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It is with great pleasure that we present the Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR) 2018 Conference Papers track. As the next evolution of the IEEE Symposium on 3D User Interfaces, the IEEE VR Conference Papers track is now a fully integrated part of IEEE VR. This has resulted in a broader scope of submissions than ever before, from many different disciplines, including virtual, augmented, and mixed reality, human-computer interaction, mobile computing, computer graphics, cognitive and perceptual psychology, games, education, and the social sciences. This breadth of submissions also reflects the recent explosion of interest in VR and 3DUI technologies; never before has VR/3DUI technology been so available to so many people. There are therefore many open research questions in VR, and more new technologies under examination.

Researches have been carried out in the past and recent years for the automation of examination systems. But most of them target on-line examinations with either choice-based or very short descriptive answers at best. The primary goal of this paper is to propose a framework, where textual papers set for subjective questions, are supplemented with model answer points to facilitate the evaluation procedure in a semi-automated manner. The proposed framework also accommodates provisions for reward and penalty schemes. In the reward scheme, additional valid points provided by the examinees would earn them bonus marks as rewards. By incremental up-gradation of the question case-base with these extra answer-points, the examiner can incorporate an automatic fairness in the checking procedure. In the penalty scheme, unfair means adopted amongst neighboring examinees can be detected by maintaining seat plans in the form of a neighborhood graph. The degree of penalization can then be impartially ascertained by computing the degree of similarity amongst adjoining answer scripts. The main question-bank as well as the model answer points are all maintained using Case Based Reasoning strategies.

Graphs have become increasingly important in many applications and domains such as querying relationships in social networks or managing rich meta data generated in scientific computing. The Panama Papers(Unprecedented leak of 11.5m data from the database of the worlds fourth biggest offshore law firm, Mossack fonseca) investigation used Neo4j as a key technology to find connections in leaked data. The International consortium of Investigative Journalists is a global network, which is used to analyze massive leaks over the world. This work attempts to analyze a subset of data from panama papers. The data is modeled as graph database using Neo4J for analysis of Panama Papers which results fraud ring over offshore entities.

Cyber Physical Systems (CPSs) refer to a new generation of embedded ICT systems (PCs, smartphones, sensors, actuators, etc.) that are interconnected and collaborating to provide users with a wide range of innovative applications and services. Many application domains, e.g., Emergency management, factories of the future, personalized healthcare, just to name a few, require the definition, design and development of systems able to carry out complex processes that coordinate the services offered by the CPS in the "physical" real world. The physical world, however, is not entirely predictable, and such processes must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this paper, we tackle the above issue and we propose an approach and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events.

In this position paper, I describe the rationale behind, and the early design of Scratch Data Blocks — a Scratch-based block-programming toolkit that allows Scratch users to program with public data from the Scratch online community.

Block-based programming environments are not accessible to users who are visually impaired. The lack of access impacts students who are participating in computing outreach, in the classroom, or in informal settings that foster interest in computing. This paper will discuss accessibility design issues in block-based programming environments, as well as present research questions and current design revisions being undertaken in Blockly.

As vehicles meet each other occasionally in VANETs, users will naturally have the requirement of protecting their privacy during VANET communications. The privacy requirements bring unique challenges for the communication setup and maintenance as any information (such as IP address and MAC address) that may relate to the users' IDs should not be used. To resolve this problem, we propose to utilize the dynamic physical level information, RSS-Ratio, as the address. This short paper briefly presents why RSS-Ratio can be used as an address while not revealing users' IDs, and our proposed method of utilizing RSS-Ratio for anonymous connection setup and maintenance.

Vehicular Ad-hoc system is currently picking up more concern because of inquisitive circumstance of transportation framework. Vehicular interchanges are typically created as a piece of Intelligent transportation System (ITS). VANET is the subclass of versatile specially appointed system (MANET). In VANET'S vehicles go about as hubs and switches which correspond with one another by sending messages. There are existing two sorts of impromptu systems in portable interchanges. One is vehicle to vehicle (V to V) correspondence and second is vehicle to roadside correspondence (V to R). Presently it is more inclined to security assaults. There are different sort of assaults like Denial of Service assault, Fabrication assault, Alteration assault, Sybil assault, Prankster assault, vindictive assault and so forth. In this paper different parts of VANET's are contemplated.

Neural networks are increasingly used as data-driven models for a wide variety of physical systems such as ground vehicles, airplanes, human physiology and automobile engines. These models are in-turn used for designing and verifying autonomous systems. The advantages of using neural networks include the ability to capture characteristics of particular systems using the available data. This is particularly advantageous for medical systems, wherein the data collected from individuals can be used to design devices that are well-adapted to a particular individual's unique physiological characteristics. At the same time, neural network models remain opaque: their structure makes them hard to understand and interpret by human developers. One key challenge lies in checking that neural network models of processes are “conformant” to the well established scientific (physical, chemical and biological) laws that underlie these models. In this paper, we will show how conformance often fails in models that are otherwise accurate and trained using the best practices in machine learning, with potentially serious consequences. We motivate the need for learning and verifying key conformance properties in data-driven models of the human insulin-glucose system and data-driven automobile models. We survey verification approaches for neural networks that can hold the key to learning and verifying conformance.

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation.

Credit card frauds, while only account for about 0.1% of all card transactions, resulting in huge financial and reputational losses. Challenges of detecting credit card frauds are from the imbalanced nature of the recorded data, the need for controlling the trade-off between miss detection and false alarm, and incomplete information due to confidentiality requirements. In this paper, we propose an innovative fraud detection framework implementing the LightGBM method under the Neyman-Pearson paradigm, which enables asymmetric error control. Performance measurement metrics are also introduced to evaluate different classification frameworks. We can successfully keep the miss detection rate under the desired upper bound and control false alarm at the same time.

The use of IoT devices in the future electricity domain (known as the smart grid) has numerous benefits, such as improved reliability of the power system, enhanced functions of SCADA (Supervisory Control and Data Acquisition), improved monitoring and management of operational power grid assets, and advanced metering infrastructure. The smart grid concept relies on the integration of high-speed and reliable communication networking technologies in order to provide twofold benefits - one for the interconnection between the existing power grid and intelligent information systems, and another for enabling real-time grid monitoring via IoT devices. However, the security of IoT devices themselves is a challenge due to the trade-off between device cost and secure communication requirements. Further, current electricity grids require robust and secure wireless communication infrastructure to realize transformation to smart grids. The 5G networks are considered as an enabler for digitalization of power grids and facilitating IoT connectivity for future smart grids with several benefits such as low latency, ultra high speed, and improved reliability. However, the use of public 5G networks may introduce new types of security risks to the IoT-based smart grids infrastructure. In this paper, we analyze the security aspects of 5G security specifications released by the 3GPP standards organization from the perspective of IoT-based smart grids. In particular, we consider a smart grid scenario utilizing 5G as a wireless communication infrastructure, and present 5G benefits to several security aspects such as authentication, confidentially, integrity, resiliency, and availability. Further, we outline security risks to IoT-based smart grids originating from compromised 5G network-related infrastructure.

Applications of conversational agents, also called chatbots, have accelerated in recent years. However, it is difficult for people with limited programming skills to implement chatbots and analyze the data collected by chatbots. In this paper, we present a new serverless implementation of conversational agents for delivering customizable surveys on smart devices, including smartphones and smart speakers, and a web-based system for applying advanced analytics techniques to survey data using cloud services. The system is called TigerAware Assistant, as part of the TigerAware platform for customizable mobile survey and sensor data collection. TigerAware Assistant enables non-technical people to easily create and deploy chatbots on various mobile devices to conduct surveys through conversations in natural languages via auditory and textual methods. It also provides a web-based system for survey data visualization, statistical analysis, and advanced machine learning-based data analysis using cloud services on survey responses collected by chatbots.

Healthcare information systems are the next big application area for Blockchain technology. However, straightforward extensions of existing digital cryptocurrency systems such as Bitcoin and Ethereum results in systems that are unsuitable for the challenges posed by healthcare systems. In this paper, we propose an architecture for a blockchain-based healthcare information system in which block validation is performed through collective signatures initiated by a designated leader and executed by a pool of witnesses. Furthermore, we describe a smart-contract based approach that allows data owners to explicitly grant or revoke authorizations for other actors to access healthcare data. All accesses, successful or not, are recorded on the blockchain as separate transactions, thus ensuring transparency and privacy protection.

Uncertainty quantification techniques have been widely used to model devices, circuits, and systems with fabrication process variations. However, most existing techniques assume that all random parameters are mutually independent or Gaussian correlated, which is rarely true in practice. How to handle non-Gaussian correlated random parameters is a fundamental and long-standing challenge. In this paper, we review existing techniques and point out their limitations. Then, we summarize our recent work to address the theoretical and computational challenges caused by non-Gaussian correlations.

Traditional signature-based malware detection techniques have been used for many years owing to their high detection rates and low false positive rates. However, signature-based techniques are ineffective as they do not detect new, polymorphic or transient malware. To beat weaknesses in signature-detection techniques, researchers have turned to behaviour-based ones, which create a malware behaviour profile by capturing malicious API calls throughout execution. In this context, API matching techniques use API calls to calculate similarities between malware. However, API concatenation techniques need significant API call process resources, which makes these methods slow owing to process quality, and thus cannot scale to large API call sequences outside the lab. In this paper, we review present malware detection strategies supported by API call sequences.

Security down to hardware (HW) has become a fundamental requirement in highly-connected and ubiquitously deployed systems, as a result of the recent discovery of a wide range of vulnerabilities in commercial devices, as well as the affordability of several attacks that were traditionally considered unlikely. HW security is now a fundamental requirement in view of the massive attack surface that they expose, and the substantial power penalty entailed by solutions at higher levels of abstraction.In large-scale networks of connected devices, attacks need to be counteracted at low cost down to individual nodes, which need to be identified or authenticated securely, and protect confidentiality and integrity of the data that is sensed, stored, processed and wirelessly exchanged. In many security-sensitive applications, physical attacks against individual chips need to be counteracted to truly enable an end-to-end chain of trust from nodes to cloud and actuation (i.e., always-on security). These requirements have motivated the on-going global research and development effort to assure hardware security at low cost and power penalty down to low-end devices (i.e., ubiquitous security).This paper provides a fresh overview of the fundamentals, the design requirements and the state of the art in primitives for HW security. Challenges and future directions are discussed using recent silicon demonstrations as case studies.

Mobile crowdsensing (MCS) is a distributed sensing paradigm that uses a variety of built-in sensors in smart mobile devices to enable ubiquitous acquisition of sensory data from surroundings. However, non-dedicated nature of MCS results in vulnerabilities in the presence of malicious participants to compromise the availability of the MCS components, particularly the servers and participants' devices. In this paper, we focus on Denial of Service attacks in MCS where malicious participants submit illegitimate task requests to the MCS platform to keep MCS servers busy while having sensing devices expend energy needlessly. After reviewing Artificial Intelligence-based security solutions for MCS systems, we focus on a typical location-based and energy-oriented DoS attack, and present a security solution that applies ensemble techniques in machine learning to identify illegitimate tasks and prevent personal devices from pointless energy consumption so as to improve the availability of the whole system. Through simulations, we show that ensemble techniques are capable of identifying illegitimate and legitimate tasks while gradient boosting appears to be a preferable solution with an AUC performance higher than 0.88 in the precision-recall curve. We also investigate the impact of environmental settings on the detection performance so as to provide a clearer understanding of the model. Our performance results show that MCS task legitimacy decisions with high F-scores are possible for both illegitimate and legitimate tasks.

The usage of single-objective similarity functions in engineering tasks of service-oriented software may reduce their effectiveness, since a single similarity value can be misleading. In particular, a single similarity value cannot be clearly interpreted, since it hides the values of its individual objectives. The state-of-the-art approaches, which use service similarity functions, typically rely on single-objective functions. Going in a complementary direction, we propose the usage of multi-objective functions for calculating service similarity. We formally define such a function and we provide emerging results, which show that the effectiveness of service engineering tasks can be improved by using multi-objective functions.

This paper presents a methodology for selecting best groups of predictor variables based on regression trees. Test results of the developed methodology applied to industrial pilot paper machine data are presented. Specifically, the results list process variable groups, which are more valuable in predicting paper quality variables. The benefit of paper quality prediction based on process variables is the timely reaction to changes happening during production process and, thus, the reduced operational costs. The proposed regression trees based group variable ranking methodology shows stable results on both data sets used in this study.

Kraft paper is widely used in oil-paper insulation of power transformer. This type of cellulose insulation paper degrades under the combined influence of a number of interrelated factors, including moisture and electric field, during the long-term running process of power transformer. And researchers have conducted a lot of researches in the field of moisture and electric field influence on the ageing process of insulation paper. Current researches are still confined to studies of moisture on the aging of insulation paper by means of macrostructure testing. And the influence of electric is carried out only by adopting Ansoft, Ansys and Matlab and other common simulation software in electrical engineering. While microcosmic process and mechanism of moisture and electric field influence on insulation paper could not be reflected by these approaches. The Material Studio software for material simulation was adopted in this article to study the electric field influence on moisture diffusion in insulation paper. Different simulation models, such as crystalline and amorphous cellulose with multiple electric fields, were established respectively, and relevant simulation results were as follows: the polarization of electric field on the surface of crystalline cellulose has obvious implication on water diffusion, which restricts the movement of water molecules on the crystalline surface. The diffusion coefficient was reduced with significant differences in different directions X, Y and Z. In comparison, the electric field influence on water diffusion in amorphous cellulose model was lack in regularity.

This paper proposes a concept of advanced spectrum database generated through the measurements of radio environment from sensors on vehicles. In the current design of TV white space spectrum databases, which is regulated by the United States Federal Communications Commission (FCC), a propagation model is utilized to decide on the availability of spectrum. To ensure protection of primary users (PUs) this approach requires a large margin for the PU coverage area. In the proposed database, the actual measurements of radio signals are used to generate the database. Since the sensors on moving vehicles gather large amount of data, an accurate propagation status can be obtained, including propagation loss and shadowing. In this paper we introduce the concept of advanced database and present initial results of experimental database generation using actual TV broadcast signals.

The lecture is focusing on some computational intelligence methods in designing of complex systems. Computational Intelligence is an emerging area which provides tools for modeling and analyzing of complex systems. It uses techniques like fuzzy logic, neural networks, genetic algorithms, etc. Nowadays fuzzy logic and expert systems are considered good approaches to solve control problems in a wide range of applications. We present practical applications of some intelligent complex systems in different fields by providing them with elements of deterministic knowledge about the processes and by assisting the design with simulations.

Cloud computing is the newly emerged technology adopted by many organizations due to its different benefits. Unfortunately, despite all the benefits offered by the cloud, there are certain concerns regarding the security issues related to the cloud platform which can threaten its widespread adoption. In this study, we suggest a scalable model to profile and prevent security attacks in the application layer of a cloud environment using an accurate and interpretable machine learning algorithm called regularized class association rules. The proposed model is based, first, on three additional security factors $(k, l$ and $m)$ , second, on the traditional authentication methods such as passwords and biometrics including keystroke to grant access to the cloud services/resources for an authorized user. Moreover, a case study of the proposal is given in order to validate the model and its usefulness. Eventually, a simulation was done to test the model performances.

It is presented theoretical statements about decentralized systems technology, blockchain networks, their structure as well as main functioning principles, structure and material about Hyperledger platform. It is examined Hyperledger services architecture, spheres of use and such important aspects as smart-contracts and their application, as well as advantages of using this platform. It is also modelled and explained the algorithm and schematic diagram of voting network built with Hyperledger platform.

With the internet having reached a level that merges with our lives, growing explosively during the last several decades, data security has become a main concern for anyone connected to the web. Data security ensures that our data is only accessible by the intended receiver and prevents any modification or alteration of data. In order to achieve this level of security, various algorithms and methods have been developed. Cryptography can be defined as techniques that cipher data, depending on specific algorithms that make the data unreadable to the human eye unless decrypted by algorithms that are predefined by the sender.

This paper summarizes opportunities and challenges concerning how we can achieve security while still ensuring privacy. It identifies research directions and includes a number of questions that have been debated by the panel.

In this paper., the application of genetic algorithm in auto-generating test paper is studied. The linear discriminant analysis(LDA)is used to personally train the weight matrix of the objective function of the test paper. The optimal retention strategy and niche technology based on exclusion mechanism are combined to solve the problem that genetic algorithm is easy to “premature” convergence, and the parallel selection method is introduced to improve the running speed of the algorithm. In addition, the adaptive technique is used to set the appropriate exchange and mutation probability for genetic algorithm, and mountain climbing algorithm(MCA)is introduced to simulate the mutation process. Based on this, a new test paper algorithm(LNPAC-GA)is proposed., and is applied to the automatic test volume. Finally, an experiment is carried out by using the test question bank, in which the new algorithm is compared with the existing improved genetic algorithm (i.e. real coding, niche technology based on exclusion mechanism, adaptive technology to improve the genetic algorithm)which is accepted in the field of automatically generating test paper. Experiments show that the new algorithm can improve the speed and success rate of automatically generating test paper.

The influence of a scientific paper in a citation network can be used as a basis for assessing the work and research results of a scientist. At present, there are lots of research determining the influence of a scientific paper in a citation network. However, combining Big Data processing technologies (such as Apache Spark) and Graph Databases (such as OrientDb) to address this problem is still challenging. In this paper, we present our research results in combining Apache Spark and OrientDb to find the influence of a scientific paper in a citation network and solve some related problems. In addition, we also present the test results of the processing time of some important functions in our experimental system. Finally, that is the conclusions and future works.

Printable electronics based electromagnetic absorbers are receiving increasing attention of the electromagnetic community because of their unprecedented advantages. This paper presents the design of printable electromagnetic absorbers for the X band. The design of the absorber is optimized using the Genetic Algorithm (GA) to enhance the absorptivity and the absorption bandwidth. The design involves the placement of several square-shaped conductive ink at optimal locations on the paper substrate such that desired absorption characteristics are obtained. Simulations are carried out using the HFSS simulation software. The optimized structure offers an absorptivity of more than 90% in the X band thereby proving to be a viable solution for stealth applications.

According to the characteristics of the online test paper composition, the genetic algorithm is preferred as the core algorithm. However, there are some problems in the traditional genetic algorithm when it is being used, such as low efficiency in the later search period and easy to fall into the local optimal solution. To solve this problem, this paper makes some optimizations for the traditional genetic algorithm's coding scheme, initial population, fitness function and genetic operator. After the improvement, the composing test paper module based on genetic algorithm was finally implemented in java language through seven parts.

The Panama Papers represent a large set of relationships between people, companies, and organizations that had affairs with the Panamanian offshore law firm Mossack Fonseca, often due to money laundering. In this paper, we address for the first time the problem of searching the Panama Papers for people and companies that may be involved in illegal acts. We use a collection of international blacklists of sanctioned people and organizations as ground truth for bad entities. We propose a new ranking algorithm, named Suspiciousness Rank Back and Forth (SRBF), that leverages this ground truth to assign a degree of suspiciousness to each entity in the Panama Papers. We experimentally show that our algorithm achieves an AUROC of 0.85 and an Area Under the Recall Curve of 0.87 and outperforms existing techniques.

In this modern world, the correct identification of paper currency is an essential need for the automation of current systems. The automatic currency identification method is highly required to reduce the human power in recognizing the currency and its associated value. Sometimes, the damaged or blurred currency and the complex design of the currency make the task of identification much difficult. In this paper, a method is proposed that can identify paper currency by considering the identification marks given on the currency. Specifically, this study focuses on the identification of Pakistani paper currency. The proposed method makes use of the fundamental image processing techniques for differentiating the major denominations. Particularly, the proposed algorithm uses Otsu’s thresholding method and circulatory matrix criteria for locating the special identity marks given on the currency. The effectiveness of the proposed approach has been shown by performing various experiments on all of the major denominations of Pakistani paper currency with varying intensities. The Proposed method shows quite satisfactory results in terms of accuracy and efficiency.

Biometric identification systems must be able to distinguish between individuals in various conditions. This paper presents a review paper on face matchers between commercial and open source SDK and techniques available for face recognition. This paper also presents experiments done in performance comparisons between a commercial face matcher, VeriLook 5.6 and an open source matcher OpenBR 0.5 from a set of images collected from those who work in the offices in various situations including the different poses, and various illuminants. The experiment results taken from those officers in NECTEC have shown that a commercial face matcher performs better but this requires proper set up of face extraction parameters to ensure the optimal results. Further improvements would require other face detection algorithms to enhance the performance of both free and commercial face matchers according to their own particular shortcomings.

Gamification is the incorporation of gaming elements into non-game situations, such as education or training. We have developed a game that can be used to teach signals concepts, and have implemented it in both a pen-and-paper version and in an electronic version. This paper discusses the game and how it applies pedagogical concepts such as active learning and game-centric curriculum design. We discuss the merits and drawbacks of each type of implementation, student reactions, and how a similar game could be incorporated into other signals-based classes.

Monitoring health of a person(s) has become a major IoT application where we need to have a system at home that helps inhabitants to have their health check done without affecting their daily routines. It is very much essential to have a IoT driven remote health monitoring system for ailing individuals which can inform caretakers in case of an emergency. These applications are constrained by the amount of data collected, managed and exchanged. The factors that constraint IoT applications are robustness, privacy, security and reliability. In this paper, a survey of various health care solutions using Internet of Things (IoT) is presented. This paper presents various challenges and problems that occur in smart health system and proposes solution to overcome them.

Exam Paper Generator provides a solution to choose challenging, well framed questions and make it easy for the instructor to generate it within a short period of time. This can be done in a few taps of the hand as it is an Android application, therefore accessible at any time and place. It contains various modules which enables the system to deal with all questions easily. The modules like admin module, user module, and question entry and question management makes it an effortless task. From the entered input the paper is generated and saved as a .pdf file which can be kept for own or distributed as per the user or admin requirements.

This article studies a paper cutting platform in a comprehensive approach to folk art, folklore, communication, computer and information science focusing on paper-cut in Hebei China. The main content includes:①the data collecting and recording of the scattered Chinese folk paper-cut work in Hebei,②the digitization, optimization, compression, classification, icon and pattern extraction, vectorization and work analysis of the first-hand material, ③the demonstration, dissemination, database construction and retrieval of the classified material, icons and patterns,④ the demonstration of the reconstruction and application of icon and pattern database, and ⑤the design and development of immersive virtual gaming platform of the multi-media scenes and production process of folk paper-cut. In this article, a virtual multi-media system framework of paper cutting is constructed.

Automatic plagiarism detection deals with retrieval of reused fragment of texts in a document and finding source documents. Due to development of various methods for plagiarism detection, large scale plagiarism corpora are needed to evaluate these methods. Despite of their importance, few plagiarism detection corpora developed in recent years, especially in low resource languages. Because of legal issues, releasing a collection of real cases of plagiarism for evaluation purposes is not ethical. Due to these limitations, simulation and artificial based methods are the two main approaches to compile a plagiarism corpus. These approaches try to simulate real cases of plagiarism, from different point of views. However, there are still fundamental differences between simulated corpora and real cases of plagiarism. In this paper a semi-real approach is proposed to create a collection of plagiarism cases as a corpus. This approach is based on eliminating correct references from scientific papers to make them as plagiarized passages. Unlike methods based on simulated and artificial approaches, the proposed corpus can correctly simulate real cases of text re-use. The evaluation result shows high accuracy of proposed corpus with respect to n-gram similarity for different ranges of N.

This paper describes wireless control and automation of various processes in paper mills using CC3200. Embedded web server is preferred over PC server in case of internet applications based on client/server architecture for its decreasing size, cost and power consumption. CC3200 has an exclusive feature of low power consumption which makes it convenient for use in industries. A user-friendly webpage interface nodered is introduced to the users and system administrators to locally (LAN) or remotely (internet) manage and control the processes. Email alerts are sent to the users in case of faults and intelligent algorithms can be implemented to rectify the faults which improves the efficiency of the system.

This paper presents an information-theoretic approach to variable selection for prediction of laboratory measurements of paper quality. Along with a well-known Principal Component Analysis we considered techniques for variable selection based on the classical Shannon Mutual Information and a novel Maximal Information Coefficient. A multilayer perceptron neural model was used to predict quality measurements and compare feature selection techniques. The suggested approach was tested on real industrial data obtained form a pilot paper machine. The presented results show that information-theoretic techniques perform better compared to Principal Component Analysis, providing higher accuracy results.

The performance of mobile devices, especially smart phones, has been quickly improved for the last few years. Most users take advantage of highly efficient smart phones, and consume the contents in the smart phones longer time than other devices usage time. As a result, users frequently share the contents and the needs of file sharing via smart phones have been increased considerably. Existing peer-to-peer sharing frequently incurs disconnections and retransmissions. A Web hard-based sharing needs to pay expensive cost for using high-volume file servers as well. In order to overcome such problems, we propose an application for seamless file sharing for the Android devices. The seamless service manager and the file manager in the proposed application share files seamlessly by choosing faster and more stable network automatically - one of the Bluetooth and the WI-FI. We expect that the proposed application could be a cost effective and reliable solution for file sharing among mobile devices.

To enhance the integration of different IoT application domains, we propose a two-layered IoT information model that consists of domain-independent and domain-specific models. In addition, for the meaningful composition of things and their services, a semantic URI as a unique identifier is allocated to a thing. Finally, a modeling approach of constructing physical mashups is proposed for the event-driven composition of IoT services. In order to show the efficiency of the proposed event-driven physical mashup approach, an energy management scenario in u-campus is demonstrated as a case study.

We describe the evolution of a system designed to allow digital grading, by a human, of traditional paper-based exams or homework assignments. We present a number of new features that build upon the system described in a paper previously published in ITiCSE 2008 [1]. In particular, our system now has the ability to monitor an enormous range of data, from individual question scores, to variances for the same questions across different graders, to question and exam statistics from one semester to the next. The system boasts a number of benefits over paper based grading. Overall grading time is reduced — small courses will see a modest improvement, while large courses can see their grading time cut in half due to the automation of many of the tasks: flipping to the correct page, summing up the pages, recording the grades, returning the exams, etc. Rapid grading of exams can also aid students in a better understanding of the material, as the exam questions are still fresh in their minds when the tests are returned. Simultaneous grading across multiple locations is possible, as there is no single paper copy to hand off to the next grader. Permanent digital records are kept of the exams, allowing for viewing of exams at a later date.

Automatic test paper generation is an important part of examination system. How to improve the test paper generating algorithm and the quality of test paper generating has become an important issue at present. Based on the researches on code tactic, adaptive function, controlling parameters, an improved genetic algorithm is used in the paper. Experiment shows that the improved genetic algorithm could compose test paper more efficiently than traditional algorithms.

Paraphrase extraction is the method of collecting words, phrases or sentences that have same meaning from free text or structured documents. Candidate monolingual pararel corpus was build from scientific papers by clustering techniques. The corpus contain couples of citation sentences that mention same index. The corpus evaluted using weighting techniques and proximity measurements based on the density of each sentence cluster. The results of this reserach are expected to simplify paraphrase labeling by language and computer science experts.

Now a days with the advancement of technology Internet of Things(IOT) has grown up day by day to cater the need of variety of applications in various domains such as consumer applications, smart homes, medical andhealthcare, transportation, home automation, agriculture and many more. IOT may be considered as things or object connected to internet. The name Internet of Things was coined by Kevin Ashton and it was name so as multiple of technologies like sensors embedded systems, wireless sensor network(WSN), control system and machine learning contributed in making the concept of IOT. This paper presents a survey on different architectures of IOT and their challenges like wear ability, security, privacy etc.

The virtualization of network functions has been proposed and gradually introduced into telecommunication networks since 2012. While the promise of NFV was the transfer of established functions from proprietary hardware to commodity servers as well as a harmonization of network management, little of this can be seen in today's networks. The reason for the slow adoption of NFV in real networks is first and foremost the tradeoff between commoditization and performance. In other words, just where I/O speed is of utmost importance, NFV has its weakest point. With the advent of programmable network devices like SmartNICs and whitebox switches the achievable performance increases dramatically. However, offloading of packet processing to hardware outside of the VM or container requires a control channel between the virtualized network function (VNF) and the network accelerator. The paper discusses options for this and introduces baseboxd, an Open Source SDN controller that uses the Linux kernel's tools to offload packet processing. Using baseboxd, a plethora of existing Open Source projects can be re-used and no “SDN controller apps” have to be written, as all networking relies on the Linux kernel.

With the continual growth of low-cost and high-throughput DNA sequence technology, the scale and amount of next-generation sequencing (NGS) datasets are continually increasing in many genomics research areas. Shotgun metagenomics sequencing provides comprehensive information on microorganisms, based on complex samples of the ecosystem. Due to challenges of its scale and computational complexity, efficient sequence processing and analyzing tools are needed. In this paper, we propose a novel high-performance shotgun metagenomics sequence analysis engine for the task of sequence comparison. It includes two major components. First, a customized shifting database, which is optimized from any existing DNA sequence dataset. Second, a high-performance sequence computation algorithm that utilizes the customized shifting reference database and accelerates GPU parallel computing. We elaborated upon the efficiency and computational complexity of our proposed approach in an HPC server, which has eight Nvidia Tesla P100 GPUs. We also conducted a case study to detect viral sequences from patients' blood samples. Our experimental result shows that we obtain similar accuracy to the conventional BLAST method, but with a computational speed that is about twenty times faster.