Reviewerly/exHarmony huggingface dataset:
E-Learning Berbasis Web Pada Ma Negeri 2 Tanjung Jabung Timur

There are several problems that occur at MA Negeri 2 Tanjung Jabung Timur so far the teaching and learning process between teachers and students is very limited. So if there are obstacles, such as students who do not understand certain subject matter, it will be difficult if they want to repeat the material and ask the teacher concerned directly. The face-to-face learning process in the classroom sometimes makes students bored because they are too tired with the monotonous learning system and at times. Covid-19 pandemic students at MA Negeri 2 study at home by being given assignments by the teacher where the assignments are taken at school and then collected again with the time that has been given by the teacher for each subject which causes the teaching and learning process to be ineffective. This research is website-based e-learning. The purpose of this study was to analyze and design a website-based e-learning system at MA Negeri 2 Tanjung Jabung Timur. The system modeling tools used are UML (Unified Modeling Language) and Balsamiq Mockups 3 to design prototypes. Based on the results of research on the analysis and design of the learning system at MA Negeri 2 Tanjung Jabung Timur, it is currently still less effective and efficient because it uses conventional learning media, so a website-based e-learning system is needed that has the features needed to support the teaching and learning process without being constrained by space. and time. Suggestions for this research are expected that further research can design an e-learning system with achievement features.
Cloud data integrity using whirlpool algorithm

Most current security solutions depend on border security.In any case, Cloud processing breaks the association borders.At the point when information lives in the Cloud, they live outside the authoritative limits.This leads clients to a lot of command over their information and raises sensible security worries that hinder the reception of Cloud processing.Is the Cloud specialist organization getting to the information?Is it truly applying the entrance control approach characterized by the client?This paper presents an information driven access control arrangement with improved job-based expressiveness in which security is centered around ensuring client information in any case the Cloud specialist organization that holds it.Novel personality based and intermediary re-encryption systems are utilized to secure the approval model.Information is scrambled and approval rules are cryptographically ensured to safeguard client information against the specialist co-op access or trouble making.The approval model furnishes high expressiveness with job progressive system and asset chain of command support.The arrangement exploits the rationale formalism gave by Semantic Web advances, which empowers propelled rule the executives like semantic clash discovery.A proof of idea usage has been created and a working prototypical sending of the proposition has been incorporated inside Google administrations.
Comparing Anomaly Detection and Classification Algorithms: A Case Study in Two Domains

Utilizing large data sets in practical scenarios usually requires identifying, annotating and classifying rare events or anomalies. Although several methods exists, there are two classes of algorithms: anomaly detection algorithms and classification algorithms. Both types of algorithms have different characteristics and in this paper, we set out to compare them on two cases. We use data from a neurointensive care unit and from microwave radio transmissions. We apply Isolation Forest and Random Forest algorithms to find events in the data that occur with a frequency of ca. 1%. The results show that classification algorithms (Random Forest) perform better and can achieve up to 100% accuracy, while the anomaly detection algorithms (Isolation Forest) can achieve only 73% at best. Based on the results, we conclude that it is better to invest in annotating data á priori and use classification algorithms, despite the lower costs of using the anomaly detection algorithms.
A Framework for Math Word Problem Solving Based on Pre-training Models and Spatial Optimization Strategies

Automatic Math Word Problem (MWP) solving plays an important role in AI-tutoring, which aims to generate corresponding math expressions and results from a series of MWP. For the applicability of the MWP solving model, two aspects are considered to be optimized. Firstly, to address the weak linguistic representation of RNN which leads to the poor accuracy of MWP solution models, we propose to use Bidirectional Encoder Representation from Transformers (BERT) as an encoder and combine it with Transformer decoder to form a model framework. It is about 8% higher on the dataset Math23K compared to GTS, reaching 82.6%. However, pre-trained models tend to be large in size, which is not conducive to the deployment on the web server. A knowledge distillation strategy integrating teacher model’s evaluation is proposed. By enabling a model to patiently learn from and imitate the teacher through multi-layer distillation, the above BERT based model is compressed into a shallow structured student model. It achieves accuracy of 76.3% on Math23K, while the model weighs only 0.61 times that of the teacher’s model, and improves the prediction speed by 1.71 times.
PCA based email spam detection utilizing machine learning approaches

Email is perhaps the most secure mechanism for online correspondence and moving information or messages through the web.A congesting expansion in notoriety, the quantity of spontaneous information has likewise expanded quickly.To sifting information, various methodologies exist which naturally identify and eliminate these unsound messages.Spam messages impact the associations monetarily as well as bother the individual email client.Presently a day's Machine learning frameworks are utilized to thus channel the spam email in an extraordinarily powerful rate.The reason for this investigation is to recognize significant diminished information highlights in building IDS that is computationally proficient and viable.In this paper, we propose a structure for highlight decrease utilizing head part investigation (PCA) to wipe out unessential highlights, choosing pertinent and nonrelated highlights without influencing the data contained in the first information and afterward utilizing choice tree and SVM calculations to group email information.The point is to lessen a few highlights of information by PCA and afterward assemble a forecast of characterization model by choice tree and SVM to acquire pertinent highlights and to improve the precision of choice tree and SVM.Observational outcomes show that chose decreased traits give better execution to configuration spam mail discovery framework that is productive and powerful for Email spam sifting.
Multi authority access control mechanism for secure cloud storage

Cloud stockpiling encourages the two people and ventures to cost adequately share their information over the Internet.In any case, this furthermore carries irksome challenges to the passageway control of shared data since relatively few cloud servers can be totally trusted.Right now, present check and monetarily wise property-based data find a good pace conveyed capacity structures.Specifically, we manufacture a multiauthority CP-ABE plot that features: 1) the system needn't waste time with a totally trusted in central force, and all property pros self-ruling issue puzzle keys for customers; 2) every trademark authority can continuously oust any customer from its space with the ultimate objective that those revoked customers can't find a good pace way redistributed data; 3) cloud servers can invigorate the mixed data from the present timespan to the accompanying one to such a degree, that the denied customers can't find a good pace available data; and 4) the update of secret keys and ciphertext is acted in an open manner.
Making the library an attractive place for learning to patrons

A complete reputable library must be able to house all the needed resources and materials for its patrons to consume.An attractive library meets all the needs of patrons.A library is a curated collection of sources of information and similar resources, selected by experts and made accessible to a defined community for reference or borrowing.It provides physical or digital access to material, and may be a physical location or a virtual space, or both.A lot needs to be done to make the library a more attractive place of learning.The library needs to be creative in bringing on board innovative activities such as open space reading environment, game centers, cafes, etc. Library staff should be moving around the various desks to assist staff to locate library materials.Student s and patrons alike are facing a lot of issues that need to be addressed by the library.Patrons should also be allowed to use mobile phones and other electronic gadgets which will be helpful to them in the library.
INCREASING STUDENTS’ VOCABULARY USING JUMBLED SENTENCES: ACTION STUDY RESEARCH

This study aims to increase students' English vocabulary by using Jumbled Sentences in a small classroom. The subjects of this study were composed of seven students. This study used Classroom Action Research (CAR) as the method and was conducted by collaboration between teacher and researcher. The data were gathered through observation, interviews, field notes, and tests. The result of the study showed there was an improvement in student abilities. Student scores have increased significantly. In the first cycle post-test, only one out of seven students got good grades, with the Kriteria Ketuntasan Minimum (KKM) of English lessons being (75). Meanwhile, the student's scores in the second cycle post-test showed that seven of seven students scored above 75. In addition, there was a positive response from the English teacher and students about this study. In conclusion, jumbled sentences could increase students' abilities, especially in their vocabulary
Single-active-element demultiplexed multi-photon source

Temporal-to-spatial mode demultiplexing routes non-simultaneous events of the same spatial mode to distinct output trajectories. This technique has now been widely adopted, because it gives access to higher-number multi-photon states when exploiting solid-state quantum emitters. However, implementations so far have required an always-increasing number of active elements and are therefore rapidly facing resource constraints. Here, we propose and demonstrate a demultiplexing approach that uses only a single active element for routing to, in principle, an arbitrary number of outputs. We employ our device in combination with a high-efficiency single-photon source based on a quantum dot, and measure up to eight demultiplexed highly indistinguishable single photons. We discuss the practical limitations of our approach, and describe in which conditions it can be used to demultiplex, e.g., tens of outputs. Our scheme thus provides a path for the preparation of resource-efficient larger-scale multi-photon sources.
Virtual Try-On System using Machine Learning

Abstract: This paper proposes a virtual try-on system forapparel shopping that generates high-resolution virtualization without pixel disruption. The system employs a Parser Free Appearance Flow Network, which simultaneously warps clothes and generates segmentation while exchanging information. The proposed methodologyoutperforms existing virtual fitting methods at 192 x 256 resolution, as demonstrated by the Fréchet inception distance (FID) performance metric. The system's technical specifications, software and hardware requirements, and user interface design are presented in detail. The proposed methodology achieves realistic try-on images and high- quality virtual try-on images in real-time. Future research areas include the incorporation of personalized measurements and the development of more accurate bodymodels.
Copyright Protection System Based Watermarking

The aim of this paper is to introduce the notion of nano -operators in term of the class of nano -open sets such as nano -interior and nano -closure with their properties in nano topological spaces. After that, by using the class of nano -open sets, we introduce the concept of nano -continuity. Also, we study the relationship among some types of nano continuous functions in nano topological spaces
Unmanned vending counter abnormal behavior recognition based on YOLOv5

In the open-door unmanned vending machine monitoring scenario, a detection scheme for multiple states of user's hand is designed to analyze the abnormal behavior during the user's shopping process, and a hand multi-state detection algorithm based on YOLOv5 is proposed.To improve the inference speed of the algorithm, the 3×3 convolution in YOLOv5 is replaced with RepVGG by using the idea of structural re-referencing.The accuracy of the algorithm is improved by adding CBAM attention mechanism.The model size is greatly reduced while ensuring the model recognition accuracy.The experimental results show that the algorithm in this paper can accurately identify the hand state of the user when shopping and has some practical application value.
Research on the Multidimensional Dilemma and Governance Strategies of Teaching Management in Colleges and Universities

Teaching management is the main content of comprehensively deepening the reform of higher education, and it is the guarantee for the realization of teaching tasks and teaching goals.At present, there are three major difficulties in the teaching management of colleges and universities in our country: insufficient credit system supporting planning, unbalanced status of scientific research and teaching, and insufficient humanistic care.In response to these difficulties, we need to continuously improve teaching management policies, improve credit-based teaching, coordinate the status of teaching and scientific research, promote the benign interaction between teaching, learning and management, provide new support conditions for teaching management reform, and improve colleges and universities.Teaching quality and teaching level.
An experimental approach for prediction of multi-classification using SVM

The multiclass classification problem is an important topic in the field of pattern recognition.It involves the task of classifying input instances into one of multiple classes.Since the class overlapping problem exists among multiple classes in most real-world problems, the multiclass classification task is much more complicated and challenging compared to the binary class problem.Classification involves the learning of the mapping function that associates input samples to corresponding target label.There are two major categories of classification problems: Single-label classification and multi-label classification.Traditional binary and multi-class classifications are subcategories of single-label classification.The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems.The results obtained are compared with state-of-the-art techniques from each of the classification types.
Toward Smart Organization: The Metaverse as breakthrough for Learning Organization

The latest buzzword, "metaverse," has gotten a lot of interest from both business and academics. The metaverse seamlessly merges the physical and digital worlds, enabling avatars to engage in a wide range of activities like trading, social networking, entertainment, and production. By exploring the metaverse, it is thus possible to create an engaging digital environment and improve the organizational efficiency. This study was aimed to empirically examine the metaverse and its impact on smart organizations with mediating role of learning organization. The study employed quantitative approach with convenient random sampling. Data collected from management department of healthcare sector including public and private hospitals in Dubai UAE. 200 valid respondent’s data used to assess the hypothesized model. the model containing four hypothesis. The findings suggested a positive association of metaverse on smart organization, while the indirect effect of metaverse and smart organization depicted as positive significant that denotes a partial mediation of learning organization between the relationship of metaverse and smart organization. As a result, metaverse technology has the potential to revolutionize the healthcare sector by improving patient care, enhancing medical training and education, and streamlining healthcare operations.
Aviation Network Security Situation Awareness Based on Game Theory

As aviation industry has progressed in recent years, the entire industry has become more and more relying on digital network connectivity and electronic data exchange. While these changes bring efficiency, they also provide opportunities for hackers and malware to threaten flight safety. For example, in 2022, the Canadian airline Sunwing experienced flight delays due to cyber attacks. In many cases, if proper defense strategies are used early, the damage caused by the threat can be greatly reduced. However, due to dynamic, heterogeneous and huge structural characteristics of aviation systems, managers are unable to assess the current system security status in a real-time and accurate way, which affects the decision-making process and leads to expose the whole system under the risk. Therefore, aviation networks need a situational awareness mechanism to help managers adjust their strategies and limit the impact of threats in a timely and accurate manner. There has been a lot of current research in network security situational awareness, but there are some problems when these studies are directly applied to aviation networks. In order to establish a situation awareness mechanism applicable to aviation networks, this paper proposes a situation awareness method based on game theory, particle swarm optimization algorithm and neural network by analyzing the strategy gain of attackers and defenders under network attack and defense, particle swarm optimization algorithm and long short-term memory networks. The innovative works of this paper are as follows. Firstly, the situation assessment model is established based on the classification of attackers; secondly, the method of situation prediction with improved particle swarm optimization algorithm and long short-term memory networks is proposed. Simulation experiment shows that the method in this paper has the advantages of high evaluation efficiency and low error rate of prediction results, which can reason about the attacker class and help managers to make reasonable decisions.
Towards a Large-Scale Empirical Study of Python Static Type Annotations

Python, as one of the most popular and important programming languages in the era of data science, has recently introduced a syntax for static type annotations with PEP 484, to improve code maintainability, quality, and readability. However, it is still unknown whether and how static type annotations are used in practical Python projects.This paper presents, to the best of our knowledge, the first and most comprehensive empirical study on the defects, evolution and rectification of static type annotations in Python projects. We first designed and implemented a software prototype dubbed PYSCAN, then used it to scan notable Python projects with diverse domains and sizes and type annotation manners, which add up to 19,478,428 lines of Python code. The empirical results provide interesting findings and insights, such as: 1) we proposed a taxonomy of Python type annotation-related defects, by classifying defects into four categories; 2) we investigated the evolution of type annotation-related defects; and 3) we proposed automatic defect rectification strategies, generating rectification suggestions for 82 out of 110 (74.55%) defects successfully. We suggest that: 1) Python language designers should clarify the type annotation specification; 2) checking tool builders should improve their tools to suppress false positives; and 3) Python developers should integrate such checking tools into their development workflow to catch type annotation-related defects at an early development stage.We have reported our findings and suggestions to Python language designers, checking tool builders, and Python developers. They have acknowledged us and taken actions based on our suggestions. We believe these guidelines would improve static type annotation practices and benefit the Python ecosystem in general.
Automated Image Reduction for Explaining Black-box Classifiers

Due to the prevalent application of machine learning (ML) techniques and the intrinsic black-box nature of ML models, the need for good explanations that are sufficient and necessary towards a model’s prediction has been well recognized and emphasized. Existing explanation approaches, however, favor either the sufficiency or necessity. To fill this gap, we present DDImage, a technique and tool that automatically produces explanations preserving dual properties for ML-based image classifiers. The core idea behind DDImage is to discover an appropriate explanation by debugging the given input image via a series of image reductions, with respect to the sufficiency and necessity properties. We conduct comprehensive experiments to compare our approach against two state-of-the-art approaches, BayLIME and SEDC, on widely-used models and datasets. The results show that our approach outperforms the other methods in producing minimal explanations preserving both sufficiency and necessity, and it matches or exceeds the other methods in terms of stability.
Proposed Convolution Architecture for Monkeypox Detection using Dermoscopy Images

Monkeypox is a skin disease that spreadsfrom animals to people and then people to people, the class of the monkeypox is zoonotic and its genus are othopoxvirus. There is no special treatment for monkeypox but the monkeypox and smallpox symptoms are almost similar, so the antiviral drug developed for prevent from smallpox virus may be used for monkeypox Infected person, the Prevention of monkeypox is just like COVID-19 proper hand wash, Smallpox vaccine, keep away from infected person, used PPE kits. In this paper Deep learning is use for detection of monkeypox with the help of CNN model, The Original Images contains a total number of 228 images, 102 belongs to the Monkeypox class and the remaining 126 represents the normal. But in deep learning greater amount of data required, data augmentation is also applied on it after this the total number of images are 3192. A variety of optimizers have been used to find out the best result in this paper, a comparison is usedbased on Loss, Accuracy, AUC, F1 score, Validation loss, Validation accuracy, validation AUC, Validation F1 score of each optimizer. after comparing alloptimizer, the Adam optimizer gives the best result its total testing accuracy is 92.21%, total number of epochs used for testing is 100. With the help of deep learning model Doctors are easily detect the monkeypox virus with the single image of infected person.
Classification of Traffic in 5G Internet of Things Networks: A New Framework

The organization formed by the connection established between computers, typically by cable, for the purpose of communicating and transmitting data is known as a network. A computer network is a collection of interconnected computers that allow for the sharing of resources including data, programs, and files. When people think of computer networks, they think of the Internet. In this paper, we proposed the usage of a new technique for the categorization of computer network traffic that is based on deep sparse autoencoders and k-nearest-neighbor (KNN) that has been optimized with Grid Search. The autoencoders took the input data and extracted high-level characteristics from it, then connected those features to the KNN. The KNN was used to divide the characteristics into three distinct kinds of assaults (normal and abnormal). In comparison to other investigations, the proposed approach demonstrated an accuracy of 98.23% in its results.