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Simulating applications and benchmarks can take hundreds of hours in full-system cycle-accurate simulators. This problem is exacerbated in many emerging applications, as they execute a large amount of dynamic instructions. For instance, in contrast to SPEC CPU2006 benchmarks, the newly released SPEC CPU2017 programs dramatically increase the total instruction count, which results in a much longer runtime. Therefore, it is of interest to analyze the program behavior to tell whether there are distinct behaviors throughout the executions or the executions are formed by the repetitions of the same behavior. Techniques that identify the repeated program behaviors (a.k.a simulation points) can narrow down the regions of interest. Researchers leverage such techniques to only simulate the regions of interest while maintaining a high simulation accuracy.In this paper, we study the phase behavior of the recent SPEC CPU2017 benchmarks and provide simulation points for them using the SimPoint methodology. We find that the number of simulation points are approximately the same as that for CPU2006, even though CPU2017 has significantly higher execution time. Besides identifying SimPoints, we also study the time-varying behavior of the SPEC CPU2017 benchmarks, and observe a strong correlation between the runtime behavior and the simulation points that are invoked.

The internet of things (IoT) or industrial internet of things (IIoT) has incredible benefits and potential for much more. It promises vast improvements in operational efficiencies and reduction of catastrophic failure. The full benefits of such technology may be limited by incompatibilities due to manufacturer protocol and device differences. These challenges will be passed on to the user resulting in diminished benefits and frustration if not anticipated and mitigated accordingly. This paper will explore available technologies and address guidelines for future standard and specification development while providing parallels to similar technology introductions in large scale industrial facilities for the review of lessons learned.

Static analysis is a great approach to find bugs and code smells. Some of the errors span across multiple translation units. Unfortunately, separate compilation makes cross translation unit analysis challenging for C family languages. In this paper, we describe a model and an implementation for cross translation unit symbolic execution for C family languages. We were able to extend the scope of the analysis without modifying any of the existing checkers. The analysis is implemented in the open source Clang compiler. We also measured the performance of the approach and the quality of the reports. The solution proved to be scalable to large codebases and the number of findings increased significantly for the evaluated projects. The implementation is already accepted into mainline Clang.

Highly configurable software systems allow the efficient and reliable development of similar software variants based on a common code base. The C preprocessor CPP, which uses source code annotations that enable conditional compilation, is a simple yet powerful text-based tool for implementing such systems. However, since annotations interfere with the actual source code, the CPP has often been accused of being a source of errors and increased maintenance effort. In our research, we have been curious about whether high-level patterns of CPP misuse (i.e., code smells) can be identified, how they evolve, and whether they really hinder maintenance. To support this research, we started a simple tool which over the years evolved into a powerful toolchain. This evolution was possible because our toolchain is not monolithic, but is composed of many small tools connected by scripts and communicating via files. Moreover, we reused existing tools whenever possible and developed our own solutions only as a last resort. In this paper, we report our experiences of building this toolchain. In particular, we present design decisions we made and lessons learned, both positive and negative ones. We hope that this not only stimulates discussion and (in the best case) attracts more researchers in using our tools. Rather, we also want to encourage others to put emphasis on building tools instead of considering them "yet another research prototype".

The susceptibility of Field Programmable Gate Arrays (FPGAs) to power analysis attacks has gained more attention lately. While this was predicted a long time ago, it was not until recently that successful attacks were reported. Xilinx FPGA families before the UltraScale architecture offer little when it comes to protecting the encryption keys from being retrieved via power analysis attacks. In this paper, we propose a scheme for securing Xilinx FPGAs against these attacks by using the on-chip Xilinx Analog-to-Digital Converter (XADC) to monitor the power consumption of the chip during an encrypted partial bitstream configuration. Our proposed solution to power analysis attacks involves activating or deactivating power consumer circuits to level out the power consumption of the FPGA with the aim of masking the power signature of the encryption key. We present here our implementation strategy and preliminary power consumption measurements of FPGA primitives that can be used to achieve this objective.

The problem of classifying a research article into one or more fields of science is of particular importance for the academic search engines and digital libraries. A robust classification algorithm offers the users a wide variety of useful tools, such as the refinement of their search results, the browsing of articles by category, the recommendation of other similar articles, etc. In the current literature we encounter approaches which attempt to address this problem without taking into consideration important parameters such as the previous history of the authors and the categorization of the scientific journals which publish the articles. In addition, the existing works overlook the huge volume of the involved academic data. In this paper, we expand an existing effective algorithm for research articles classification, and we parallelize it on Apache Spark –a parallelization framework which is capable of sharing large amounts of data into the main memory of the nodes of a cluster– to enable the processing of large academic datasets. Furthermore, we present data manipulation methodologies which are useful not only for this particular problem, but also for most parallel machine learning approaches. In our experimental evaluation, we demonstrate that our proposed algorithm is considerably more accurate than the supervised learning approaches implemented within the machine learning library of Spark, whereas it outperforms them in terms of execution speed by a significant margin.

Blockchain technologies are secure massive power in the last few years. Block chains are distributed ledgers that facilitate parties who do not steady trust each other to maintain a set of ecumenical states. The parties concur on the easiness, standards and histories of the states. As the technology backdrop is enlarge swiftly, it is mutually consequential and arduous to have a rigid grasp of what the foundation technologies have to offer, particularly with difference to their data dealing out capabilities. In this paper, we first survey the state of the art, fixating on secretive block chains. We analyze both in-engenderment and research systems in four dimensions: disseminated ledger, intrusion detection, consortium Blockchain, consensus protocol and perspicacious agreement. We then present BLOCKBENCH, a criterion skeleton for understanding performance of private block chains and public Blockchain. In this chapter we have discussed the circumscriptions of Blockchain.

In this paper, an integrated fault reconstruction and fault-tolerant control (FTC) synthesis for a class of nonlinear systems represented by Takagi-Sugeno models is considered. The scheme is based on the design of an augmented proportional-integral (PMI) observer for joint system-state estimation / fault reconstruction and hiding approach for active fault-tolerant control by controller reconfiguration. Sufficient conditions for the existence of integrated solutions are formulated by linear matrix inequalities. A numerical example is used to illustrate the effectiveness of the proposed method.

This paper develops an innovative scalable and low-cost QoS-based user equipment (UE) virtualization (UEV) scheme that capitalizes on the massive connectivity and the enhanced resources of the new UE generation in terms of connectivity, computing, battery/power, etc. Exploiting the need/excess duality and the heterogeneity in such resources at the UE plan, we form virtual UEs (VUE)s, dynamically, owing to a carefully-designed time-adjusting scheme for the selection of the proper cooperative UE sets. The new UEV scheme is able to adapt to each target UE (TUE) environment, meet its demands, and scale with its needs, offers a reliable and efficient yet low-cost inter-UE cooperation, reduces the overhead and power consumption with respect to conventional approaches, and substantially reduces the number of communication links and, hence, incurs much less interference. System-level simulation results show that the proposed QoS-based UEV scheme largely outperforms the “dummy UEs” approach.

With rapid increases in Cloud users and their workload, the Qos (quality of service) is decreasing proportionally. Providing better computing services, managing large number of cloud users and reducing time as well as energy on cloud data center can only be achieved by efficient live VM migration technique. Modern cloud computing service mainly depends upon live VM migration. In this paper we have reviewed all the existing live VM migration techniques with their advantages and disadvantages. Their working and application with future scope has been stated briefly. Comparisons have been done between Pre-copy and Post-copy VM Migration techniques by simulation corresponding to CPU uses, Memory and Network as parameters. Then a Hybrid VM Migration technique has been proposed and by comparison we proved it is better than the pre copy and post copy.

The Intelligent vehicle is experiencing revolutionary growth in research and industry, but it still suffers from a lot of security vulnerabilities. Traditional security methods are incapable of providing secure IV, mainly in terms of communication. In IV communication, major issues are trust and data accuracy of received and broadcasted reliable data in the communication channel. Blockchain technology works for the cryptocurrency, Bitcoin which has been recently used to build trust and reliability in peer-to-peer networks with similar topologies to IV Communication world. IV to IV, communicate in a decentralized manner within communication networks. In this paper, we have proposed, Trust Bit (TB) for IV communication among IVs using Blockchain technology. Our proposed trust bit provides surety for each IVs broadcasted data, to be secure and reliable in every particular networks. Our Trust Bit is a symbol of trustworthiness of vehicles behavior, and vehicles legal and illegal action. Our proposal also includes a reward system, which can exchange some TB among IVs, during successful communication. For the data management of this trust bit, we have used blockchain technology in the vehicular cloud, which can store all Trust bit details and can be accessed by IV anywhere and anytime. Our proposal provides secure and reliable information. We evaluate our proposal with the help of IV communication on intersection use case which analyzes a variety of trustworthiness between IVs during communication.

This paper introduces some trust issues for blockchain transactions. Such transactions rely on ECDSA signature based on 32 bytes secret keys. Because these keys can be stolen or hacked, we suggest to prevent these risks by using javacard secure elements. We detailed the interface of a Crypto Currency Smart Card (CCSC) designed over a JC3.04 standard platform. We details two use cases. First deals with deployment of the CCSC application in low cost low power object powered by an open hardware (arduino) platform, and integrates sensor data in ethereum transactions. Second proposes the deployment in the cloud of CCSC javacards plugged to RACS servers, in order to enable the remote and safe use of ECDSA signature.

This work aims to formulate a procedure to establish a graded trust system within autonomous vehicular networks. A trust management system in VANETs typically falls into one of two categories: centralized or distributed. Our solution is a hybrid system which merges the two canonical designs of a centralized and distributed structure that utilizes to capabilities of both systems. In this paper we provide the design of a trust system that takes into account both central and local evaluations to establish trust. In order to validate the trust system we design a simulation model in MATLAB that captures the interaction between different vehicles and between the vehicles and the Central Authority. The simulations show a high decision performance for the trust management system and validate the proposed scheme as a coherent grading system.

In this position paper, I present a view of education in 50 years from now and how a 100 percent success rate is achievable by every high school and college student, via the use of virtual teachers and virtual psychologists, knowledge bases and artificial intelligence.

This paper summarizes opportunities and challenges of big data. It identifies important research directions and includes a number of questions that have been debated by the panel.

As pricing and margin pressures in the pulp & paper industry continue to increase, overall equipment effectiveness (OEE) has become more than just a catch phrase; it is an essential element in the viability of any player in the industry. Every level of the organization can have an impact on OEE - positive, and negative. This paper discusses how remote monitoring and expert diagnostic support fill the need to improve OEE by supporting operations, maintenance, engineering and management. These programs assure assets are in operation longer, machines perform at or above design production speed for a given product and ultimately delivering more saleable product. This is accomplished by reducing or eliminating instances of unscheduled down time, reducing the duration of such down time, increasing the effectiveness, availability and value of support resources, increasing quality by optimizing system performance and by delivering return on investment (ROI) to management. Real-world pulp & paper industry case examples are used to demonstrate how remote monitoring and expert diagnostic support programs actually affect ROI for existing users. These case examples range from predictive failures of simple devices to deep dive (in-depth), system design, performance and product issue analyses and resolutions.

We present in this paper a developing project attempting to integrate the strengths of mobile technology into paper-based reading activities to enhance learners’ reading comprehension. While conventional print text provides very limited information in fostering learners’ comprehension, integrating mobile technology with paper prints is a possible way to offer learners essential content-related resources to make sense of the text. We explored the possibility of this integration, and to verify the practicability of such a system in an English reading practice.

Using computers for assessment can provide several benefits for educators and test-takers. However, in the literature, there is no consensus on the equivalence of paper-and-pencil and computer-based test environments. Accordingly, more evidences are needed especially for the engineering education. In this study, students' performance on different test modes was evaluated on 209 first year engineering students of a chemistry course. The results of this study showed that, there is no significant performance difference between paper-and-pencil and computer based tests. By comparing results with the previous studies, this study concludes that, personal characteristics of test takers, the features of computer-based testing systems and the test content are all possible confounding factors when comparing test modes and need to be considered by the implementers. The results of this study show that, once these factors are controlled, students' performance on computer-based tests and paper-and-pencil tests in chemistry courses for the engineering students will not vary. This finding is encouraging the educators to get benefits of computer-based tests without any affect on students' performance.

Some distance education institutions have invested heavily in print-based resources, and learners still show a preference for print; but some concepts and practical or skills based courses, for example science or engineering, do not translate well into the print medium. This paper presents Augmented Reality (AR) as a possible solution and discusses the development and implementation of an AR application into traditional print learning materials.

A bibliometric analysis was applied in this work to evaluate global scientific production of data mining papers of the Science Citation Index (SCI), which was compiled by Institute for Scientific Information (ISI), Philadelphia, USA. The published output analysis showed that data mining research steadily increased over the past 20 years and the annual paper production in 2007 was about three times that in 1999. The analytical results eventually lead to several key findings. Several author keywords became the focus in the last few years, and might be a new research direction in the future. There are clear distinctions among author keywords used in publications from the five most productive countries (USA, China, Germany, England and Canada) in data mining research. Bibliometric methods could quantitatively characterize the development of global scientific production in a specific research field.

With today’s eye-tracking technologies, it is possible to analyze cognitive processes such as reading. There are many studies on that topic. We have studied some of these studies that analyze eye movements recording during reading by various machine learning algorithms.

The objectives of this study are: (1) developing LMS technology by using Makerspace approach on unique experiments-based through MOOCs; (2) testing product validation; (3) testing the effectiveness of MOOCs products. This study uses R & D method. The results of this study are: (1) test results in aspects of material content and product usefulness of each indicator have a percentage of: 85% (material novelty), 73.30% (material relevance), 82.70% (political), 75% (material validity), 82% (target suitability), 87.3% (product accuracy), 75% (function), and 81.5% (ease of access); (2) LMS technology by using Makerspace approach on unique experiments-based through MOOCs has proven effective in improving the professional competence of vocational students. This can be seen by the significant differences between experimental classes (using MOOCs products) and control classes (not using MOOCs products); and (3) LMS Technology Unique experiment-based Makerspace approach through MOOCs needs to be developed in a valid manner to improve the professional competence of vocational students.

Whole-genome multiple alignments are widely used in genomics and evolution, and yet their accuracy is imperfect, due in part to the computational complexity of the task at hand. Identifying portions of these alignments that are likely to be incorrect would allow researchers to either work on improving them or flagging them for exclusion from downstream analyses. We introduce MSA-ED, a machine learning tool for the detection of errors in whole-genome multiple alignments. MSA-ED uses random forests or artificial neural networks to identify and classify several types of alignment errors. It is trained on labeled data obtained by using an evolution simulator to generate fake orthologous sequences and their correct alignment, and comparing it to the alignment produced by Multiz, a popular whole-genome aligner. Key to the success of MSA-ED is the engineering of several types of evolutionarily-inspired features that boost prediction accuracy. MSA-ED is shown to be able to detect certain types of errors with good accuracy. It is then applied to actual genomic alignments to identify putative alignment errors. Availability: https://github.com/jaspal1329/MSA-ED

Genetic network inference methods using random forests have shown promise. Some of the random-forest-based inference methods have an ability to analyze both time-series and static gene expression data. We think however that, as the gene expression levels at two adjacent measurements of a time-series data are often similar to each other, the gene expression levels at each measurement in the time-series data are less informative than those in the static data. On the basis of this idea, we proposed a new inference method that relies more on static gene expression data than time-series ones. Through the numerical experiments, we showed that the quality of the inferred genetic network is slightly improved by giving greater importance to static data than time-series ones. Although we develop the new method by modifying the random-forest-based inference method proposed by the authors, we could introduce the idea in this study into any inference method that is capable of analyzing both time-series and static gene expression data.

Precise prediction of severe diseases resulting in mortality is one of the main issues in medical fields. Even if pathological and radiological measurements provide competitive precision, they usually require large costs of time and expense to obtain and analyze the data for prediction. Recently, end-to-end approaches based on deep neural networks have been proposed, however, they still suffer from the low classification performance and difficulties of interpretation. In this study, we propose a novel disease prediction method, EHAN (EHR History-based prediction using Attention Network), based on the recurrent neural network (RNN) and attention mechanism. The proposed method incorporates (1) a bidirectional gated recurrent units (GRU) for automated sequential modeling, (2) attention mechanism for improving long-term dependence modeling, (3) RNN-based gradient-weighted class activation mapping (Grad-CAM) to visualize the class specific attention-weights. We conducted the experiments to predict the occurrence of risky disease containing cardiovascular and cerebrovascular diseases from more than 40,000 hypertension patients' electronic health records (EHR). The results showed that the proposed method outperformed the state-of-the-art model with respect to the various performance metrics. Furthermore, we confirmed that the proposed visualizing methods can be used to assist data-driven discovery.

The widespread of 3D-printing technology has resulted in the appearance of many open-source prosthetic hand models, especially for partial hand amputations. However, most of these designs are not editable and while some are parametric to some degree, customization for every user is limited to scaling the size of a base design. As consequence, most prostheses fail to closely match the user specific anthropometry and have poor aesthetics, which could result in abandonment of the device. Furthermore, achieving a high degree of customization could be a time-consuming task and requires previous knowledge of CAD design. This work presents a prosthetic hand easy to customize by changing parametric dimensions of the finger phalanges and palm on an Excel sheet. Additionally, the design tackles common issues from previous 3D-printed body-powered prosthetic hands by incorporating new features such as the use of linkages instead of cables as finger flexors and a new cable-adjusting system which requires no additional tools and makes the tensioning of finger tendons easier and quicker.

In the new era of genetic epidemiology, there have been growing interest in studying genetic variants and their associations to complex diseases. Advances in modern computational approaches have led to the search for useful interacting genetic variants that are associated to the manifestation of a disease. However, these conventional strategies face number of challenges in predicting interesting interactions when data acquisition and dimensionality increases. Deep learning promises empirical success in number of applications including bioinformatics to drive insights of biological complexities. A deep neural network was previously proposed to identify true causative two-locus SNP interactions. The method was evaluated on various simulated and real datasets. In this study, the performance of the previously proposed deep learning method is maximized by improving network learning and avoiding overfitting. The method is further extended for performing sensitivity analysis. The performance of the method is evaluated on chronical dialysis patient's data for identifying higher-order interactions. It was observed that the highly ranked two-locus and three-locus SNP interactions in mitochondrial D-loop has the highest risk for the manifestation of disease.

Software systems naturally evolve, and this evolution often brings design problems that cause system degradation. Architectural smells are typical symptoms of such problems, and several of these smells are related to undesired dependencies among modules. The early detection of these smells is important for developers, because they can plan ahead for maintenance or refactoring efforts, thus preventing system degradation. Existing tools for identifying architectural smells can detect the smells once they exist in the source code. This means that their undesired dependencies are already created. In this work, we explore a forward-looking approach that is able to infer groups of likely module dependencies that can anticipate architectural smells in a future system version. Our approach considers the current module structure as a network, along with information from previous versions, and applies link prediction techniques (from the field of social network analysis). In particular, we focus on dependency-related smells, such as Cyclic Dependency and Hub-like Dependency, which fit well with the link prediction model. An initial evaluation with two open-source projects shows that, under certain considerations, the predictions of our approach are satisfactory. Furthermore, the approach can be extended to other types of dependency-based smells or metrics.

In the last decade, data security has become a primary concern for an increasing amount of companies around the world. Protecting the customer's privacy is now at the core of many businesses operating in any kind of market. Thus, the demand for new technologies to safeguard user data and prevent data breaches has increased accordingly. In this work, we investigate a machine learning-based approach to automatically extract sources and sinks from arbitrary Java libraries. Our method exploits several different features based on semantic, syntactic, intra-procedural dataflow and class-hierarchy traits embedded into the bytecode to distinguish sources and sinks. The performed experiments show that, under certain conditions and after some preprocessing, sources and sinks across different libraries share common characteristics that allow a machine learning model to distinguish them from the other library methods. The prototype model achieved remarkable results of 86% accuracy and 81% F-measure on our validation set of roughly 600 methods.

Composite commits are a common mistake in the use of version control software. A composite commit groups many unrelated tasks, rendering the commit difficult for developers to understand, revert, or integrate and for empirical researchers to analyse. We propose an algorithmic foundation for tool support to identify such composite commits. Our algorithm computes both a program dependence graph and the changes to the abstract syntax tree for the files that have been changed in a commit. Our algorithm then groups these fine-grained changes according to the slices through the dependence graph they belong to. To evaluate our technique, we analyse and refine an established dataset of Java commits, the results of which we also make available. We find that our algorithm can determine whether or not a commit is composite. For the majority of commits, this analysis takes but a few seconds. The parts of a commit that our algorithm identifies do not map directly to the commit's tasks. The parts tend to be smaller, but stay within their respective tasks.

The online system is rapidly developing and widely used for companies to market their products. The products can always be more diverse and abundant. However, it creates difficulties for the company to provide recommendations to users about products which are suitable with users' interest. This condition encourages a recommendation system. One of the popular methods of this recommendation system is Collaborative Filtering (CF) by using rating-based and ranking-based approaches. Some of the ranking-based methods are Copeland method and Borda method. Both methods use a user-rating approach limited to the user preference profiling processes. Therefore, this research proposes the use of the user-rating to get the normalized rating frequency (NRF). Normalization process was done through the calculation of the frequency of a user-rating, which eventually generated a product ranking for recommendations to users. Experimental results of the NRF method can improve the performance of the recommendation system. This can be seen from the recommendations produced by the NRF method was more relevant in accordance with the wishes of the user, which is indicated by the average value of Normalized Discounted Cumulative Gain (NDCG) higher than Copeland and Borda methods. In addition, the NRF method has a faster computation time with a simpler algorithm than the Copeland and Borda methods.

As the quality and quantity of open source code increase, semantics-based code search has become an emerging need for software developers to retrieve and reuse existing source code. We present an approach of semantics-based code search using input/output examples for the Java language. Our approach encodes Java methods in code repositories into path constraints via symbolic analysis and leverages SMT solvers to find the methods whose path constraints can satisfy the given input/output examples. Our approach extends the applicability of the semantics-based search technology to more general Java code compared with existing methods. To evaluate our approach, we encoded 1228 methods from GitHub and applied semantics-based code search on 35 queries extracted from Stack Overflow. Correct method code for 29 queries was obtained during the search and the average search time was just about 48 seconds.

The cognitive infocommunication based IT tools can support the education abilities from the point of view of mathability, edu-coaching, 3D/VR etc. In the modern mathematical and engineering education the contemporary tools of cognitive infocommunication, coaching in computer assisted education can be a valuable method for discovering and applying engineering knowledge using problem solving abilities. The edu-coaching offers an alternative way to improve the efficiency of education based on the support of disruptive technologies, cooperative 3D VR learning environments, which fits properly to the digital life of new generation of students. The cognitive infocommunication based ICT can improve the effectiveness of learning because of the effect on motivation and creativity.

Local differential privacy (LPD) is a distributed variant of differential privacy (DP) in which the obfuscation of the sensitive information is done at the level of the individual records, and in general it is used to sanitize data that are collected for statistical purposes. LPD has the advantage it does not need to assume a trusted third party. On the other hand LDP in general requires more noise than DP to achieve the same level of protection, with negative consequences on the utility. In practice, utility becomes acceptable only on very large collections of data, and this is the reason why LDP is especially successful among big companies such as Apple and Google, which can count on a huge number of users. In this talk, we propose a variant of LDP suitable for metric spaces, such as location data or energy consumption data, and we show that it provides a much higher utility for the same level of privacy. Furthermore, we discuss algorithms to extract the best possible statistical information from the data obfuscated with this metric variant of LDP.

© 2018 IEEE. During the secure boot process for a trusted execution environment, the processor must provide a chain of certificates to the remote client demonstrating that their secure container was established as specified. This certificate chain is rooted at the hardware manufacturer who is responsible for constructing chips according to the correct specification and provisioning them with key material. We consider a semi-honest manufacturer who is assumed to construct chips correctly, but may attempt to obtain knowledge of client private keys during the process. Using the RISC-V Rocket chip architecture as a base, we design, document, and implement an attested execution processor that does not require secure non-volatile memory, nor a private key explicitly assigned by the manufacturer. Instead, the processor derives its cryptographic identity from manufacturing variation measured by a Physical Unclonable Function (PUF). Software executed by a bootloader built into the processor transforms the PUF output into an elliptic curve key pair. The (re)generated private key is used to sign trusted portions of the boot image, and is immediately destroyed. The platform can therefore provide attestations about its state to remote clients. Reliability and security of PUF keys are ensured through the use of a trapdoor computational fuzzy extractor. We present detailed evaluation results for secure boot and attestation by a client of a Rocket chip implementation on a Xilinx Zynq 7000 FPGA.

Zerocoin (Miers et. al, IEEE S&P'13), designed as an extension to Bitcoin and similar cryptocurrencies, was the first anonymous cryptocurrency proposal which supports large anonymity sets. We identify a cryptographic denial-of-spending attack on the original Zerocoin protocol and a second Zerocoin protocol (Groth and Kohlweiss, EUROCRYPT'15), which enables a network attacker to destroy money of honest users. The attack leads to real-world vulnerabilities in multiple cryptocurrencies, which rely on implementations of the original Zerocoin protocol. The existence of the attack does not contradict the formal security analyses of the two Zerocoin protocols but exposes the lack of an important missing property in the security model of Zerocoin. While the security definitions model that the attacker should not be able to create money out of thin air or steal money from honest users, they do not model that the attacker cannot destroy money of honest users. Fortunately, there are simple fixes for the security model and for both protocols.

Blockchain and Internet of Things are supposed to have big impact on logistics. This study presents a prototypical smart contract using smart storage containers in order to investigate the potential and the maturity of Blockchain and Internet of Things for logistical processes. A smart storage container is developed and connected to an Ethereum-based smart contract. The smart contract is based on a multi signature wallet of three parties to process the payment and arbitrate disagreements. Further research implications for the development of smart contracts for supply chains are derived from the experiences of this prototype study.

This paper presents the issues and benefits experienced in the migration from a paper based Technical Publications process to a Multimedia interactive DVD based format. The paper aims to present the benefits of using such a format as a future strategy to replace the paper system. It also attempts to address the financial and time gains should such a format be adopted.

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.

In recent years, data mining had become very popular to provide a facility in various fields. Large amount of data elements are stored in the database. Various data mining algorithms are used to classify the data elements and perform some operations to find the best solution on the basis of some relational parameters. One of the automatic architecture designs in this research is to find the solution for examination in terms of level wise question paper. The entire examination process is a vital component for direct assessment of an individual learning. So, preparing a complete test paper and the setup is relatively necessary. Currently, the customary technique of making question paper has been handbook. In current scenario the question paper generation is a manual approach leading to unproductive at times owing to bias, repetition and security concerns. The current paper presents an automatic procedure of question paper group which can be modified, streamlined, synchronized and secured. Each task done by this system is automatic, such that storing space, bias and security is not an apprehension any longer. Earlier, the question paper was generated by concerned subject teacher manually and was very time consuming, man power was required and sometimes the question paper lacked accuracy. Outcome Based Education (OBE) designates what students will know and be intelligent to do, as they advance in a program. The information related to student learning was collected for question paper generation and assessment using OBE. This information can be used to predict students ability, advancements in education system, betterment in teaching method, future interest of student etc. Various data mining classifiers like oneR, ZeroR, J48, Naive Bayes, IBk are used for prediction of the course outcome. The comparative study contains some parameters like time, detection accuracy, classification error etc. to find the performance of the proposed architecture as compared to other techniques to verify the enhancements.

Our paper on “Mica” from the 2006 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCCO6 [1] presented a prototype tool that helped programmers use a description of desired functionality to find specific relevant API classes and methods. Mica worked by using the Google Web APIs to find pages relating to a given query, and then using a list of known API classes and methods to recognize and aggregate the API-relevant words contained in the result pages. Mica used weighted word frequencies to find the API-relevant words correlated with the search and displayed the most relevant words alongside regular web search results. We had observed that when programmers create new software or add functionality to existing software, a common and often difficult task is that of using libraries, toolkits and SDKs to achieve the desired functionality. Internet search engines, in particular Google, have emerged as new and effective tools for this task, providing quick access to a large collection of tutorials and example code. However, our observations of how programmers used these resources revealed problems and inefficiencies in their use as programming tools. Mica was the first of long series of tools that we built as part of the “Natural Programming Project” [2] to help programmers more easily use APIs. We speculate that the impact of the Mica paper is because: •The problem of helping users find and use the right API from a growing number of available options has become increasingly relevant. •The data that comes from other developers' experience, found and ranked through web search, continues to be an invaluable starting point for finding and figuring out how to use APIs. •The strategy of extracting and displaying relevant information alongside Google search results has proven effective. Since Mica came out Google and Bing have started to support this pattern for movies, people, events, and many other pieces of information, although not yet APIs. •That sweet logo!

The advantages of electronic paper or what is called raster technology are discussed. Rasters capture the engineering drawing in a format that a personal computer (PC) can use. Raster files can then be shipped across the continent or an office building via computer networks, stored on diskettes or laser storage systems, retrieved simultaneously by hundreds of reviewers, annotated electronically, and then transmitted back to the design organization. Additional benefits accrue from the fact that tedious paper scanning is eliminated, delay in handling and mailing is avoided, rooms of flat file storage cabinets are replaced by a single laser juke box, and in general, the need for paper is reduced. Equipped with a raster system, an engineer can conduct the searching, retrieving, and mailing of the drawings without having to move away from the PC. In the case presented, the DoD CALS standard is used in a raster drawing system for reviewing the design of Space Station Freedom.

Personal computers (PCs) are increasingly being used in industrial applications. Graphical user interface (GUI) software and Windows-based operating systems are proving to be cost-effective, rugged and reliable. These advances have allowed over the the successful installation of several human-machine interface (HMI) systems for paper machine winders. The HMI monitors the operation of a winder and provides complete machine control. An operator interface was developed to both monitor and control a multi-section paper machine drive. The application described highlights the use of a redundant multi-computer network to replace existing benchboard controls. New monitoring and control features and a unique reel roll-density-control scheme were included. The goals achieved were increased paper production and improved control of reel building.

With the development of network technology, the design of the automatic generative system of examination papers that is based on the web has become a trend. In order to reduce the burden on teachers and avoid duplication of work, we design the system to automatically generate test papers. It is based on a constantly updated examination question bank system, according to the teachers' demands, which can automatically generate the test paper with a certain degree of difficulty, the number and value. The client configuration is simple and it can be free from geographic constraint. Users only need to go through their own identity authentication to enter the automatically generated system of test papers, and to manage and maintain examination questions, exam and user information and so on. The exam contents can be constantly enriched and improved and the data sharing among teachers can be easily performed. It will have important practical significance to improve work efficiency.

Advances in nanotechnology, large scale computing and communications infrastructure, coupled with recent progress in big data analytics, have enabled linking several billion devices to the Internet. These devices provide unprecedented automation, cognitive capabilities, and situational awareness. This new ecosystem--termed as the Internet-of-Things (IoT)--also provides many entry points into the network through the gadgets that connect to the Internet, making security of IoT systems a complex problem. In this position paper, we argue that in order to build a safer IoT system, we need a radically new approach to security. We propose a new security framework that draws ideas from software defined networks (SDN), and data analytics techniques; this framework provides dynamic policy enforcements on every layer of the protocol stack and can adapt quickly to a diverse set of industry use-cases that IoT deployments cater to. Our proposal does not make any assumptions on the capabilities of the devices - it can work with already deployed as well as new types of devices, while also conforming to a service-centric architecture. Even though our focus is on industrial IoT systems, the ideas presented here are applicable to IoT used in a wide array of applications. The goal of this position paper is to initiate a dialogue among standardization bodies and security experts to help raise awareness about network-centric approaches to IoT security.

We present an industrial design perspective on power management. We focus primarily on server and mainframe class processors. However, recent advances in very low voltage (VLV) research in support of cognitive platforms of the future are also considered. A particular focus is on the special attention that needs to be placed on resilience and energy-secure design principles. The paper discusses how reliability gaps in power management hardware and/or algorithms can expose gaps in security in terms of so-called power attacks. We discuss current industrial practice and describe key challenges that need to be addressed by the research community in this regard. This is designed to be a tutorial-style survey and analysis paper.

The paper presents a method to classify electromyographic (EMG) signals according to the postures of rock-paper-scissors by using multi-layer perceptrons (MLPs). The EMGs are first applied to He-Zajac-Levine bilinear activation model and then the output of model is utilized to be inputs of the MLPs. Cross validation method is used to evaluate the classification performance of MLPs and its outcome also shows that accuracy of the proposed method is over 97%.

The 12th Workshop on Silicon Errors in Logic - System Effects (SELSE) was held on March 21-22, 2017, in Boston, MA , USA. Detailed information about SELSE 2017 and previous SELSE workshops, including links to presentations, is available at http://www.selse.org. Three papers were selected as the Best of SELSE papers and are presented in this special DSN session.

This survey paper emphasis on the word alignment of English-Hindi language pair. Word alignment defines the process of establishing the better translation relationship between the words of a parallel or bilingual corpus. Word alignment's primary step is SML. A few word alignment approaches have been progressed in NLP. Word corpus is applied to fetch multiword phrases with linguistics sense. This survey paper uses a combined technique to progress the implementation of word alignment for English-Hindi language pair with limited resources. This paper introduces the different types of techniques of word alignment for English to Hindi translation system.