Reviewerly/exHarmony huggingface dataset:
Aplikasi Pemesanan Barang di Nancy Toys Menggunakan Aplikasi Web

Nancy toys toys that deal in retail sales for children/hobbyists have more than 90 regular customers outside the area, so there is a system of sales information needed to help the customer buy and receive the product information from the store. The web based retail information system aimed at helping the customer at Nancy toys can make online purchases without having to visit the store directly. In addition, this information system can be used as a platform for the promotion of child toy products/hobbies that can be easily transmitted directly. In this study using a qualitative type of descriptive research using the data-collection methods of observation, interviews and library studies. The purpose of this study is to design a system that can improve quality and bookmaking services as an effort to support the online business process. The design of the system USES PHP programming language, and mysql for its database and for testing it USES the black box methods. From this study results in a bookmaking system application, and it can be summarized to design and build a web-based bookmaking service system that can help Nancy toys to improve the quality of service and business from the effort.
Extended Time in the Digital Environment

Several aspects of human life have been extended through the digital environment: programs and apps help to expand cognitive capacity of human brain, drives and memories of ICT devices as well as cloud services provide an external memory storage for human remembrance, portable devices serve as multiple communication supply including text, voice, and video channels, social networking sites offer extensions for social life – and this development is in continual expanse. Nevertheless, time is also involved in this process. On the one hand, the relativity of time is obvious since the technological development transformed not only the sense of time but the culturally or traditionally determined frames of time itself. On the other hand, extended parts of human life can have an independent existence: regardless of time, regardless of human life they can subsist long after the death of the person. In other words, digital environment provides the possibility to extend the time for humanity. Is this extension a device for eternal life, or at least for a longer lifespan? Will this extension transform our thinking about time – or about our life?
The Relationship Of Metacognitive Awareness And Family Support To Online Learning Outcomes Students Of Senior High School

&lt;p&gt;Many factors are related to student learning outcomes in bold learning, including metacognitive awareness and family support. This study is a correlational study that aims to determine the relationship between metacognitive awareness and family support on the learning outcomes of students of class XI MIA (Mathematics and Natural Sciences) SMA Negeri 3 Martapura on the digestive system material in 2021/2022. 73 samples of this study were determined randomly. Questionnaires were given to determine metacognitive and family relationships and were given multiple choices to determine learning outcomes. The results showed that there was a positive but low correlation (r = 0.228) between metacognitive awareness and courageous learning outcomes. There is a negative correlation (r = -0.053) between family support and courageous learning outcomes (Y = 12.917+0.152 X1 - 0.108 X2) which indicates a low relationship between metacognitive awareness and family support on courageous learning outcomes. The implication of this research is that metacognitive awareness and family support are not closely related to learning outcomes and there are many other factors that have a closer relationship.&lt;/p&gt;
An approach for Preserving Privacy in Public Auditing of Cloud data

Abstract: Cloud users can remotely store their data and appreciate the on demand high quality applications and services from a shared pool of configurable computing resources, regardless of local data storage and maintenance. However, the fact that users no longer have physical possession of the out sourced data makes the data integrity protection in Cloud computing a formidable task, especially for users with constrained computing resources. This paper study the problem of ensuring the integrity of data storage in Cloud Computing. In particular, wet ask of allow in gathird party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic datastored in the cloud. To securely introduce an effective third-party auditor (TPA), and determine various techniques and algorithms for strengthen the Security. Keywords: TPA, Cloud Computing, CSP, RSA
Evaluation-oriented Knowledge Distillation for Deep Face Recognition

Knowledge distillation (KD) is a widely-used technique that utilizes large networks to improve the performance of compact models. Previous KD approaches usually aim to guide the student to mimic the teacher's behavior completely in the representation space. However, such one-to-one corresponding constraints may lead to inflexible knowledge transfer from the teacher to the student, especially those with low model capacities. Inspired by the ultimate goal of KD methods, we propose a novel Evaluation oriented KD method (EKD) for deep face recognition to directly reduce the performance gap between the teacher and student models during training. Specifically, we adopt the commonly used evaluation metrics in face recognition, i.e., False Positive Rate (FPR) and True Positive Rate (TPR) as the performance indicator. According to the evaluation protocol, the critical pair relations that cause the TPR and FPR difference between the teacher and student models are selected. Then, the critical relations in the student are constrained to approximate the corresponding ones in the teacher by a novel rank-based loss function, giving more flexibility to the student with low capacity. Extensive experimental results on popular benchmarks demonstrate the superiority of our EKD over state-of-the-art competitors.
Pedagogical and self-reflecting approach to improving the learning within a cyber exercise

: In the digitalized world, there is a growing need not only to improve one’s cybersecurity skills and knowledge, but also to find ways to optimize the learning process, for example by motivating the learners or optimising the learning facilities, material and the learners for the process. Cyber exercises ran within cyber ranges/arenas (CR) are an efficient way for the exercise participants to improve their cybersecurity skills and knowledge level. The pedagogical way of orienteering the participant to a learning situation is to have a preliminary survey, which prepares the participant for the upcoming event, adds self-reflection, and may even provide feedback and background information for the educator about the upcoming event. The objective of the survey is to improve the quality of the exercise by knowing the interest areas, preferences and other useful information about the participants that is then be used optimise the exercise accordingly. &#x0D; This study analyses the structure of one preliminary survey targeted for the cyber exercise event to be held in January 2022. The questions are justified according to existing frameworks. We have collected a set of structured questions presenting different topics related to the participants’ professional background and expectations towards the exercise. In addition to the short-term goal of analysing the survey for one cyber exercise, this work benefits the long-term goal for improving the skills of cybersecurity professionals. Our further work will validate the results of our preliminary analysis and analyse its correspondence with the survey results, and the final analysis constructed after the cyber exercise.
Supervised Learning: A First Approach

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming

We consider statistical inference of equality-constrained stochastic nonlinear optimization problems. We develop a fully online stochastic sequential quadratic programming (StoSQP) method to solve the problems, which can be regarded as applying Newton's method to the first-order optimality conditions (i.e., the KKT conditions). Motivated by recent designs of numerical second-order methods, we allow StoSQP to adaptively select any random stepsize $\bar{\alpha}_t$, as long as $\beta_t\leq \bar{\alpha}_t \leq \beta_t+\chi_t$, for some control sequences $\beta_t$ and $\chi_t=o(\beta_t)$. To reduce the dominant computational cost of second-order methods, we additionally allow StoSQP to inexactly solve quadratic programs via efficient randomized iterative solvers that utilize sketching techniques. Notably, we do not require the approximation error to diminish as iteration proceeds. For the developed method, we show that under mild assumptions (i) computationally, it can take at most $O(1/\epsilon^4)$ iterations (same as samples) to attain $\epsilon$-stationarity; (ii) statistically, its primal-dual sequence $1/\sqrt{\beta_t}\cdot (x_t - x^\star, \lambda_t - \lambda^\star)$ converges to a mean-zero Gaussian distribution with a nontrivial covariance matrix depending on the underlying sketching distribution. Additionally, we establish the almost-sure convergence rate of the iterate $(x_t, \lambda_t)$ along with the Berry-Esseen bound; the latter quantitatively measures the convergence rate of the distribution function. We analyze a plug-in limiting covariance matrix estimator, and demonstrate the performance of the method both on benchmark nonlinear problems in CUTEst test set and on linearly/nonlinearly constrained regression problems.
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles

To promote new scientific discoveries from complex data sets, feature importance inference has been a long-standing statistical problem. Instead of testing for parameters that are only interpretable for specific models, there has been increasing interest in model-agnostic methods, often in the form of feature occlusion or leave-one-covariate-out (LOCO) inference. Existing approaches often make distributional assumptions, which can be difficult to verify in practice, or require model refitting and data splitting, which are computationally intensive and lead to losses in power. In this work, we develop a novel, mostly model-agnostic and distribution-free inference framework for feature importance that is computationally efficient and statistically powerful. Our approach is fast as we avoid model refitting by leveraging a form of random observation and feature subsampling called minipatch ensembles; this approach also improves statistical power by avoiding data splitting. Our framework can be applied on tabular data and with any machine learning algorithm, together with minipatch ensembles, for regression and classification tasks. Despite the dependencies induced by using minipatch ensembles, we show that our approach provides asymptotic coverage for the feature importance score of any model under mild assumptions. Finally, our same procedure can also be leveraged to provide valid confidence intervals for predictions, hence providing fast, simultaneous quantification of the uncertainty of both predictions and feature importance. We validate our intervals on a series of synthetic and real data examples, including non-linear settings, showing that our approach detects the correct important features and exhibits many computational and statistical advantages over existing methods.
Audio to Indian Sign Language Translator

Abstract: This project's primary purpose is to bridge the gap between deaf and hearing persons, which will benefit those with hearing impairments who employ a simple and effective way of sign language. Sign language is a visual language used by the deaf community. It employs body language, hand gestures, and facial expressions. Indian Sign Language is one of the most significant and commonly utilised modes of communication for individuals with speech and hearing difficulties. This web application facilitates communication for deaf and speech-impaired individuals. The primary focus of these new web application and natural language processing technologies is the conversion of spoken or written language into sign language. In this web application, users can record their speech using a microphone or text as input utilising NLP-based speech recognition. If the video is missing from the database, the word is spit out and the associated video is displayed. This technique has made communicating with deaf individuals simple and practical. Keywords: NLP, Speech to text, sign language translation.
Surface Analysis with Vision Transformers

The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as the generalisation of convolutions to irregular surfaces is non-trivial. Recent state-of-the-art performance of Vision Transformers (ViTs) demonstrates that a general-purpose architecture, which implements self-attention, could replace the local feature learning operations of CNNs. Motivated by the success of attention-modelling in computer vision, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence problem and propose a patching mechanism for surface meshes. We validate the performance of the proposed Surface Vision Transformer (SiT) on two brain age prediction tasks in the developing Human Connectome Project (dHCP) dataset and investigate the impact of pre-training on model performance. Experiments show that the SiT outperforms many surface CNNs, while indicating some evidence of general transformation invariance. Code available at https://github.com/metrics-lab/surface-vision-transformers
Decentralized Computing Architecture for Decentralized and Unstructured Data Recommendation Options

The convergence of new technologies based on Electrical and Electronic Engineering, such as the use of sensors as nodes in the internet of things, the distribution of processing systems in networks, and cloud computing, among others, has resulted in the generation of a volume of data that, in the last two years, has accounted for nearly all of humanity's data. In a cloud environment, data storage and processing might have certain inherent constraints, such as high latency and poor availability events. This paper proposes using Enterprise Service Bus (ESB) as a fog computing application, with the goal of enhancing availability and lowering latency.
Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation

Despite their high computation and communication costs, Newton-type methods remain an appealing option for distributed training due to their robustness against ill-conditioned convex problems. In this work, we study ommunication compression and aggregation mechanisms for curvature information in order to reduce these costs while preserving theoretically superior local convergence guarantees. We prove that the recently developed class of three point compressors (3PC) of Richtarik et al. [2022] for gradient communication can be generalized to Hessian communication as well. This result opens up a wide variety of communication strategies, such as contractive compression} and lazy aggregation, available to our disposal to compress prohibitively costly curvature information. Moreover, we discovered several new 3PC mechanisms, such as adaptive thresholding and Bernoulli aggregation, which require reduced communication and occasional Hessian computations. Furthermore, we extend and analyze our approach to bidirectional communication compression and partial device participation setups to cater to the practical considerations of applications in federated learning. For all our methods, we derive fast condition-number-independent local linear and/or superlinear convergence rates. Finally, with extensive numerical evaluations on convex optimization problems, we illustrate that our designed schemes achieve state-of-the-art communication complexity compared to several key baselines using second-order information.
APPROACHES TO MANAGEMENT PROVISION EDUCATIONAL PROCESS

The article deals with the substantiation of the structure of integrative-cultural approach.
Enhancement of Super Sort Sorting Algorithm Using Identical Value Search Concept

The Super Sort Sorting algorithm is a sorting algorithm that uses forward selection, backward selection, merging, and partition to sort a given list or data set.This study will focus on the enhancement of the Super Sort Sorting Algorithm on the forward selection part.This paper introduces an enhanced version of the algorithm where the concept of Identical Value Search was implemented on the first forward pass.This method eliminated the small sublists left behind by the original algorithm's forward pass and reduced the overall runtime of the algorithm.
FORMATION OF STUDENTS' READINESS FOR SELF-MANAGEMENT AS A CURRENT PEDAGOGICAL PROBLEM

The article is devoted to revealing of objective and subjective factors of forming of social-communicative activity of teenagers personality in process of touristik activit
Virtual affective consciousness and raw social AI

In this paper, we attempt to philosophically eliminate the conceptual problems in developing affective artificial agents, and then put forward an industry and research direction called Raw Social AI. We first provide a set of laws for affective concepts understanding and then suggest building unconscious concepts with uncertainty. We address the problem of qualia supply and discuss how to implement the initiative of affective agents. We suggest that initiative be the cue of the utility of affective agents. We advocate that the initiative of affective agents must be realized as social service. Raw Social AI is about using AI to record, model, and predict people's emotional reactions in the real or virtual world. It is necessary for us to establish the infrastructure of Raw Social AI.
Computer-Assisted Language Learning Tools for Punctuation in Dyslexia: Development and Evaluation

The purpose of this paper is to discuss tools relevant to dyslexic students and language teachers with a focus on punctuation. First, the paper briefly discusses existing software in dyslexia. Following this short summary, the paper exemplifies the steps in Computer-Assisted Language Learning (CALL) development by presenting _PunkBuddy_, an original tool aiming at assisting students in post-primary education in Ireland with the use of punctuation. The tool development draws on Second Language Acquisition (SLA) theories and various available technologies, including Text-to-Speech (TTS) technologies, Natural Language Processing (NLP), and dialogue systems in the form of a chatbot. On the basis on this tool, the paper provides a set of desiderata for teachers and developers focusing on dyslexia and proposes a combination of relevant evaluation frameworks.
General Store Management System

Abstract: As the world is continuously advancing and software to automate everything is available already. General stores are a very basic need of every citizen as they provide a variety of services like stationary, grocery, daily necessities etc. So, an efficient way to manage and run a general store is very important. Also, the paper bills are not very handy and are not reliable as well as they degrade overtime, stock calculations get unmanageable and hard to keep records of, the retailer also faces hardships on employee tracking. Keywords: Software, automation, Service, Productivity, System.
Research at the central regional library: The history and the subject scope at the present stage

The purpose of this article is to identify new trends in the research activities of regional libraries of the Russian Federation. The authors analyze in detail the documents that provide the legislative and regulatory framework for research activities of libraries. The authors emphasize that the well-defined legal status of central scientific libraries and their mandate are to support clear understanding of the concept of their research activities. Currently, the research activities are actualized in diverse formats. Library scientists have not achieved the uniform understanding of the object of these activities. Using the example of the State Universal Scientific Library of the Krasnoyarsk Kray [Territory], the content and main directions of modern scientific library practice are characterized; the number of general research patterns inherent to larger Russian libraries are revealed; and features related to regional specifics are identified. The authors suggest what the main motivations in choosing the thematic focus of library research are. At the present stage, these are: the national project “Culture” and the issues related to COVID-19 pandemic situation. Based on the analysis of various approaches to understand the research activities of libraries, the authors conclude that there is a need for comprehensive revision of content and formats of scientific activities of central regional libraries.
Development of higher education final judgment management information system using user-centered design method

Final Judgment is a process of consideration to declare a student has met the academic and administrative requirements to hold a scholarship academic degree from the college. The Final Judgment management process business in universities involves many processes and stakeholders. Based on the result of observations and interviews, it is known that Final Judgment Management at the Informatics Faculty of ITTP is currently done manually by filling out the registration form and takes a long time for its processing. This study aims to develop Information Systems using User-Centered Design (UCD) methods that can meet the users' needs in implementing and managing Final Judgment processes. The developed system was tested for usability using a questionnaire to users with the Nielsen Attribute Usability (NAU) method. The method consists of five criteria, they are Learnability, Memorability, Efficiency, Errors, and Satisfaction. Usability test results showed: Learnability rate of 3.60, Memorability of 3.35, Efficiency of 3.90, Errors of 3.12, and Satisfaction of 3.63. Therefore, the average usability level of the Final Judgment system is 3.52. The user agrees that the developed Final Judgment information system has met the expected functional needs and is considered more effective and efficient.