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Network coding has been applied successfully in peer-to-peer systems to shorten the distribution time, particularly in extreme conditions where peers in the network are clustered and separated by physical links with limited bandwidth. In this paper, we focus more closely on the use of source coding, i.e. encoding is done only at the source, as an alternative to network coding to facilitate content distribution under such limited bandwidth configuration. We observe analytically and experimentally that, in this specific case, with appropriately chosen expansion factors, source coding can have comparable performance to network coding in terms of distribution time, yet consuming much less computational resources than the latter approach does.

Air quality and fuel efficiency has become important factors in decision-makings on urban traffic planning and management. To support the process, simulation models have potential to play essential roles in evaluation of planning alternatives and control strategies. However, traffic and its environmental impacts are different processes and often require various levels of models. With concerns on high computing performance and rich functionalities, it may be not appropriate to model emission inventory within traffic simulation. In this paper, we present a distributed simulation approach, and an independent emission/energy computing platform is built to simulate, visualize and analyze online emission outputs, given a microscopic traffic simulation tool, KTH-TPMA. Two distributed computing frameworks, common objects request broker architecture (CORBA) and service oriented architecture (SOA), are adopted in the distributed software design and implementation. Several emission models are implemented and generally evaluated in micro simulation runs of two road networks.

Detecting roadside safety attributes and distances in point cloud data is a challenging task. The major problems are accurate detection of attributes and attribute centers for calculating safety distance among attributes. In this paper, we propose a point cloud classification framework for roadside safety attributes detection. In addition, we propose an object center approximation technique for distance calculation that has been integrated into the proposed framework. The proposed framework has been evaluated on large real-world point cloud data, and the experimental results are promising. The framework achieved 100% object-wise accuracy on detecting poles and trees, while the overall point-wise accuracy on detecting all seven attributes was 86%.

In nature, many species became extinct as they could not adapt quickly enough to their environment. They were simply not fit enough to adapt to more and more challenging circumstances. Similar things happen when algorithms are too static to cope with particular challenges of their "environment", be it the workload, the machine, or the user requirements. In this regard, in this paper we explore the well-researched and fascinating family of adaptive indexing algorithms. Classical adaptive indexes solely adapt the indexedness of the data to the workload. However, we will learn that so far we have overlooked a second higher level of adaptivity, namely the one of the indexing algorithm itself. We will coin this second level of adaptivity meta-adaptivity. Based on a careful experimental analysis, we will develop an adaptive index, which realizes meta-adaptivity by (1) generalizing the way reorganization is performed, (2) reacting to the evolving indexedness and varying reorganization effort, and (3) defusing skewed distributions in the input data. As we will demonstrate, this allows us to emulate the characteristics of a large set of specialized adaptive indexing algorithms. In an extensive experimental study we will show that our meta-adaptive index is extremely fit in a variety of environments and outperforms a large amount of specialized adaptive indexes under various query access patterns and key distributions.

Vehicles may move at high speed in vehicular ad-hoc networks (VANETs) and the network topology changes arbitrarily as the nodes move. Vehicles may not find suitable resources because sharing information could quickly become obsolete or irrelevant resources. This paper proposes an efficient geo-aware Peer-to-Peer (P2P) search mechanism to enable resource discovery and sharing in VANETs. We propose discovery and sharing schemes based on the virtual backbone architecture, and then limit the data exchanging region to reduce unnecessary message transmissions. Because the resource node changes very fast, the coordinator must be set to maintain the local node of geographic information in the network. In this scheme, coordinators are established to store information and manage local resource information. In order to ensure dissemination performance, it must guarantee that vehicles possess enough resource pieces to allow efficient bartering. Furthermore, three kinds of piece selection mechanisms, including Nearest-First, Rarest-First, and In-Order are proposed to download resource pieces from other vehicles and to improve the dissemination efficiency. Experiment results demonstrate that our searching and sharing schemes are efficient and outperforms other schemes in terms of satisfactory ratio and download time.

Video synopsis provides a means for fast browsing of activities in video. Principal background selection (PBS) is an important step in video synopsis. Existing methods make PBS in an offline way and at a high memory cost. In this paper we propose a novel background selection method, ``online principal background selection'' (OPBS). The OPBS selects n principal backgrounds from N backgrounds in an online fashion with a low memory cost, making it possible to build an efficient online video synopsis system. Another advantage is that, with OPBS, the selected backgrounds are related to not only background changes over time but also video activities. Experimental results demonstrate the advantages of the proposed OPBS.

Current component interfaces, such as those defined by CORBA, are devoid of additional constraints that make helps in the task of composing software, therefore the dependent relations and interaction protocols among components are hidden within the implementations. The "implicity" reduces the composability and makes it difficult to reuse, validate and manage components. This paper presents a message based architectural model and an XML-message based Architecture Description Language (XADL). XADL promotes the description of dependent relations from the implementation level to the architectural level and enhances the interface specification by adding the sequencing, behavior and quality constraints. Furthermore, we formulate the notion of architectural mismatch and propose a checking algorithm to prevent systems from potential mismatches. The use of XML facilitates not only the specification descriptions but also the system implementations, and increases the openness of the whole system as well.

Robustness, security, anonymity, and scalability are valued features for mobile ad hoc network routing protocols. Unfortunately, the existing ad hoc routing protocols have limitations in each area. In this paper, we first analyze several existing mobile ad hoc routing protocols for robustness, security, anonymity, and scalability. Based on the analysis, we propose a new robust anonymous ad hoc on-demand routing protocol based on a proposed anonymous neighborhood trust model. We demonstrate that the protocol has strong security and anonymity protection, better scalability and is more robust when compared with other protocols., MILCOM 2009 - 2009 IEEE Military Communications Conference, October 18-21 2009, Boston, MA, USA

Conventional traffic light detection methods often suffers from false positives in urban environment because of the complex backgrounds. To overcome such limitation, this paper proposes a method that combines a conventional approach, which is fast but weak to false positives, and a DNN, which is not suitable for detecting small objects but a very powerful classifier. Experiments on real data showed promising results.

This paper presents a comparative assessment of Pole Placement and Linear Quadratic Regulator to control a balancing of two wheels mobile robot. Disturbance is applied to to test the balancing of the robot. The mathematical model of this robot which is highly nonlinear is derived. The final model is then represented in state space form using MATLAB/Simulink application. Simulation on MATLAB application is analyzed and the performance of position, speed, angle, and angle rate of the balancing robot are observed to find out the best controller for two wheels balancing robot model.

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, which causes a localized dilation or ballooning of the blood vessel. It is life-threatening; hence, an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel Lattice-Boltzmann simulation software, which is designed to provide the radiologist with estimates of flow rates, pressures, and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualize the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this article, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low-cost embedded platform and to visualize the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second. 2007-2012 IEEE. Manuscript received February 19, 2019; revised September 20, 2019; accepted November 4, 2019. Date of publication December 2, 2019; date of current version June 3, 2020. This paper was supported by the National Priorities Research Program (NPRP) under Grant 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). X. Zhai is partially supported by the UK Engineering and Physical Sciences Research Council under Grants EP/R02572X/1 and EP/P017487/1. (Corresponding author: Xiaojun Zhai.) X. Zhai is with the School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ Colchester, U.K. (e-mail: xzhai@essex.ac.uk). Scopus

Sparse coding is a very powerful method to learn high-level features from raw data input. It is able to learn an over complete basis that has the potential to capture robust and discriminative patterns within the data. However, like many other feature learning algorithms, it is unable to detect very similar features or stimuli on different input channels. In this paper, we propose a novel method to build general features that can be applicable to different sets of channels. This succinct representational model will express the stimuli independent of the locality in which they appeared. As a result, it prepares the groundwork for transferring the learned features from a set of input channels to other possible sets of input channels.

Script image segmentation of a document image is the most decisive step to the success of the process of transliteration of the script image into another script, such as automatically transliterating a printed Javanese manuscript image into a Latin manuscript. This paper gives an example of the application of profile projection modification to the segmentation of Javanese script document image of the entire 87 pages of the document image of HamongTani book. Based on the output of the developed system, the average percentage of correctness is 84.255% with the average standard deviation of 14.093%. This value of average percentage of correctness shows that the model developed for the Java script document image segmentation of the HamongTani book is relatively good.

Password security of user accounts is a major concern in today's fast moving and advanced computing world with password authentication the most commonly used identification mechanism. Its security can be enhanced using typing biometrics as a transparent layer of user authentication. This paper describes a password hardening mechanism that is able to recognize different users based on their time latencies. Our research focuses on only using the time period between keystrokes as the measure of the individual's typing pattern. Seven individuals used the same 7-digit password which allowed the system to record the time interval between each digit. Identification improvements up to 3% were achieved with Subtractive Clustering Fuzzy Inference when rule conflicts were removed.

Re documentation and design recovery are two important areas of reverse engineering. Detection of recurring organizations of classes and communicating objects, called Software Patterns, supports this process [1]. Many approaches to detect Software Patterns which have been published in the past years suffer from the problems of necessity of reference library, performance and language compatibility. This paper presents a model to solve those problems in software pattern detection. The proposed model solves the problem of necessity of reference library by detecting software patterns using Formal Concept Analysis (FCA). The proposed model solves the problem of performance by using the most efficient algorithm CMCG (Concept-Matrix based Concepts Generation) for the construction of concept lattice, which is the core data structure of FCA [3]. The proposed model solves the problem of language compatibility by using the language independent Meta model called MOOSE for taking the input information. The validity of this model was proved in theory and by experiment.

A questionnaire method of the fuzzy rating scale data is actively put to practical use with high efficiency because of the relatively high reliability or easy handling of the data. As a structural analysis method of the rating scale data, the semantic structure (SS) analysis is proposed. The SS analysis is a method to interpret human daily activities or psychological sides from the item order, whose structure attention is paid with the intention to build the structure of the items. In the SS analysis, the rating allows a discrete fixed-point on the scale to be designated (fixed-point rating). However in some cases, it is difficult in reality for evaluators to be confident of a specific discrete fixed-point as a rating value. In this paper, a method of the structural analysis allowing the fuzzy rating scale data to be dealt with as fuzzy data is proposed. >

In oriented service computing area, the applications of theory and technology of muti-agents can make it possible to change computing model from a centralized controlling type to a distributed cooperation form. In the meantime, we have to take some steps to constrain and lead global behaviors of muti-agents so as to ensure that an effective consultation alliance will bring out smoothly, for agent with self-interest feature in the traditional muti-agents theory. This paper presents an approach for proposing solutions to specific requirement based on Automated Mechanism Design(AMD) techniques. The main contribution is to describe how to built up the computational settings in mechanism designing process for active service environment, such as agency group, type sets, special outcomes for requirement, utility functions and an objective goal. After that, a simulation experiment is put forward to verify the rationality of mechanism designed in this paper as an ending.

Object tracking and avoidance is a vital functionality in autonomous vehicles. Object tracking techniques must be both fast and accurate for this safety-critical application. Neither of the two factors mentioned can be compromised in this safety critical application. Much of the work done in this field uses the point clouds themselves for object matching. This ensures greater accuracy at the expense of run time. This paper posits that such a fine level of registration is not required for comparable levels of accuracy. The object tracking technique proposed uses a coarse technique for object matching via voxels which reduces run time without compromising on accuracy. It also allows for evaluation of quality of matches.

The paper provides an introduction into the area of inductive formation of knowledge bases. It presents traditional definitions of main problems in this area and highlights the current topical questions including the interpretability of the results. For solving of current problems in defined area the method of inductive formation of easily interpretable medical diagnostic knowledge bases is suggested. It includes the new definitions of classification and clustering problems for dependence models with parameters and the learning algorithm (solving mentioned problems in their new definitions) developed for the practically useful and easily interpretable mathematical dependence model with parameters which is a near real-life ontology of medical diagnostics (defined by a system of logical relationships with parameters). Also it includes the software package InForMedKB (INductive FORmation of MEDical Knowledge Bases) which implements above mentioned learning algorithm. InForMedKB allows to create training sets (consisting of clinical histories from various therapeutic areas) and to use them for inductive formation of medical diagnostic knowledge bases. These knowledge bases are presented in form accepted in the medical literature and contain descriptions of diseases (from specified therapeutic areas) as well as an explanation of these knowledge bases based on descriptions of clinical histories from used training sets. The formal representation of medical knowledge bases enables their usage for intelligent systems for medical diagnostics.

The study of this paper is based on VRML (Virtual Reality Modeling Language) and virtual scene dynamic model realization. In order to overcome the insufficiency of using VRML on generating dynamic scenes, this paper mainly embeds JavaScript virtual scene into the virtual external language and viewer interaction; it also use JavaScript VRML node in perfect position and orientation interpolator node, which cannot be obtain information about visitors from the interpolator browser due to the lack of the JavaScript language processing results. Consequently, with the interpolator node as input events, the paper achieves better interaction between different nodes and the scenes with the viewer's interactive features.

Network virtualization, as a fundamental technology of Future Internet Architecture, has been used to overcome the ossification of the current Internet. This paper focuses on virtual nodes splitting for aggregating the diverse physical resources in the issue of virtual network embedding (VNE), which is used for the resource allocation in network virtualization. A mixed integer programming (MIP) problem has been proposed by taking both path-splitting and node-splitting into considerations. The feasibility of virtual node splitting is analyzed and a corresponding formal expression is formulated. The VNE performance is evaluated using by a generic MIP solver over a NSFNET topology. The numerical evaluations in this paper show that, comparing with the approaches for VNE without node-splitting, the proposed algorithm is beneficial to reduce VNE cost, as well as to increase VN acceptance ratio.

Recently most of the modern industries or buildings are aiming at efficient usage of the power energy available without compromising on occupant comfort. The energy once distributed cannot be stored back. The best way for efficient usage is an intelligent way of the distribution of power. This can be achieved through Decision support systems. These expert systems help in decision making activities to make decisions on the amount of distribution of power based on the conditions prevailing. DSSs are very much essential and emerging technology in industrial automation. This paper describes how the decision on energy distribution activities can be taken using fuzzy logic controllers with the help of clustering algorithms and association rule mining.

In order to predict heart failure disease in advance and keep heart failure patients away from pain, this paper combines genetic algorithms (GA) to optimize Extreme Learning Machine (ELM) and builds a network model that predicts heart failure disease based on some of the characteristic factors that may induce heart failure. The ELM model has the characteristics of fast training speed and strong generalization ability, but its randomly generated input weights and hidden layer bias will cause instability in the prediction results. In this paper, we use GA’s principle of survival of the fittest to find the most suitable parameter configuration of the ELM algorithm, and analyzes the simulation results, and compares them with the prediction results of the original ELM model. The results show that the GA-optimized ELM model can better predict the occurrence of heart failure disease and has better predictive performance. The ELM algorithm, which has undergone parameter optimization has made up for its original defects, which may affect clinical practice and become a new assistant for doctors to predict patients with heart failure.

SaaS has become a typical cloud computing delivery model. In sparse table, one of the most popular multi-tenant data storage schemas for SaaS, all tenants' data are stored into sparse table and mapped to tenant's logical view by metadata. During the data storage schema upgrade in SaaS, all tenants' data need to be migrated into the new data schema before it becomes effective to ensure the integrity of the tenants' data. However, the migration is complex and brings overhead workload. Worse still, it may cause the system unusable. In this paper, we propose metadata evolution technology. We can realize the mapping from the old data schema to new data schema smoothly via metadata evolution during schema upgrade. So the schema upgrade can be completed without data migration.

In this paper, we propose an optical orthogonal code (OOC) acquisition scheme for optical code division multiple access (CDMA) systems. By employing multiple thresholds, the proposed scheme provides a better mean acquisition time (MAT) performance than the conventional scheme. Numerical results demonstrate that the MAT of the proposed scheme is shorter than that of the conventional scheme.

Pulse-coupled neural networks, PCNN, have arisen as an alternative for image preprocessing. However, the PCNN model is a member of the artificial neural networks model and hence it inherits the problem of parameter specification. This paper presents a PCNN model that was generated by analysis of other PCNN models and describes what we call a PCNN processor, PCNNP. The PCNNP is a PCNN simulator with a user interface. The PCNN simulated in the PCNNP is a model generated from other PCNN models analysis. The PCNNP allows the user to analyze the behavior of the net by changing the linking and feeding kernels and the parameters of the neuron. The interface also incorporates different visualization tools to facilitate the analysis of the information generated by the net. The visualization modes have proved to be helpful to understand the internal activity of the PCNN and how this activity is modified by the parameters of the PCNN.

With the rapid growth of data in various domains, models for data processing become more and more complicated and require higher and higher computing power of local machines. In our opinion, it is a good solution to put models into the "cloud". Integrating and categorizing these models in different domains is convenient for users. They don't have to establish similar systems for different models in different domains. This paper demonstrates how we employ the Web Processing Service in the Open Geospatial Consortium as a service engine to establish a scientific model integration platform to handle requests and responses. And a study case that integrates some geospatial models based on the platform is presented at last.

In today's globalized business world, outsourcing, joint ventures, and cross-border collaborations have led to work environments that are geographically distributed across organizational and national boundaries. There are critical research needs to develop highly secured collaborative work environments and security solutions for deployment, configuration, monitoring, and device control of interoperating services. This paper presents a well-shaped security framework for distributed system control with a focus on device-level system control, monitoring and services re-configuration in open and dynamic environments. The characteristics of portability, reconfigurability, interoperability, and interchangeability of these new environments are considered as key factors to produce new security risks and challenges. By adopting Public Key cryptography, software agent and XML binding technologies, the major security problems of authenticity, integrity, confidentiality, and safe execution are addressed in this framework. The core modules for secure task delivery and execution are presented in detail., The 1st IEEE Conference on Industrial Informatics, August 2003, Banff, Alberta, Canada

Nowadays, data is seen as one of the most valuable assets of organizations. Representing and exposing data in a suitable manner is mandatory for allowing consumers – either human beings or software systems – to properly retrieve and interpret such data. Web Services along with semantic Web techniques may be adopted to address this issue. This paper presents the semantic data-driven microservice, a cloud service capable of providing linked data based on non-semantic data sources. Its main goal is to work as a solution for publishing linked data and for maximizing data reuse.

We analyze the RTO algorithm standardized for TCP and SCTP (V. Paxon et al., 2000) that is widely deployed in the Internet, referred to as the RFC2988-RTO in this paper. We briefly demonstrate three well-known problems of the RFC2988-RTO. We then develop a new RTO algorithm called the peak-hopper-RTO (PH-RTO) that eliminates the mentioned problems. Through extensive simulations in ns2, we evaluate the effectiveness of both RTO algorithms. The key advantage of the PH-RTO over the RFC2988-RTO is its predictability. Although the RFC2988-RTO often exhibits low loss detection times, it also exhibits frequent "RTO outliers" that can lead to exceptionally long loss detection times. The loss detection times of the PH-RTO are much more closely spread around the mean. Finally, our results show that the PH-RTO is much more robust to sudden delay spikes that are particularly common in wireless networks.

Aiming at the shortcoming that obtained from comprehensive evaluation by principal component analysis, this paper points out the method of based on the EXCEL/VBA one-click technology to achieve principal component comprehensive evaluation. Namely, according to the algorithm of Eigenvalue and Eigenvector with the function in Excel-macro, in this article, an easy-to-implement solution to comprehensive evaluation by principal component analysis with VBA in Microsoft EXCEL is provided, which makes users be completely free from manual operation. It is raising the comprehensive evaluation by principal component analysis method practical. And besides, it analyses an example with the method and gets better effects.

As a sunrise and national supporting industry, the current specialty of computer science and technology is in an awkward situation. Many students cannot get offer, but enterprises want to employ thousands of talents. The reason is not difficult to find out that abilities of undergraduates cannot meet industrial demands. So reform of computer science and technology education is imperative. Industry-linked and practice-oriented cultivating mode need be established. Responding to Chinese "excellent engineer education plan", the cultivating scheme of excellent software engineer is proposed. The scenario details are also discussed in this paper, such as cultivating objective, curricula setting and practice education system. And the scheme is being performed.

This paper presents ASC03 algorithm, aimed at finding mappings between entities of two ontologies represented in OWL DL/Lite. ASC03 algorithm searches the maximal common subgraph of two graphs representing two ontologies, and relies on an algorithm of search of the maximal clique of their association graph.

A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.

Machine learning (ML) algorithms provide a good solution for many security sensitive applications, they themselves, however, face the threats of adversary attacks. As a key problem in machine learning, how to design robust feature selection algorithms against these attacks becomes a hot issue. The current researches on defending evasion attacks mainly focus on wrapped adversarial feature selection algorithm, i.e., WAFS, which is dependent on the classification algorithms, and time cost is very high for large-scale data. Since mRMR (minimum Redundancy and Maximum Relevance) algorithm is one of the most popular filter algorithms for feature selection without considering any classifier during feature selection process. In this paper, we propose a novel adversary-aware feature selection algorithm under filter model based on mRMR, named FAFS. The algorithm, on the one hand, takes the correlation between a single feature and a label, and the redundancy between features into account; on the other hand, when selecting features, it not only considers the generalization ability in the absence of attack, but also the robustness under attack. The performance of four algorithms, i.e., mRMR, TWFS (Traditional Wrapped Feature Selection algorithm), WAFS, and FAFS is evaluated on spam filtering and PDF malicious detection in the Perfect Knowledge attack scenarios. The experiment results show that FAFS has a better performance under evasion attacks with less time complexity, and comparable classification accuracy.

This paper will describe a numerical approach to simulating biologically-plausible spiking neural networks. These are time dependent neural networks with realistic models for the neurons (Hodgkin-Huxley). In addition the learning is biologically plausible as well, being a Hebbian approach based on spike timing dependent plasticity (STDP). To make the approach very general and flexible, neurogenesis and synaptogenesis have been implemented, which allows the code to automatically add or remove neurons (or synapses) as required.

Based on the analysis of the status of archives social media information at home and abroad, it is not only a strategic step in the construction of archives informatization, but also an inevitable choice for its effective construction and utilization. This paper draws lessons from the international standards of document management system and the reference model of open archives information system, and designs the framework of archives social media integration system at the top level. It also takes the heterogeneous data of various archives social media information as the analysis source, and excavates the contents in depth. The LDA model is improved and extended by utilizing certain attribute information in microblog messages. At the same time, the relevant features of data sources are fused into the traditional document sorting function, which significantly improves the retrieval performance of multi-source social media information.

In this paper, we explore building classifiers to detect Salsa dance step primitives in choreographies available in the Huawei 3DLife data set. These can collectively be an important component of dance tuition systems that support e-learning. A dance step is reasoned as the shortest possible extract of bodily motion that can uniquely identify a particularly repeatable movement through time. The representation of dance steps adopted is a concatenation of vectorized matrices involving the 3D coordinates of tracked body joints. Under this modeling context, a Salsa dance performance is seen as an ordered sequence of Salsa dance steps, requiring a multiple of the variables allocated in the representation of a single step. Following a previous work by Masurelle & Essid that discusses the classification of six Salsa dance steps from 3DLife, we show that it is possible to obtain better classifiers under a similar experimental protocol in terms of both test accuracy and F-measure. By carefully re-annotating the data in 3DLife, we refocus on the six-step classification problem and then extend the protocol to the case of 20 dance steps. In comparison to common classifiers of the trade operating on full-dimensions, we show that it is possible to produce more accurate models by computing a subspace of the data. At the same time it is possible to reduce problematic bias in resulting models due to the uneven distribution of samples across step data classes. We provide and discuss experimental findings to support both hypotheses for the two experimental settings.

In this paper, we propose a rate adaptation algorithm for Hypertext Transfer Protocol (HTTP) streaming. The proposed algorithm selects the video bit rates based on the estimated throughput and playback buffer occupancy. The performance of the algorithm is compared with that of throughput- and buffer-based adaptation algorithms. The proposed method selects high-quality video segments, while minimizing video quality changes and the risk of playback interruption. We evaluate the algorithm for single- and multi-user environments and demonstrate that it performs remarkably well under varying network conditions. Furthermore, we determine that it efficiently utilizes network throughput to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that the proposed algorithm performs well regardless of the buffer size and segment duration.

Photovoltaic power generation system gradually develops toward high density and multi-point access. However, the traditional islanding detection method, especially the active method, is not ideal for the detection of multi-inverter in distributed power generation system. In this paper, the Sandia frequency shift (SFS) is used to analyze the adverse effects of dilution and high load quality factor on the islanding detection. Then, it proposes an improved Sandia frequency shift method. The simulation model is established in MATLAB/Simulink environment. The results show that the detection method can achieve fast and precise islanding detection.

Clustering plays an important role in data mining and machine learning. Then, intuitionistic fuzzy sets (IFSs) are flexible and practical in dealing with vagueness and uncertainty problems. To cluster the information expressed by intuitionistic fuzzy data, this paper proposes the joint training auto-encoder based intuitionistic fuzzy clustering algorithm. Firstly, we propose the auto-encoder based intuitionistic fuzzy clustering by utilizing similarity measure of IFSs, auto-encoder and k-means algorithm. Then, we propose the joint training auto-encoder based intuitionistic fuzzy clustering algorithm by utilizing the proposed auto-encoder based intuitionistic fuzzy clustering and two kinds of similarity measures for the clustering analysis of intuitionistic fuzzy data. Lastly, several experiments are provided to verify the effectiveness of the proposed intuitionistic fuzzy clustering algorithms.

This paper discusses the design and implementation of a cluster file system, called PVFS-PM, on the SCore cluster system software. This is the first attempt to implement a cluster file system on the SCore system. It is based on the PVFS cluster file system but replaces TCP with the PMv2 communication library supported by SCore to provide a scalable, high-performance cluster file system. PVFS-PM improves the performance by factors of 1.07 and 1.93 for writing and reading, respectively, with 8 I/O nodes, compared with the original PVFS on TCP on a Gigabit Ethernet-connected SCore cluster.

An under-resourced language may be defined as a language that lacks the presence on the web and has limited electronic resources for the advancement of Human Language Technology (HLT) such as inadequate digital corpora, few transcribed speech data, and pronunciation dictionaries. Languages from rapidly developing countries such as Malay, Bengali, Vietnamese and Urdu are among the languages categorized as under-resourced languages. HLT research in the Malay language is tremendously increasing over the years since it started in 2000 with text-to-speech (TTS) synthesis moving to automated speech recognition (ASR). TTS in the Malay language has also progressed to expressive speech synthesis in the last five years adding emotions such as happy, sad, fear and angry. Our proposed work further extended emotion speech synthesis for storytelling of Malay folklores. In this paper, an improved pitch contour formulation is introduced by modifying the existing pitch contour sinusoidal function. The aim is to convert neutral speech into storytelling speech in the Malay Language. Our speech datasets (neutral and storytelling speech) were recorded by a male and a female professional speaker. They contain 116 speech sentences, 1,164 words, and 2,732 syllables. For storytelling speech, 124 prominent syllables are detected using Prosogram tool. These prominent syllables are further categorized into six clusters of pitch contour. Distance measurements using one minus Pearson correlation is done to assess the similarity of the proposed pitch contour formulae to the original storytelling pitch contour. The proposed pitch contour sinusoidal function is also compared with the existing pitch contour function used by previous work. The results showed that the proposed pitch contour formulation performed better than the existing pitch contour formulae.

A trend in the consumer electronics market is the demand for new applications that have a lot of similarities to older applications but the new ones impose more challenging and special-purpose performance requirements. In the digital signal processing (DSP) industry, this clearly reflects a transition from the design regime of general DSP to the application-specific DSP. A key in effecting this transition is the engineering capability to make the design specification "match" the application, before detailed design starts. In this paper, we present an effective simulation tool to assist in the generation of accurate design specifications. It models the application by a parameter-driven conditional data-flow graph (CDFG) and the hardware (HW) architecture by a configurable HW graph. The simulator takes the application CDFG and HW graph as the input and carries the simulation at a low level to catch the detailed HW activities. Experimental results show that our tool is not only able to accurately estimate system performance, but also helps to identify performance bottlenecks and to find the optimal software (SW) implementation solution for video applications.

Internet of Things (IoT) is a scientific advancement that is likely to transfigure our traditional way of life by automating everything connected to the internet. Smart physical devices/objects have become indispensable part of our lives thereby making our being simplified and way more organized. Internet of Things encompass and empower several heterogeneous physical devices/objects for sensing and gathering information from surroundings, consequently exchanging it in a seamless mode to realize smart application goals without human intervention, for instance, home automation, smart cities, smart agriculture, and smart industries etc. The growth in the number of such wired/wireless physical devices/objects and the speed of that growth raises concern over security. To be able to implement a smart world with such billions of physical devices/objects, appropriate mechanisms are required to provide secure end-to-end communications amongst them. In addition to it, a variety of attacks may occur in various layers of Internet of Things. Without a well-founded and congruous Internet of Things environment, budding Internet of Things applications cannot be exploited to their full potential. Hence, reliability, security and recovery from attacks are amongst the most unsparing sides of the Internet of Things. In this paper, we have presented a comprehensive review of Internet of Things security issues followed by security measures in Internet of Things. The focus in this paper is also on research challenges and application domains of Internet of Things.

Modeling and simulation is a fundamental part of control and design. This paper describes a new suite of software that has a combination of features that makes it unique. It is professionally designed and developed and is a mixture of open source and freely distributed software. It uses a language syntax similar to the most commonly used ones today. It includes also a graphical modeling package. The software is designed to model continuous-time, discrete-time, and hybrid systems. The distribution also includes a Modelica part that allows easy incorporation of Modelica blocks within the general diagrams. A control toolbox is also included. There are a number of other features that are described in this paper.

A web service (WS) is called compound or composite when its execution involves interactions with other WS to use their features. The composition of WS specifies which services need to be invoked, in what order and how to handle exception conditions. This paper gives an overview of research efforts of WS composition. The approaches proposed in the literature are diverse, interesting and have opened important research areas. Based on many studies, we extracted the most important role of WS composition use in order to facilitate its introduction in WS concept.

Emerging as a widely accepted technique for malware analysis, YARA rules due to its flexible and customisable nature, allows malware analysts to develop rules according to the requirements of a specific security domain. YARA rules can be automatically generated using tools, however, they may require post-processing for their optimisation, and may not be effective for the specific security domain. This compels the requirement to enhance automatically generated YARA rules and increase their effectiveness for malware analysis without increasing computa- tional overheads. Reflecting on the above requirement, this paper initially evaluates automatically generated YARA rules using three YARA tools: yarGen, yaraGenerator and yabin. These tools are Python-based open-source tools used to generate YARA rules automatically utilising different underlying techniques. Subsequently, it proposes a method to enhance automatically generated YARA rules using a fuzzy hashing method. This proposed enhancement method can improve the effectiveness of YARA rules irrespective of the chosen YARA tool used to generate YARA rules, which is demonstrated through several experiments on samples of collected malware and goodware.

With the development of network technology, there exists large number of identity management systems with different architectures and different application areas in cyberspace. How to manage various network identities is becoming a challenging problem. In this paper, we begin by reviewing the characteristics of traditional identity management system and propose a new unified trust service scheme based on blockchain to integrate the isolated identity management system. We then present the theory verification that we show the analysis and experiment under our new scheme. Interestingly, our new constructions are more feasible than traditional centralized solutions. Furthermore, traditional solutions for heterogeneous identity alliance only consider the central authority to integrate different identity management system. We consider the natural extension where different identity management system can join alliance autonomously rather than compulsively. We formally define a unified trust service scheme for Heterogeneous identity alliance, and present an efficient construction.

Cardiovascular disease (CVD) is a cause of 17.9 million deaths each year globally. Among the risk factors of CVD are hypertension, hyperlipidemia, hyperglycemia, and hyperuricemia. In hyperuricemia, serum uric acid (SUA) level can be detected only with a blood test, a microinvasive procedure that is costly and incur minimal pain and requires patients to travel to a hospital for regular monitoring. In this paper, a machine learning technique is proposed for SUA level classification. A total of 74 subjects participated in this study, and 20 features were extracted from the pre-processed photoplethysmogram (PPG) morphology. Blood tests were performed, and the results were used as inputs and labeled accordingly. Results show that based on p-value, the onset-diastolic width of the right and onset-systolic width of the left hand are the most significant features for SUA classification. Artificial neural network has an accuracy of 86.36%, which is higher than that of three machine learning methods: k-nearest neighbor, support vector machine, and decision tree.