Reviewerly/exHarmony huggingface dataset:
Intelligent garbage sorting system based on MobileNetV2

In order to better solve the social hot issue of garbage classification difficulties, we designed an intelligent garbage classification system based on the lightweight convolutional neural network MobileNet V2. The system can automatically realize the intelligent classification operation of garbage, and liberate people from the tedious and mechanical garbage classification. The system uses the Pytorch deep learning framework to build the MobileNet V2 network and train the garbage classification model, so that the model can be mounted on the Raspberry Pi to achieve embedded integration. This design realizes the highly integrated, intelligent, and data visualization of the garbage classification system, with accurate classification It has the characteristics of high density, simple and convenient use, high practical value and good applicability, which can greatly simplify the process of garbage classification.
Artificial Intelligence and Human Intelligence

As with academics in other fields, law academics are concerned about artificial intelligence (AI) and its effects on society, specifically its impact on law and its possible role in the legal system. Less attention seems to be paid to the effects that AI might have on human intelligence and the legal implications that might arise from those effects. Law scholars have researched and discussed many aspects of the existence and effects of AI, including the large-scale use of data in the development and lifecycle of AI, algorithmic decision-making by AI and various types of AI acts ‒ from accidents involving self-driving cars to case reporting by psychotherapy apps (e.g. Brauneis and Goodman (2018), Stein (2022), Solaiman and Cohen (2023 forthcoming)). AI has also caused concern among IP law scholars, who have debated the status and ownership of inventions and other works created by AI, its impact on patent prosecution and other IP grant and registration processes, and AI’s effects on innovation and creativity (e.g. Samuelson (1986), Dornis (2020), von Lewinski (2022), Leistner (2022), Abbott (2022 forthcoming)).
Problems of Involving Students in Scientific Research Activities (On the Example of Kurgan State University)

In the work, a study was conducted aimed at studying the problems of involving students in research activities using the example of Kurgan State University. The results of the study allowed the authors to identify a number of significant problems that are present in the student scientific field of the university and are an inhibitory factor in the involvement of students in research activities. Based on these problems, recommendations were developed aimed at creating more effective and positive conditions for the research activities of university students.
Education as the Supporting Structure of the Knowledge Society

The article analyzes the role of educational system in reproduction of the intellectual life of a modern society, which, according to certain scientists, is striving for a state referred to by them as the Knowledge Society. It is pointed out that among various interpretations of this concept the authors are closest to understanding it as the social ideal, since it is too early to talk about any noticeable and even more so final transition to a fundamentally new type of the social reality. Another interpretation is also proposed, which means that any knowledge society as a social attractor appears to be a possible and desirable new quasi-stable state of the society based on systematic production of the rational knowledge in the course of widely held theoretical (aimed at finding propositional truth), moral and practical (aimed at finding normative correctness) discussions. The simulated attractor will make it possible to avoid catastrophic scenarios for the development of Civilization associated with unpreparedness of the human mind to cope with the growing instrumental power and other challenges of the history. Relevance of the problem under discussion is due to unprecedented acceleration of the evolutionary processes in the 21st century noted by many researchers of the Universal History (Big History). An original procedural concept of rational knowledge is presented, which basic component is the widely understood principle of sufficient reason serving as the universal mechanism for accumulating the cultural capital. It is noted that the introduced principle is not any law of logic, but is represented by a five-step scheme for formation and selection of knowledge and could be substantiated by the transcendental-pragmatic method proposed by Karl-Otto Apel and Jürgen Habermas. To analyze the functions of education, fundamental theory of the intellectual networks by R. Collins is introduced, which includes theory of interactive rituals, social concept of creativity and model of the intellectual attention space dynamics.
Formation of the Value and Motivational Sphere of the Personality of A Public Servant

Values are one of the most important elements of the personality structure and underlie motives, priorities are chosen in accordance with them, significance is attached to certain events. Values are intertwined with the moral sphere, determine human behavior. The idea of public service is based on a certain system of values that should be shared by employees. This is the basis of both the moral image of a civil servant and the system of motivation, the creation of which is very complicated by the specifics of this activity. The purpose of this work is to identify the main factors that form the value-motivational sphere of the personality of a civil servant, to study its features. The empirical basis of the study was the survey of students in the direction of training “State and municipal management” and civil servants themselves. As part of the study, a classification of terminal and instrumental values of civil servants was developed, and a comparison was made of the value-motivational sphere of the personality of students studying in the direction of training “State and municipal management” and civil servants. The data obtained are of interest to personnel services in the selection and assessment of the personnel potential of civil servants.
A study of high school students' perceptions on their online English classes under the COVID-19 pandemic.

The purpose of this study was to find out how high school students view emergency remote teaching and learning in the form of online English class operations during the COVID-19 pandemic. Two hundred and twenty high school students from a high school in Seoul participated to complete the questionnaire with 30 closed questions and 5 open-ended questions. For the analysis of the study, exploratory factor analysis, t-test, and descriptive statistics analysis were conducted by the use of SPSS 27. In teacher factor, learning effect factor, and system factor, students with high- intermediate English proficiency were more satisfied than students with low-intermediate English proficiency. There was no significant difference in the learner factor. They cited accessibility and convenience as advantages of online classes whereas lack of communication and interaction, difficulty in concentration and immersion, and online network instability as disadvantages. Based on the results of the study, several pedagogical implications for future online English classes were proposed.
Comparative Study of Manual and Automated Ki-67 Proliferative Indices in Classifying Pulmonary Neuroendocrine Neoplasms (NENs) in Fine Needle Aspiration (FNA) versus Biopsy and Surgical Specimens: A Single Institution Experience

Being able to characterize the dynamic characteristics of the air traffic system and embody the dynamic evolution thereof, the complexity of air traffic flows has attracted the broad attention of scholars and researchers. To fully consider amplitude information, we adopt the improved multiscale weighted permutation entropy (IMWPE) and propose an improved multivariate multiscale weighted permutation entropy (IMMWPE) to explore the complexity of flight flows of the top ten busy airports in China from a univariate and a multivariate perspective, respectively. In addition, we propose an improved multiscale weight contingency to investigate the inhomogeneity between fluctuation pattern distributions in departure and arrival flight flows. At the advantages of the multiple coarse-graining procedures, we obtain more reliable and stable entropy and contingency results, considering amplitude information. Comparisons between the weighted and unweighted results show that the weighted methods are as capable as the unweighted in exactly characterizing the dynamic characteristics of flight flow fluctuations, i.e., temporal scale dependency, spike distributions, differences between univariate and multivariate perspectives. Results also show that the weight has a stronger impact on the complexity of univariate time series than the multivariate. From the univariate perspective, the complexity of departure and arrival flight flows of ZLXY and ZGGG is more sensitive to the amplitude information. From the multivariate perspective, ZUCK could be a typical representative of the complexity of the total flight flows of the ten airports.
Challenges in Corpus Construction for Thai-English Machine Translation

This paper presents the challenges identified during research into the construction of a parallel text corpus of 18,000 sentences for Thai-English machine translation (MT). The research approach was to perform the essential tasks of locating, extracting, and formatting web text suitable for the corpus whilst identifying the primary issues. The project included the creation and development of core components to extract web text, segment the text into sentences, and align these sentences into the parallel text corpus. Despite the success of several techniques related to these stages of corpus construction, their performance is often poor when applied to text from languages that are considerably different from English, although these issues are not always reported. The primary challenge to corpus construction for Thai-English machine translation was identified as the segmentation of text into sentences. Sentence segmentation tools did not split Thai text into sentences that corresponded to the English sentences. Machine translation is reliant on parallel text having corresponding sentences with the same meaning. This issue was further compounded by the nature of web text, which made the text difficult to segment correctly when there were source-specific idiosyncrasies. Traditional alignment approaches are not as effective when applied independently to Thai text due to its nature and a lack of linguistic resources. Despite this, a combination of approaches for both the text extraction and alignment was successful when the sentence segmentation was not problematic.
A Digital Archive of Participatory Location Rhythm Performances

The global COVID-19 pandemic influenced many to change, lastingly, how they listen – how they attend to their surroundings, consciously or not, through the sonic. This chapter presents a notion of listening informed by the hybrid auditory realities that the pandemic has amplified: the sonic blend of events from the virtual and the physical worlds, as physical matter, in our ears – described using the metaphor of the sonic window through which the world leaks in. While this modality of listening is not new, this chapter investigates how it is increasingly gaining importance during and after the pandemic because of its affordance to attend to, and provide information about, the multidimensional hybrid auditory collages many spend their everyday with when using digital communication tools. The author’s art piece #otherbeats is described as an explanatory listening device that takes this idea of the sonic window to a website. Users navigate a window full of sounds: an archive of participatory location rhythm performances illustrates the effects of sonic leakage, mediation, temporal, and spatial distortion. The website gives its users a tool to practice more consciously their hybrid everyday listening, emphasising that such listening will lastingly gain in significance from the pandemic forward.
The GeFI (Italian Forensic Geneticists) code of conduct

<h2>Abstract</h2> The Ge.F.I. (Italian Forensic Geneticists) board has set up a working group aimed to develop the "Code of conduct of a Forensic Geneticist". This group includes all practitioners working in the field of forensic genetics. The rules of the code of conduct apply to forensic geneticists belonging to the GeFI group and all of those sharing the GeFI's mission, vision and values applicable to their professional practice, reciprocal relationship and relationship with third parties.
CHS: a clustering-based hybrid sampling algorithm for the imbalanced classification

The traditional SVM has poor performance in imbalanced classification. To address the issue, a clustering-based hybrid sampling algorithm CHS is proposed in this paper. The majority samples are divided into different clusters by CFSFDP clustering algorithm for hierarchical undersampling. The minority samples are oversampled, and new minority samples are generated near the boundary based on the support vectors. Finally, the moth-flame optimization algorithm (MFO) is used to optimize the new minority samples in order to avoid the generation of noisy samples. The algorithm is applied to 16 KEEL datasets with different imbalance rates and compared with other sampling algorithms. The performance is evaluated by G-mean and AUC, and the experimental results show that CHS can effectively improve the performance of SVM for imbalanced classification.
Data center disaggregation: when and how much?

Disaggregation has been proposed by both the industry and academia as an approach to reduce resource fragmentation in cloud data centers. With disaggregation the entire rack is viewed as a single machine in which all resources are remotely accessible from anywhere in the rack. Because a large number of resources may be used remotely, the average access time to resources tends to be elongated compared to a non-disaggregated rack organization. This elongation represents a technology penalty of disaggregation and impacts application performance. This paper presents a model to capture basic tradeoffs between virtual machine (VM) lifetime elongation and improved memory mutualization in a rack with disaggregation. Our results show that disaggregation has the potential to significantly improve the maximum VM launch throughput provided the technology penalty remains moderate. Additionally, our model provides guidance for the number of servers to combine through disaggregation.
Building Individual and Community Resilience through Open Education Resources for Health Literacy Education in sub-Saharan Africa

Health literacy is the ability of individuals to understand health information and to make informed health decisions. As part of a larger project, we completed a scoping review to consider the question “To what extent does teacher education in sub-Saharan Africa (SSA) currently include a focus on health literacy?” In this paper, we specifically address the question, “How might OER support teacher education in health literacy to support individual and community resilience in sub-Saharan Africa?” We examine the extent literature on health literacy, its relevance to teacher education and OER, and how OER can be used to support health literacy education in SSA. A discussion on the ways in which health literacy education in SSA can be fostered through governments and through other partnership is explored.
English Skill of Traditional Transportation Drivers in Malioboro Yogyakarta

English skills are very important for traditional transportation drivers in Malioboro Yogyakarta. This research analyzes the latest facts about the English skills of traditional transportation drivers in Malioboro Yogyakarta, barriers to mastering English, and their impact. The subject of this research is the traditional driver transportation (pedicab and andong) in the tourist area of Malioboro. The data collection techniques are interviews, observation, and documentation. The data analysis technique used in this research is data reduction, data presentation, and conclusion drawing. The results of this study indicate that : (1) the English ability of traditional transportation drivers in Malioboro is still low (2) The inhibiting factors in mastering English are low economy, old age, limited time, low literacy, low education, no training Free English from the government (3) The impact of the inability to communicate in English well is difficulty in making agreement on service rates and less than optimal service delivery.
Research on text classification technology based on natural language processing

Natural Language Processing (NLP) is a science that integrates computer knowledge, mathematical knowledge and linguistic knowledge, and text classification and recognition is considered to be an important research field and direction of natural language processing. This paper mainly studies the realization of text classification model through the processing method of text data in natural language processing and the theoretical knowledge and technical means of machine learning. We summarize the existing text classification algorithms, analyze their applicable scenarios, and optimize on the basis of these algorithm models. The paper proposes a Chinese text classification algorithm based on weight preprocessing. Algorithm based on the weight preprocessing link, so that the optimized classifier model can improve the accuracy of the existing text classifier. In this paper, the English Newsgroups corpus is used for experimental verification. The experimental results show that the classification accuracy and accuracy of the improved algorithm are better than those of the traditional text classification algorithm, thereby improving the accuracy of English text classification.
Python based crawling program for collecting data from websites

Web crawler (also known as web spider) is a program or script that automatically grabs data from websites according to certain rules. Like a spider crawling along the silk thread of URLs on the internet, it downloads the web page pointed to by each URL, and extracts and analyzes the contents of the page. Through the web crawler program, the massive data on the target website can be automatically collected and saved in a structured file or in database. Therefore, crawler owners can obtain a large amount of data with potential economic value at very low time cost and tiny economic cost. This article takes http://novel.tingroom.com/ as the target, and introduces in detail the general steps of how to use the Python based crawler program to obtain massive data (novel content): Firstly, analyze the structure of the target page. Secondly, use the requests module to get the target page. Thirdly: use the parsel module to extract the valuable parts of the target page. Finally, save the information into structured files. The empirical research shows that the crawler program based on Python has significant practical value.
Identifying and clustering of COVID-19 cases in Jakarta using K-medoids algorithm

Indonesia has been hit by an outbreak of the corona virus (COVID-19). Every day the spread of COVID-19 in Indonesia continues to increase. People practice social distancing to break the chain of the spread of COVID-19 which is spreading in various regions in Indonesia. Many people have experienced losses and difficulties in their MSMEs due to COVID-19. However, when the COVID-19 pandemic emerged, product marketing management experienced a decline because there was no smooth operation in the distribution process for product delivery which was not carried out normally. Therefore, it is necessary to formulate a marketing management strategy that can reveal the condition of product sales for MSME craftsmen. People can contract COVID-19 from other people who are infected with this virus. Therefore, it is necessary to explore the infected area, so that the hotspot areas can be guarded by both the government and citizens. Therefore, research is needed on the spread of the COVID-19 virus in DKI Jakarta. The ultimate goal of this research is to create a cluster system to classify areas affected by COVID-19 by applying the yahoo K-medoids method, which is well-known for its accuracy. To perform clustering, data for the DKI Jakarta City area infected with COVID-19 were taken from a web portal provided by the DKI Jakarta Provincial Government. In the implementation of the K-medoids algorithm, a clustering simulation (grouping) of the DKI Jakarta area that is infected with COVID-19 is carried out using the rapid miner application. From the results of clusters that have been tried, the optimal BDI value was obtained in clusters in simulation 4 (five clusters) and a value was obtained of 0.263.
How to circumvent common anti-crawler mechanism of target websites via Scrapy

In the field of artificial intelligence and machine learning, only when enterprises obtain a large amount of data can they train enough reliable models [1]. How to obtain massive data at a low cost has become one of the key prerequisites for the success of data intelligence enterprises. Mastering a large amount of data is an important prerequisite for gaining competitive advantage [2]. There is a cognitive trend among enterprises with massive data. If the massive data as their advantages are collected by peers, their advantages will be weakened or even lost. Therefore, more and more massive data owners adopt various mechanisms to protect their public data in network applications and avoid data being crawled by crawlers [3]. From the perspective of data collectors, this paper introduces some common anti crawling mechanisms in details based on the Scrapy framework and the recruitment website of a well-known internet enterprise, and then gives some techniques to circumvent the above crawling mechanism. Finally, it successfully crawls all the job information on the recruitment website of the enterprise. The experimental results show that the techniques provided by the paper can effectively bypass the anti-crawling mechanism of some large websites, so as to help collectors obtain massive data.
Collaborative Signal of Opportunity Doppler Navigation with Inverse Covariance Intersection

As the need for robust position, velocity, and timing (PVT) data grows across different industries, signal of opportunity (SOOP) navigation has arisen as a promising source of PVT measurements in addition to traditional GNSSs. The accuracy of PVT estimates delivered by many SOOP navigation schemes, however, is unacceptably poor. Collaborative navigation techniques can improve the performance of such systems by fusing information across multiple navigation receivers. In this work, the inverse covariance intersection (ICI) data fusion algorithm is applied to a group of collaborating unmanned aerial vehicles (UAVs). The UAVs are navigating using only Doppler measurements from SOOPs derived from low earth orbit (LEO) satellites, and are collaborating using peer-to-peer (P2P) ranges derived from two-way time of flight (TW-ToF) measurements. An analysis of the positioning accuracy of the navigators is conducted, which compares a control group of UAVs using no collaborative navigation techniques to a selection of well-known collaborative navigation schemes, as well as a proposed scheme utilizing ICI. This comparison relies on a measurement-level simulation framework, which generates inertial measurements, SOOP range rates, and P2P ranges which can be supplied to each different navigator configuration to create a Monte Carlo test suite. The results of these tests allow for an analysis of the positioning accuracy of each method, as well as the accuracy of each navigator’s own covariance estimate in each case.