Reviewerly/exHarmony huggingface dataset:
Leveraging Google BERT to Detect and Measure Innovation Discussed in News Articles

In this paper, we leverage non-survey data (i.e., news articles), natural language processing (NLP), and deep learning methods to detect and measure innovation, ultimately enriching innovation surveys. Our dataset is composed of 1.9M news articles published between 2013 and 2018 acquired from Dow Jones Data, News, and Analytics. We use Bidirectional Encoder Representation from Transformers (BERT), a neural network-based technique for NLP pre-training developed by Google. Our methods involve: (i) utilizing Google’s BERT as a binary classifier to identify articles that mention innovation, (ii) developing BERT’s named-entity recognition algorithm to extract company names from these articles, (iii) leveraging BERT’s question and answering capabilities to extract company and product names. As a result, we obtain innovation indicators, i.e., company innovations in the pharmaceutical sector.
Digital Transformation of Academic Management: All the Tigers Come at Night

Latin America higher education institutions have been participating in several academic networks promoted by European Commission. There are cooperation, mobility, and research programs to help and support institutions development. However, the Fourth Industrial Revolution brings a disruptive scenario, challenging these institutions to go beyond academic cooperation and undergo a digital transformation, offering answers to this new revolution that is changing the world in never-before-seen form and speed. Tools and approaches originated in the business sector may help higher education institutions to promote their digital transformation. Nevertheless, these institutions have characteristics that differentiate them from the traditional managerial business. Moreover, these characteristics are a source of tensions, night tigers that are adversaries but invisible on a day-to-day transformation activity, impacting the digital transformation process. This chapter presents the double triad model: “rethink the business” and “rethink where is the business” to create an integrative framework, a strategic playing field to understand, manage, and overcome the challenge of dealing with adverse digital transformation scenarios.
Estimation of the Features of Application of the M-Sequences Subset with the Possibility of Joint Processing

The object of consideration is asynchronous address systems using code division of subscribers. The subject of the analysis is noise immunity of quasi-orthogonal signal ensembles based on a subset of M-sequences, allowing joint processing of code sequences. The purpose of the analysis is to determine the noise immunity characteristics of system using M-sequences subset that have the stated properties of joint processing. The solution is based on the principles and mathematical models developed by authors: the principle of the fast conversions in Galois fields, algebraic model of representation and structure of fast delay acquisition devices for a subclass of the sequences as well as on the simulation of the corresponding devices. This article is devoted to the study of the correlation properties of signal ensembles possessing the property of joint fast processing, as well as to the finding of the noise immunity characteristics of the corresponding signal receiving and processing devices. It is shown that the considered subset of code sequences, allowing joint fast processing, can be used in various scenarios, including in the scenarios of "distinguish-measure" objects, simultaneously using different code features from the ensemble under consideration. In this case, the considered devices are resistant to changes in the signal structure within the established ensemble. The proposed variants of the search scheme perform reception and primary processing of code sequences in real time, which favorably distinguishes them from known schemes that perform fast transformations by other methods. The results of the work will be useful to specialists involved in the design of asynchronous-address systems using complex signals based on code sequences with a large base.
A Policy-Agnostic Programming Language for the International Data Spaces

It is of utmost importance to maintain digital sovereignty in the context of Industry 4.0 and data-driven business models. As data itself becomes a valuable asset, this is a challenge that many companies have to face. This is particularly true as data sharing with third parties is a mandatory component of many modern business models. For its participants, the International Data Spaces (IDS) provide an ecosystem that supports the establishment and protection of their digital sovereignty. In this extended paper, we present the requirements in the area of usage control that have emerged from the IDS. We recapitulate the current state of the domain-specific language D $$^\circ $$ , which has usage control mechanisms as a core functionality. We then introduce extensions to the policy system of D $$^\circ $$ and the language itself to meet these requirements. We demonstrate how the policy system can be extended by label-based mechanisms in order to increase the expressiveness of the policy system. These mechanisms can be used to attach metadata to data as well as other language components of D $$^\circ $$ and therefore allow easy tracing of data and sharing of information between language components such as policies. We introduce a mechanism to D $$^\circ $$ that allows to dynamically map external identifiers (like the ones used in the IDS) to those used in D $$^\circ $$ . This allows D $$^\circ $$ to be combined with other usage control solutions as it facilitates the use of global identities that exceed D $$^\circ $$ .
System and method for stereoscopic image acquisition

This paper discusses stereoscopic images gain using library in OpenCV on Python platform. The simple stereo camera system that can provide stereoscopic images in both as a picture (PNG) or as a video (AVI) is developed. The system comprises a stereo camera, a USB connector, a tripod, and a notebook with programming language of Python 3.7, along with the free library of OpenCV 4.0 installed inside the notebook. The system has successfully provided both images and videos. However, the spatial resolution and the frame rate depend to the specifications of the stereo camera being used. The camera resolution of 1280 × 360 with a frame rate of 5 fps is obtained based on the stereo camera specifications in this study. The disparity of the stereo camera as a function of distance of the camera to the object of interest is measured. From the experimental results, the p constant is 1059.4, q constant is -0.527 over a distance of 10–100 cm. It shows that farther the object from the camera, the disparity decreases. This system will be used for performing physical measurement in the future.
DeepMix: A Real-time Adaptive Virtual Content Registration System with Intelligent Detection

This demo proposes a novel virtual content registration system (DeepMix) for MR applications, which integrates state-of-the-art computer vision technology and allows real-time interaction between virtual contents and arbitrary real objects in the physical environment for MR devices. DeepMix effectively utilizes different sensors on MR devices to measure the dimension and spatial location of real objects in the physical environment and improves the quality of experience (QoE) of users adaptively under various situations. Compared with state-of-the-art virtual content registration methods, DeepMix is a light-weight registration system with more flexibility, higher intelligence, and stronger adaptability.
USER EXPERIENCE STUDY ON IDEATING WEARABLES IN VR

Abstract More than ever, the ability to quickly and effectively shape ideas in a 3D environment is essential for industrial designers and with the rise of XR technology, a shift from traditional, screen-based CAD to VR-based CAD could improve time to market and personal effectiveness for product designers. In this study, this shift is assessed from a user experience perspective. Ten professional product developers are asked to design respirator masks. The experiment takes place in the Gravity Sketch VR app, using a HTC Vive Pro HMD. The participants are observed, surveyed and interviewed regarding different parameters on their experience. Participants experienced VR Aided Design as quick and intuitive. They personally felt performant and they enjoyed the process. As of now, VRAD is not seen as an alternative to neither sketching nor CAD. Instead, new users experience it being a new tool that can be positioned either parallel to or in between ideation sketching, clay modeling and detailed CAD design. Lastly, this paper includes a preliminary look at a VR Stylus for virtual reality aided design.
Redundancy Features Detection and Removal for Simplification of Convolutional Neural Networks

Since the rapid development of GPUs, the AI model of the convolutional neural network (CNN) has also made great progress. Researchers have gradually developed the model in a deeper and wider direction, hoping to have better accuracy. Although this is indeed effective, it also causes the model has too many parameters, and it takes a lot of time to calculate. In such a complex model, some operations are no effect on the output results. In this paper, we use several methods to remove the less important operations from the CNN model. This algorithm can reduce the amount of parameters and calculations while maintaining accuracy.
Performance Analysis of Retinex Based Algorithms for Enhancement of Low Light Images

Abstract The images captured under low and poor light conditions may suffer low contrast and blurring problems which will affect its interpretation and recognition. Hence it is important to enhance and restore such low contrast images. The enhancement of the captured images in the low light environment through various image enhancement algorithms is discussed in this work. The set of algorithms tested includes single scale retinex, multiscale retinex color preservation, multiscale retinex color restoration, contrast limited enhanced adaptive histogram equalization, dark channel prior, and gamma correction algorithms. This paper discusses the various variants of retinex models, which are considered powerful methods for image enhancement and restoration. The proposed work involves the study of various image enhancement algorithms and their comparison on the basis of a reference original low light image and human eye perception models. The performance of various retinex derivative algorithms is experimented with using the LIME dataset, and the histogram equalization is analyzed for metrics such as peak signal to noise ratio, structural similarity index, and discrete entropy.
EScALation

Spatio-temporal action localization aims to detect the spatial location and the start/end time of the action in a video. The state-of-the-art approach uses convolutional neural networks to extract possible bounding boxes for the action in each frame and then link bounding boxes into action tubes based on the location and the class-specific score of each bounding box. Though this approach has been successful at achieving a good localization accuracy, it is computation-intensive. High-end GPUs are usually demanded for it to achieve real-time performance. In addition, this approach does not scale well on a large number of action classes. In this work, we present a framework, EScALation, for making spatio-temporal action localization efficient and scalable. Our framework involves two main strategies. One is the frame sampling technique that utilizes the temporal correlation between frames and selects key frame(s) from a temporally correlated set of frames to perform bounding box detection. The other is the class filtering technique that exploits bounding box information to predict the action class prior to linking bounding boxes. We compare EScALation with the state-of-the-art approach on UCF101-24 and J-HMDB-21 datasets. One of our experiments shows EScALation is able to save 72.2% of the time with only 6.1% loss of mAP. In addition, we show that EScALation scales better to a large number of action classes than the state-of-the-art approach.
Augmented Reality Enhanced Traditional Paper Book Reading Experience Design: A Case for University Library

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.
Enhanced Static Secure Logic for Hardware Security

As the electronic activity is penetrating into various situations of life like electronic governance, banking and reservation systems, the need for higher and higher security in protecting the banking transactions, passwords, PNR numbers, etc. is increasing. The security lapses occur through both software and hardware. The side channel attacks (SCAs) use the details of power pattern, timing details and leakage information. Differential Power Analysis (DPA) attack is also a major threat, where the intruder analyzes the fluctuations in the power consumption to estimate the secret key of the implemented encryption algorithm in the IC as security. As a remedy, logic gates having constant power dissipation that are not dependent on input signals are used in security ICs, i.e., for every input pattern, consumed power and circuit characteristics like identical leakage current, instantaneous current, input–output delay, independent of logic value and sequence of input data. Implementation of the encryption module inside logic will protect the device against side channel attacks. This paper aims at developing an Enhanced Static Secure Logic (ESSL) for basic circuits required for implementing a larger module. They are compared for area, power consumption and variations in power consumption against Static Complementary CMOS logic, genuine Dynamic and Differential Logic.
Working with readers under quarantine conditions

The main purpose of the article is to present the experience in organizing users’ services at the Universal Literature Circulation Department of the Central Scientific Library (CSL) of the Federal Research Center “Krasnoyarsk Science Center” of the Siberian Branch of the Russian Academy of Sciences (FRC KSC SB RAS) in the period of spring - summer of 2020. Ways of solving the problems that arose in the libraries’ work due to restrictions caused by the COVID-19 pandemic are considered. During the quarantine period, it became necessary to ensure the return and extension of literature in the conditions of limited contacts between the librarian and the reader. The following mechanisms for solving the problems were proposed: the possibility of remote extension of the period of documents usage with the help of a mobile communication device as well as of books return through the “book-box” at the checkpoint of the FRC KSC. When informing users in a remote mode, telephone calls, SMS and e-mail messages were used. The preferences of readers when using various communication systems were analyzed: among the subscribers who used the remote method of extending books usage period, about half of the readers used Viber, one third used WhatsApp and about a fifth – e-mail. The efficiency of the work performed was determined: almost all users settled the matters with the library before August 31. At the same time, most of the books were returned or the period of usage was extended. Several reasons for effective work were highlighted: convenience of online renewal; no time limits for returning books; prompt notification of readers about new opportunities. The advantages of the proposed technology (the fulfillment of quarantine requirements, convenience of remote work for readers, and, as a result, promptness of performing actions on their part) and disadvantages (additional work to preserve information when using the employee’s personal smartphone, lack of personal contact between the librarian and the reader are emphasized. The activity of the Circulation Department is assessed as satisfactory according to the selected set of criteria. The considered method of the Department work, taking into account the quarantine requirements, is recognized as promising.
Toward a Theory-Driven Model of Emotional Interaction Design in Mobile Games Research

The rapid development of mobile information technology provides good opportunities for the maturity and growth of mobile games. In busy modern life, mobile games are playing an increasingly important role in people’s social connection. In addition to entertainment functions, mobile games in the near future have the potential to become a new method of teaching, by which users can learn a variety of knowledge in a lively and interesting way. What factors contributed to the birth of an excellent mobile game? Obviously, the emotional factors in interaction design play a vital role. Emotion-based interaction design can evoke various positive emotions of players, such as the sense of accomplishment, satisfaction, flow experience, awe, and other psychological experiences. Thus, it is of importance to analyze the emotional factors in mobile game interaction design and discuss emotional interaction design principles and methods. The purpose of this paper to establish an emotional interaction design model, which could provide the theoretical basis for improving mobile game design theory, expanding mobile game functions and value, meeting users’ more advanced emotional needs and guiding mobile game development and design practice. This paper first discusses the emotional design theory proposed by Professor Don Norman. And then it analyzes the intersection and integration of interaction design and psychology, human factors engineering, aesthetics, and other disciplines. Corresponding to the three levels of emotional design: visceral, behavioral, and reflective, it puts forward an emotional interaction design model consisting of user interface design, interaction operation design, and interaction experience design. Finally, it improves and applies the emotional interaction design model in mobile games through case studies.
The Role of ICT in Modulating the Effect of Education and Lifelong Learning on Income Inequality and Economic Growth in Africa

This study assesses the role of ICT in modulating the impact of education and lifelong learning on income inequality and economic growth. It focuses on a sample of 48 African countries from 2004 to 2014. The empirical evidence is based on the generalised method of moments (GMM). The following findings are established. First, mobile phone and internet each interact with primary school education to decrease income inequality. Second, all ICT indicators interact with secondary school education to exert a negative impact on the Gini index. Third, fixed broadband distinctly interacts with primary school education and lifelong learning to have a positive effect on economic growth. Fourth, ICT indicators do not significantly influence inequality and economic growth through tertiary school education and lifelong learning. These main findings are further substantiated. Policy implications are discussed.
Broadcasting coherence via incoherent operations

Quantum coherence has recently emerged as a key candidate for use as a resource in various quantum information processing tasks. One of the main problems in any quantum resource theory is the characterization of the conversions between resources by means of the free operations of the theory. In this work, we ask if coherence, already present in a reference system, can be broadcast to the auxiliary system via incoherent operations. We prove that it is impossible for a weaker form of broadcasting via incoherent operations. This shows that broadcasting of coherence is forbidden via incoherent operations in every finite-dimensional system. We also prove that a generalized form of broadcasting via incoherent quantum operations can occur if and only if the state of system is coherent. In this case, we are able to give a method for constructing an incoherent quantum operation to broadcast coherence.
Verifiable Model Construction for Business Processes

Business process specified in Business Process Execution Language (BPEL), which integrates existing services to develop composite service for offering more complicated function, is error-prone. Verification and testing are necessary to ensure the correctness of business processes. SPIN, for which the input language is PROcess MEta-LAnguage (Promela), is one of the most popular tools for detecting software defects and can be used both in verification and testing. In this paper, an automatic approach is proposed to construct the verifiable model for BPEL-based business process with Promela language. Business process is translated to an intermediate two-level representation, in which eXtended Control Flow Graph (XCFG) describes the behavior of BPEL process in the first level and Web Service Description Models (WSDM) depict the interface information of composite service and partner services in the second level. With XCFG of BPEL process, XCFGs for partner services are generated to describe their behavior. Promela model is constructed by defining data types based on WSDM and defining channels, variables and processes based on XCFGs. The constructed Promela model is closed, containing not only the BPEL process but also its execution environment. Case study shows that the proposed approach is effective.
Aspect Term Extraction Using Deep Learning-Based Approach on Indonesian Restaurant Reviews

Aspect term extraction is a fundamental process in aspect-based sentiment analysis. Aspect term extraction aims to identify the review text span that contains the aspect mentions. In this paper, we present our work on aspect term extraction for Indonesian restaurant reviews, using a deep learning-based approach. We collected and annotated an Indonesian restaurant reviews dataset, obtained from a restaurant review website. We performed the annotation at a token-level and used the following aspect labels to annotate the reviews: FOOD, PRICE, AMBIENCE, SERVICE, and MISCELLANEOUS. This paper treats aspect extraction as a token-level classification. We employed a Convolutional Neural Network (CNN) model and Long Short-Term Memory (LSTM) model for the classification. The experimental result showed that the LSTM method gives the best performance, with the micro average F1-score is 55,1%.