Reviewerly/exHarmony huggingface dataset:
Opportunities, barriers and enablers of blockchain adoption in organizations

In this chapter, we provide details of key enablers of the diffusion and adoption of blockchain in organizations. It will discuss major opportunities that can result from blockchain's use in organizations. These issues are discussed from the perspective of blockchain's potential to act as an effective governance mechanism so that it can contribute to produce trust. It evaluates initiatives of organizations, inter-organizational networks and industries on this front. It provides a perspective on the use of blockchain-based solutions to enhance branding, empower consumers and increase their satisfaction with services. This chapter also offers an analysis of blockchain's potential to bring major economic, political and social transformations. We also offer an overview of barriers in implementing blockchain. We also explain why deployment of blockchain-based solutions is difficult due to complex socio-economic, political and infrastructural challenges. Also discussed is how the lack of clarity in the regulatory environment and limited interest among the incumbent players in the market has been a major obstacle to the adoption of cross-border blockchain payment solutions.
Using ISEF Edge Detection, Text Extraction from Natural Images of Different Languages

In this research, we presented a method for word extraction from various linguistic imagery. In computer vision research, text in images is crucial. Here, we employ edge-based text extraction with ISEF (infinite symmetrical edge filter). The best edge detector, known as ISEF, provides reliable findings for text in images. Detection, localization, tracking, and improvement are all involved in text extraction. For text extraction, numerous techniques have been proposed. Our goal is to propose a reliable method for text extraction from photos in many languages.
THE RELATIONSHIP BETWEEN KNOWLEDGE SHARING AND INNOVATION PERFORMANCE: A CASE OF MANUFACTURING SECTOR OF THE EMERGING ECONOMY

This study investigates the relationship between knowledge sharing and innovation performance. Quantitative survey using cross-sectional sampling was used. Sample included 355 different staff members. A questionnaire that was self-administered and was based on previously used metrics was chosen. The percentage of those that responded was 73.9%. A statistical method known as partial least squares structural equation modelling was used in order to examine and evaluate hypotheses that were proposed. The software program SPSS and Smart PLS were used in order to do the analysis on the data that were obtained. Findings reveal that knowledge sharing has a significant effect on innovation performance. In addition to this, data demonstrated a favorable and substantial association between the sharing of knowledge and innovation of both products and processes. The findings of research have significant implications for further developing and enhancing the relation between knowledge sharing and innovative performance.
Utilization of e-Book Kotobee Reader as an Interactive Learning Tool in Developing 8th Grade Mathematical Skill

When the COVID-19 pandemic broke out in our country, the education sector explored the availability of technologies that might be helpful to ensure the delivery of quality education to the learners despite the sudden shift in learning modality, that is, from face-to-face to blended learning. The Sulong Edukalidad, a move of the Department of Education to innovate Philippine Education under COVID-19 provided online training and webinars which prepared Filipino teachers in facing the new normal of the education setting. One of which is the utilization of the e-book Kotobee Readers as an interactive learning tool. E-books are one of the many software and programs being developed to enhance teaching in the field of education. E-books are publications that can give students information and instructions in the form of multimedia flexibly and efficiently, thereby supporting and improving learning. In this regard, our team aims to explore the potential of the utilization of the e-book Kotobee reader as an interactive learning tool in developing 8th Grade Mathematical skills during the Covid-19 pandemic, focusing on the average percentage of the participants before and after the utilization of the e-book, their level of performance during the utilization of the said learning material, the challenges encountered by the participants and also identifying the positive traits of Kotobee that helped the students learn. The research design to be used for this research will be a quasi-experimental approach. This research design is intended for an interventional study used to estimate the impact of an interactive–based lesson using the Kotobee e-book on the Grade 8 students in developing their mathematical skills The participants involved in this research are the selected Grade 8 – Rose learners in Mataas na Paaralang Neptali A. Gonzales. Our team implemented a series of interventions about topics in Quarter III which the lectures and activities can be found in the said e- book. Students’ usage of the Kotobee e-book app on a daily basis was tracked in an Inquiry report. Students’ scores in each activity will be tallied at the end of each quarter for assessment. This will assist us in determining how well the students performed after they have used the Kotobee e-book. A separate learning activity sheet (LAS), validated by the Master Teacher in charge of JHS Mathematics, was administered weekly to be able to validate the performance of the learners for a specific topic taught while using the e-Book. At the end of the Quarter, a post-test was administered to be able to determine the performance of the students. Data collected showed that by using the Kotobee e-book, it has affected how well the participants do in Mathematics 8 Quarter III, as shown by the significant difference between the mean percentage scores on the pre-test and the mean percentage scores on the summative test. Students learn while using the Kotobee e-book, based on the results of their weekly assessments and their quarterly test.
Minimalistic in-flight odometry based on two optic flow sensors along a bouncing trajectory

Estimating the distance traveled while navigating in a GPS-deprived environment is key for aerial robotic applications. For drones, this issue is often coupled with weight and computational power constraints, from which stems the importance of minimalistic equipment. In this study, we present a visual odometry strategy based solely on two optic flow magnitudes perceived by two optic flow sensors oriented at ±30° on either side of a drone’s vertical axis. As results, (i) we measured the local optic flow divergence and the local translational optic flow respectively as the subtraction and the sum of the two optic flow magnitudes perceived (ii) we validated experimentally the visual odometer on a hexarotor oscillating up-and-down while following a 50m-long circular trajectory under three illuminance conditions (117lux, 814lux and 1518lux). The measured optic flow divergence was used to estimate the flight height by means of an Extended Kalman Filter. The estimated flight height scaled the measured translational optic flow, which was integrated to perform minimalistic visual odometry.
Medical image cryptosystem using improved Quadratic Congruential Generator and logistic map

The medical images, patient history, doctor's note, and clinical review contained in an electronic health record are highly sensitive data. Digital images are multimedia data that comprise of hidden information in medical field. Conversely, designing aproficientsafe cryptosystem to safeguard the confidential images during sharing is complex and perplexing. The improved Pseudo Random Number Generators (PRNGs) centered Quadratic Congruential Generator (QCG) generators and a nonlinear dynamical method, improved logistic chaotic map are utilized in this work to present a 24-bit color DICOM image encryption technique. The cryptosystem proposes a unique methodology to generate confusion and diffusion sequences for scrambling and substituting image pixels to defy various adversarial attacks by portraying randomness to enciphered images. To demonstrate the effectiveness and reliability of suggested image cryptosystem, it is tested on a collection of color DICOM images. The findings and different validation metrics lists that the proposed image cryptosystem offerunique level of performance as well as security for 24-bit color DICOM images.
AI Effects (AIX) in Computer Graphics (CG)

Artificial intelligence (AI) is a powerful tool and has increased operational efficiency and improved user experience in many areas. Computer Graphics (CG) is a domain that empowers Virtual Reality, Augmented Reality, and Metaverse. Many AI methods have been studied to boost CG and have achieved startling effects. In this talk, I will show how we innovate and apply AI in the CG pipeline to enhance user experience and productivity. We will see how to enable intelligence in media manipulation, media creation, and media engagement.
Chinese sentiment analysis with multi-granularity vector representation and multi-channel network

Aiming at the problem that normal vector representation method cannot fully represent the emotional semantic information contained in the text when dealing with Chinese text sentiment analysis task, a multi-granularity convolutional capsule neural network model is constructed. The input of sentiment analysis model is vector representation of the texts trained through the language model. As there exists the problem that a single language model is not enough to abstract text features, a multi-granularity text vector representation method based on BERT is proposed. The text vector representations with different granularities are input into the improved multi-channel convolutional capsule neural network. The capsule layer can associate the low-level and high-level text features, and it will extract information selectively through the dynamic routing algorithm to construct emotional and semantic features of the whole text. Multiple comparative experiments confirm that the method proposed in this paper will efficiently improve the accuracy of Chinese sentiment analysis.
Semantic analysis of the basic concepts of research on the construction of an information-analytical system for the training of scientific personnel

The relevance of support for research on the methodology of building information and analytical systems is determined by the separation and justification of basic concepts. In this context, based on view of the relevance of the topic of the article on the results obtained analytically, before proceeding to the practical aspects of building any information-analytical system, given the transdisciplinarity of scientific knowledge, it is always appropriate to conduct structural-logical or semantic analysis terminology. This makes it possible to clarify the essence and content of the basic concepts of research and to outline the vector of further research. It is noted that in the conditions of modern information communications all objects of information-analytical interaction, regardless of their origin, are directly or indirectly interconnected. The purpose of the research is to provide basic categorical-conceptual support for doctoral research on the methodology of building information and analytical systems. In this sense, the methods of content analysis, semantic and comparative analysis, ontological modelling, etc. were used, which allowed identifying several types of approaches to determining the amount of information exchanged by objects in the process of information interaction: algorithmic, entropic, combinatorial, ontological, semantic, synergistic, transdisciplinary and pragmatic. The results and conclusions of the research are in the range of issues that allow us to understand the essence of the definition of «Information-Analytical System»; the semantics of the terms «Information», «Information Object», «Information Environment», «Information Space», «Information-Analytical Interaction», «Semantic Environment», etc.; the special nature of information as such, which distinguishes it from matter and energy, is emphasized; it is noted that information exists only in the presence of its carriers, which have a material and energy form; the relationship between the concepts of «Information Environment», «Information-Analytical Field» and «Information Space» is revealed, which is derived from the content of the basic concept of «Information». The ontological approach as a basic tool of semantic (informational) analysis is studied (within which it is possible to calculate the semantic similarity of semantic interpretations of text tokens in relation to the immediate semantic environment, i.e. context); it is noted that the construction of ontologies is based on the fixation of logical constructions that contain dictionaries of terms of the thematic area and on the description of the definitions of these terms, their theoretically possible and impossible semantic connections; it is noted that the basis for building an ontology can be user requests; the mathematics of the process of ontological modeling is briefly presented. The current definition of «Information-Analytical System» based on the results of semantic analysis is given.
Digital citizenship under lockdown: promoting the healthy use of technology for adolescents growing-up in Perú during COVID-19

Since 2018, the Transitions project has promoted digital citizenship, the set of abilities that allow for safe and positive online participation, among adolescents in Latin America through a culturally appropriate school curriculum informed by developmental science. COVID-19 motivated readers to redesign the author's research to promote well-being during a crisis, highlighting the role digital technology plays during lockdown. This project presents a case study for the cultural adaptations necessary to implement digital citizenship lessons for Peruvian youth during remote instruction. Recognizing the elevated importance of digital technology as a social bridge concurrent with physical distancing, the authors aimed to capture the dynamic changes and adaptations that adolescents made as they tried to cope. As a result of the lockdown, digital technology has become utterly essential for adolescents; education, relatedness, and autonomy.
User Clustering Based on the Topic-Sentiment Analysis

Nowadays, more and more people communicate and comment on what they care about on social platforms. In order to determine the distribution of topic-sentiment in microblog comment text more accurately and efficiently, this paper combined the improved TF-IDF parallel algorithm and Latent Semantic Model to construct the topic-sentiment model for user clustering. Firstly, we carried out data pre-processing and semantic role tagging. Then, we used the improved TF-IDF parallel algorithm to calculate the eigenvalues and sentimental polarity values of each sentimental word, and then the topic-sentiment words table was established. Finally, we established a Latent Semantic Model for the data of users' topic sentimental polarity value, extracting eigenvectors of users, and clustering users with the same eigenvectors by K-means algorithm. Compared with the traditional models, the result of the experiment showed that the proposed model has better accuracy, recall rate, F1 and time performance.
Energy efficient computing devices with voltage control of nanoscale magnetism (Conference Presentation)

Electrical field control of magnetization in nanoscale magnets has the potential to be extremely energy efficient. We will discuss voltage-induced strain and acoustic wave switching of the magnetization as well as direct voltage control of magnetic anisotropy (VCMA) to control magnetic skyrmion states in nanomagnets with confined geometry. We will then discuss various voltage-controlled nanoscale magnetic device proposals towards implementation of energy efficient, memory, deep neural networks whose synaptic weights can be reprogrammed online and reservoir computing devices that are amenable to online training. This could be a key enabling technology for edge computing in IOT devices.
Realization of reservoir computing by delayed oscillators with active microparticles (Conference Presentation)

Reservoir computing as a highly efficient architecture for recurrent neural networks has been implemented in a variety of ways, including anharmonic oscillators, liquid surfaces, and optical and electronic circuits. Here, we investigate whether active particle networks that mimic fundamental dynamical processes of living systems can serve as reservoirs. In particular, we realize active particle oscillators, each consisting of an immobile and an active colloidal microparticle suspended in a layer of a liquid solution. The motion of the active particles is manipulated by a feedback system using a focused laser that stimulates the particles to float in 2D by thermophoresis [1]. The active particle is programmed to be attracted to the immobile particle with a delayed response that exhibits a pitchfork bifurcation, which introduces nonlinearity and memory into the response of a single active oscillator. Using time multiplexing, the propulsion of the oscillator is selected at different times as virtual nodes of a reservoir that are coupled to an input layer. Since the motion of the active particle is affected in a nonlinear manner with a memory of its previous state, the last node state is naturally coupled to the other nodes from different iterations due to the intrinsic property of the delayed oscillator. We illustrate the performance of the reservoir consisting of multiple oscillators with different delays by the tasks of nonlinear prediction and classification. [1]. F. Martin et al. ACS Nano 15, 2, 3434-3440 (2021)
MZM Nonlinearity Enabled Uneven PAM-4 for EVM Reduction in Digital Mobile Fronthaul

We propose and experimentally demonstrate a digital mobile fronthaul scheme utilizing the non-linearity of MZM. The EVM can be reduced by 6.17% and 2.19% at −9dBm ROP for BtB and after 10km SSMF transmission, respectively.
Dengue prediction using time series forcasting I.E ARIMA model

Dengue fever is a mosquito-borne tropical disease caused by the dengue virus. It is a life threatening disease lots of person died due to dengue because its symptoms are not detected at early stages many parson thought that it was a normal fever or headache so that they just ignore it which cause there are in danger situations and in worst case they loss their life.
Probabilistic UI Representation and Reasoning in Touch Interfaces

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A study of compound verbs in high school Japanese textbooks: Frequent lists in the past and important lists in the future

This study aims to compile a list of compound verbs that have been used frequently over the past decades for a total of 104 high school Japanese textbooks, and then compare it with high-frequency lists from previous studies, and propose a list of compound verbs that will be considered important in future Japanese textbooks. The principal results are as follows:BR (1) The list of frequent compound verbs in the past are as follows: 言い替える、受け取る、落ち付く、思い出す、書き表す、書き入れる、書き替える、書き直す、繰り返す、差し上げる、出会う、出掛ける、似合う、乗り替える、話し合う、見付ける、召し上がる、申し上げるBR (2) The list of important compound verbs in the future are as follows: 出掛ける、付き合う、出会う、似合う、引っ越す、召し上がる、受け取る、落ち付く、思い出す、繰り返す、乗り替える、話し合う、見付ける、申し込むBR The study also suggests that it will be necessary in the future to proactively offer highly productive syntactic compound verbs such as
Context-Related Recommendation Algorithm Based on Graph Embedding

Most personalized recommendation systems make recommendations based on the similarity of users and items, such as collaborative filtering and mass diffusion, and graph embedding techniques. Although graph embedding technology can mine the information in the network, its embedding performance becomes unstable due to the influence of parameters. The bipartite graph-based recommendation algorithm can continuously mine the information in the bipartite graph network through the resource allocation process, but its information mining is not comprehensive. Based on the complementary nature of the two methods, this paper proposes a context-relevant recommendation algorithm combined with graph embedding, which uses the embedding representation of users and items to obtain the degree of association between items and integrates them into the resource allocation process. The purpose is to fully mine the information of user-item interaction data. Finally, the algorithm in this paper not only reduces the parameter sensitivity of embedding, but also improves the recommendation accuracy. The experimental results show that the proposed algorithm consistently outperforms the baseline method in common recommendation indicators.
Unexpectedness as a Measure of Semantic Learning When Training Transformer Models

Many problems in NLP such as language translation and sentiment analysis have shown a lot of improvement in recent years. As simpler language problems are solved or better understood, the focus shifts to more complex problems such as semantic analysis and understanding. Unfortunately, a lot of studies in the literature suffer from a too much specificity problem. The algorithms and datasets are too domain specific. In this study, we analyze and elaborate on this notion of generality. Instead of selecting a highly specialized data set for semantic analysis, we take a generic and possibly dry data set, and we study how a plain vanilla Transformer performs in learning higher level semantic patterns beyond what was obvious or expected. We tune our Transformer model on a classic language task to ensure correct performance. Once tuned, the goal is to select sentences with specific key words and study whether higher level semantic patterns may have been learned by our model. We believe that we obtained promising results. The average BLEU score for sentences less than 25 words is equal to 39.79. Our initial qualitative analysis of possible semantic content of interest shows a 17 percent rate in finding interesting semantic patterns. We provide discussion of data driven results of unexpectedness as a measure of semantic learning.