Reviewerly/exHarmony huggingface dataset:
Strengthening gender equality in small business and achieving sustainable development goals (SDGs): Comparative analysis of Kenya and Nigeria

Many small business owners and entrepreneurs in Africa still see sustainable development goals as a misery. The study investigates whether sustainable development goal number five (5) is being used to motivate gender equality among small and medium-sized enterprises in Africa, particularly in Kenya and Nigeria. The study focuses on female entrepreneurs and female-owned businesses. Design/methodology/approach. A qualitative approach involving semi-structured in-depth interviews was considered with a target population of 110 participants selected from businesses operating in Embakasi East constituency in the Embakasi ward in Nairobi County, Kenya, and in Ikeja Local Government in the Lagos Mainland of Lagos state Nigeria. The primary interview data was gathered from businesses in the manufacturing, services, information technology, transport, trading, and communications sectors. Findings. From the population of 110 participants, the response rate was 90.91%. Results demonstrated that a significant number of women working in SMEs are unaware of the sustainable development goal number five (5) in the two countries despite the United Nations (UN) supporting gender equality globally. The research concludes that there is still a considerable difference between men and women in small and medium-sized SMEs in Africa, and the awareness of sustainable development goals is limited. Research limitations/implications. The restriction of the dataset from a small sample size of women-owned businesses operating in Embakasi East constituency in the Embakasi ward in Nairobi County, Kenya, and in Ikeja Local Government in the Lagos State Nigeria. The research criteria used to choose study participants were unique to the current research; future researchers may consider changing the research criteria and sample size to broaden the study's focus. Practical implications. The study advances empirical research on sustainable development goal number five (5) by offering evidence of its impact on women-owned businesses. Further, the study throws light on the need to the fact that gender equality is necessary and why women should receive funding and mentoring to help them succeed as business owners. Originality/value. The study provides a comparative study with evidence from two countries, Kenya and Nigeria, on sustainable development goal number five (5) and throws light on the relevance of gender equality on business performance as funding and mentoring will assist women-owned businesses. For Africa to flourish and grow economically, gender equality is crucial.
Blockchain Technology Based Supply Chain Management System

One of the most important inventions and innovative developments playing an important role in the business world today is blockchain technology.Blockchain technology can be defined as a peer-to-peer distributed ledger database that is verifiable by parties and cannot be permanently updated.The hereditary features like data integrity and immutability in Blockchain is to optimize the processing model in several domains, such as financial services, healthcare, educational system, IoT, supply chain and many more.A decentralized blockchain system in which products are anti-counterfeit and manufacturers can use this system to supply genuine products without managing directly operated stores.The main objective of this study is to present a detailed overview of the use of blockchain technology in supply chain management systems.The present study examines all the relevant research done in the field related to the application of blockchain technology in supply chain operations.
Open string pair production from the interacting dressed-angled D-strings

We calculate the rate of the open string pair production from the interaction of two non-intersecting D-strings at angle. The D-strings have been equipped with the $U(1)$ gauge potentials in the presence of the Kalb-Ramond field. This rate will be discussed when the D-strings intersect each other. Our computations is in the framework of the bosonic string theory.
GABSA-PT: Graph Neural Networks for Aspect-level Sentiment Analysis in Portuguese Language

Aspect-based Sentiment Analysis is a Natural Language Processing task that aims to extract aspects from an opinionated text and identify the sentiment expressed by the opinion holder towards these aspects. Graph-based text representation has been shown to bring benefits to this task. While studies have demonstrated the effectiveness of this representation for ABSA using Graph Neural Networks in English, more work currently needs to be done evaluating this methodology for Portuguese language. In this article, we adapt a GNN known as DualGCN for sentiment classification using Brazilian Portuguese data. The model created using this approach achieved satisfactory results with a balanced accuracy of 75%
Deployed QKD Networks in Europe

We report several use-case demonstrations for quantum key distribution in deployed fiber networks. The tests were carried out under real world conditions at the end-user premises using commercial QKD systems.
Information-Driven Security Analysis: Tools and Techniques for the Study and Practice of Security Engineering

This article reviews concepts and methods for security analysis found in the professional practice and academic study of security engineering. We also describe a set of software learning aids that may have value as interactive tools for any security engineer.
Hand Gesture Recognition From Wrist-Worn Camera for Human–Machine Interaction

In this work, we study the ability to use hand gestures for human-machine interaction from wrist-worn sensors.Towards this goal, we design a wrist-worn prototype to capture RGB video stream of hand gestures.Then we built a new wrist-worn gesture dataset (named WiGes) with various subjects in interaction with home appliances in different environments.To the best of our knowledge, this is the first benchmark released for studying hand gestures from a wrist-worn camera.We then evaluate various CNN models for vision-based recognition.Furthermore, we deeply analyze the models that produce the best trade-off between accuracy, memory requirement, and computational cost.We point out that among studied architectures, MoviNet produces the highest accuracy.Then, we introduce a new MoviNet-based two-stream architecture that takes both RGB and optical flow into account.Our proposed architecture increases the Top-1 accuracy by 1.36% and 3.67% according to two evaluation protocols.Our dataset, baselines, and proposed model analysis give instructive recommendations for human-machine interaction using hand-held devices.
EXPLORING EFFECTS OF MOBILE APPLICATIONS ON ENGLISH VOCABULARY LEARNING AND ACQUISITION

The current research aims to explore the effects of mobile applications (apps.) on English lexis learning. This paper explored the interests of learners in mobile apps., especially in the English vocabulary learning process, and investigated the efficacy of mobile apps in their academic environment. It also observes that Interactive use of technology is showing effectiveness in the performances of the learners. This study adopted a quantitative approach, using a questionnaire that consists of 20 items. 150 students from the English department of National University of Modern Languages (NUML) contributed to this study who were regular users of mobile phones and apps. The data was analyzed by the SPSS program through frequency and mean deviation. The study concludes that it becomes easier for the students to learn vocabulary proficiently when vocabulary is accessible in numerous ways such as Pictures, Definition, Sound, etc. Findings also showed that it is easier for the participants, to learn vocabulary by apps, because it provides them visuals which leave a long lasting impact on their memory. Investigation also proved that the majority of the participants considered learning vocabulary through mobile app an enjoyable experience. Keywords: Mobile Applications, English Vocabulary Learning and Acquisition, Interactive use of Technology.
Formation of Future Teachers’ Leadership Competence at a Higher Education Institution

Abstract The article substantiates the need to form leadership competence among future teachers in the conditions of university education. The authors generalized and concretized the concept of “leadership competence” of the future teacher, defining it as a particular competence, namely the meta-competence. The research aimed to define the possible ways to form future teachers’ leadership competence in mastering the educational program “Pedagogy and Psychology.” The algorithm for future teachers’ leadership competence development is suggested in the given article. The confirmation of the accepted hypothesis is justified by the presented results of the study, which showed positive dynamics in the level of formation of the studied competence. The effectiveness of the consistent and purposeful formation of the future teacher’s leadership competence in studying psychological and pedagogical disciplines and passing pedagogical practice has been proven.
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization

In this paper, we conduct a comprehensive study of In-Context Learning (ICL) by addressing several open questions: (a) What type of ICL estimator is learned by large language models? (b) What is a proper performance metric for ICL and what is the error rate? (c) How does the transformer architecture enable ICL? To answer these questions, we adopt a Bayesian view and formulate ICL as a problem of predicting the response corresponding to the current covariate, given a number of examples drawn from a latent variable model. To answer (a), we show that, without updating the neural network parameters, ICL implicitly implements the Bayesian model averaging algorithm, which is proven to be approximately parameterized by the attention mechanism. For (b), we analyze the ICL performance from an online learning perspective and establish a $\mathcal{O}(1/T)$ regret bound for perfectly pretrained ICL, where $T$ is the number of examples in the prompt. To answer (c), we show that, in addition to encoding Bayesian model averaging via attention, the transformer architecture also enables a fine-grained statistical analysis of pretraining under realistic assumptions. In particular, we prove that the error of pretrained model is bounded by a sum of an approximation error and a generalization error, where the former decays to zero exponentially as the depth grows, and the latter decays to zero sublinearly with the number of tokens in the pretraining dataset. Our results provide a unified understanding of the transformer and its ICL ability with bounds on ICL regret, approximation, and generalization, which deepens our knowledge of these essential aspects of modern language models.
The Concept of "INFORMATION SPACE" of the Present: Sources, State, Prospects

Information has always been valued, now it has become even more valuable. Today it is considered the most important resource for the development of society. Recently, issues related to information warfare have been actively discussed not only in our country but also abroad. Despite thirty years of independence of our country, the concept of information space has not lost its relevance. Therefore, our task was to demonstrate with the help of which frames (in their broad interpretation from the point of view of linguistics, psychology, communication in general and knowledge as a picture of the world, the source of which is time, circumstances and actions) information is framed. The purpose of the study is to present the concept of "information space" in the projection to the present. The multidimensionality of the coverage of this issue in scientific discourse is explained by various factual materials. Therefore, the paper uses traditional methods of scientific research - analysis and synthesis - to identify the most important features of the phenomenon under study. Discussion. Linguistic units have different thematic and genre load, and therefore compositional build their own caches in the communicative space. There are codes - rubrics - symbols that help to decipher any information. We tried to highlight the concept of information space, based on our own observations and developments, which may not coincide with the generally accepted ones. Its main advantages are a high level of mobility. Conclusions. The article analyzes how information security occurs and what influences it. The author's own interpretation of "information space" in the context of modern communication is presented. This is encoded information that you need to be able to decipher. And everyone chooses the tools and platforms himself. This research can be used in teaching the theory of disciplines related to algorithmization, programming, artificial intelligence, linguistic experiments, mathematical linguistics and logic. Information space is a generalized concept of the acquired linguistic experience of mankind based on the objects of the physical world. Information can either be copied or acquire a new sound. Information only becomes valid when it is guaranteed by its use.
LiTrix: A Lightweight Live Light Field Video Scheme for Metaverse Stereoscopic Applications

Aiming to bring 3D immersive and personalized experiences, the metaverse converges many emergent technologies, including Internet of Things (IoT)-based data collection, 5G networks, and Artificial Intelligence (AI)-assisted computer vision. One of the efforts in the metaverse supports light field video (LFV) collection and processing to create high-fidelity digital twins, which act as a fabric link between the physical world and the cyber world. However, the research on the LFV system is still hindered by complicated capturing systems and huge computational resource requirements. We propose LiTrix, including a lightweight device for capturing live LFV with real-world scenes, and the device-edge-cloud collaboration under 5G networks to conduct stereoscopic reconstruction with less consumption of resources. LiTrix is fabricated by leveraging IoT devices that include a portable 100-camera array and dedicated edge and cloud servers. A novel LFV dataset is recorded through the devices, providing a diversity of the texture of the scene varying from paper products to ceramic to cotton. The dataset is open source and shared for public usage. In addition, we present the results of point cloud and mesh reconstruction, and show that the IoT-based collaboration framework saves 19.6 percent of the time in the ablation experiment. We expect that LiTrix can be used for metaverse stereo-scopic applications in the future.
Architecture and protocols for all-photonic quantum repeaters

An all-photonic repeater scheme based on a type of graph state called a repeater graph state (RGS) promises tolerance to photon losses as well as operational errors, and offers a fast Bell pair generation rate, limited only by the RGS creation time (rather than enforced round-trip waits). Prior research on the topic has focused on the RGS generation and analyzing the secret key sharing rate, but there is a need to extend to use cases such as distributed computation or teleportation as will be used in a general-purpose Quantum Internet. Here, we propose a protocol and architecture that consider how end nodes participate in the connection; the capabilities and responsibilities of each node; the classical communications between nodes; and the Pauli frame correction information per end-to-end Bell pair. We give graphical reasoning on the correctness of the protocol via graph state manipulation rules. We then show that the RGS scheme is well suited to use in a link architecture connecting memory-based repeaters and end nodes for applications beyond secret sharing. Finally, we discuss the practicality of implementing our proposed protocol on quantum network simulators and how it can be integrated into an existing proposed quantum network architecture.
GTdownloader: A Python Package to Download, Visualize, and Export Georeferenced Tweets From the Twitter API

This article describes GTdowloader, a Python package that serves as both an API wrapper and as a geographic information pre-processing helper to facilitate the download of Twitter data from the Twitter API. Specifically, the package offers functions that enable the download of Twitter data through single functions that integrate access to the API call parameters in the form of familiar Python functions syntax. In addition, the data is available for download in common formats for further analysis, which includes standard Geographic Information Systems (GIS) vector format. This software package is especially useful for users with little to no experience with building API calls but who would highly benefit from access to Twitter data.
Innovative trend analysis of hydro-meteorological variables in Ibadan, Nigeria

Analysis of trend in hydro-meteorological variables is extremely important for improving water resources planning and management. This study examines trend in monthly rainfall and temperature using the recently developed Innovative Trend Analysis Method (ITAM) and Mann-Kendall (M-K) with Sen’s slope test. Record of precipitation and temperature data was obtained from the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria for the period of 1973-2018. The precipitation trend during the period of 45years has been analyzed using the ITAM and M-K test. Most of the results obtained from these two methods for detecting trend are found to be matching. The result of this study shown that precipitation for the month of February, March, April, May, June, July, August and September indicated an increasing trend with the rate of 0.01, 0.01, 0.004, 0.025, 0.063, 0.028, 0.006 and 0.061 mm / year respectively. A significant increasing trend was shown in June and September at 1% and 10% significance level. The result of temperature obtained over the study area shown that almost all the months indicated increasing trend. The month of February, April, May and November shown an increasing trend at 10% significance level.
Automated Text Translation

The paper analyzed the problem of accessibility of content in other languages, it was found that many content may not be translated into the native language of users who want to access it, but at the same time there are many who want to help other users with this problem. The solution is a special information system that allows you to easily register and create your own translation, in which other users can participate, or join another already created one and help. As a result, the interested user can easily download the translation result and use it at his own discretion. The analysis of business processes for the creation and translation of the text was carried out. Based on this analysis, requirements for a future solution were developed. Business requirements were also identified. Among other things, a system use case model was developed and use case specifications were described. Lists with functional and non-functional requirements have also been developed. The functional model of the system was shown - algorithms: authorization, registration, password recovery, creating a new translation, generating a file with a new translation, generating a list of translations, managing users, viewing a translation, editing a translation text, checking the correctness of a translation, and moderating translations. A class diagram was developed, where you can see the main entities of the system and their relationships. A sequence diagram was also developed. The architecture of the information system was described. The system was implemented using the React.JS library and the Spring framework. The main processes of the system users were also described.
Visualization of algorithms on graphs with a large number of vertices: The features of applications design

The task of visualization of large graphs as a special data structure and algorithms on them is considered by scientists and practitioners as a complex and non-trivial problem. The analysis of scientific works and existing software applications that implement similar functions of the subject domain testifies the relevance of expanding exploration in the lines of identifying the features of the development of applications for the visualization of large graphs and algorithms on them. The formulation of features and recommendations for the development of such software and presentation of the software module designed by the authors is the aim of the article. In the course of the work, the main features of the development of a program for the visualization of graphs with a large number of vertices were identified and formulated using methods of analysis and graph theory. Special recommendations on the essence of each of the stages of development of such applications were provided and those steps that are most important for developers in terms of the complexity of processing and visualization of large graphs, metrics of their layout in the application screen, etc. were identified. A software module developed by the authors, that provides a unified application programming interface for visualizing any algorithm on graphs, which allows to save time working on utility software and focus more on solving algorithmic problems is also presented. The presented module was developed by the authors taking into account the identified recommendations. A comparative analysis of the developed software module and analogues was carried out, which proved the extended functionality of the module for the visualization of graphs with a large number of vertices. The module is a practically valuable tool for data structures researchers and other experts working on graph algorithms, since it enables data visualization at debugging software and simplifies the analysis of large data structures
Differentially Private Learning with Per-Sample Adaptive Clipping

Privacy in AI remains a topic that draws attention from researchers and the general public in recent years. As one way to implement privacy-preserving AI, differentially private learning is a framework that enables AI models to use differential privacy (DP). To achieve DP in the learning process, existing algorithms typically limit the magnitude of gradients with a constant clipping, which requires carefully tuned due to its significant impact on model performance. As a solution to this issue, latest works NSGD and Auto-S innovatively propose to use normalization instead of clipping to avoid hyperparameter tuning. However, normalization-based approaches like NSGD and Auto-S rely on a monotonic weight function, which imposes excessive weight on small gradient samples and introduces extra deviation to the update. In this paper, we propose a Differentially Private Per-Sample Adaptive Clipping (DP-PSAC) algorithm based on a non-monotonic adaptive weight function, which guarantees privacy without the typical hyperparameter tuning process of using a constant clipping while significantly reducing the deviation between the update and true batch-averaged gradient. We provide a rigorous theoretical convergence analysis and show that with convergence rate at the same order, the proposed algorithm achieves a lower non-vanishing bound, which is maintained over training iterations, compared with NSGD/Auto-S. In addition, through extensive experimental evaluation, we show that DP-PSAC outperforms or matches the state-of-the-art methods on multiple main-stream vision and language tasks.