Reviewerly/exHarmony huggingface dataset:
Boosting human Insight by Cooperative AI: Foundations of Shannon-Neumann Logic

We present the logical foundation of an artificial intelligence (AI) capable of dealing with complex dynamic challenges, that would be very hard to handled using traditional approaches (e.g. predicate logic and deep learning). The AI is based on a cooperative questioning game, to boost insight. Insight gains are measured by information, probability, uncertainty (Shannon), as well as utility (von Neumann). The framework is a two-person cooperative iterated Q&amp;A game, in which both players (human, AI agent) benefit (positive-sum): the human player gains insight and the AI player learns to improve its suggestions. Generally speaking, valuable insight is typically gained by asking ’good’ questions about the ’right’ topic, at the ’appropriate’ time and place: by posing insightful questions. In this study, we propose a logical and mathematical framework, for the meanings of ’good, right, appropriate’, within clearly-defined classes of human intentions.
Exploratory Data Analysis of Exact Science and Social Science Learning Content on Digital Platform

Data is one of the essential aspects in providing new information and new knowledge so that the data exploration process can provide policies on a decision for many sectors. Exploratory Data Analysis in this paper begins with collecting datasets contained on the Youtube digital platform. The dataset used was 30 samples found on the top page of youtube in each keyword. After conducting the Exploratory Data Analysis process, we found new learning content on the digital youtube platform. From the Exploratory Data Analysis that has been carried out, we also find different variations of the analysis's variables. The duration variable shows the result that the total duration of the overall duration in mathematics learning content that includes the Exact Science field is less than the psychology learning content included in the Social Science field. Meanwhile, the overall number of views on mathematics learning content is more than the number of views on psychology learning content. From the collecting dataset that we have made, showing a considerable number of views is undoubtedly the key to equitable distribution of information and knowledge for all users. More innovation and creating learning content are expected to encourage increased human development.
Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification

We address the overlooked unbiasedness in existing long-tailed classification methods: we find that their overall improvement is mostly attributed to the biased preference of tail over head, as the test distribution is assumed to be balanced; however, when the test is as imbalanced as the long-tailed training data -- let the test respect Zipf's law of nature -- the tail bias is no longer beneficial overall because it hurts the head majorities. In this paper, we propose Cross-Domain Empirical Risk Minimization (xERM) for training an unbiased model to achieve strong performances on both test distributions, which empirically demonstrates that xERM fundamentally improves the classification by learning better feature representation rather than the head vs. tail game. Based on causality, we further theoretically explain why xERM achieves unbiasedness: the bias caused by the domain selection is removed by adjusting the empirical risks on the imbalanced domain and the balanced but unseen domain. Codes are available at https://github.com/BeierZhu/xERM.
An Improved Differentiable Binarization Network for Natural Scene Street Sign Text Detection

The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network’s low-level and high-level semantic information to enhance the network’s effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017- Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection.
E3W—A Combined Model Based on GreedySoup Weighting Strategy for Chinese Agricultural News Classification

With the continuous development of the internet and big data, modernization and informatization are rapidly being realized in the agricultural field. In this line, the volume of agricultural news is also increasing. This explosion of agricultural news has made accurate access to agricultural news difficult, and the spread of news about some agricultural technologies has slowed down, resulting in certain hindrance to the development of agriculture. To address this problem, we apply NLP to agricultural news texts to classify the agricultural news, in order to ultimately improve the efficiency of agricultural news dissemination. We propose a classification model based on ERNIE + DPCNN, ERNIE, EGC, and Word2Vec + TextCNN as sub-models for Chinese short-agriculture text classification (E3W), utilizing the GreedySoup weighting strategy and multi-model combination; specifically, E3W consists of four sub-models, the output of which is processed using the GreedySoup weighting strategy. In the E3W model, we divide the classification process into two steps: in the first step, the text is passed through the four independent sub-models to obtain an initial classification result given by each sub-model; in the second step, the model considers the relationship between the initial classification result and the sub-models, and assigns weights to this initial classification result. The final category with the highest weight is used as the output of E3W. To fully evaluate the effectiveness of the E3W model, the accuracy, precision, recall, and F1-score are used as evaluation metrics in this paper. We conduct multiple sets of comparative experiments on a self-constructed agricultural data set, comparing E3W and its sub-models, as well as performing ablation experiments. The results demonstrate that the E3W model can improve the average accuracy by 1.02%, the average precision by 1.62%, the average recall by 1.21%, and the average F1-score by 1.02%. Overall, E3W can achieve state-of-the-art performance in Chinese agricultural news classification.
Open Mapping with Official Cartographies in the Americas

Abstract Governments need data to be effective and accountable to the SDGs. Spatial data are key to meeting these goals, and youth open mapping represents one way to support official cartographies, especially to advance SDG 16. We explored how youth could add value by integrating OSM data into public agency maps while offering education opportunities (SDG 4).
Small experiment, Big Data: the data production of the Muon $g-2$ Experiment

The Muon -2 Experiment at Fermilab aims to measure the muon anomalous magnetic moment with the unprecedented precision of 140 parts per billion (ppb).In April 2021 the collaboration published the first measurement, based on the first year of data taking.The result confirmed the previous experiment at Brookhaven National Laboratory (BNL), and increased the long-standing tension with the Standard Model prediction to 4.2 .The experiment is now running the sixth year of data acquisition of positive muon data, having accumulated a total of ∼19 times the statistics of the BNL experiment.A collaboration-wide effort is now in place to help produce the multi-petabyte-sized data sets, a challenge typically faced by much bigger experiments.Having a quick production turnaround time is of critical importance in order to achieve a timely analysis and publication schedule.In this paper I will describe the production workflow, the former and current challenges, the resources, tools, and the future prospects of the Muon -2 Experiment.
Power Grid Mapping in West Africa

Abstract YouthMappers helps to map fundamental features of rural communities across the countryside of Sierra Leone as an innovative model for how to bring power to villages across West Africa. Charting location of buildings, tracing streets, and pinpointing where utility poles dot the landscape inform efforts to design and install mini-grids in places without power or with insufficient service. By understanding settlement patterns, road connectivity, and the layout of current low-voltage distribution networks, the team speeds up and scales up design for rural electrification, contributing directly to SDG 7 to bring affordable and clean energy to communities and to SDG 9 to build resilient infrastructure in ways that foster innovation for mapping and beyond.
Analysis of Merchandise Inventory Accounting Information System at PT Konimex Bengkulu City

The company PT Konimex Bengkulu City is one of the companies engaged in the distribution of food, vitamins, medicines and medical devices, especially in the Bengkulu City area. In the face of competitive competition, the company must improve the company's performance, through increasing the marketing of merchandise inventory to strengthen and improve the company's competitiveness. The purpose of this study was to determine the accounting information system for merchandise inventory at PT Konimex Bengkulu City based on the latest stock of goods in the merchandise warehouse. This type of research uses a qualitative approach, which is presented in a descriptive form that is not in the form of numbers and supports quantitative data as information, data collection methods are observation, interviews and documentation. In accounting there are several methods, namely FIFO, LIFO and Average. The results of this study found that the accounting information system for merchandise inventory at PT Konimex Bengkulu City uses the FIFO method where this method is used by manufacturing-based companies. PT Konimex Kota Bengkulu uses the FIFO method analysis because this company sells products that have an expiration date such as vitamins, food, and medicines.
Mapping Access to Electricity in Urban and Rural Nigeria

Abstract YouthMappers use OpenStreetMap and IoT tools to help map where rural communities have good, poor, or no access to electricity throughout Nigeria, contributing to efforts that bring stable power across West Africa. An ongoing energy crisis is hampering other efforts to make sustainable cities and communities, so assessing and locating the reality of power access are critical not only to address SDGs related to affordable and clean energy but also as a fundamental element that serves as an engine for sustainable development. Through these efforts, we students have built our technology capacity and community networks, while also advancing practices about how to build inclusive, sustainable cities and communities in both urban and rural places where we live and study.
Drones for Good: Mapping Out the SDGs Using Innovative Technology in Malawi

Abstract Drones are being used in various industries, focusing on youth involvement and mapping activities as a key component of the multiple initiatives. YouthMappers are creating open-source geospatial data using geospatial technology to address local developmental and humanitarian issues in Malawi and beyond. As the world works to meet the Sustainable Development Goals, this new technology, combined with significant youth involvement, is being used to its full potential because of its sophistication, which allows for more efficient operations in support of industry, innovation and infrastructure (SDG 9) and responsible consumption and production (SDG 12) in the health sector for delivery, precision agriculture and the education sector, to name a few.
Research on the Image Segmentation by Watershed Transforms

Segmentation is a very important step in medical image processing. The mathematical morphology is very suitable for the pretreatment and segmentation of medical images, which present rich information content. In this work we presented a segmentation paradigm by Watershed preceded by a filtering to eliminate insignificant minima, a marking to remove unmarked minima, and finally we implemented a hierarchical segmentation using the mosaic image of the original image. In principle, watershed segmentation depends on ridges to perform a proper segmentation, a property that is often fulfilled in contour detection where the boundaries of the objects are expressed as ridges. Watershed is normally implemented by region growing, based on a set of markers to avoid over segmentation. The diversity of segmentation offers us several ways to segment the image, always we must look for the right method to get good results.
Professional Activities of Practical Psychologists: Philosophical Counseling in the Context of Postmodernism

The current state of Ukrainian society is characterized by socio-economic instability, there are dynamic, unique processes in which each person is involved. In this connection, there are increasing demands on the professionalism of specialists in the field of practical psychology, their philosophical counseling in the context of postmodernism, aimed at helping people become the subject of their life, work, social and value relationships, to teach them to find meanings and maximize self-actualization. This is represented in the change of priorities in the professional development of practical psychologists, its focus on the development of specialists' subjectivity, their ability to independently solve professional and life tasks. An important role in enhancing the development of professionalism is the understanding of the essential characteristics of existential professional activity, expressed most often in professional meanings and values. The basis of a meaningful professional and personal life is the key ability of the practical psychologist to see and accept the needs of the profession and life and to find the most correct explanations for them. The purpose of the article is to investigate the importance and necessity of the activity of a practical psychologist, its peculiarities, the study of types of psychological aid: counseling, psychological rehabilitation, correction, psychotherapy, training, education and prevention; as well as the need to identify the main areas of professional activity of practical psychologists in the context of postmodernism.
DIGITAL UNIVERSITY LIBRARIES – STUDENT PERCEPTIONS

The university libraries “Svetozar Marković” in Belgrade and “Nikola Tesla” in Niš were among the first to be actively involved in the process of digitization of libraries in Serbia. They are working on new projects supported by the European Union in order to modernize and develop new ways to transfer scientific and professional information. Consequently, they are a valuable source of information for writing professional and scientific papers, as well as obligations provided by curricula, especially at higher levels of study. The main goal of this paper is to examine the attitudes of doctoral students at the University of Belgrade and the University of Niš on the usefulness of digital resources of the university libraries “Svetozar Marković” and “Nikola Tesla”. Accordingly, research tasks include issues related to informing students about the potential of the digital libraries, and the use of electronic resources that they offer. For the purpose of the research, an instrument that was used was a questionnaire, which was created purposely and delivered electronically to the respondents. Research data were processed through qualitative and quantitative methods and presented graphically. The results of the research show that students are partially informed about the potential of the digital university library, that improvements in information about audio and video files are possible; also, that students recognize the benefits of using electronic resources and their usefulness in the study process.
Impact of Automatic Image Classification and Blind Deconvolution in Improving Text Detection Performance of the Craft Algorithm

Text detection in natural scenes has been a significant and active research subject in computer vision and document analysis because of its wide range of applications as evidenced by the emergence of the Robust Reading Competition. One of the algorithms which has good text detection performance in the said competition is the Character Region Awareness for Text Detection (CRAFT). Employing the ICDAR 2013 dataset, this study investigates the impact of automatic image classification and blind deconvolution as image pre-processing steps to further enhance the text detection performance of CRAFT. The proposed technique automatically classifies the scene images into two categories, blurry and non-blurry, by utilizing of a Laplacian operator with 100 as threshold. Prior to applying the CRAFT algorithm, images that are categorized as blurry are further pre-processed using blind deconvolution to reduce the blur. The results revealed that the proposed method significantly enhanced the detection performance of CRAFT, as demonstrated by its IoU h-mean of 94.47% compared to the original 91.42% h-mean of CRAFT and this even outperformed the top-ranked SenseTime, whose h-mean is 93.62%.
Leaving No One Behind: Can Rising Africa Beat the Odds Against Poverty?

The number of poor people continues to rise in Africa, despite a slow decline in the poverty rate. Africa with a population of 422 million poor people, representing about 70 per cent of the world's poorest people shows that the global burden of poverty has shifted from the rest of the world to Africa. This paper discussed the causes of poverty on the continent and various responses by stakeholders toward accelerating its poverty reduction. It was found that with the available statistics and projections, Africa will still fall short of eradicating poverty by 2030, but it can bring it to a low level. The study, therefore, recommends that African policymakers should aim for growth that is inclusive and sustainable. International support from the world bank, ODA, G7, and others will play a vital role, especially through technology and resource transfers, also African continent need to improve its resource mobilization.
Ambiguity Detection in Requirements Classification Task using Fine-Tuned Transformation Technique

The software requirement specification (SRS) document is essential in software development. This document influences all subsequent steps in software development. Nevertheless, requirements problems, such as insufficient or ambiguous specifications, can cause misunderstandings during the requirement analysis stage. This influences testing activities and increases the project’s duration and cost overrun risk. This paper represents an intuitive approach to detecting ambiguity in software requirements. The classifiershould learn ambiguous features and characteristics extracted from the text on a training set and try to detect similar characteristics from a testing set. To achieve this, this study experimented with two main approaches. The first approach is feature extraction, which uses the hidden states as features and trains asupport vector machine (SVM) classifier to assess software requirement ambiguity without modifying the pre-trained model. Unfortunately, this approach only identified 68% of the requirement ambiguity. The second approach is training an end-to-end model that updates the parameters of the pre-trained model. This approach enhanced the baseline results by 13%.
An Object Detection and its Educational Effect on Aspiring Film Directors using Computer Vision and Machine Learning

The issue that is being addressed is how those who wish to be involved in film production in the future can be more educated regarding what goes into making a movie. An application was created to help these people keep track of movie props and characters and where they are placed throughout a movie. This is performed by using videos or images as input for the application, and the application uses an object detection model from MediaPipe to determine what types of objects are in a scene and where exactly they are located in the scene [4]. Then, bounding boxes are placed around the objects with the classification of the object next to each bounding box, and the result is returned. The results of the experiment indicate that the application is consistent at detecting objects when the object is fully shown on the screen. However, when the application used the cropped images as input as opposed to the full version of the image, the application was not consistently accurate, as it was not able to recognize objects that were partially cut out of the picture. More work will be needed to improve both the object detection capabilities of partially obstructed objects and the overall user interface of the application [5].
The Role of Cryptocurrency in the Sustainable Development of Business

Money is a social convention, a method of acquiring various goods and services designed to facilitate a person's life.Over time, money has taken different forms, from gold coins to fiat coins.Along with digitization, the emergence of a payment method that should not involve a banking institution or a government that should have an influence on its purchasing power was desired, and in 2008 cryptocurrencies appeared.Cryptocurrencies are virtual currencies, unregulated by a financial institution, used as a payment method, and their system of use is based on a peer-to-peer technology, without the need for a third party intervention.The purpose of this paper is to present an overview of the cryptocurrency phenomenon and the impact that cryptocurrencies have on sustainable business development.