Reviewerly/exHarmony huggingface dataset:
Compilation Complexity of Multi-Winner Voting Rules (Student Abstract)

Compiling the votes of a subelectorate consists of storing the votes of a subset of voters in a compressed form, such that the winners can still be determined when additional votes are included. This leads to the notion of compilation complexity, which has already been investigated for single-winner voting rules. We perform a compilation complexity analysis of several common multi-winner voting rules.
8 Steps to Protect Against Rising Third Party Cyber Risks

Third parties are often your weakest link making you vulnerable to data breaches.So, it’s becoming very clear that you need strong processes in place to protect your organization from third party cyber risk.
Research on Fault Location Method for Low-Voltage Active Distribution Network Based On Correlation Of Main Frequency Components

In order to solve the problems of poor fault tolerance and low fault identification rate, a fault location method based on the correlation of main frequency components is proposed. Based on the distribution network of main frequency components extracted by Prony algorithm, this paper introduces the fault location model of low-voltage active distribution network built by hierarchical algorithm, and realises the correlation of main frequency components based on the research of fault location method of low-voltage active distribution network based on intelligent algorithm. The experimental results show that the fault tolerance rate of the proposed method based on the correlation of main frequency components is over 76% and the fault recognition rate is over 60%, which appears to be higher than several other methods. It is consequently believed that the proposed method may stand a good chance to offer better fault location performance.
Penerapan Model Pembelajaran Discovery Learning Berbantuan Media Interaktif untuk Meningkatkan Hasil Belajar Peserta Didik di SDN 1 Kebonadem Kabupaten Kendal

The purpose of this study is to apply a discovery learning model assisted by interactive media to improve the learning outcomes of fifth grade students at SDN 1 Kebonadem in the 2020/2021 academic year. This research method uses classroom action research. The subjects of this study were the fifth grade students, totaling 25 students. This research instrument uses observation, tests, and documentation. While the data analysis technique used descriptive analysis. The results of this study indicate that there is an increase in learning outcomes for Theme 9 by using a discovery learning model assisted by interactive media for the fifth grade students of SDN 1 Kebonadem. This can be seen from the increase in the average student learning outcomes from the initial condition which was only 61 with 16% complete learning outcomes, an increase in the first cycle of 69 with 60% learning outcomes, increasing again in the second cycle with average learning outcomes. by 72 with 72% completeness learning outcomes and in the third cycle the average student learning outcomes increased again by 82 with 88% complete learning outcomes. So that the performance indicator, namely complete learning can be achieved.
KEMAMPUAN MENGIDENTIFIKASI KONJUNGSI DALAM TEKS EKSPOSISI PADA SISWA KELAS VIII SEMESTER GENAPSMP UTAMA 3 BANDAR LAMPUNG TAHUN PELAJARAN 2018/2019

The purpose of this research is to find out and describe the ability of Grade VIII students in identifying conjunctions of exposition text. The population of class VIII students was 47 and a sample of 22 students. Data is collected by assignment and analyzed by quantitative methods and supported by qualitative descriptions. The result is that the ability of eighth grade students in the category is sufficient because it reaches a score of 73. There are 13 students classified as sufficient with a relative frequency of 59%. The percentage of errors of students of class VIII even semester at SMP Negeri 3 Bandar Lampung in the 2018/2019 academic year in identifying conjunctions in exposition texts was 70%. The percentage was obtained from 14 students because the number of errors was greater than the correct number. If classified as error intervals, the percentages of 67% and 75% are included in moderate errors.
Robust Model Compression Using Deep Hypotheses

Machine Learning models should ideally be compact and robust. Compactness provides efficiency and comprehensibility whereas robustness provides resilience. Both topics have been studied in recent years but in isolation. Here we present a robust model compression scheme which is independent of model types: it can compress ensembles, neural networks and other types of models into diverse types of small models. The main building block is the notion of depth derived from robust statistics. Originally, depth was introduced as a measure of the centrality of a point in a sample such that the median is the deepest point. This concept was extended to classification functions which makes it possible to define the depth of a hypothesis and the median hypothesis. Algorithms have been suggested to approximate the median but they have been limited to binary classification. In this study, we present a new algorithm, the Multiclass Empirical Median Optimization (MEMO) algorithm that finds a deep hypothesis in multi-class tasks, and prove its correctness. This leads to our Compact Robust Estimated Median Belief Optimization (CREMBO) algorithm for robust model compression. We demonstrate the success of this algorithm empirically by compressing neural networks and random forests into small decision trees, which are interpretable models, and show that they are more accurate and robust than other comparable methods. In addition, our empirical study shows that our method outperforms Knowledge Distillation on DNN to DNN compression.
Quantum advantage with noisy boson sampling and density of bosons

Inevitable noise is the main problem in demonstration of computationaladvantage of quantum devices, such as boson sampling, over digital computers.Can a noisy realization of boson sampling be efficiently and faithfullysimulated classically? It is shown how one can distinguish the outputdistribution of noisy $N$-boson sampling from that of classical approximationswith mixtures of quantum interferences of up to $K\ll \sqrt{N}$ bosons, with anumber of samples that depends solely on $K$, noise amplitude and density ofbosons $\rho = N/M$, where $M$ is network size. The surprising result is thatnoisy boson sampling in a regime of finite density of bosons $\rho\le 1$, i.e.,on a small network $M = N/\rho$, retains scalable quantum advantage toarbitrary large number of bosons despite the presence of finite noise.
멕시코의 부패가 경제성장에 미치는 요인

Purpose : This study considers corruption to be another important area to be aware of for economic growth and would like to conduct an analysis. Research design, data, methodology : This paper uses Mexico’s time series data by VECM. The variables include the Corruptions Perception Index (CPI), Remittance, FDI, and ODA. Results : FDI, ODA and Transparency have been shown to have a positive effect on economic growth. It was analyzed that remittance from abroad had a negative effect. Conclusions : The inflow of simple capital is not conducive to economic growth. Mexico should refrain from quantitative economic growth in developing countries.
Unsupervised Discovery of Actions in Instructional Videos

In this paper we address the problem of automatically discovering atomic actions in unsupervised manner from instructional videos. Instructional videos contain complex activities and are a rich source of information for intelligent agents, such as, autonomous robots or virtual assistants, which can, for example, automatically `read' the steps from an instructional video and execute them. However, videos are rarely annotated with atomic activities, their boundaries or duration. We present an unsupervised approach to learn atomic actions of structured human tasks from a variety of instructional videos. We propose a sequential stochastic autoregressive model for temporal segmentation of videos, which learns to represent and discover the sequential relationship between different atomic actions of the task, and which provides automatic and unsupervised self-labeling for videos. Our approach outperforms the state-of-the-art unsupervised methods with large margins. We will open source the code.
Development of Class Attendance via QR Code

Majority of higher learning institution nowadays established online system for student attendance in conjunction of new digital era. In UC TATI, the current implementation of attendances system using attendances sheet to be sign by the students. Thus, this paper aim is to develop an application that can replace current class attendance system (paper) in class. The methodology used in this development is the combination of waterfall model and Rapid Application Development (RAD).  Feedback from users who volunteered to test the app shows that this project is capable to replace current attendance system even though it shows a slight increment in time taken for attendance marking.
Sistem Pengambilan Keputusan Pemilihan Bibit Ikan Air Tawar untuk Dibudidayakan Menggunakan Metode AHP Berbasis Web

Farmers in fish farming in the context of selecting suitable fish seeds for cultivation have not paid too much attention to geographical conditions and water quality. The purpose of this study is to assist freshwater fish farming farmers in selecting the types of fish seeds to be cultivated in accordance with geographical conditions and quality. water in the area. The method used is the AHP method, where the AHP method is a functional hierarchy with the main input being human perception. With a hierarchy, a complex and unstructured problem is solved into groups arranged into a hierarchical form. The research design used is UML (Unified Modeling Language), which is designed in a structured manner consisting of usecase diagrams, activity diagrams, sequence diagrams and class diagrams. The language used in building this system is the PHP programming language with a sublime text editor and using the xampp database. The results of this study are a Freshwater Fish Seed Selection Decision Making System that can make it easier for farmers to make decisions in selecting fish seeds.
Investigasi Awal Perilaku Pengguna Terhadap Penerimaan Aplikasi e-Transportasi di Kabupaten Nganjuk: Studi Kasus Aplikasi HeehJek

Abstract E-Trasnportasi is a transportation application service that provides pick-up and delivery of service products through internet-based applications. Today, E-Trasnportasi are not only in urban areas but have entered rural areas. HeehJek is one of the E-Trasnportasi Applications that appear in Nganjuk Regency with nuances of local wisdom. The existence of cultural development in the community, from riding conventional transportation to switching to on-line transportation becomes an interesting phenomenon to get special attention on research into the behavior of users of the HeehJek application. As a preliminary study this research uses a quantitative method with a conceptual approach to technology acceptance models. Respondents were taken as many as 300 by cluster sampling, which then performed SEM-PLS analysis. The results of this study address the acceptance of the Nganjuk community towards HeehJek due to the belief, ease, and usefulness of the HeehJek application in supporting daily activities carried out.
Deep Learning for Multi-View Stereo via Plane Sweep: A Survey

3D reconstruction has lately attracted increasing attention due to its wide application in many areas, such as autonomous driving, robotics and virtual reality. As a dominant technique in artificial intelligence, deep learning has been successfully adopted to solve various computer vision problems. However, deep learning for 3D reconstruction is still at its infancy due to its unique challenges and varying pipelines. To stimulate future research, this paper presents a review of recent progress in deep learning methods for Multi-view Stereo (MVS), which is considered as a crucial task of image-based 3D reconstruction. It also presents comparative results on several publicly available datasets, with insightful observations and inspiring future research directions.
An Ameliorated Methodology to Determine Relations among Objects

Data is a collection of facts or things from which the information to inferred. To infer the data there is a need for clustering the facts or things, but some information are unstructured and difficult to access. because, it is large in volume and it contains different verities of data. This paper presents a methodology to cluster the data/things based on the verb (method) and Noun technique.
Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers

Recently, vision transformers and MLP-based models have been developed in order to address some of the prevalent weaknesses in convolutional neural networks. Due to the novelty of transformers being used in this domain along with the self-attention mechanism, it remains unclear to what degree these architectures are robust to corruptions. Despite some works proposing that data augmentation remains essential for a model to be robust against corruptions, we propose to explore the impact that the architecture has on corruption robustness. We find that vision transformer architectures are inherently more robust to corruptions than the ResNet-50 and MLP-Mixers. We also find that vision transformers with 5 times fewer parameters than a ResNet-50 have more shape bias. Our code is available to reproduce.
DECENTRALISED VOTING SYSTEM USING ETHEREUM BLOCKCHAIN

We are investigating the troubles within-side the election balloting structures and seeking to suggest the e-voting version with the usage of blockchain which could solve the issues. Also, our attempts aim to assess the software of blockchain as provider to enforce dispensed digital voting structures. The phase of paper will spotlight a number of famous blockchain structures that provide blockchain as a provider and related digital e-balloting machine, that’s primarily based totally on decentralised system that addresses all obstacles respectively, it additionally preserves participant’s anonymity whilst nonetheless being open to public inspection.
LRP-DS: Lightweight RepPoints with Decoupled Sampling Point Set

Most object detection methods use rectangular bounding boxes to represent the object, while the representative points network (RepPoints) employs a point set to describe the object. The RepPoints can provide more fine-grained localization and facilitates classification. However, it ignores the difference between localization and classification tasks. Therefore, a lightweight RepPoints with decoupling of the sampling point set (LRP-DS) is proposed in this paper. Firstly, the lightweight MobileNet-V2 and Feature Pyramid Networks (FPN) is employed as the backbone network to realize the lightweight network, rather than the Resnet. Secondly, considering the difference between classification and localization tasks, the sampling points of classification and localization are decoupled, by introducing classification free sampling method. Finally, due to the introduction of the classification free sampling method, the problem of the mismatch between the localization accuracy and the classification confidence is highlighted, so the localization score is employed to describe the localization accuracy independently. The final network structure of this paper achieves 73.3% mean average precision (mAP) on the VOC07 test dataset, which is 1.9% higher than original RepPoints with the same backbone network MobileNetV2 and FPN. Our LRP-DS has a detection speed of 20FPS for the input image of (1000, 600), on RTX2060 GPU, which is nearly twice as fast as the backbone network of ResNet50 and FPN. Experimental results show the effectiveness of our method.
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU

Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances has a critical effect in practical use. Recent works have shown that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data. This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection. Our method designs a novel domain-regularized module (DRM) to reduce the overconfident phenomenon of a vanilla classifier, achieving a better generalization in both cases. Besides, DRM can be used as a drop-in replacement for the last layer in any neural network-based intent classifier, providing a low-cost strategy for a significant improvement. The evaluation on four datasets shows that our method built on BERT and RoBERTa models achieves state-of-the-art performance against existing approaches and the strong baselines we created for the comparisons.
One-class Steel Detector Using Patch GAN Discriminator for Visualising Anomalous Feature Map

For steel product manufacturing in indoor factories, steel defect detection is important for quality control. For example, a steel sheet is extremely delicate, and must be accurately inspected. However, to maintain the painted steel parts of the infrastructure around a severe outdoor environment, corrosion detection is critical for predictive maintenance. In this paper, we propose a general-purpose application for steel anomaly detection that consists of the following four components. The first, a learner, is a unit image classification network to determine whether the region of interest or background has been recognised, after dividing the original large sized image into 256 square unit images. The second, an extractor, is a discriminator feature encoder based on a pre-trained steel generator with a patch generative adversarial network discriminator(GAN). The third, an anomaly detector, is a one-class support vector machine(SVM) to predict the anomaly score using the discriminator feature. The fourth, an indicator, is an anomalous probability map used to visually explain the anomalous features. Furthermore, we demonstrated our method through the inspection of steel sheet defects with 13,774 unit images using high-speed cameras, and painted steel corrosion with 19,766 unit images based on an eye inspection of the photographs. Finally, we visualise anomalous feature maps of steel using a strip and painted steel inspection dataset
UPDATING THE IDEAS OF THE REPRESENTATIVES OF THE NEW SCHOOL OF SCIENTIFIC MANAGEMENT TO IMPROVE THE EDUCATIONAL MANAGEMENT OF MULTIDISCIPLINARY INSTITUTIONS OF EXTRACURRICULAR EDUCATION

531.5 Updating the ideas of the representatives of the new school of scientific management to improve the educational management of multidisciplinary institutions of extracurricular education Over the past decades, we have seen a number of transformations in the education system and in its separate subsystem -extracurricular education.In our opinion, these radical changes will be one of the most significant events for the life of the future generation.Significant changes are made with the help of external and internal factors.An external factor of transformation is in the discovery of ideas for the transformation of additional, in-depth, non-formal education outside the walls of ordinary schools; an internal factor is a deep study of these ideas and their reflexive implementation in practice.Understanding the pedagogical work of the representatives of extracurricular activities in Ukraine, the implementation of educational management both at the regional, national and international levels -all these are qualitative historical changes that affect extracurricular education in terms of form, processes, and technology of problem solving.We believe that the current state of educational management development as a separate science and practical activity indicates imperfect procedures and insufficient study of practices for the adoption of new models in management by heads of extracurricular educational institutions.The situation is aggravated by the socioeconomic crisis, which forces the manager of an educational institution to be not just a «teacher of teachers», but also to have an idea of various management functions, which today are extremely difficult for educational activities, because extracurricular education faces the mutual influence of various organizations and shows both the achievements and best experience, and also the shortcomings of educational policies.Nowadays the educational management of multidisciplinary institutions of extracurricular education goes beyond the existing knowledge of management and it is currently in search of the best theories, concepts and models.To improve this process, it is necessary to conduct an analysis of the ideas presented by the best representatives of the new school of management science, which include such scientists as R. Akoff
EXPERIMENTAL ANALYSIS ON DIFFERENT EFFECTS OF PV MODULE WITH RESPECT TO PANEL ANGLE USING MACHINE LEARNING ALGORITHMS

Finding the optimum panel tilt angle for a photovoltaic device is crucial because it will efficiently convert the solar radiance into electricity. A variety of testing methods were used to determine the tilt angle so as to maximise the amount of radiation received by the solar panel. Recent research, however, has discovered that conversion efficiency is not exclusively determined by theamount of radiation received and it is also dependent on the tilt angle of the solar panel. Solar panel tilt angle optimization model based on machine learning algorithms is proposed in this paper. Concentration on tilt angle is done, that maximises photovoltaic (PV) device radiance into electricity. Five forecasting models were developed using linear regression (LR), Ensemble, random forest (RF), support vector machine (SVM), and Gaussian Process Regression (GPR), all of which took into account various factors such as weather and dust level. Our model showed an increase in PV yield as compared to ideal point models when we used the best model..
Reform and Practice of Advanced Mathematics Course in the Context of New Engineering.

The Advanced Mathematics course is a highly applied and closely related course to modern economics, finance, statistics, and management. This course provides the necessary foundation for students to learn professional knowledge. It has strong application and is also of great significance for the study of subsequent courses. Therefore, it is particularly important to improve the effect of teaching Advanced Mathematics course. This paper analyzes the necessity and importance of Advanced Mathematics teaching reform in private colleges and universities and points out the ways and methods of Advanced Mathematics teaching reform.