Reviewerly/exHarmony huggingface dataset:
Regulating online content moderation: Taking stock and moving ahead with procedural justice and due process rights

The practice of content moderation, whereby online platforms monitor and remove harmful content, has proliferated in the digital sphere. Private corporations controlling digital speech raises challenges for democracies. Should, therefore, private power in the digital sphere be regulated, and if yes, should content moderation practices be restrained to protect fundamental rights? The EU has initiated a line of legislative efforts that together generate a body of digital governance norms for regulating content moderation practices. Inspecting these initiatives reveals that although they are intended to enshrine human rights guarantees in the digital sphere, they have failed to provide individual end users with procedural justice rights, known also as procedural due process rights, to protect them against the online platforms. Although the principles of transparency and accountability are praised in the preambles of these legislative initiatives, they are not translated into concrete obligations.
PENINGKATAN HASIL BELAJAR DENGAN MODEL PICTURE AND PICTURE PADA PEMBELAJARAN MATEMATIKA BAGI SISWA KELAS VI SDN KALICARI 01

This study aims to improve student learning outcomes in learning mathematics with the Picture and Picture model. This study used a Classroom Action Research (CAR) design which was carried out for two cycles. The stages of conducting research include planning, implementing, observing, and reflecting. This study shows that student learning outcomes have increased in each cycle. At the pre-cycle stage, the average learning completeness was only 25%, in the first cycle, the average learning mastery was 65.5%, in the second cycle, the learning mastery average was 93.5%. It can be concluded that student learning outcomes with the Picture and Picture model in mathematics learning which starts from the pre-cycle, cycle I, and cycle II have increased.
The Self-Shrinking Conflation Generator: A Proposed Improvement to the Self-Shrinking Generator

The backbone of many cybersecurity applications and algorithms require random numbers. One of the most commonly used pseudo-random number generators is the Linear Feedback Shift Register (LFSR), which is fast, computationally inexpensive, and has excellent statistical properties. Unfortunately LFSRs have a number of weaknesses, some of which were addressed by decimation-based sequence generators such as the self-shrinking generator (SSG). Regrettably, the SSG was also found to be vulnerable to attack. In this paper, we propose an improvement to the SSG called the self-shrinking conflation generator (SSCG). Our approach is based on the observation that what is discarded during the self-shrinking process of the SSG, is from a cryptographic perspective, just as good as that which is kept. By combining the bits the SSG would normally discard with those it retains, using the exclusive OR (XOR) operation, we create a modified SSG bitstream with several improved characteristics. To highlight these improvements, we provide some mathematical security analysis associated with this approach, apply the NIST statistical test suite to several different bitstreams created using LFSRs driven by different degree primitive polynomials, and compare our results to that of the SSG.
Derin Öğrenme Modellerinin Doğruluk, Süre ve Boyut Temelli Ödünleşme Değerlendirmesi

Machine Learning and especially deep learning models need to be optimized over three main criteria concurrently, to be operationalized in real-time field applications. These criteria are model’s accuracy, training-testing times and file size. Related work only considers two criteria (e.g. accuracy-time) together. However, it is observed that deep neural networks (DNN) designed to improve model accuracy can increase training time and size, while efforts to reduce model size can lead to lower accuracy. A trade-off needs to be made among these three criteria. In this paper, to demonstrate the effects of different optimization techniques on model performance, we tested ResNet50, ResNet101, VGG16, VGG19, EfficientNet pre-trained models with CIFAR10, CIFAR100 image datasets, which are commonly utilized in the DNN research field. Important performance results obtained over Google Colab Pro and TensorFlow system show that weight quantization is the most successful technique so far in multi-dimensional optimization, while weight clustering and transfer learning techniques remain useful in 2-dimensions. In addition, we designed and tested a new DNN operational score and model-to-model layer transfer method for the first time in literature. We hope that our framework will constitute a multi-dimensional evaluation reference for DNN models before they are operationalized.
Digital Literacy Using Protexan to Detract Herd Stupidity in The Era Of Freedom to Learn

There are many problems in implementing the teaching-learning process in the era of freedom to learn. The specific purpose of this research is to provide education on digital literacy learning in English subjects through the Protexan application to detract the herd stupidity in society through students as agents of change in implementing health protocols in the era of freedom to learn. The method used in this research is the research &amp; development (R&amp;D) method. The research was carried out in several stages such as preliminary, prototype creation, expert validation, and media testing. The design and teaching media materials are matched through sorting the data processing at an early stage. This Protexan was tested on educators and students with satisfactory results above 70%. The material is in the form of procedural texts in English following the curriculum in high schools in the independent era of learning. The Covid-19 socialization that is integrated with the learning application is used as additional information for students to continue or to carry out post-pandemic health protocols so that students know what actions need to be taken in the family, friendship, and social spheres to avoid Covid-19.
eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features

We propose eBoF, a novel time-varying ensemble data visualization approach based on bag-of-features (BoF). In the eBoF model, we take a simple and monotone interval from all target variables of ensemble scalar data as a local feature patch of BoF model and the duration time of each interval (i.e., feature patch) as its frequency. The feature clusters in ensemble runs are then identified based on the similarity of temporal correlations. eBoF generates the clusters together with their probability distribution across all the feature patches while storing the geo-spatial information, which is often lost in the traditional topic modelling or clustering algorithms. The probability distribution across different clusters can help to generate reasonable clustering results evaluated by the domain knowledge. We conduct several case studies and performance analyses. We also consult the domain experts to evaluate the proposed eBoF model. Evaluation results suggest the proposed eBoF can provide insightful and comprehensive evidence on ensemble simulation data analysis.
VLSI Architecture for Depth Invariant Real-time Fixed/Random Valued Impulse Noise Removal Algorithm for Back-end of Ultrasonography Systems

Ultrasound images often get distorted by impulse noise during data acquisition and processing in the Back-end of the system, which overlay the finer details of the scanned body parts. Generally, a portable low-cost USG system doesn’t have an impulse noise-cleaning module which hinders detections of smaller details in the images. A Depth Invariant Impulse Noise Removal (DIINoR) algorithm and its hardware architecture for real-time impulse noise removal from the corrupt USG image are proposed in this paper. In this decision-based algorithm, the corrupt pixel is first detected depending on the homogeneity of the processing window and is restored with the median of the window or previous pixel value. Testing of the DIINoR algorithm on different USG images establishes that the denoised images have superior quantitative performance compared to those of existing schemes. Implementation of this architecture in VIRTEX-7 FPGA gives a maximum clock frequency of 357.96 MHz. Synthesis of this architecture using UMC 90nm technology gives 103 mW power consumption at a clock frequency of 100 MHz with a gate count of 63K (NAND2) including two memory buffers which proves its suitability for the real-time fixed and random valued impulse noise cleaning in the Back-end of the portable USG system.
Discovering New Stories in What AI Doesn't Know: An Interview with Audiovisual Performance Artist Debashis Sinha

&lt;p&gt;An interview between media artist and scholar Debashis Sinha and professor Shanti Pillai, regarding Sinha's sound practice and exploration of machine learning and AI. Published in Johns Hopkins' Theatre Journal, Sept 2021 on a special issue devoted to AI.&lt;/p&gt;
DEKOMPOSISI GRAF POHON PISANG Bm,n

A decomposition of graph G is collection of subgraphs 〖{H_i}〗_(i=1)^n from G such that H_i [E_i] for E_i is a subset of E(G) and 〖{E_i}〗_(i=1)^n is a partition of E(G). The purpose of the research was to determine the decomposition of the banana tree graph B_(m,n), for m≥1 and n≥2. The research method used in this research is library research. The steps used to determine the decomposition of the banana tree graph B_(m,n) are as follow: (a) Draw a banana tree graph B_(m,n) and label each edge and vertex, (b) Determine the partition on the edges of the banana tree graph B_(m,n), (c) Induced subgraph of from partitions of the banana tree graph B_(m,n), (d) Determine the decomposition of the banana tree graph B_(m,n), (e) Tabulate a conjecture on the decomposition of the banana tree graph B_(m,n), (f) Construct theorem of the decomposition theorem of of the banana tree graph B_(m,n) and its proof. The result of the reasearch is with m≥1 and n≥2, because banana tree graph B_(m,n) is decomposed by the complete graph 〖mK〗_2-decomposition.
A Robotic System for Remote Teaching of Technical Drawing

Abstract This paper describes a robotic system that supports remote teaching of technical drawing. The system aims to enable a remote class of paper-based technical drawing, where the students draw the drawing in a classroom, and the teacher gives instructions to the students from a remote place while confirming the paper drawing. The robotic system has a document camera for confirming the paper, a projector, a flat screen to project a cursor on the paper, and a video conference system for communication between the teacher and the student. We conducted two experiments; the first experiment verified that use of a projected cursor was useful for pointing the paper remotely. The second experiment was conducted in an actual drawing class, and the result showed that the students had a good impression of the system.
Contextualising the global information revolution in a development arena:

The focus of this article is to contextualise information communication technology(ICT) development within the global information arena through reference to a particularcase study where an ICT was implemented in various areas in South Africa. The articlesheds light on often-conflicting ideologies relating to the effect of informationtechnology in developing countries in general and in South Africa in particular. Itpresents an overview of problems and possible solutions regarding acceptability, use,sustainability, and best practices. From a research perspective the article investigateshow development initiatives can be supported by adapting this computerised systemto the needs of users within specific contexts. It also intends to discuss generalprohibiting factors in a development arena extending from implementation to usageat various levels involving diverse role players.
Noiseless Codelength in Wavelet Denoising

&lt;p&gt;We propose an adaptive, data-driven thresholding method based on a recently developed idea of Minimum Noiseless Description Length (MNDL). MNDL Subspace Selection (MNDL-SS) is a novel method of selecting an optimal subspace among the competing subspaces of the transformed noisy data. Here we extend the application of MNDL-SS for thresholding purposes. The approach searches for the optimum threshold for the data coefficients in an orthonormal basis. It is shown that the optimum threshold can be extracted from the noisy coefficients themselves. While the additive noise in the available data is assumed to be independent, the main challenge in MNDL thresholding is caused by the dependence of the additive noise in the sorted coefficients. The approach provides new hard and soft thresholds. Simulation results are presented for orthonormal wavelet transforms. While the method is comparable with the existing thresholding methods and in some cases outperforms them, the main advantage of the new approach is that it provides not only the optimum threshold but also an estimate of the associated mean-square error (MSE) for that threshold simultaneously.&lt;/p&gt;
Examining Students’ Interaction and Satisfaction with Online Learning

Technology has made it possible for the higher education ecosystem to adapt to the different needs of students and to interact with them remotely when face-to-face interaction is impossible. This study aimed to investigate the relationship between interaction and online learning satisfaction and whether this relationship is mediated by academic self-efficacy and student engagement among 175 Bosnian high school students during the COVID-19 pandemic. The questionnaire was used to collect the data from the participants. The findings suggested that the participants feel confident while using the Internet, are pretty self-directed, and do not lack interactions or satisfaction with online learning. In addition, the findings indicated that while grade level and GPA do not significantly influence students' satisfaction levels, the amount of time spent online and gender significantly influence it, with males reporting higher levels of satisfaction. There is no correlation between gender or the amount of time spent online and online interaction. However, grade level and grade point average have a significant impact. Further, studies show that students who put less effort into their studies are much more likely to be satisfied with the online learning environment. Time spent online substantially affects internet self-efficacy and self-regulated learning, while grade, GPA, and gender have little to no effect. The results of this investigation may help educators design virtual classrooms that stimulate student engagement, discussion, confidence in using the web for learning, and self-directed study. Online learning will become more efficient as a result of this.
Perancangan Maskot Kampanye PROKES (Program Kembali Ke Sekolah) Untuk Mendukung PTM Terbatas di SMP Bina Taruna Bandung

Mascot is a visual identity that can be used to represent an organization, event, or product. Mascot design can be applied in a campaign activity to strengthen the image and identity that is conveyed to the audience. PROKES is a socialization campaign held at SMP Bina Taruna Bandung in order to support limited PTM (Face-to-Face Learning) activities in the school environment. The method used for the design and data collection is interpretative qualitative, with the design process being carried out in stages and in a structured manner. Based on the evaluation of the campaign, both as a whole and in the context of visual identity, the response from the residents of SMP Bina Taruna was considered good and positive. The PROKES campaign mascot is expected to be an identifier that reflects the concept of the activity.&#x0D; &#x0D; Keywords: campaign, mascot, SMP Bina Taruna, visual identity
Pendampingan Penulisan Artikel Penelitian Tindakan Kelas di Sabu Raijua

The significance of classroom action research in improving the learning process and quality in the classroom and classroom action research for teacher career advancement in terms of rankings and suggested career paths. The aim is to help teachers understand the importance of writing. The method used is, first, the basic concept of classroom action research. Second, practice writing action research in the classroom. And a summary of the results. From the training results, it is known that after following the material, understanding and mastery of CAR increased compared to before. Teachers can understand the duties and responsibilities in writing activities in class. So the teacher can understand the importance of references in writing articles.
Literasi Gizi Remaja Melalui Permainan Edukasi Goaksi (Go Aksi Bergizi)

Nutritional problems in adolescents are increasingly complex, one of which is nutritional anemia and skipping breakfast. It happens because of limited knowledge about balanced nutrition in adolescents. With the increasing nutritional problems of adolescents, the Community Service (PKM) team collaborates with partners, namely students of SMAN 1 Perhentian Raja, Kampar to carry out adolescents' nutrition literacy through the educational game GOAKSI (Go Aksi Bergizi). This activity effort to convey adolescent nutritional literacy through counseling the contents of my plate in meeting nutrition needs. The PKM was attended by 28 student 11th graders. All students were able to understand well the concept of balanced nutrition through the contents of my plate by an increase in the average value of knowledge from 5.4 to 8.0 (pre-test and post-test results) of 2.6 points. It is hoped that PKM activities can be carried out continuously with different topics, so that the formation of knowledge and positive attitudes of adolescents in fulfilling balanced nutrition and good food habits in overcoming adolescent nutritional problems.&#x0D;
A Study of RobustNet, a Domain Generalization Method for Semantic Segmentation

Domain Generalization alleviates the domain gap between training set and test set, improving the performance of deep neural networks on out-of-dataset data.This opens the possibility of deploying models on unlabelled data that were previously pretrained on other datasets.In this article, we study the ideas and performance of RobustNet [Choi et al.CVPR 2021], a recent method for Domain Generalization in Urban-Scene Semantic Segmentation.Instead of exposing the network to a wide range of domains, RobustNet tries to separate domain-variant from domain-invariant features via a whitening transformation.Then, only the domain invariant features are used for training, which allows to reduce training time since no combination of datasets is needed to achieve domain invariance.In addition, we provide an easy-to-use demo where users can quickly test their own data and compare the results of RobustNet against the state of the art for semantic segmentation.
Markov Chain for Keywords Extraction from News Articles

Abstract Keywords extraction, that is the process of extracting the most important and relevant words from a text or a set of texts, is one of the core components of every News Aggregator Service. This paper elaborates how Markov Chain can be used for extracting nouns from a set of news articles, and then ranking them in order to extract the top five. The process involves noun extraction using POS tagger, construction of a graph of extracted nouns, and PageRank algorithm for ranking nouns.
Improving Explanations of Image Classification with Ensembles of Learners

In explainable AI (XAI) for deep learning, saliency maps, heatmaps, or attention maps are commonly used to identify important regions for the classification of images of explanations. Recent research has shown that many common XAI methods do not accurately identify the regions that human experts consider important. We propose averaging explanations from ensembles of learners to increase the accuracy of explanations. Our technique is general and can be used with multiple deep learning architectures and multiple XAI algorithms. We show that this method decreases the difference between regions of interest of XAI algorithms and those identified by human experts. Furthermore, we show that human experts prefer the explanations produced by ensembles to those of individual networks.
Adaptive Phase Error Suppression Concerning 3D surface Deformation Measurement on Color Digital Fringe Projection Profilometry

3D surface measurement based on phase-shifting profilometry (PSP) has been actively developed in recent years.Three color channels of RGB that are modulated to generate a one-shot PSP method is a concept of color digital fringe pattern profilometry (CDFPP).The CDFPP is a promising technique for the 3D imaging profile of dynamic surface deformation if several phase errors in the one-shot PSP method can be suppressed.This work proposes a processing scheme for phase error suppression schemes (PESS) based on retrieving the modulated sinusoidal fringe and color fringe normalization in PSP using RGB color channel.The processing of PESS consists of tunable bandpass filtering (BPF) followed by fringe normalization.The initial BPF function is defined based on a smoothing spline data set of frequency and power spectrum from the baseline color fringe image.The predefine BPF function could be tunable during the imaging process by considering each frame's condition and RGB channel spectrum mapping.The corrected fringe images are then normalized from the color imbalance, and the phase shift is calculated using the conventional three-step PSP.For evaluation, PESS is performed to reconstruct simulator membrane deformation from four different static profiles and tested to observe the 3D surface of continuous membrane deformation.The PESS could suppress the phase errors of less than 30% less absolute errors than the conventional method and successfully reconstruct the 3D surface for low-frequency continuous membrane deformation with minimizing phase errors.
C-Type Ops Transformation

Many indexes are involved on ordinary power series calculation. One of the most valuable discovery on previous research is Ops Transformation which able to simplify notations of ordinary power series to avoid some more complicated manipulations. The aim of this research is to generalize the concept of Ops Transformation to gain more benefit and wider applications. The main result of this research is a new formula of generalization of Ops Transformation which called C-Type Ops Transformation. Such transformation is able to solve some differential equations which involving Taylor series