Reviewerly/exHarmony huggingface dataset:
Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest

Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse today. The opaque nature of the algorithms these platforms use to curate content raises societal questions. Prior studies have used black-box methods led by experts or collaborative audits driven by everyday users to show that these algorithms can lead to biased or discriminatory outcomes. However, existing auditing methods face fundamental limitations because they function independent of the platforms. Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users' private data and platforms' proprietary algorithms. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to relevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms' business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase (6.34x as an upper bound, and 4× for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.
Classifying the Competency Development of Middle School Students in Seoul and Analyzing Determinants

본 연구는 중학생의 인지, 사회·정서, 참여자치의 측면에서 역량 실태를 다면적으로 평가하고, 중학생의 균형 있는 역량 발달을 위한 교육적 시사점을 제공하기 위해 인지·사회·정서·참여자치 역량 발달의 현황에 따른 잠재계층을 분류하고, 이에 영향을 미치는 학생 및 학교 특성을 탐색하였다. 이를 위해 『서울학생종단연구(SELSS) 2020』 1차년도 중학교 패널 자료를 활용하여, 총 97개 학교에 소속된 서울 지역 중학생 3,993명을 대상으로 다층 잠재프로파일분석을 실시하였다. 본 연구의 주요 결과는 다음과 같다. 첫째, 서울 지역 중학생의 인지·사회·정서·참여자치 역량에 따른 하위유형을 분류한 결과, ‘전 역량 부족’, ‘인지역량 부족’, ‘전 역량 우수’, ‘사회·정서역량 부족(인지역량 우수)’과 같은 4가지의 이질적인 잠재계층이 도출되었다. 둘째, 잠재계층 분류에 영향을 미치는 학생 특성을 살펴본 결과, 성별, 수업태도, 교우관계, 사회경제적지위(SES)와 같은 학생 특성이 역량에 따른 잠재계층 분류에 유의미한 영향을 미치는 것으로 나타났다. 셋째, 잠재계층 분류에 영향을 주는 학교 특성을 살펴본 결과, 교사분위기, Wee클래스 운영 여부, 비대면수업 비율 등은 인지역량 및 사회·정서역량 측면에서 정적인 영향을 미치는 것으로 나타났다. 본 연구의 결과를 바탕으로, 중학생의 인지·사회·정서·참여자치 역량의 균형 있는 발달을 위한 교육적 시사점을 제시하였다.This study classified the latent classes of cognitive, social-emotional, and autonomy competencies and analyzed the effects of student and school factors in order to provide educational implications for balanced competencies development of middle school students. For this purpose, 3,993 middle school students data from the first-year middle school panel of the Seoul Educational Longitudinal Study of Students 2020 were used and multilevel latent profile model analysis was conducted. The main analysis results are as follows. First, four latent classes were classified according to competencies. Second, the student factors such as gender, class attitude, friendship and socioeconomic status affected the classification the classes. Third, the school factors such teacher atmosphere, the operation of the WeeClass, and the ratio of online class had a positive effect on cognitive and social-emotional competencies. Based on the results of this study, educational implications for the balanced development of middle school students' competencies were suggested.
Promoting the English language learning through the use of effective andragogical strategies

Introduction. This research work implemented andragogical strategies for English learning in teachers of the Educational Unit "El Oro" in Ambato. Objective. Promote the English language learning through the use effective andragogical strategies. Methodology. It was used quasi-experimental research; it was executed with 30 teachers (adults) from different areas who were motivated to learn and help each other to update their knowledge of English. We worked with two groups of the population under study. Both groups were made up of 15 teachers. The experimental group was treated with an intervention proposal containing andragogical strategies, such as discussion, analysis, project elaboration, simulation, demonstrations, and problem solving, which had a series of tasks with a communicative approach through the Zoom Platform due to the COVID-19 pandemic. Nevertheless, the control group worked with online activities who had documents to fill grammar exercises. Both groups responded two tests: the first was taken before the intervention and the second or post-test after it. Results. The means of the two exams were compared in the pre-test and it was evidenced that the two groups of teachers had a low level of English. On the other hand, in the post-test, that is, after the intervention, the means of the two groups showed significant difference, since the experimental group experienced a considerable improvement. Conclusion. In this way, it was determined that the use of andragogic strategies helped teachers to improve their learning of the English language. Therefore, it is recommended to apply andragogic strategies for better teachers’ learning and thus, to improve their professional quality.
Effects and elimination of image grating defocusing on a double-Ronchi shearing interference field

Double-Ronchi shearing interferometry is a promising wavefront aberration measurement system for advanced lithography projection lens systems. The image grating defocusing is a key systematic error of the interferometer. However, the effects and elimination of this error have not been systematically researched. In this work, the interference field effects caused by the image grating defocusing are analyzed based on the theories of scalar diffraction, and a method to eliminate the effects is proposed. The theoretical analysis has been verified by a simulation and experiments. The results show that the error of image grating defocusing is mainly expressed as the Zernike Z 4 term and Z 9 term in the reconstructed wavefront, and the coefficients of Z 4 , and Z 9 , respectively, are related to NA 2 , NA 4 , and the defocus distance z . When the numerical aperture (NA) of the under-test projection lens is 0.6, 99.8384% of the errors caused by the image grating defocusing can be removed. When the NA is reduced to 0.3, 99.9854% of the errors can be removed. Additionally, when the NA is less than 0.1, almost all the errors can be eliminated.
Status of Primary School Specialty Education - case study of Shantou City

This study takes Shantou City's primary education occupational labor force demand as a starting point，which determines the relevant concepts of vocational education for primary school teachers. By the means of literature analysis, investigation as well as interview methods, this paper investigated the training status and employment status of various majors in primary education, mathematics education, and primary education (general subjects) in Shantou Vocational and Technical College. First, the current situation of the school's current vocational education training is analyzed in terms of the existing professional training plans of each major in recent years. Second section is about the interviews and surveys on the current situation of the above-mentioned students of each major. The survey will be based on the students' personal information, students' acceptance of training, employment tendency, existing employment opportunities, long-term career and other pain points. The last section is about the investigation the needs of employers for primary school teachers in recent years.
<i>How Sonata Forms: A Bottom-up Approach to Musical Form</i> Yoel Greenberg

Journal Article How Sonata Forms: A Bottom-up Approach to Musical FormYoel Greenberg Get access How Sonata Forms: A Bottom-up Approach to Musical Form. By Yoel Greenberg. Pp. 264. Oxford Studies in Music Theory. ( Oxford University Press, New York and Oxford, 2022. ISBN 978-0-19-7552628-6, $74.) Richard Cohn Richard Cohn Yale University, New Haven, CT Search for other works by this author on: Oxford Academic Google Scholar Music and Letters, Volume 104, Issue 2, May 2023, Pages 307–310, https://doi.org/10.1093/ml/gcad008 Published: 04 May 2023 Article history Editorial decision: 24 February 2023 Received: 24 February 2023 Accepted: 24 February 2023 Corrected and typeset: 04 May 2023 Published: 04 May 2023
A Study on the sentence embedding by contrastive learning

We discuss the sentence representation method using contrastive learning, in which various data augmentation methods are used to automatically generate positive samples such as word replacement, deletion, duplication, and word order change. These methods can make sentences grammatically incorrect or create sentences with different meanings, which seems to have bad or unexpected effects on the accuracy and reliability of the expression vector. We proposed a two-step model in which the self-supervised learning model is followed by the supervised learning with the data sets reflecting the special characteristics of Korean language. The Korean language allows word order scrambling that a positive dataset can be secured by word order change augmentation. In addition, various positive datasets can be proposed from a set of honorific-ordinary augmentation as well as active-passive, or passive-active augmentation in Korean. We confirmed that the proposed method is efficient via an experiment using a dataset with word order-changing augmentation.
Research on Topic Discovery and Evolution Trend Based on Temporal Keyword Characteristics Analysis

In order to mine the research topics in massive articles, sort out the evolution context and correlation relationship of research topics, enhance the scientificity and vividness of evolution results, this paper proposes the concept of temporal influence factor as an important feature in keyword extraction.Using the time window method, the topic model is used to mine and identify topics and perform visual analysis.It is verified that keyword extraction with time series features can improve the effect of topic model.Through visualization, we can not only observe the overall trend of topic popularity, but also analyze the evolution of topic content in each time period and observe the trend of splitting and merging.
Research on Pavement Crack Detection and Recognition Algorithm

In order to solve the shortcomings such as low efficiency and great risk of detection and recognition algorithm of road surface, this paper studies the algorithm program of detection and recognition of road surface from the Angle of digital image processing based on computer road and track automation technology, and makes many parameter calculations for horizontal, longitudinal, massive and mesh cracks of road surface.The results show that compared with the traditional detection and recognition methods, the algorithm program studied in this paper effectively improves the measurement accuracy of pavement cracks and the accuracy of crack identification, and the program has high feasibility in road detection and recognition.
Vehicle Object Tracking Based on Feature Fusion Siamese Network Under Unstructured Terrain for Unmanned Ground Vehicle

Vehicle target tracking technology is an important research component for unmanned ground vehicle. It has wide applications in areas such as cooperative operations and battlefield surveillance. In the actual complex unstructured terrain, the object appearance is susceptible to interference such as light changes, similar backgrounds, and deformations, which bring great challenges for vehicle target tracking. In recent years, deep learning techniques have shown potential in solving target tracking problems under interference conditions due to their powerful feature representation capabilities. To solve the problem, a vehicle target tracking method based on Feature Fusion Siamese Network (FFSN) is proposed. It designs a two-stream feature extraction network. Low-level visual features and high-level semantic features are integrated together, which can distinguish deep features and improve its resolution. The experimental results demonstrate that the method can achieve accurate tracking under complex unstructured terrain in real time, the tracking speed under the GPU is 111 fps. Compared with the baseline GOTURN algorithm, the overlap accuracy and distance accuracy of the proposed method are improved by 8.17% and 13.16%.
A GAN-Based UAV Platform Complex Weather Image Restoration Technology

With the continuous development of UAV technology, the application of UAV is more and more extensive. In aerial photography, reconnaissance, detection and autonomous driving navigation, the application of UAV is inseparable from the acquisition of clear images. However, in some complex weather conditions, such as foggy weather, windy and rainy weather, etc., the quality of the target image obtained by the UAV imaging system may be affected to a certain extent. In these processes, there will be some unavoidable external factors and hard conditions, which will reduce the quality of the image and make the information that people can obtain from the image become blurred. The data used in most UAV image system training is a data set composed of clear images. In order to solve the image acquisition of UAV in complex weather environment, this paper studies an image inpainting method based on Cycle GAN. This paper uses Jun-Yan Zhu’s open source Cycle GAN program to train a repair network model for complex weather images suitable for UAV use.
Research on Task Assignment of Multi-UAVs Based on Improved Two-Stage Hierarchical Auction in Collaborative Defense Scenario

The great role played by UAVs in high-tech wars such as the Naka conflict and the Russia-Ukraine conflict has received widespread attention and importance from the militaries of various countries, and UAV technology has entered a period of rapid development. With the increasing scale of participating UAVs, the human decision-making capability can no longer meet the rapidly changing requirements of modern warfare. In this paper, we propose a two-stage hierarchical auction-based Multi-UAVs dynamic task assignment algorithm for the multi-type complex task assignment problems of “decoy” “deception” and “interception” in collaborative defense combat task scenarios. A dynamic Multi-UAVs task assignment algorithm based on a two-stage hierarchical auction is proposed to cope with the multi-type task assignment of heterogeneous UAVs and to enhance the cooperation among UAVs. At the same time, a simulation environment is established and dynamic verification is carried out to provide technical support for improving the success rate and adaptability of UAV equipment system combat tasks.
MAD Clarinet 2.1.: Sound Travels

This article describes a clarinet performance augmented by new media art and its technical and conceptual design. MAD Clarinet 2.1. is a sound and visual journey made through an installation/performance, which offers the audience a sensorial experience conducted by a panoply of (un)recognizable sounds. The performance is realized by a clarinetist, who, using his traditional instrument, physically places himself in predefined positions to interact with the digital component of the installation. In turn, this digital component acts through motion sensors and sound capture. These two processes, besides triggering the computational digital sound component which works in a duet with the instrumentalist and also triggering an acoustic augmentation of the clarinet’s possibilities, namely delay and reverb, allowing the sound captured from the performance to serve as a parameter for the creation of generative computational visual art, which develops in real-time throughout the performance. In short, this work uses the connection between the instrumentalist of a traditional instrument with the new media art to extrapolate what would be expected from an instrumental musical performance.
How I Learned to Stop Worry[in]g and Love the Big Brother: videosurveillance in the domestic place

This paper briefly describes the Digital Media- Art project How I Learned to Stop Worry[in]g and Love the Big Brother, an interactive installation that allows the public to police a domestic space through a video surveillance system and, simultaneously and remotely, control a set of smart home devices. This artistic artifact aims to take a critical and reflexive look at the gradual loss of privacy in one of its last strongholds: the place we call home.
Tracking of Aerial Cooperative Target by Satellite-Based Optical Cameras

In this paper, we propose a real-time window tracking algorithm for cooperative high-speed aerial targets via optical cameras on satellites. It includes three parts: (1) The relative motion relation between the predicted orbits of the satellite and the aerial target is constructed based on the orbital dynamic theory; (2) The relative position vector is projected to the camera coordinate system; (3) The angular deviation from the relative Line-of-Sight (LoS) between the target and the camera to the optical axis of the camera is calculated. Finally, the window tracking algorithm is implemented on a typical aerial target. Simulation results show that, by using the proposed window tracking algorithm, the angular dispersion of the target in the focal plane of the camera is lower than 0.11% when the swing mirror adjustable speed is less than 2.40 degree per second. The tracking precision is better than 0.53% during the whole tracking measurement process.
Deep Learning Neural Networks for Detection of Onset Leukemia from Microscopic Images

The major challenge in the medical field is the detection of blood cancer or leukemia in its early stage detection. Once the blood cancer has been diagnosed in its early stage, treatment becomes easy and also it works out well. Presently, only manual identification of blood cancers from microscopic images is available. Manual identification, at times many lead to improper diagnosis. The automation of blood cancer identification depends on the availability of the database to form a medical expert system. Leukemia is caused due to deficiency of white blood cells in the blood stream in human body. This research work focuses on detecting the deficiency of white blood cells at the initial stage itself. For this reason, fuzzy C-means (FCM) algorithm and active contours are used. The features are extracted from the region of interest (RoI) by segmentation of the microscopic images. The outcome of the proposed visual recognition system is that the features are extracted from the specific region of the microscopic blood cell image using FCM algorithm to detect the blood cancer at the early stage using Convolutional Neural Networks (CNN). Various algorithms are implemented and a trail has been conducted using standard simulation package to demonstrate that active contours yield optimal results with high accuracy, recall, and a low rate of false classification ratio.
Edge computing technology engineering analysis

The paper analyzes the problems arising in edge computing technology. A mathematical model describing the time delays in data processing and transmission is constructed, and suggestions are made to improve the efficiency of using the technology. The devices' examples developed taking into account the edge computing technology requirements are given.
Estimate of remainder term in CLT for the number of empty cells after allocation of particles

This work belongs to the field of limit theorems for separable statistics. In particular, this paper considers the number of empty cells after placing particles in a finite number of cells, where each particle is placed in a polynomial scheme. The statistics under consideration belong to the class of separable statistics, which were previously considered, where necessary statements for the layout of particles in a countable number of cells were proved. The same scheme was considered, in which the conditions for the asymptotic normality of random variables were established. In this paper, the asymptotic normality of the statistics in question is proved and an estimate of the remainder term in the central limit theorem is obtained.
Bioinspired algorithm for selecting semantically significant features in linguistic information processing tasks

The article proposes bioheuristics for selecting informative features of classification in the problems of processing linguistic information. Bioheuristics is based on combining, generalizing, and adapting simpler heuristics depending on the current state of the solution. The experiments used a metric algorithm to automatically classify objects with k nearest neighbors and a classification tree algorithm. The effectiveness of bioheuristics was evaluated on information from the UCI repository of real and model machine learning problems. In the comparative evaluation of methods, non-parametric statistical tests were used, such as the Wilcoxon signed-rank test and Friedman test.
The Computing Chips in Unmanned Systems from Electron to Photon

The unmanned systems enable to complete collaboratively complex tasks within a certain time and space. All these real-time tasks require chips with high-capacity, large calculation ability, and low latency for the unmanned systems. Nowadays, the electronic computing chips were widely used in unmanned vehicles, autonomous cooperative operations of drone swarms, etc. However, electronic computing chips are suffering from the failure of Moore's Law and electronic bottlenecks. Photonic integrated chips (PIC) provide a new and feasible complementary solution for addressing the bottleneck. In this paper, we separately discussed several electronic computing chips and PICs in terms of architecture, performance, and applications. The results showed that electronic computing chips could meet the needs of tasks, but with the further growth of data volume and the arrival of electronic bottlenecks, photonic computing chips, a type of PICs, had orders of magnitude improvement in latency, computing capacity and energy efficiency. The PICs shows much potential in the unmanned systems.
Quantitative Analysis of Various 2D CNN Structures based on Dataflow

Convolutional Neural Networks (CNNs) are used in a wide range of fields due to their excellent accuracy. Previous researchers have proposed convolution architectures for the typical convolutional layer in CNN whose input is larger than the kernel size. However, neural networks are continuously evolving and developing, and there is a deep convolutional layer unlike classic CNNs demands a larger kernel size than the input. In this paper, we conduct a quantitative analysis based on dataflow for various CNN structures. A total of eight 2D CNN structures are described and compared in terms of processing time, total area, and energy efficiency, based on different dataflow graphs. As a result, the comparison provides advantages and disadvantages of the different CNN structures and aids in determining the optimal hardware structure solution for various neural network types.