Reviewerly/exHarmony huggingface dataset:
A Survey on Blockchain Based Documentation Verification

Abstract: Secure Doc verifier is a document verifier, a model that is used to verify educational certificates and organizational documents. The Prime factor of this model is to give a possible solution to the duplication of certificates in the educational system. And also, time consumption in the verification process and money used to spend on the third-parties services for the verification process. the proposed model achieves the security in the verification process and gives the transparent in the verification process.to achieve these all factors this model is implemented on blockchain network. blockchain is one of the secure and transparent networks. Apart from blockchain, IPFS is used in this model to store documents on a decentralized storage space. IPFS is a decentralized storage network, that stores any type of data on decentralized file storage. with decentralization this model achieves more secure and kept private. Not like centralized system that takes ownership on users’ data.
Static Polycode Text Modeling Using Network Analysis (Demotivator Dedicated to Problems of Self-Isolation)

The features of modeling a graphic-verbal polycode text, including a static image and an accompanying inscription, are considered. The study was conducted on the example of a demotivator dedicated to the problems of mass self-isolation at the very beginning of the pandemic and the introduction of restrictive measures. Significant semantic components, represented as part of only the iconic component, only the verbal component, and also as part of the verbal and iconic components at the same time are established. The semantic relations between the selected semantic components are revealed, the types of these links, revealing the different nature of their correlation are determined. On the basis of the data obtained, a network model of the considered static polycode text in the form of a semantic network was built. Cases of semantic components correlation are considered, reflecting the generally objective aspects of the situation and unrealistic ideas based on irony and hyperbole to create a comic effect. Based on quantitative analysis, representative semantic relations were established: “partitive”, “localization (in)”, “attributive”, “subject-object”. Non-representative semantic relations between the semantic components in the analyzed polycode text are revealed: “coincidence”, “localization (on)”, “temporal”, “subject-instrument”, “subject-result”.
Unlocking High-Accuracy Differentially Private Image Classification through Scale

Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points. Differentially Private Stochastic Gradient Descent (DP-SGD), the most popular DP training method for deep learning, realizes this protection by injecting noise during training. However previous works have found that DP-SGD often leads to a significant degradation in performance on standard image classification benchmarks. Furthermore, some authors have postulated that DP-SGD inherently performs poorly on large models, since the norm of the noise required to preserve privacy is proportional to the model dimension. In contrast, we demonstrate that DP-SGD on over-parameterized models can perform significantly better than previously thought. Combining careful hyper-parameter tuning with simple techniques to ensure signal propagation and improve the convergence rate, we obtain a new SOTA without extra data on CIFAR-10 of 81.4% under (8, 10^{-5})-DP using a 40-layer Wide-ResNet, improving over the previous SOTA of 71.7%. When fine-tuning a pre-trained NFNet-F3, we achieve a remarkable 83.8% top-1 accuracy on ImageNet under (0.5, 8*10^{-7})-DP. Additionally, we also achieve 86.7% top-1 accuracy under (8, 8 \cdot 10^{-7})-DP, which is just 4.3% below the current non-private SOTA for this task. We believe our results are a significant step towards closing the accuracy gap between private and non-private image classification.
Improve Object Detection with Knowledge Distillation

Knowledge distillation is an effective method for model lightweighting. However, the previous distillation methods in object detection, in order to solve the problem of extreme imbalance between positive and negative examples, rely on manually adjusted hyper-parameters and lack generalization. And ignore the relationship information between different detection instances. Therefore, we propose a new distillation algorithm for the task. This algorithm automatically selects the Top k detection instances that need distillation most based on the network output, and fully considers the feature distillation based on the attention mechanism and the instance relationship distillation based on the Euclidean distance. Our results show that under various object detection frameworks, the student model has achieved a significant effect improvement with a lighter structure.
Sensor Device Capable of Autonomously Keeping Accurate Time Information Using a Chip Scale Atomic Clock for Earthquake Observation and Structural Health Monitoring of Buildings

Sensor devices were developed in this study for use in seismic observation to understand damage conditions after earthquakes and structural health monitoring for building and civil infrastructure maintenance. To be used, the sensor devices must be densely installed over a large area and measurement data with synchronized time must be obtained. It is preferable that the sensor devices themselves maintain accurate time information even when no network or Global Positioning System (GPS) signals are available. As a result, a sensor device was created that maintains accurate time information autonomously by employing a Chip Scale Atomic Clock (CSAC), which consumes ultra-low power, can be mounted on a small board, and is an ultra-high precision clock. This paper describes the CSAC and a mechanism for using the CSAC to add highly accurate time information to measured data. Following that, the study outlines the development process from prototype to practical device, as well as improvement results to address challenges identified in actual bridge use. A new procedure for time synchronization between devices is presented in this paper. Furthermore, the communication system of measurement data that was recently constructed using "fluentd," an open source software for data collection, is detailed. Eventually, the paper demonstrates the applicability of the developed sensor device through a case study in which seismic observation and structural health monitoring were implemented by installing the improved devices in a real-world building. The practical device developed in this research securing autonomous time synchronization made it possible to monitor the structural health of the building based on the evaluation of inter-story deformation. &nbsp
Resource-efficient domain adaptive pre-training for medical images

The deep learning-based analysis of medical images suffers from data scarcity because of high annotation costs and privacy concerns. Researchers in this domain have used transfer learning to avoid overfitting when using complex architectures. However, the domain differences between pre-training and downstream data hamper the performance of the downstream task. Some recent studies have successfully used domain-adaptive pre-training (DAPT) to address this issue. In DAPT, models are initialized with the generic dataset pre-trained weights, and further pre-training is performed using a moderately sized in-domain dataset (medical images). Although this technique achieved good results for the downstream tasks in terms of accuracy and robustness, it is computationally expensive even when the datasets for DAPT are moderately sized. These compute-intensive techniques and models impact the environment negatively and create an uneven playing field for researchers with limited resources. This study proposed computationally efficient DAPT without compromising the downstream accuracy and robustness. This study proposes three techniques for this purpose, where the first (partial DAPT) performs DAPT on a subset of layers. The second one adopts a hybrid strategy (hybrid DAPT) by performing partial DAPT for a few epochs and then full DAPT for the remaining epochs. The third technique performs DAPT on simplified variants of the base architecture. The results showed that compared to the standard DAPT (full DAPT), the hybrid DAPT technique achieved better performance on the development and external datasets. In contrast, simplified architectures (after DAPT) achieved the best robustness while achieving modest performance on the development dataset .
COMPARISON STUDY BETWEEN IMAGE RETRIEVAL METHODS

Searching for a relevant image in an archive is a problematic research issue for the computer vision research community. The majority of search engines retrieve images using traditional text-based approaches that rely on captions and metadata. Extensive research has been reported in the last two decades for content-based image retrieval (CBIR), analysis, and image classification. Content-Based Image Retrieval is a process that provides a framework for image search, and low-level visual features are commonly used to retrieve the images from the image database. The essential requirement in any image retrieval process is to sort the images with a close similarity in terms of visual appearance. The shape, color, and texture are examples of low-level image features. In image classification-based models and CBIR, high-level image visuals are expressed in the form of feature vectors made up of numerical values. The researcher's findings a significant gap between human visual comprehension and image feature representation. In this paper, we plan to present a comparison study and a comprehensive overview of the recent developments in the field of CBIR and image representation.
Can deep learning match the efficiency of human visual long-term memory in storing object details?

Humans have a remarkably large capacity to store detailed visual information in long-term memory even after a single exposure, as demonstrated by classic experiments in psychology. For example, Standing (1973) showed that humans could recognize with high accuracy thousands of pictures that they had seen only once a few days prior to a recognition test. In deep learning, the primary mode of incorporating new information into a model is through gradient descent in the model's parameter space. This paper asks whether deep learning via gradient descent can match the efficiency of human visual long-term memory to incorporate new information in a rigorous, head-to-head, quantitative comparison. We answer this in the negative: even in the best case, models learning via gradient descent require approximately 10 exposures to the same visual materials in order to reach a recognition memory performance humans achieve after only a single exposure. Prior knowledge induced via pretraining and bigger model sizes improve performance, but these improvements are not very visible after a single exposure (it takes a few exposures for the improvements to become apparent), suggesting that simply scaling up the pretraining data size or model size might not be a feasible strategy to reach human-level memory efficiency.
Advanced Deep Learning Technique for Gender Data Classification

Advanced deep learning (ADL) is advantageous at any instance when dealing with large image data sets. Gender identification from either the image is both a challenging and complicated task. There are many software programs in the areas of facial recognition, security, tracking, communication between people and computers, etc. Researchers operating in this region had suggested various techniques for gender recognition to extract characteristics from picture. Etc. Convolution Neural Network (CNN) is now commonly used in various vision apps to extract and classify features. Here is proposed an assessment on the use of MCNN for sexuality recognition. Findings from either the trials performed assist the assessment. To proposed Advanced Deep Learning pre-trained MCNN model (VGG16)for Gender Data Classification.
Implementing Bogoliubov Transformations Beyond the Shale-Stinespring Condition

We provide two extensions of a dense subspace of Fock space, such that Bogoliubov transformations become implementable on them, even though they violate the Shale-Stinespring condition, so they are not implementable on Fock space. Both the bosonic and fermionic case are covered. Conditions for implementability in the extended sense are stated and proved. From these, we derive conditions for a quadratic Hamiltonian to be diagonalizable by a Bogoliubov transformation that is implementable in the extended sense. Three examples illustrate situations, in which an implementation in the extended sense is possible although the Shale-Stinespring condition fails to hold.
Pembuatan Alat Pendeteksi Banjir di Sungai Kawasan Wisata Desa Rindu Hati Bengkulu Tengah

Rindu Hati village is a village in Bengkulu province which developing natural tourism by utilizing the village budget managed by the village-owned enterprise. The tourism developed in Rindu Hati Village such as Glamping, Tubing, Waterfall Tracking, Endu Hill cliff climbing. The location of the village and tourist attractions are along the river and under the foot of the hill. In addition, the scene is very beautiful and it has the potential for a natural disaster if the rainy season occurs floods which can occur and the impact is cause loss of property and life. In the tourist attraction and residential areas, the residents of Rindu Hati Village have not been equipped with flood detectors. So, the negative impacts in the event of a flood are very large. To reduce the impact of floods, a flood detector was made as a warning sign to tourist visitors and residents when the flood or an increase in river water levels. The flood detector was made using a solar cell system and two controllers which will read the condition of the water level directly and could stand for 68 hours if there is no sunlight and could last for 8 hours when the siren sounds. The flood detection equipment has been installed and socialized in the term of theory and practice to tourism managers and the local community with the participant of 16 people. The flood detection was installed in the Rindu Hati Tourism River, it will reduce the impacts caused by floods and will increase the comfort of visitors from the dangers of flooding
SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN BANTUAN PENDIDIKAN MENGGUNAKAN METODE ELECTRE DI SMP NEGERI 2 RENGAT BARAT

At SMP Negeri 2 Rengat Barat every year there is educational assistance from BAZNAS, Indragiri Hulu Regency, several years ago in determining decision making based on a proposal from the homeroom teacher by proposing a certain number of students with modest criteria so that it was difficult to determine who received educational assistance.&#x0D; By using the Elimination Et Choix la Realite (ELECTRE) method to facilitate the process and to be transparent and objective in making decisions on the recipients of educational assistance, criteria are made, including: Student attendance, attitude at school, GPA or report cards and Income Certificate issued by the Village or lurah and each criterion is given a score or value from 1 to 5.&#x0D; The researcher uses the Elimination Et Choix la Realite (ELECTRE) method using the Laravel application, the admin will be easy to input data and process and the results are quickly visible. The results of calculations through the system, the admin provides information to the principal to see data that are eligible to receive educational assistance. The results of the calculations to assist the principal's decision, after approval, a printing process will be carried out, then the recipients of educational assistance will be informed through the bulletin board, homeroom teacher, or class group.&#x0D; With the application created and practiced by the Admin, the results obtained are very helpful for all schools to determine the recipients of educational assistance because this system shows transparent and objective results.&#x0D; With this electre application, the school accepts it to be used this year as well as for the coming year, because the school realizes the importance of this application which can assist in objective decisions in determining recipients of educational assistance..
Pelatihan Penggunaan Aplikasi Faststone Capture dalam Menyusun Bahan Ajar untuk Pembelajaran Matematika Model Flipped Classroom

This activity was motivated by the problem of not optimal online learning in the Covid-19 pandemic situation. In particular, there are still teachers who do not know how to learn with the flipped classroom model, especially in the preparation of flexible teaching materials. Problems with mathematics teacher partners in Surakarta city can be solved, one of which is training in the use of technology applications, namely the Faststone Capture application which functions to capture screens, record, and edit to the screenshots to produce better teaching materials. The stages of activities carried out to solve problems are as follows: (1) socialization of mathematics learning with the flipped classroom model assisted by IT-based media with the aim of making mathematics learning plans; (2) introduction and use of faststone capture applications with the aim of developing mathematics teaching materials by utilizing faststone capture; and (3) dissemination of training results. The training activities provide knowledge to mathematics teachers regarding the use of faststone capture applications in compiling teaching materials that support mathematics learning through the flipped classroom model.
Real Quantum Amplitude Estimation

We introduce the Real Quantum Amplitude Estimation (RQAE) algorithm, an extension of Quantum Amplitude Estimation (QAE) which is sensitive to the sign of the amplitude. RQAE is an iterative algorithm which offers explicit control over the amplification policy through an adjustable parameter. We provide a rigorous analysis of the RQAE performance and prove that it achieves a quadratic speedup, modulo logarithmic corrections, with respect to unamplified sampling. Besides, we corroborate the theoretical analysis with a set of numerical experiments.
Applying social media for studying challenges of COVID-19 for students

This conference paper contributes to understanding opportunities to use social media for identifying the priorities and challenges of students from different countries in online and face-to-face learning and networking activities during the COVID-19 pandemic. The COVID-19 crisis has resulted in more intensive online learning and hybrid learning applications in higher education. When there is a shift from a teacher-controlled class environment to a more learner-controlled social media, the role of the educator becomes more of a facilitator. The main research question in this paper is: How social media activities facilitated by instructors can support international learner-driven online networking and knowledge sharing during the COVID-19 pandemic? Our action research aim was to reveal learning preferences and networking challenges that students do not usually share in a class environment. Despite critics of Facebook and the increasing popularity of alternative social media among young people, Facebook groups offer tools for discourse between different age cohorts and conduct polls to assess alternative educational tools and COVID-19 administrative restrictions that influence student mobility and socialisation. To study the challenges of COVID-19 for students in online learning and in physical interaction, we applied netnography methods in combination with interventions by educators to study students’ preferences in the Facebook group Challenges in online learning – COVID-19. Among the most popular Facebook polls for students were questions about group work and exam arrangements during the pandemic and work from anywhere practice implications, cross-border mobility regulations during the COVID-19 pandemic and reasons why some students do not participate in web conferences using their computer cameras. Students shared the view that flexible online work from anywhere solutions will be for many organisations among trends that will remain after the COVID-19 crisis is over. Some students, both from Europe and developing countries, believed that this trend would improve job opportunities for the workforce living in low-cost countries. Facebook group enabled international knowledge sharing, where both students and educators could share their views on many issues that influence the rapidly changing online learning and networking environment.
Adaptation of Android and Digital Applications in Education System

Main purpose of this review paper is to emerge early writing tools with new digital writing tools as computers, tablets, and laptops introduce to android application, which recognizes information through QR code in form of audio, video, text, etc., since last few years’ smart phones and wireless internet connection has no ambiguity. Mobile technology plays an important role in combination between advanced mobile technology and computer technology application. Location based interactions or Wi-Fi identification this shows the necessity of digital applications. This paper examines the practical outcomes from study and learning that has been taken place. In education society learning is general with continuing digital revolution in education and business relationships.
FedShuffle: Recipes for Better Use of Local Work in Federated Learning

The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck in Federated Learning (FL). Such methods are usually implemented by having clients perform one or more epochs of local training per round while randomly reshuffling their finite dataset in each epoch. Data imbalance, where clients have different numbers of local training samples, is ubiquitous in FL applications, resulting in different clients performing different numbers of local updates in each round. In this work, we propose a general recipe, FedShuffle, that better utilizes the local updates in FL, especially in this regime encompassing random reshuffling and heterogeneity. FedShuffle is the first local update method with theoretical convergence guarantees that incorporates random reshuffling, data imbalance, and client sampling - features that are essential in large-scale cross-device FL. We present a comprehensive theoretical analysis of FedShuffle and show, both theoretically and empirically, that it does not suffer from the objective function mismatch that is present in FL methods that assume homogeneous updates in heterogeneous FL setups, such as FedAvg (McMahan et al., 2017). In addition, by combining the ingredients above, FedShuffle improves upon FedNova (Wang et al., 2020), which was previously proposed to solve this mismatch. Similar to Mime (Karimireddy et al., 2020), we show that FedShuffle with momentum variance reduction (Cutkosky & Orabona, 2019) improves upon non-local methods under a Hessian similarity assumption.