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Paper-based books in university libraries are important learning materials for university students, but there are some drawbacks in traditional reading methods, and it is necessary to utilize scientific and technological means to solve the pain points of users, i.e. the students. With the development of information technology, augmented reality (AR) technology has gradually penetrated into all areas of the students’ lives. The fusion of AR technology and traditional paper books brings a potential new approach of reading and a sense of experience. At present, the application of AR technology in library is mainly for book navigation and book protection. AR technology has also appeared in some children’s books, but most of the books are still in the traditional way of reading. Therefore, this research aims to implement AR technology with traditional reading methods, optimize the reading experience of paper books, and enhance the interactivity and interest of paper book reading. This article selects university students in the library of South China University of Technology for the case study. Firstly, it finds the application method of AR technology in the library through the literature, and then observes and interviews the students to summarize the pain points and design opportunities from traditional reading. Based on AR technology, this paper designed a social reading application with a human-oriented design concept, thereby optimizing the reading experience of paper books for library.

Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data. This study builds on prior work that investigated to what extent “best practices” around labeling training data were followed in applied ML publications within a single domain (social media platforms). In this paper, we expand by studying publications that apply supervised ML in a far broader spectrum of disciplines, focusing on human-labeled data. We report to what extent a random sample of ML application papers across disciplines give specific details about whether best practices were followed, while acknowledging that a greater range of application fields necessarily produces greater diversity of labeling and annotation methods. Because much of machine learning research and education only focuses on what is done once a “ground truth” or “gold standard” of training data is available, it is especially relevant to discuss issues around the equally important aspect of whether such data is reliable in the first place. This determination becomes increasingly complex when applied to a variety of specialized fields, as labeling can range from a task requiring little-to-no background knowledge to one that must be performed by someone with career expertise.

Keyphrases associated with research papers provide an effective way to find useful information in the large and growing scholarly digital collections. In this paper, we present KPRank, an unsupervised graph-based algorithm for keyphrase extraction that exploits both positional information and contextual word embeddings into a biased PageRank. Our experimental results on five benchmark datasets show that KPRank that uses contextual word embeddings with additional position signal outperforms previous approaches and strong baselines for this task.

Low energy surface coatings have found wide range of applications for generating hydrophobic and superhydrophobic surfaces. Most of the studies have been related to use of a single coating material over a single substrate or using a single technique. The degree of hydrophobicity is highly dependent on fabrication processes as well as materials being coated and as such warrants a high-level study using experimental optimization leading to the evaluation of the parametric behavior of coatings and their application techniques. Also, a single platform or system which can predict the required set of parameters for generating hydrophobic surface of required nature for given substrate is of requirement. This work applies the powerful machine learning algorithms (Levenberg Marquardt using Gauss Newton and Gradient methods) to evaluate the various processes affecting the anti-wetting behavior of coated printable paper substrates with the capability to predict the most optimized method of coating and materials that may lead to a desirable surface contact angle. The major application techniques used for this study pertain to dip coating, spray coating, spin coating and inkjet printing and silane and sol–gel base coating materials.

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. Preprint: https://arxiv.org/abs/2102.06251. Data and materials: https://doi.org/10.5281/zenodo.3996696.

Wireless communication base stations have strong requirements in flexibility, performance and real-time responsiveness. Building an application-specific lightweight LibOS on a general OS is a good technique to make a balance among real-time responsiveness, performance and flexibility. Compared to traditional ext-kernel systems, the fast progress of virtualization technology brings a more efficient implementation of LibOS. In this paper, we propose a virtualization assisted real-time LibOS system named Tenma for wireless Baseband Unit (BBU). In Tenma, lightweight process virtualization, enhanced isolation between host OS and guest OS, interrupt direct-pass and application specific lightweight scheduling are used to guarantee the requirements of performance and real-time responsiveness while keeping system flexibility. The Tenma system is already in commercial deployment. Experiments in practice prove that it can meet the requirements of wireless base station applications.

Constantly increasing botnets powered by vulnerable IoT devices perform record-breaking DDoS attacks to critical infrastructures. Therefore, it is imperative to find vulnerabilities in IoT devices ahead of attackers. In this paper, we first present a systematic analysis on various kinds of IoT malware to further explore the challenges of IoT Honeypot design. We then propose HoneyIoT, a scalable IoT Honeypot framework, which aims at attracting IoT attacks and recording malicious behaviors with configurable vulnerabilities and firmware support. During a 7day real-world industrial experiment, HoneyIoT observed over 12,500 malicious connections and 3,423 distinct login attempts.

Using machine learning (ML) services, both service customers and providers need to monitor complex contractual constraints of ML service that are strongly related to ML models and data. Therefore, establishing and monitoring comprehensive ML contracts are crucial in ML serving. This paper demonstrates a set of features and utilities of the QoA4ML framework for ML contracts.

In this invited paper, applications for Machine Learning (ML) in several areas of semiconductor manufacturing and test are reviewed and potential opportunities are discussed.

—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.

The rapid growth of Internet technologies and further dependence on online services, increase the demand for keeping these networks and data secure. The protection of online information is becoming even more vital to the national security and economic stability. Recently, network security has become one of the most concerning subjects in the current research and industry fields. Intrusion Detection Systems (IDSs) are considered as the backbone for network and data protection. Throughout time, different IDS approaches have been implemented to attain maximum detection accuracy. Machine learning IDS is one of the promising IDS techniques that have been created to detect known as well as unknown attacks. This paper investigates various machine learning techniques used to deploy Network-based Intrusion Detection System (NIDS). This survey could provide a more robust understanding of the existing techniques and assists intrigued researchers to identify research opportunities and investigate more in this direction.

Autism spectrum conditions (ASC) develop early in life and are thought to last a lifetime. However, ASC research has two major knowledge gaps that hinder progression in understanding the concept of ASC and in providing proper support for autistic adults: 1) the majority of knowledge about ASC mainly stems from childhood studies so little is known about older autistic adults; 2) while it is broadly recognized that ASC is a heterogeneous condition, we do not yet understand the differences in trajectories leading to their future outcome. We aim to fill both knowledge gaps.A multistage overlapping cohorts design assessing (cognitive) aging in ASC, is designed to obtain an accelerated longitudinal dataset. Data, including a multitude of questionnaires, diagnostics, and cognitive tests, are collected over four waves within a 10-year time frame. This will provide information regarding actual changes in quality of life, co-occurring health conditions, and cognition as well as the possibility to test external validity and temporal stability in newly formed behavioral subtypes. Participants consist of three groups of adults aged 20 to 90 years: 1) with a clinical diagnosis of ASC; 2) with a clinical diagnosis of ADHD, but no ASC; 3) no ASC/ADHD (i.e., comparison group; COM). The sample size differs between waves and instruments. Detailed analysis plans will be preregistered in AsPredicted or at the Open Science Framework. Ethical approval for this study was obtained from the ethical review board of the Department of Psychology of the University of Amsterdam (Wave 1 2011-PN-1952 and 2013-PN-2668, Wave 2 2015-BC-4270, Wave 3 and 4 2018-BC-9285). In line with the funding policies of the grant organization funding this study, future papers will be published open access.

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.

In this paper, we demonstrate IllumiPaper: a system that provides new forms of paper-integrated visual feedback and enables multiple input channels to enhance digital paper applications. We aim to take advantage of traditional form sheets, including their haptic qualities, simplicity, and archivability, and simultaneously integrate rich digital functionalities such as dynamic status queries, real-time notifications, and visual feedback for widget controls. Our approach builds on emerging, novel paper-based technologies. We describe a fabrication process that allow us to directly integrate segment-based displays, touch and flex sensors, as well as digital pen input on the paper itself. With our fully functional research platform we demonstrate an interactive prototype for an industrial form-filling maintenance application to service computer networks that covers a wide range of typical paper-related tasks.

The current demand for apps has greatly moved the software development market. Due their massive distribution and use by thousands of different users, special attention needs to be paid to the interfaces design and the ways of interaction with these apps. An efficient way to evaluate these aspects in advance is prototyping. However, prototyping is often overlooked because of the additional cost it has put into the app development process and the time that may require it. The aim of this paper is to present a prototyping process for apps based on the paper construction of interface drafts and their interactions. Later these drafts are captured by the camera of a smartphone, generating a version of the app that can be used by the client to evaluate the interactions. This process also generates a digital version of the project based on a Domain Specific Language (DSL), whereby this prototype can be refined, generating, if necessary, a new executable version for evaluation or a new paper model to be presented and modified by the client. The validation of the proposal is done through a case study, in which an application is modeled on paper through a stencil for Android interface. Thus, generating the digital version of the project as well as the codes that implement the screens and the navigation structures between them.

It is an amazing fact that the very first chess program in history was written a few years before computers had been invented. It was designed by a visionary man who knew that programmable computers were coming and that, once they were built, they would be able to play chess. The man, of course, was Alan Turing, one of the greatest mathematicians who ever lived. Soon after the war he wrote the instructions that would enable a machine to play chess. Since there was as yet no machine that could execute the instructions he did so himself, acting as a human CPU and requiring more than half an hour per move. A single game is recorded, one in which Turing's "paper machine" lost to a colleague.We sketch the historical context of Turing’s involvement in chess and then go on to describe how the chess computer experts reconstructed the paper machine to run on a modern day computer. In the process they encountered a problem: the chess engine refused to duplicate all of Turing’s moves as recorded in the historical game. The debugging process, in which computer chess pioneer Ken Thompson was involved, left the programmers baffled. Then someone called Donald Michie, a colleague from Bletchley, who advocated debugging not the program but Turing! “Alan did not care about details; he was interested in general principle.” In this paper we also discuss the points of deviation from the recorded game.

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.

Statins are one of the most commonly prescribed drugs in clinical practice. They are usually well tolerated and effectively prevent cardiovascular events. Most adverse effects associated with statin therapy are muscle-related. The recent statement of the European Atherosclerosis Society (EAS) has focused on statin associated muscle symptoms (SAMS), and avoided the use of the term 'statin intolerance'. Although muscle syndromes are the most common adverse effects observed after statin therapy, excluding other side effects might underestimate the number of patients with statin intolerance, which might be observed in 10-15% of patients. In clinical practice, statin intolerance limits effective treatment of patients at risk of, or with, cardiovascular disease. Knowledge of the most common adverse effects of statin therapy that might cause statin intolerance and the clear definition of this phenomenon is crucial to effectively treat patients with lipid disorders. Therefore, the aim of this position paper was to suggest a unified definition of statin intolerance, and to complement the recent EAS statement on SAMS, where the pathophysiology, diagnosis and the management were comprehensively presented.

In order to automate the task of correcting handwritten subjective answers, we envision a system that reads such answers and grades them. In this project, we have created a mobile application to achieve this task. We have used the RAKE algorithm to extract key-phrases, which are then embedded using Word2vec model and compared using Word Mover's Distance. Our created model achieved almost human like evaluation accuracy. We have also provided accessibility features to the users with the aim of making this application a one-stop solution to automatic evaluation of handwritten subjective answers.

Sentiment analysis and opinion mining have developed significantly throughout the last decades. This study aims to assess, amongst many other aspects, people's feelings, opinions, and emotions of someone or something. This review's principal objective is to automatically assess the direction of the review and contrast this with the evaluation made by the reviewers. This will allow scientists to categorize and evaluate reviews cross-sectionally and encourage a scientific article's overall evaluation more critically. An EM-VC ensemble approach has been proposed for analyzing the reviews. This approach uses POS to achieve a syntactic sentence structure. With dictionaries, this syntactic structure enables the semantic orientation of the analysis via the scoring algorithm to be determined. To test the proposed approach's efficiency and performance about the baseline method, some tests have been performed using typical performance metrics like precision, accuracy, F1-score, and recall. The findings reveal that improvements are achieved in binary classification, ternary classification, & five-point classification compared to the existing HS-SVM method. Still, it provides a slight improvement in accuracy in the context of multiclass classification compared to others.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

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.

This thesis examines the comparison between film and novel of Paper Towns by John Green. In both novels and films, has two elements, namely the intrinsic element and the extrinsic element. In this thesis, the researcher focuses on the intrinsic elements, there are in characters, characterization, and setting. The comparison between novel and the film version of Paper Towns. The method used by researchers is a qualitative method. Researchers used the original novel in English, as well as watched the film on Netflix. To include a table in the form of snippets of scenes from Paper Towns film. The theory used is the book Comparative Literature: A Critical Introduction by Susan Bassnett. The characters in the novel are diverse, when the novel become a film, many of these characters disappear. Usually the characters that are not used in the film are characters that do not have an impact on the threads of the story. The setting in the novel is presented in a more complex and detailed manner than in the film. The characterization in the novel is also complex, but it does not mean that after being a film the characterization that is already in the novel changes.Keywords: comparative literature, novel, film, Paper Towns

In today's scenario cloud computing in growing concept in the computer and information technology field for storing the data. Because on the physical storage device whatever the huge amount of data which is sensitive or confidential that cannot be stored. So the cloud computing comes into the picture for storing the data as per their requirement. But if we store the data on cloud it will be easily accessible to all and the confidentiality of that data is not maintained. So for that purpose we are going make the data on the cloud to be secure with the help of cryptography. In this Paper we talk about earlier work in this field of encryption algorithm and the key issues for encrypting the data and decoding the data in cloud environment and proposed the objectives for improving the encryption algorithm and key issues which would help to store the data in cloud environment securely and efficiently. The paper proposed the defined objectives for the secrecy of the data in the storage of cloud computing environment which is the main aspect of our paper.

Cryptography is wide domain with many subs. The objective of this review paper is to give light review for readers and students of the three common used algorithm two in symmetric cryptography DES, AES and one in asymmetric cryptography RSA. Allowing the reader to have simple understanding of the background history of the algorithm in review and the key functional cipher operation of the algorithm. Accordingly summary of strength and weakness of each algorithm under the review will be highlighted. And how the security goals is such confidentiality and integrity are achieve using the mentioned algorithms. The paper will sequentially list and review the said algorithms and clarify the relational between them. Such as the relation between the symmetric and asymmetric algorithm the one with secret key and the ones with key pairs. The paper may insight some of the research window for upcoming scholar research in the field of cryptography.

This paper reflects the technology-induced novelty of translation, which is perceived as a bridge between languages and cultures. We debate the extent to which the translation process maintains its specificity in the light of the new technology-enhanced working methods ensured by a large variety of Computer-Assisted Translation (CAT) and Machine Translation (MT) tools that aim to enhance the process, which includes the translation itself, the translator, the translation project manager, the linguist, the terminologist, the reviewer, and the client. This paper also hints at the topic from the perspective of the translation teacher, who needs to provide students with transversal competencies that are suitable for the digital area, supported by the ability to tackle Cloud-based translation tools, in view of Industry 4.0 requirements.

Industrial archived process data represent a convenient source of information for data-driven models, such as artificial neural network (ANN), that can be used for safety and efficiency improvement like early or even predictive fault detection and diagnosis (FDD). Nonetheless, most of the data used for model generation are representative of the process nominal states and therefore are not enough for classification problems intended to determine abnormal process conditions. This work proposes the use of techniques to augment the original real data standards, dismissing the need for experiments that could jeopardize process safety. It uses the Monte Carlo technique to artificially increase the number of model inputs coupled to the nearest neighbor search (NNS) by geometric distances to consistently classify the generated patterns in normal or faulty statuses. Finally, a radial basis function neural network is trained with the augmented data. The methodology was validated by a study case in which 3381 pulp and paper industrial data points were expanded to monitor the formation of particles in a recovery boiler. Only 5.8% of the original process data were examples of faulty conditions, but the new expanded and balanced data collection leveraged the classification performance of the neural network, allowing its future use for monitoring purpose.

There is a large number of high-profile cyberattacks identified in the year of 2017, i.e., Ransomware attacks are one of the areas of cybercrime growing the fastest. These increasingly sophisticated cyberattacks are forcing various organisations to face security challenges and invest money building security and trust models. There will also be an increase in the use of recent development of security solutions that can help improve the detection performance and react to malicious events. In this position paper, we mainly introduce recent development trends in cybersecurity, including legal issues (e.g., GDPR), Artificial intelligence (AI), Mobile security and Internet of Things.

Blockchain technology enables a transaction to be handled in a decentralized fashion. In this position paper, we aim to introduce the background of blockchain technology, discuss one of its important component — smart contract, and present its recent applications in many fields such as cryptocurrency, financial services, risk management, and Internet of Things.

This paper describes a new e-paper system, which is suitable for office applications. As a replacement of paper based work, it is necessary to have the same readability as paper, capability of handwriting, and convenience of IT equipment. In order to satisfy these requirements, there are three main features of this system. One is a high resolution electrophoretic display (EPD) which provides printed paper image quality. Another is binary display driving which provides high speed image writing on a high resolution display. The last is an original EPD control scheme which gives quick page turning or handwriting performance.

This special section presents an updated version of the paper that won the first prize award in the ITU (International Telecommunication Union) Kaleidoscope conference (K-2020).

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

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

Developers of applications relying on cryptographic libraries can easily make mistakes in their use. Popular dynamic languages such as JavaScript make testing or verifying such applications particularly challenging. In this paper, we present our ongoing work toward a methodology for automatically checking security properties in JavaScript code. Our main idea is to attach security annotations to values that encode properties of interest. We illustrate our idea using examples and, as an initial step in our line of work, we present a formalization of security annotations in a statically typed lambda calculus. As next steps, we will translate our annotations to a dynamically typed formalization of JavaScript such as λJS and implement a runtime checked type extension using code instrumentation for full JavaScript.

The Affect Misattribution Procedure (AMP) (Payne et al., 2005) can measure implicit attitudes based on a conceptually different procedure from the Implicit Association Test (IAT) (Greenwald et al., 1998). We aim to improve the administrative advantage of the AMP by converting it into a paper-based performance test so that it can collect massive data easily without using a computer for each examinee. We conduct three Paper-based AMP experiments to achieve the typical AMP results with large effect sizes (Study 1), obtain sufficient reliability by the test-retest method (Study 2), and find a similar phenomenon of the disappearance of misattribution when participants evaluate the primes consciously prior to the target evaluation (Study 3).

This position paper proposes CsPI (Cybersecurity Performance Index) as a new way to evaluate the cyber security investment decisions and measure the progresses and effects of the investments and projects. In a conventional way, cyber security budgets are usually evaluated by the return on investment (ROI), which has some undesirable side effects. Since we don't ask for return on investment for other similar expenses (like IT or capital), why should we use ROI for cybersecurity? Here, we propose to adopt a well-established technique in the project management field called "earned value" to the field of cyber security. Specifically, what we propose is to measure the progress of cybersecurity expenses against a plan. The plan should include protection, detect, respond, recover goals that cybersecurity expenses (people, process, technology) attempt to meet – and this has to take into account business objectives and threat/vulnerabilities. Similar to the "Cost Performance Index" or "Schedule Performance Index" in Earned Value, we can define "Cybersecurity Performance Index" (CsPI). The main idea is to show how well current cybersecurity expenses are performing against a plan. At the end, future research directions are presented.

A Trusted Execution Environment (TEE) provides an isolated environment for sensitive workloads. However, the code running in the TEE may contain vulnerabilities that could be exploited by attackers and further leveraged to corrupt the TEE. The increasing buggy code inside the TEE concerns the security of the entire TEE. In this position paper, we present the challenges towards securing trusted execution environments and potential mitigation.

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.

Now a days due to the development in color printing technology the rate of counterfeit notes production and distribution is increasing. This is a massive problem, faced by almost all the countries. It affects the economy, sine it compromises the security of the real economy. Such counterfeit currencies are used to fuel nefarious motives, usually involving terrorist activities. According to the research, developing countries like India have been impacted by this very negatively. Even after the steps taken in 2016 to remove the counterfeits, by executing the demonetization of 500 and 1000 rupees bank notes in India the counterfeits of the new notes have begun circulating. This is due to the highly advanced technology adopted by the counterfeiters which makes the tracking of these counterfeit notes hard. This has become a very critical issue and the negative impact due to the counterfeit currency keeps rising. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for a small business or for a common man these machines are not affordable. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion etc. The proposed system will have advantages like simplicity, reliability and costs less.

Peer review is an important means of academic evaluation. The evaluation words in peer review texts reflect the important viewpoints of reviewers. In this paper, An evaluation words recognition method for peer review texts (TransPeerBCC) is proposed based on transfer learning method and BiLSTM-CNN-CRF framework. TransPeerBCC first classifies direct evaluation word and indirect evaluation word, and then uses BiLSTM-CNN-CRF framework to identify these words. At the same time, in order to improve the recognition accuracy, the model parameters of public domain data are transferred to the peer review texts’ using the transfer learning method. The effectiveness of the method is verified by the experimental dataset, and the identified evaluation words are quantitatively analyzed.

Tech mining is the application of text mining tools to science and technology information resources. The ever-increasing volume of scientific outputs is a boom to technological innovation, but it also complicates efforts to obtain useful and concise information for problem solving. This challenge extends to tech mining, where the development of techniques compatible with big data is an urgent issue. This article introduces a semi-supervised method for extracting layered technological information from scientific papers in order to extend the reach of tech mining. Our method starts with several pre-set seed patterns used to extract candidate phrases by matching the dependency tree of each sentence. Then, after a series of judgements, phrases are divided into two categories: ‘main technique’ and ‘tech-component’. (A technique, for the purposes of this study, is a method or tool used in the article being analysed.) In order to generate new patterns for subsequent iterations, a weighted pattern learning method is also adopted. Finally, multiple iterations of the method are applied to extract technological information from each paper. A dataset from the field of optical switcher is used to verify the method’s effectiveness. Our findings are that (1) by two loops of extraction process in each iteration, our method realises the layered technological information extraction, which contains the ‘part–whole’ relationships between main techniques and tech-components; (2) the recall rate for main techniques is superior to the baseline after iterating 23 rounds; (3) when layering is disregarded, in the aspect of the precision and the volume of techniques, the new method is higher than that for the baseline; and (4) adjusting another two parameters can optimise the efficiency – however, the effect is neither pronounced nor straightforward.

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.