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In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies.

Headline generation is a challenging subtask of abstractive text summarization, which its output should be a summary, shorter than one sentence. It would be precious to develop a dataset for the evaluation of abstractive summarization methods on this task in the Persian language. There are several datasets for headline generation in Persian, most of which are not large enough to be used by more sophisticated methods of text summarization, such as deep learning models. Moreover, all of these datasets are focused on daily news and there is no dataset for summarizing scientific Persian papers. In this article, we present “ElmNet,” a headline generation dataset of about 400,000 abstract/headline pairs of scientific papers, gathered from six major publishers for scientific articles in Persian. We, moreover, evaluate the performance of the most important deep learning-based headline generation methods, on the proposed dataset. The results prove the comparability of the performance of the state-of-the-art methods on this task, to their results on the existing English datasets.

Background In Software Engineering (SE), conference publications have high importance both in effective communication and in academic careers. Researchers actively discuss how a paper should be organized to be accepted in mainstream conferences. Aiming This work tackles the problem of generalizing and characterizing the type of papers accepted at SE conferences. Method The paper offers a new perspective in the analysis of SE literature: a categorization scheme for SE papers is obtained by merging, extending and revising related proposals from a few existing studies. The categorization scheme is used to classify the papers accepted at three top-tier SE conferences during five years (2012–2016). Results While a broader experience is certainly needed for validation and fine-tuning, preliminary outcomes can be observed relative to what problems and topics are addressed, what types of contributions are presented and how they are validated. Conclusions The results provide insights to paper writers, paper reviewers and conference organizers in focusing their future efforts, without any intent to provide judgments or authoritative guidelines.

We propose new solutions that can efficiently address the problem of malicious memory access from pluggable computer peripherals and microcontrollers embedded within a system-on-chip. This problem represents a serious emerging threat to total-system computer security. Previous work has shown that existing defenses are insufficient and poorly deployed, in part due to performance concerns. In this paper we explore the threat and its implications for system architecture. We propose a range of protection techniques, from lightweight to heavyweight, across different classes of systems. We consider how emerging capability architectures (and specifically the CHERI protection model) can enhance protection and provide a convenient bridge to describe interactions among software and hardware components. Finally, we describe how new schemes may be more efficient than existing defenses.

The security of software systems relies so heavily on the use of secrets, ranging from credentials, API keys, and tokens to secret keys for cryptographic security. This paper examines approaches for managing and handling secrets in software development life cycle, focusing on mobile applications where software must be distributed to devices and operate in an untrusted environment. This paper points out the shortcomings in activities related to secrets management and handling in mobile application development life cycle and outlines future directions in this area of research.

Since English is probably the most popular second language, helping students learn English through technology is a critical issue in non-English-speaking countries. With the growth of digital pen technologies, developing an interactive learning environment that combines printed textbooks and a digital pen to support English-language classroom learning has become feasible. This work presents an attention-based diagnosing and review mechanism (ADRM) based on brainwave detection to help learners identify the passages with low attention level in a lesson as review targets in order to perform efficiently and accurately review processes while reading paper-based English texts with digital pen support in autonomous learning environments. Based on the true experimental design, this work aims to confirm whether the ADRM improves the review performance and sustained attention of learners while reading paper-based English texts with digital pen support. The research participants were a total of 108 students at an industrial vocational high school in Taipei City, Taiwan. All research participants were male and aged from 17 to 18 years. The experimental group used the ADRM while reading paper-based English texts with digital pen support, whereas the control group used the autonomous review while reading paper-based English texts with digital pen support. Experimental results reveal that the review performance of the experimental group was significantly better than that of the control group, proving that the ADRM improved review performance. The results also show that the field-dependent learners in the experimental group exhibited a great improvement in review performance in comparison with the field-independent learners. Additionally, the low-ability learners in the experimental group exhibited better review performance compared to those in the control group. Furthermore, learners with high attention level in the experimental group have exhibited better review performance and sustained attention than the learners in the control group. This work confirms that developing an ADRM based on brainwave detection to assist learners’ review processes is practicable. However, the usability and acceptability of using ADRM instead of human autonomous review should be further considered in the information society.

We envision a richer support from Virtual Reality (VR) development environments than the one available today. We believe this will be part of an Operating System (OS) for VR applications, since those services should be uniform and widespread for all experiences within a VR system, they are generic enough to be part of any application, and possibly they will not have to be developed from scratch. This position paper explores some of the shortcomings in current technology we are identifying while developing an application for the intervention of kids with ADD (Attention Deficit Disorder), and how these OS services might be defined and used.

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied.

Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services, and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection system has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

The aim of this contribution is to show the detection of seminal papers using data mining techniques. To achieve the objective of this research, Rapidminer Studio software and its data mining tools are used, based on data created with information extracted from Google Scholar and Scopus, in three different areas of knowledge. In this process, other softwares such as Microsoft Excel and Publish or Perish are used. Comparing the results obtained for the searches in Knowledge Management, Entrepreneurship and Marketing, it was obtained that there is no marked similarity between the sets of articles that were obtained in Google Scholar and Scopus. The values for the Similarity Index remained below 0.52%, similar between Knowledge Management and Entrepreneurship but decreasing for Marketing. The detection of outliers using Data Mining techniques and in particular using Rapidminer, allowed to determine the seminals papers for the three search terms analyzed and allowed to characterize these in the space, in Google Scholar and Scopus. It was shown that the seminal articles can be different if Google Scholar or Scopus is used. The results suggest determining for other search terms whether the trend found is maintained or not.

Paper microfluidic or lateral flow devices have found many applications, especially in medical diagnostics. Their low cost and ease of use makes them particularly valuable in resource-limited and point-of-care applications. However, the process of developing new paper microfluidic devices is slowed by the need to find optimal values for their various design parameters, which determine the overall size and fluid volume requirements of the device. Often, researchers must design and test several different versions of a device to find a combination of parameters that functions as intended. To accelerate the development of new paper microfluidics, we developed a software framework that automatically designs custom paper microfluidic devices for a given application. Once the user specifies the desired device parameters, the software generates printable image files of the resulting devices, ready to output to a conventional wax ink color printer and test. As a proof-of-concept, we used our software to automatically design 51 different paper microfluidic devices we needed to create a functional lateral flow assay that detects protein and glucose in urine. These designs took only a few seconds to generate and were used in 120 lab experiments we performed in 16 h in the lab. Thus, with the help of our software framework, we went from an idea to a functional device in just two work days. By accelerating device development and enabling researchers without microfluidics experience to create custom devices, our software can help spread paper microfluidic technology to important new application areas.

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.

Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task. Throughout the process, we identify the main features that are used as keys to play a significant role in terms of predicting the new rank under the supervised learning setup. First, we propose an overview of the work that has been realized in ranking scientific research papers. Second, we evaluate and compare some of state-of-the-art for the classification by supervised learning, semi-supervised learning and non-supervised learning. During the preliminary tests, we have obtained good results for performance on realistic corpus then we have compared performance metrics, such as NDCG, MAP, GMAP, F-Measure, Precision and Recall in order to define the influential features in our work. Keywords: Scientific research, Ranking scientific research papers, Data mining, Supervised learning, Multilayer perceptron algorithm

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

The author claimed for the artifact associated with his paper the following ACM Reproducibility badges:(1) Artifact Available,(2) Artifact Evaluated-Functional,(3) Results Reproduced. After an in-depth review process, we agree to assign all the requested badges as we found it to meet the following requirements:i) it is uploaded on a persistent repository, accessible via a DOI; ii) it is well documented, consistent with the presented data, complete of all the necessary software sources and packages, and exercisable; iii) it is exhaustive in the reproduction of all the relevant data of the paper. Some curves in some reproduced plots are truncated, due to the computational limits imposed by the short-term deadline of the review process. Nevertheless, the overall trends are respected, and the curves are supporting the paper's claims.

Based on the concept of explanation sheets, we present an approach to make spreadsheets easier to understand and thus easier to use and maintain. We identify the notion of explanation soundness and show that explanation sheets which conform to simple rules of formula coverage provide sound explanations. We also present a practical evaluation of explanation sheets based on samples drawn from widely used spreadsheet corpora and based on a small user study.In addition to supporting spreadsheet understanding and maintenance, our work on explanation sheets has also uncovered several general principles of explanation languages that can help guide the design of explanations for other programming and domain-specific languages., This work is partially supported by the National Science Foundation under the grant CCF-1717300. Work financed by European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization COMPETE 2020 Programme and by National Funds through the Portuguese funding agency FCT project POCI-01-0145-FEDER-016718 and NOVA LINCS UID/CEC/04516/2013, and by FLAD project 233/2014.

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit., Comment: Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm

Critical flaws continue to exist at the level of domain, requirement, and/or design specification, and specification verification (i.e. to check whether a specification has desirable properties) is still one of the most important challenges in software/system engineering. CafeOBJ is an executable algebraic specification language system and domain/requirement/design engineers can write proof scores in CafeOBJ for improving quality of specifications by the specification verification. This paper describes advances of the proof scores for the specification verification in CafeOBJ from the author’s point of view.

Trusted execution as offered by Intel's Software Guard Extensions (SGX) is considered as an enabler to protect the integrity and confidentiality of stateful workloads such as key-value stores and databases in untrusted environments. These systems are typically long running and require extension mechanisms built on top of dynamic loading as well as hot-patching to avoid downtimes and apply security updates faster. However, such essential mechanisms are currently neglected or even missing in combination with trusted execution. We present sgx-dl, a lean framework that enables dynamic loading of enclave code at the function level and hot-patching of dynamically loaded code. Additionally, sgx-dl is the first framework to utilize the new SGX version 2 features and also provides a versioning mechanism for dynamically loaded code. Our evaluation shows that sgx-dl introduces a performance overhead of less than 5% and shrinks application downtime by an order of magnitude in the case of a database system.

The Electric Field and Voltage Distribution (EFVD) are an important parameter for assessing high voltage bushing transformer performance. However, conducting laboratories experiment is dangerous, difficult and expensive due to several aspects. Therefore, Finite Element Method (FEM) software is the best option used as a tool for the assessment of bushing transformer's performance in terms of EFVD. But, before an assessment of analysis could be carried out, an accurate model of bushing transformer must first to be designed. In this research, Computer Aided Design (CAD) software has been employed to design the 145kV bushing transformer based on actual dimension. Upon completion, the design has been exported to FEM software for further analysis. In FEM software, measurement and analysis of electric field and voltage distribution (EFVD) have been carried out. The measurements are performed at various locations of bushing transformer such as of the porcelain surface (both air and oil side), along with aluminum foils, and at oil-impregnated paper (OIP). The results obtained have been compared with other researchers and it is found very satisfactory.

Background Electrocardiograms (ECGs) are routinely recorded and stored in a variety of paper or scanned image format. Current ECG recording machines record ECG on graph paper, also it provides digitized ECG signal along with automated cardiovascular diagnosis (CVD). However, such recording machines cannot analyse preserved paper ECG records as it requires input in terms of digitized signal. Therefore, it is important to extract ECG signal from these preserved paper ECG records using digitization method. There are different paper degradations that adversely affect digitization process. The purpose of this work is to perform an image enhancement and digitization of the degraded ECG images to extract continuous ECG signal. Methods In this paper, we propose entropy-based bit plane slicing (EBPS) algorithm in which pre-processing is done using dominant color detection and local bit plane slicing. Maximum entropy based adaptive bit plane selection is applied to the pre-processed image. Discontinuous ECG correction (DECGC) is then done to produce continuous ECG signal. Results The algorithm is tested on 836 different degraded paper ECG records obtained from various diagnostic centers. After analysis with 101 known ground truth ECG signals the accuracy, sensitivity, specificity and overall F-measure of ECG is 99.42%, 99.69%, 99.81% and 99.26% respectively. The RMS error and correlation between the extracted digitized signal and ground truth for 101 cases is 0.040 and 99.89% respectively. Conclusions The EBPS method is able to remove all types of degradation in paper ECG records to generate a uniform digitized signal. Instead of manual measurement and prediction from archived paper ECG records, automated prediction (using already existing cardiovascular diagnosis software) is possible with the help of extracted digitized signal obtained using proposed digitization method, which will also help retrospective cardiovascular analysis.

—Industries have a significant amount of data in semi-structured and unstructured formats which are typically captured in text documents, spreadsheets, images, etc. This is especially the case with the software description documents used by domain experts in the automotive domain to perform tasks at various phases of the Software Development Life Cycle (SDLC). In this paper, we propose an end-to-end pipeline to extract an Automotive Knowledge Graph (AutoKG) from textual data using Natural Language Processing (NLP) techniques with the application of automatic test case generation. The proposed pipeline primarily consists of the following components: 1) AutoOntology, an ontology that has been derived by analyzing several industry scale automotive domain software systems, 2) AutoRE, a Relation Extraction (RE) model to extract triplets from various sentence types typically found in the automotive domain, and 3) AutoVec, a neural embedding based algorithm for triplet matching and context-based search. We demonstrate the pipeline with an application of automatic test case generation from requirements using AutoKG.

This paper will discuss the integration of electronic Case Report Forms (e-CRFs) into an already existing Android-based Audio Computer-Assisted Self-Interview (ACASI) software solution that was developed for a public health project in Kampala, Uganda, the technical outcome results, and lessons learned that may be useful to other projects requiring or considering such a technology solution. The developed product can function without a connection to the Internet and allows for synchronizing collected data once connectivity is possible. Previously, only paper-based CRFs were utilized at the Uganda project site. A subset or select group of CRFs were targeted for integration with ACASI in order to test feasibility and success. Survey volume, error rate, and acceptance of the system, as well as the operational and technical design of the solution, will be discussed.

Paper-based microfluidic devices have many applications in biomedical and environmental analysis. However, the process of prototyping device designs can be tedious, error-prone, and time-consuming. Here, we present a cross-platform, open-source software tool—AutoPAD—developed to quickly create and modify device designs and provide a free alternative to commercial design software. The capabilities that we designed to be inherent to AutoPAD (e.g., automatic zone alignment and design refactoring) highlight its potential use in nearly any paper-based microfluidic device application and for creating nearly any desired design, which we demonstrate through the recreation of numerous device designs from the literature.

At present, the protection, inheritation, innovation and development of intangible cultural heritage in China are facing more severe opportunities and challenges. In order to overcome the shortcomings of too rigid and conservative expression, and poor interactive of traditional digital means, the virtual reality (VR) technology is applied to the protection and inheritance of the paper-cut art in this paper. Firstly, a variety of paper-cut basic symbols are put into a database, and then the paper-cut symbols are summarized. Now we put them into computer aided design software of CDDataBase, and we can use CorelDRAW for graphic expression and artistic design. Secondly, the virtual digital museum display system is constructed by using the 3D model database. At the same time we can create a digital platform for Mangshi Dai paper-cut art, in order to maximize the utilization of resources and sharing. Finally, using WeChat, weibo, community sites and other mediums of the Web platform to connect digital Dai paper-cut art with social docking platform can make public deeply understand Dai paper-cut arts. So the VR technology can achieve wide protection and inheritance of Dai paper-cut art.

This work is centered on IMC (Internal Model Control) control system design and its robustness analysis for controlling the basis weight of the paper produced by the paper milll. The headbox has a significant role in a paper mill. It maintains the pressure inside the box and discharges stock at an optimum required speed. In this paper, the robustness of the IMC based controllers will be investigated for the two parameters, setpoint tracking and disturbance rejection. The MATLAB software is used for carrying out all simulations done in this paper.

The notion of attribute-based encryption (ABE)was proposed as an economical alternative to public-keyinfrastructures. It is the set of descriptive attributes, used as anidentity to generate a secret key, as well as serving as theaccess structure that performs access control. ABE is also auseful building block in various cryptographic primitives suchas searchable encryption. . It successfully integratesEncryption and Access Control and is ideal for sharing secretsamong groups, especially in a Cloud environment. Mostdeveloped ABE schemes support key-policy or ciphertextpolicyaccess control in addition to other features such asdecentralized authority, efficient revocation and keydelegation. This paper surveys mainstream papers, analyzesmain features for desired ABE systems, and classifies theminto different categories. With this high-level guidance, futureresearchers can treat these features as individual modules andselect related ones to build their ABE systems on demand.For ABE, it is not realistic to trust a single authority tomonitor all attributes and hence distributing control over manyattribute-authorities is desirable.A multi-authority ABE scheme can be realized with a trustedcentral authority (CA) which issues part of the decryption keyaccording to a user's global identifier (GID). However, this CAmay have the power to decrypt every cipher text, and the useof a consistent GID allowed the attribute-authorities tocollectively build user's attributes. Decentralized ABE schemecan eliminate the burden of heavy communication andcollaborative computation. It is observed that privacypreservingdecentralized key-policy ABE scheme has claimedto achieve better privacy for users and is provably secure inthe standard model

We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe. A critical challenge to our—and any—autonomous machine is accurate and efficient localization under resource constraints, which has fueled specialized localization accelerators recently. Prior acceleration efforts are point solutions in that they each specialize for a specific localization algorithm. In real-world commercial deployments, however, autonomous machines routinely operate under different environments and no single localization algorithm fits all the environments. Simply stacking together point solutions not only leads to cost and power budget overrun, but also results in an overly complicated software stack.This paper demonstrates our new software-hardware co-designed framework for autonomous machine localization, which adapts to different operating scenarios by fusing fundamental algorithmic primitives. Through characterizing the software framework, we identify ideal acceleration candidates that contribute significantly to the end-to-end latency and/or latency variation. We show how to co-design a hardware accelerator to systematically exploit the parallelisms, locality, and common building blocks inherent in the localization framework. We build, deploy, and evaluate an FPGA prototype on our next-generation self-driving cars. To demonstrate the flexibility of our framework, we also instantiate another FPGA prototype targeting drones, which represent mobile autonomous machines. We achieve about $2 \times$ speedup and $4 \times$ energy reduction compared to widely-deployed, optimized implementations on general-purpose platforms.

With paper map use in decline, one of the strategies that libraries and archives can adopt to make the information contained within them more accessible and usable is to extract features of interest from their scanned raster maps and convert those to geospatial vector data. This process adds valuable unique data to library geospatial collections and enables those previously map-bound features to be used separately in geographic information systems (GIS) software for custom mapping and analysis. Advances in partially automating most of the process have made this a much more viable option for libraries and archives. Although there is no one-size-fits-all automated solution for all maps and map features, this paper provides a complete description of the entire process incorporating examples of the various techniques and software used in selected studies that would be applicable in the library and archive environment.

This paper introduces a novel software framework to support automated development of paper-based microfluidic devices. Compared to existing lab-on-a-chip technologies, paper-based microfluidics differs in terms of substrate technologies and point-of-care usage across a wide variety environmental conditions. This paper addresses the contexts in which the software can address these challenges and presents several initial case studies that demonstrate the capabilities of the framework to produce workable and usable paper microfluidic devices.

The paper mill is one of the major industries of India. The various processes that are present in paper mill are not simple to model and control because of presence of nonlinearities and the dead time. The present work is focused on a prime section of a paper mill that is the headbox. The regulation of its consistency is a major concern. In this paper, first of all, the FOPDT model of the head box consistency dynamics is approximated as a polynomial model using the Pade approximation and hence control system is designed for it. The Simulated Annealing (SA) technique is used for the performance optimization of this control system. MATLAB software has been used to obtain all simulation results.

With the aim of identifying good structures and examples for papers in the software engineering field, we conducted a study of the type of papers accepted along four decades in the Research Track of the International Conference on Software Engineering (ICSE). We used for this purpose a categorization scheme for Software Engineering papers that was obtained by merging, extending and revising a few existing paper scheme proposals. This paper summarizes some outcomes relative to what topics and problems are addressed, what types of contribution are presented and how they are validated. Insights from the study could help ICSE authors, reviewers and conference organizers in focusing and improving future efforts.

For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic

Ascertaining the impact of research is significant for the research community and academia of all disciplines. The only prevalent measure associated with the quantification of research quality is the citation-count. Although a number of citations play a significant role in academic research, sometimes citations can be biased or made to discuss only the weaknesses and shortcomings of the research. By considering the sentiment of citations and recognizing patterns in text can aid in understanding the opinion of the peer research community and will also help in quantifying the quality of research articles. Efficient feature representation combined with machine learning classifiers has yielded significant improvement in text classification. However, the effectiveness of such combinations has not been analyzed for citation sentiment analysis. This study aims to investigate pattern recognition using machine learning models in combination with frequency-based and prediction-based feature representation techniques with and without using Synthetic Minority Oversampling Technique (SMOTE) on publicly available citation sentiment dataset. Sentiment of citation instances are classified into positive, negative or neutral. Results indicate that the Extra tree classifier in combination with Term Frequency-Inverse Document Frequency achieved 98.26% accuracy on the SMOTE-balanced dataset.

In order to satisfy the demands for both safety and global warming reduction, a high-strength seismic reinforced steel bar is required in the structural steel market. Recent developments in computational thermodynamics and related application software have made it possible to design a suitable material as well as support engineers of steel manufacturing companies in the production of the designed material with minimum benchmarks in practical operations. This paper reports our recent success in developing grade 600 MPa reinforced steel bars for seismic safety in South Korea. First, conventional alloy design based on CALPHAD-type computational thermodynamics was carried out. For this purpose, a typical alloy system of Fe-0.30C-0.23Si-1.37Mn-0.14V-0.22Cu (in wt%) was selected, and thermodynamic and kinetic calculations were carried out using MatCalc and JMatPro software. Second, in order to reduce V content in the steel for economic reasons, a cooling process designed using finite element (FE) simulation based on the thermodynamic database was performed. For this application, Fe-0.34C-0.22Si-1.34Mn-0.04 V (in wt%) alloy was chosen, and the FE software ABAQUS was applied for modeling the TempCore process. The mechanical properties of the steel products with a diameter of 32 mm produced based on the simulated results satisfy the required properties for grade 600 MPa seismic reinforced steel bars.

JavaScript has seen meteoric growth in popularity as it has in- creasingly become the language of choice for developers, both for front-end web development and server code development through various JavaScript frameworks and Node.js. Part of the reason for its wide use is that it is a prototype based language with dynamic types, making it easy to learn and program in. This flexibility and ease of programming comes at the cost of performance. There are two sources of significant slowdown. First, since the number and type of properties of prototypes is dynamic, accessing a property involves a slow dictionary lookup, as opposed to it being present at a fixed offset from the base address. Second, the dynamism in type of values necessitates wrapping and unwrapping of values into objects with a variety of checks including for type of the value. To mitigate these performance problems, this paper proposes JSCore, a core specialized for JavaScript execution, that vastly reduces the performance degradation due to the above two causes. It uses a hardware lookup table to accelerate property access, and extends the data path to store data types with the data, nearly eliminating the second source of slowdown. Combining the two, JSCore accelerates real world JavaScript applications by 23%.

Deep Neural Networks (DNNs) are the algorithm of choice for image processing applications. DNNs present highly parallel workloads that lead to the emergence of custom hardware accelerators. Deep Learning (DL) models specialized in different tasks require a programmable custom hardware and a compiler/mapper to efficiently translate different DNNs into an efficient dataflow in the accelerator. The goal of this paper is to present a compiler for running DNNs on Snowflake, which is a programmable hardware accelerator that targets DNNs. The compiler correctly generates instructions for various DL models: AlexNet, VGG, ResNet and LightCNN9. Snowflake, with a varying number of processing units, was implemented on FPGA to measure the compiler and Snowflake performance properties upon scaling up. The system achieves 70 frames/s and 4.5 GB/s of off-chip memory bandwidth for AlexNet without linear layers on Xilinx’s Zynq-SoC XC7Z045 FPGA.

This Embedded Graphics libraries have evolved from OpenGL to next gen Vulkan libraries. OpenCL and CUDA have been used for many years for GPU compute on a range of SoC devices with powerful embedded GPU chipsets. OpenCL and CUDA were never able to be certified, due to the contributions from many developers. Next generation autonomous vehicles, unmanned aircraft, transportation and even military vehicles will require both graphics, as well as GPU compute to implement DSP or FPGA logic is software, as well as Deep Machine Learning. Vulkan libraries are available now and are defined by the Kronos Group, as well as this, a next generation of Vulkan safety critical library for safe Shader and GPU Compute is now available. This white paper will look at Vulkan architectures, embedded system use and the implications for using GPU Compute, where certification may be a critical requirement for ISO 26262, EN50182, IEC 61508, DO178C certifiable embedded systems.

In the veterinary professional curriculum, methods of examination in many courses are transitioning from the traditional paper-based exams to electronic-based exams. Therefore, a controlled trial to evaluate the impact of testing methodology on examination performance in a veterinary physiology course was designed and implemented. Formalized surveys and focus group discussions were also used to determine student attitudes toward the examination formats. In total, 134 first-year veterinary students and 11 PhD/MS students were administered a total of 4 exams throughout 1 semester (2 on paper and 2 electronically) using a split-halves design. The paper (P) and electronic (E) exams contained 25 identical multiple-choice questions. Students were randomly assigned to two groups and were given exams in one of two sequences (E-P-E-P or P-E-P-E). Participants consented to and completed two anonymous surveys vis à vis their experience. Out of a maximum raw score of 25, the mean score for electronic examinations (20.8; 95% confidence interval, 20.3–21.2) was significantly ( P = 0.01) greater than that for paper examinations (20.3; 95% confidence interval, 20.0–20.7). However, students expressed numerous concerns with the electronic examination format, and, at the completion of the study, 87% preferred to take their examination on paper rather than the electronic format. These data show that student attitudes concerning the examination format are not primarily determined by examination results, and that the additional anxiety related to the electronic examination format plays a large role in student attitudes.

One of the biggest problems of agriculture is the reckless use of pesticides and their incorrect application with consequent waste of product and environmental pollution. The spray application characterization is a good preventive technique to limit the volume of a distributed product, to perform a more efficient application and to restrict the spray drift. Since there is no specific sampling technique useful for every context, it is necessary that every methodology is known in all its aspects before being employed. The colorimetry, fluorimetry and spectrometry methods are very accurate, but they are costly and time-consuming compared to a Water-Sensitive Paper (WSP) assessment performed by an image analysis software. This kind of software can detect and estimate many drops features using an image often obtained from a scanner. The objective of work was to develop a MATLAB code to evaluate the spray distribution over WSPs. After a pre-processing step in which the WSPs were isolated inside the image, the individual drops were identified using the difference between their color (blue) and the remaining dry part of the paper (yellow). Once the surface of every drop was estimated, it was possible to assess the number of the drops per cm2, the Normal Median Diameter (NMD) and the Normal Volume Diameter (VMD).

Standby efficiency for connected devices is one of the priorities of the G20’s Energy Efficiency Action Plan. We propose transparent programming language mechanisms to enforce that applications remain in the deepest standby modes for the longest periods of time. We extend the programming language Céu with support for interrupt service routines and with a simple power management runtime. Based on these primitives, we also provide device drivers that allow applications to take advantage of standby automatically. Our approach relies on the synchronous semantics of the language which guarantees that reactions to the environment always reach an idle state amenable to standby. In addition, in order to lower the programming barrier of adoption, we show that programs in Céu can keep a sequential syntactic structure, even when applications require non-trivial concurrent behavior.

Systems neuroscience studies involving in-vivo models often require realtime data processing. In these studies, many events must be monitored and processed quickly, including behavior of the subject (e.g., movement of a limb) or features of neural data (e.g., a neuron transmitting an action potential). Unfortunately, most realtime platforms are proprietary, require specific architectures, or are limited to low-level programming languages. Here we present a hardware-independent, open-source realtime computation platform that supports high-level programming. The resulting platform, LiCoRICE, can process on order 10e10 bits/sec of network data at 1 ms ticks with 18.2 µs jitter. It connects to various inputs and outputs (e.g., DIO, Ethernet, database logging, and analog line in/out) and minimizes reliance on custom device drivers by leveraging peripheral support via the Linux kernel. Its modular architecture supports model-based design for rapid prototyping with C and Python/Cython and can perform numerical operations via BLAS/LAPACK-optimized NumPy that is statically compiled via Numba’s pycc. LiCoRICE is not only suitable for systems neuroscience research, but also for applications requiring closed-loop realtime data processing from robotics and control systems to interactive applications and quantitative financial trading.

The need to analyze and execute binaries from legacy ISAs on new or different ISAs has been addressed in a variety of ways over the past few decades. Solutions using complementary static and dynamic binary translation techniques have been deployed in most real-world situations. As new ISAs are designed and legacy ISAs re-examined, the need for binary translation infrastructure re-emerges, and needs to be re- engineered all over again. Work is in progress with a goal to make such re-engineering efforts easier by using some of the software tools that would irrespectively be developed or available for a new or existing ISA. To that end, this paper presents a static binary raiser that translates binaries to LLVM IR. Native binaries for a new ISA are generated from the raised LLVM IR using the LLVM compiler backend. This technique enables development of a single raiser per legacy ISA, irrespective of the new target ISA. The result of such a raiser can then leverage compiler back-ends of new ISAs, thus simplifying the development of binary translator for the new ISA . This work leverages the existing LLVM infrastructure to implement a static raiser that currently supports raising x64 and Arm32 binaries to LLVM IR. The raiser is built as an LLVM tool – similar to llvm-objdump or clang and does not have any dependencies outside of those needed to build LLVM. This paper describes the phases of the raiser and gives the current status and limitations.

YARN , yet another resource negotiator is a large-scale, distributed operating system for big data applications and software rewrite that is capable of decoupling MapReduce's resource management and scheduling capabilities from the data processing component. In this study we have briefly described the evolution of YARN for big data analysis from the year 2017 to the recent times. Keywords: Apache HADOOP, HDFS, MapReduce, Apache Samza, Apache Flink

This paper reviews several dimensions in terms of which electronic/Internet and paper voting can be compared (vote secrecy, verifiability, ballot box integrity, transparency and trust base). We conclude that, for many vulnerabilities of Internet voting systems, there exist related weakness in paper systems as well. The main reason why paper-based elections are perceived as more secure is historical experience. We argue that recent criticisms of Internet voting have unfairly concentrated on the associated risks and neglected the benefits. Remote electronic voting lowers the cost of election participation and provides the most secure means for absentee voting. The latter is something that is urgently required in the contemporary, increasingly mobile world. Hence, we need to give Internet voting a chance, even if it means risking unknown threats and learning by trial and error.

International audience; Chameleon is a software that combines computer vision feature-matching algorithms with an open database format to allow the incorporation of dynamic HTML5 interactive content over any type of document (e.g. PDF files, Power-Point documents, etc.) without modifying existing applications or the source document. It thus allows the provision and viewing of an enhanced version of a research paper with embedded interactive demonstrations or videos. It can also be used to perform live demonstrations of interaction techniques while giving a presentation without having to switch tools.

Organizing and assigning papers into sessions within a large conference is a formidable challenge. Some conference organizers, who are typically volunteers, have utilized event planning software to ensure simple constraints, such as two people can not be scheduled to talk at the same time. In this work, we proposed utilizing natural language processing to find the topics within a corpus of conference submissions and then cluster them together into sessions. As a preliminary evaluation of this technique, we compare session assignments from previous conferences to ones generated with our proposed techniques.