Reviewerly/exHarmony huggingface dataset:
MediaWiki as a Tool for Creating a Digital University Model

The article is devoted to the software product MediaWiki, proposed by the authors of the article for use as a platform for creating the software and technological core of the faculty, which, in turn, is a typical element for building prototypes of the faculty and models. The model considered in the article is proposed by the authors as a comprehensive solution to the problem of digital transformation in the field of education. According to the authors of the article, the MediaWiki software complex, supplemented by domestic software and open source products, will effectively solve the problem of managing information flows in the educational process. The article describes the experience of creating standard wiki systems for faculties and scientific schools of Moscow State University and their use as central elements of the digital transformation of university faculties. The article discusses the creation of a network tool that ensures the management of digital information flows that are formed between teachers and students in the learning process, as well as the development of a system of rules for the interaction of participants in the educational process in the educational process. A digital environment that creates conditions for the successful development of the system. Currently, three systems are being tested, created by specialists from the Faculty of Computational Mathematics and Cybernetics of Moscow State University in the framework of inter-faculty agreements with the Faculty of Law and the branch of Moscow State University in Sevastopol. The system is built taking into account the experience gained by the authors during the inter-faculty course Introduction to Wikipedia.
ATTENTION AND CONCENTRATION ABILITIES, THE DIFFERENCE BETWEEN STUDENTS WHO HAD BREAKFAST AND STUDENTS WHO DIDN’T HAVE BREAKFAST IN UPTD SMA NEGERI 2 NGANJUK

Indonesia had the lowest position in cognitive skills and educational achievement according to the research of the Economist Intelligence Unit in 2014. Cognitive skills are the brain abilities to reach knowledges and understanding from experiences and information. Parts of cognitive domain are attention and concentration. Attention is the ability to focus on problems. Concentration is the ability to maintain the attention in the longer periode of time. The availibility of adequate nutrition can be achieved by consuming good and various foods, especially at the breakfast. This research aimed to study the difference of attention and concentration abilities between students who had breakfast and those who didn’t in UPTD SMA Negeri 2 Nganjuk. The subjective assessment used questionnaire and the objective assessment used Moca-Ina assessment. This was an analytic research with cross sectional research design applied to 42 eleventh grade students in UPTD SMA Negeri 2 Nganjuk. The sampling technique was Simple Random Sampling by distributing the questionnaire and performing Moca-Ina assessment grade students in UPTD SMA Negeri 2 Nganjuk. The data were processed with version 20 SPSS. The result showed that there were significant differences of the attention and subjective concentration abilities between students who had breakfast and students who didn’t have, based on the breakfast menus (p=0.012), breakfast calories (p=0.012), and breakfast time between who had breakfast at 06.00-06.59 AM and who didn’t have or had breakfast before 06.00 AM (p=0.004). Also, there were significant differences ot attention and objective concentration abilities among students who had breakfast and students who didn’t have (p=0.035) based on Moca-Ina assessment. The researcher could conclude that subjectively and objectively there were significant differences of attention and objective concentration abilities between students who had breakfast and those who didn’t. Respondents who had breakfast had better attention and concentration abilities than the ones who didn’t have.
Privately Learning Mixtures of Axis-Aligned Gaussians

We consider the problem of learning mixtures of Gaussians under the constraint of approximate differential privacy. We prove that $\widetilde{O}(k^2 d \log^{3/2}(1/\delta) / \alpha^2 \varepsilon)$ samples are sufficient to learn a mixture of $k$ axis-aligned Gaussians in $\mathbb{R}^d$ to within total variation distance $\alpha$ while satisfying $(\varepsilon, \delta)$-differential privacy. This is the first result for privately learning mixtures of unbounded axis-aligned (or even unbounded univariate) Gaussians. If the covariance matrices of each of the Gaussians is the identity matrix, we show that $\widetilde{O}(kd/\alpha^2 + kd \log(1/\delta) / \alpha \varepsilon)$ samples are sufficient. Recently, the local covering technique of Bun, Kamath, Steinke, and Wu has been successfully used for privately learning high-dimensional Gaussians with a known covariance matrix and extended to privately learning general high-dimensional Gaussians by Aden-Ali, Ashtiani, and Kamath. Given these positive results, this approach has been proposed as a promising direction for privately learning mixtures of Gaussians. Unfortunately, we show that this is not possible. We design a new technique for privately learning mixture distributions. A class of distributions $\mathcal{F}$ is said to be list-decodable if there is an algorithm that, given heavily corrupted samples from $f\in \mathcal{F}$, outputs a list of distributions, $\widehat{\mathcal{F}}$, such that one of the distributions in $\widehat{\mathcal{F}}$ approximates $f$. We show that if $\mathcal{F}$ is privately list-decodable, then we can privately learn mixtures of distributions in $\mathcal{F}$. Finally, we show axis-aligned Gaussian distributions are privately list-decodable, thereby proving mixtures of such distributions are privately learnable.
Progression in Cyber Security Concerns for Learning Management Systems

In today’s technology driven environment, the educational sector is adopting technologies, and online deliveries of sessions have increased the scope for discussion and knowledge sharing multifold. This has also opened up new ground for various opportunities in cyberspace. Educational bodies are facing several challenges as dependency on cyber-physical systems (CPSs) is increasing. A learning management system (LMS) is one of the CPSs used as a platform for online delivery of sessions. Many professional bodies disseminate online education and are responsible for their respective assessments. However, on the down side it tags itself with many cyber threats, with which users or participants are not familiar. The perception and knowledge of cyber security is predominantly required to address the assessments and thereby the outcome of delivery by professional bodies supporting higher education. The infrastructure used to build and deploy online session delivery (i.e., LMSs) plays a considerable role in implementing security standards and guidelines, hence policies, for an organization. The distributed nature of online LMSs with the greater support of advancement of scalability and functionality has increased the complexity of the system, which has spawned various threads for non-legitimate users to exploit the vulnerabilities in the existing architecture of LMSs. In addition, the invention of various mobile devices to access online LMSs serves as a catalyst for such exploitation. There is a need to bring forward the essentials of cyber security in education and its adoption by participants (instructors or learners). The authors have analyzed various vulnerabilities present in LMSs and studied the role and adoption intentions of instructors regarding cyber security measures.
F-ONTOCOM: A Fuzzified Cost Estimation Approach for Ontology Engineering

Estimating effort is an essential prerequisite for the wide-scale dispersal of ontologies. Not much attention has yet been paid to this essential aspect of ontology building. To date, ONTOCOM is the most prominent model for ontology cost estimation. Many factors influencing the building cost of an ontology are depicted by linguistic terms like Very High, High, . . . and so on; making them vague and indistinct. This fuzziness is quite uncertain and must be taken into consideration. The available effort estimation models do not consider the uncertainty of fuzziness. In this work, we propose an effort estimation methodology for ontology engineering using Fuzzy Logic i.e. F-ONTOCOM (Fuzzy-ONTOCOM) to overcome of uncertainty and imprecision. We have defined the corresponding Fuzzy sets for each effort multiplier and its associated linguistic value, and represented the same by triangular membership functions. F-ONTOCOM is applied to a dataset of 148 ontology projects and evaluated over various evaluation criteria. FONTOCOM outperforms the existing effort-estimation models; it has been concluded that F-ONTOCOM improves the cost estimation accuracy and estimated cost is very close to actual cost.
Inference Methods for Evaluable Knowledge Bases

This paper investigates inference from evaluable knowledge bases without reasoning by contradiction. These knowledge bases are comprised of non-Horn rules with partial predicates and functions, some of them are defined as recursive functions. This inference corresponds to model elimination without the reduction rule, which is equivalent to forward and backward chaining extended with rule contrapositives, to input resolution, and to constrained resolution without factoring including its ordered variant.
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates

Three Operator Splitting (TOS) (Davis & Yin, 2017) can minimize the sum of three convex functions effectively when an efficient gradient oracle or proximal operator is available for each term. This requirement often fails in machine learning applications: (i) instead of full gradients only stochastic gradients may be available; and (ii) instead of proximal operators, using subgradients to handle complex penalty functions may be more efficient and realistic. Motivated by these concerns, we analyze three potentially valuable extensions of TOS. The first two permit using subgradients and stochastic gradients, and are shown to ensure a $\mathcal{O}(1/\sqrt{t})$ convergence rate. The third extension AdapTOS endows TOS with adaptive step-sizes. For the important setting of optimizing a convex loss over the intersection of convex sets AdapTOS attains universal convergence rates, i.e., the rate adapts to the unknown smoothness degree of the objective. We compare our proposed methods with competing methods on various applications.
Multiparticle quantum interference in Bogoliubov bosonic transformations.

We explore the multiparticle transition probabilities in Gaussian unitaries effected by a two-mode Bogoliubov bosonic transformation on the mode annihilation and creation operators. We show that the transition probabilities can be characterized by remarkably simple, yet unsuspected recurrence equations involving a linear combination of probabilities. The recurrence exhibits an interferometric suppression term - a negative probability - which generalizes the seminal Hong-Ou-Mandel effect to more than two indistinguishable photons impinging on a beam splitter of rational transmittance. Unexpectedly, interferences thus originate in this description from the cancellation of probabilities instead of amplitudes. Our framework, which builds on the generating function of the non-Gaussian matrix elements of Gaussian unitaries in Fock basis, is illustrated here for the most common passive and active linear coupling between two optical modes driven by a beam splitter or a parametric amplifier. Hence, it also allows us to predict unsuspected multiphoton interference effects in an optical amplifier of rational gain. In particular, we confirm the newly found two-photon interferometric suppression effect in an amplifier of gain 2 originating from timelike indistinguishability (arXiv:2012.15165 [quant-ph]). Overall, going beyond standard two-mode optical components, we expect our method will prove valuable for describing general quantum circuits involving Bogoliubov bosonic transformations.
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning

We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy. Motivated by stochastic optimization and the federated learning (FL) paradigm, we focus on the case where a small fraction of data samples are randomly sub-sampled in each round to participate in the learning process, which also enables privacy amplification. To obtain even stronger local privacy guarantees, we study this in the shuffle privacy model, where each client randomizes its response using a local differentially private (LDP) mechanism and the server only receives a random permutation (shuffle) of the clients' responses without their association to each client. The principal result of this paper is a privacy-optimization performance trade-off for discrete randomization mechanisms in this sub-sampled shuffle privacy model. This is enabled through a new theoretical technique to analyze the Renyi Differential Privacy (RDP) of the sub-sampled shuffle model. We numerically demonstrate that, for important regimes, with composition our bound yields significant improvement in privacy guarantee over the state-of-the-art approximate Differential Privacy (DP) guarantee (with strong composition) for sub-sampled shuffled models. We also demonstrate numerically significant improvement in privacy-learning performance operating point using real data sets.
On Calibration and Out-of-Domain Generalization

Out-of-domain (OOD) generalization is a significant challenge for machine learning models. Many techniques have been proposed to overcome this challenge, often focused on learning models with certain invariance properties. In this work, we draw a link between OOD performance and model calibration, arguing that calibration across multiple domains can be viewed as a special case of an invariant representation leading to better OOD generalization. Specifically, we show that under certain conditions, models which achieve \emph{multi-domain calibration} are provably free of spurious correlations. This leads us to propose multi-domain calibration as a measurable and trainable surrogate for the OOD performance of a classifier. We therefore introduce methods that are easy to apply and allow practitioners to improve multi-domain calibration by training or modifying an existing model, leading to better performance on unseen domains. Using five datasets from the recently proposed WILDS OOD benchmark, as well as the Colored MNIST dataset, we demonstrate that training or tuning models so they are calibrated across multiple domains leads to significantly improved performance on unseen test domains. We believe this intriguing connection between calibration and OOD generalization is promising from both a practical and theoretical point of view.
Context-based video frame interpolation via depthwise over-parameterized convolution

Video frame interpolation is used to generate intermediate frames by estimating the movement of pixels between the input frames. However, problems of blurring, object occlusion, and sudden brightness changes occur in naturally obtained video frames. We propose a context-based video frame interpolation method via depthwise over-parameterized convolution. First, the proposed network obtains the context graphs of the input frames. Subsequently, an adaptive collaboration of flows is adopted to warp the input frames and the context graphs. Then, the frame synthesis network is used to fuse the warped input frames and context graphs to obtain a preliminary estimate of the interpolated frame. Finally, a post-processing module is employed to refine the result. Experimental results on several datasets demonstrate that the proposed method performs qualitatively and quantitatively better than state-of-the-art methods.
Web-Based Data Visualization To Identify the Spread of Covid-19 in Indonesia

This study aims to produce a data visualization or picture based on Covid-19 website deployment in Indonesia. The model in this study uses Rapid Application Development (RAD ). The results and discussion of the research show that, the RAD model with three phases, namely requirements planning, design, and implementation can produce a website application system for urgent needs. The website that was designed has made it easy for users, especially to find out the data distribution of Covid-19 in Indonesia Keywords : Covid-19, RAD Model, Website, Data DOI : http://dx.doi.org/10.23960/jpvti.v2.i1.202003
Assessment of transition metal oxides, post transition metal oxides in suspended or settled dust particles by wavelength dispersive X-rays fluorescence (WD-XRF)

Settled dust or suspended particles of dust in air contains transition and post-transition metal oxides like Chromium oxide {Cr2O3}, Manganese oxide {MnO}, Nickel oxide {NiO}, Copper oxide {CuO}, Zinc oxide {ZnO}, Titanium oxide {TiO2}, Iron oxide {Fe2O3}, Zirconium oxide {ZrO2}, Lead oxide {PbO} and Aluminum oxide {Al2O3} and the study was carried out to find out the composition of these metallic oxides and the comparison between them with X-Ray Fluorescence Spectrometer (Chemical Analysis Instrument) by making solid beads of settled dust. Their average percent composition is found as 0.043, 0.0109, 0.013, 0.049, 0.425, 0.485, 5.39, 0.365, 0.031, and 5.55 respectively. Total of four areas were chosen to study within Karachi, a busiest and highly populated city of Pakistan. This study provides helpful data for future research and assignments as the studied metal oxides are the dynamics for environment pollution.Key words: Settled dust, X-ray fluorescence spectrometer, environment, earth sciences.
Compressive Visual Representations

Learning effective visual representations that generalize well without human supervision is a fundamental problem in order to apply Machine Learning to a wide variety of tasks. Recently, two families of self-supervised methods, contrastive learning and latent bootstrapping, exemplified by SimCLR and BYOL respectively, have made significant progress. In this work, we hypothesize that adding explicit information compression to these algorithms yields better and more robust representations. We verify this by developing SimCLR and BYOL formulations compatible with the Conditional Entropy Bottleneck (CEB) objective, allowing us to both measure and control the amount of compression in the learned representation, and observe their impact on downstream tasks. Furthermore, we explore the relationship between Lipschitz continuity and compression, showing a tractable lower bound on the Lipschitz constant of the encoders we learn. As Lipschitz continuity is closely related to robustness, this provides a new explanation for why compressed models are more robust. Our experiments confirm that adding compression to SimCLR and BYOL significantly improves linear evaluation accuracies and model robustness across a wide range of domain shifts. In particular, the compressed version of BYOL achieves 76.0% Top-1 linear evaluation accuracy on ImageNet with ResNet-50, and 78.8% with ResNet-50 2x.
Turnitin--The Impact of Different Types of Tests in EFL Writing on theTeachers’ Classroom Practices

To promote the quality of national education, a test,especially high-stake test, has received special concern from thegovernment. Such kind of test brings serious consequence(impact) to some related parties like teachers, students, schoolsystems, and society. This article is intended to highlight howdifferent type of testing affects the teachers’ teaching practices inthe classrooms. In Indonesian secondary schools, two types oftests, National Examination (NE) and School Examination (SE)are administered to measure students’ achievement in aparticular level as well as to decide students’ graduation. The twotests are different at some points. First, the test for NE wasdeveloped and administered by the government through theBoard of National Education Standard. Meanwhile, thepreparation and administration of the test for SE were done bythe English teachers at every school. Second, the test of NE wasin the form of objective test, whereas, the test of SE is subjectivein which the students should create writing products. Usingdescriptive qualitative research design, the current researchinvestigated how four Indonesian secondary school teacherscarried out the teaching of EFL Writing in their classroom as theimpact of the two types of test. The findings of this researchrevealed that the administration of the different types of testsbrings different impacts on the preparation and classroomspractices in which the teachers were more enthusiastic andserious to prepare for the SE rather than the NE. It can beconcluded that a particular type of test which requires fullinvolvement of the teachers either in the preparation or theadministration like SE brings positive impact on their classroompractices.
Unsupervised Part Discovery from Contrastive Reconstruction

The goal of self-supervised visual representation learning is to learnstrong, transferable image representations, with the majority of researchfocusing on object or scene level. On the other hand, representation learningat part level has received significantly less attention. In this paper, wepropose an unsupervised approach to object part discovery and segmentation andmake three contributions. First, we construct a proxy task through a set ofobjectives that encourages the model to learn a meaningful decomposition of theimage into its parts. Secondly, prior work argues for reconstructing orclustering pre-computed features as a proxy to parts; we show empirically thatthis alone is unlikely to find meaningful parts; mainly because of their lowresolution and the tendency of classification networks to spatially smear outinformation. We suggest that image reconstruction at the level of pixels canalleviate this problem, acting as a complementary cue. Lastly, we show that thestandard evaluation based on keypoint regression does not correlate well withsegmentation quality and thus introduce different metrics, NMI and ARI, thatbetter characterize the decomposition of objects into parts. Our method yieldssemantic parts which are consistent across fine-grained but visually distinctcategories, outperforming the state of the art on three benchmark datasets.Code is available at the project page:https://www.robots.ox.ac.uk/~vgg/research/unsup-parts/.
Research on optimal path planning of indoor robot based on wireless sensor

Abstract Based on the work tasks and positioning characteristics of indoor robots, the environment is divided into grids, and wireless sensors are used to detect obstacles, and the density of obstacles in each grid is given. At the same time, the path planning algorithm is combined to realize the optimal path planning of indoor robot. The simulation results show that the wireless sensor network can realize the obstacle density detection, so that the robot can achieve fast optimal path planning and reach the target point.