Reviewerly/exHarmony huggingface dataset:
Sentiment Analyzing from Tweet Data’s Using Bag of Words and Word2Vec

Twitter sentiment classification is an artificial approach for examining textual information and figuring out what people's publicly tweets from a variety of industries are experiencing or thinking. For instance, a large number of tweets containing hashtags are posted online every minute from one user to some other user in the commercial and politics fields. It can be challenging for scientists to correctly comprehend the context in which specific tweet terms are used, necessitating a challenge in determining what is actually a positive or negative comment from the vast database of twitter data. The system's authenticity is violated by this issue and user dependability may be significantly diminished. In this study, twitter data sent to interpret movies were classified using various classifier and feature methods. In this context, the IMDB database consisting of 50000 movie reviews was used. For the purpose of anticipating the sentimental tweets for categorization, a huge proportion of twitter data is analyzed. In the proposed method, bag of words and word2vec methods are given by combining them instead of giving them separately to the classifier. With both the suggested technique, the system's effectiveness is increased and the data that are empirically obtained from the real world situation may be distinguished well. With experimental efficiency of 90%, the suggested approach algorithms' output attempts to assess the reviews tweets as well as be able to recognize movie reviews.
Risks to the Public

ChatBots and Generative AI more generally have really opened up a deluge of RISKS items in the past few months, along with an increased awareness of risks relating to arti - cial intelligence. This issue includes just the tip of the iceberg with one-liners on that subject, where it is almost impossible to summarize all the risks. PGN
Improved elephant herding optimization algorithm based on sine cosine search

Inspired by the renewal process of elephant clans, elephant herding optimization (EHO) is proposed and successfully applied to many optimization problems. However, EHO is prone to fall into local optimum during the optimization process, resulting in poor global search ability. To overcome this deficiency, an improved elephant herding optimization (SCEHO) is proposed, which integrate good point set strategy, improved sine cosine search strategy, nonuniform gauss mutation strategy and greedy strategy. First, good point set is introduced to initialize the population. Secondly, sine cosine algorithm is improved and incorporated into EHO to update the position of individuals. Then, the separation operator is improved from two aspects, on the one hand, non-uniform Gauss mutation is introduced to separate individuals. On the other hand, the number of separated individuals is increased, and half of the individuals with poor fitness are selected. Finally, the greedy strategy is introduced into SCEHO to update population according to the fitness. Our SCEHO is tested on 10 benchmark functions from CEC 2019 and the results imply the superiority of SCEHO to other algorithms. SCEHO is also applied to solve WSN coverage optimization problem. The results show that coverage model based on SCEHO has higher coverage and more uniform node distribution.
Internet Computer Protocol: democratic evolution of a web3 platform

Recent technological advances have enabled the efficient execution of decentralized web3 applications and smart contracts. The Internet Computer Protocol (ICP) is a fast and efficient decentralized blockchain-based system for the execution of general-purpose applications in the form of smart contracts. In particular, the ICPs execution, governance and evolution are controlled by different parties in a trustless and fault-tolerant manner instead of a central entity. In this talk, I will give an overview of the ICP, followed by a discussion of the challenges the IC faces to facilitate upgrading itself through voting by ICP token holders and present our approach to tackle them.
Vision-based Cooperative Moving Path Following for Fixed-Wing UAVs

This paper addresses the problem of collaborative ground target tracking by a group of fixed-wing Unmanned Aerial Vehicles (UAVs) using vision in the loop. The UAVs adopt a circular path formation centered at the target’s coordinates and move together with it, using the Moving Path Following (MPF) method. A distributed control architecture is implemented, where each vehicle obtains the telemetry data from the preceding vehicle, through a chained communication network. A computer vision system based on machine learning techniques is proposed to close the cooperative MPF control loop. The obtained results show the efficiency of the proposed control system in realistic software-in-the-loop simulations.
Deepfake Creation Using Gans and Autoencoder and Deepfake detection

Deep learning techniques is useful in a myriad of applications such as computer vision processing, natural language processing, as well as deepfake detection. The development of deep learning algorithms for imaging detection has resulted in the development of deepfakes. These fakes employ advanced algorithms for deep learning to generate fake images that are extremely difficult to differentiate from authentic images. In a fake video, the face, expression or speech is replaced with an image of another's face, with a distinct speech or emotion with the help of the technology of deep learning. These videos are typically so sophisticated that the traces of manipulation are hard to spot. Social media are among the most frequently targeted and most serious because they are vulnerable platforms that are susceptible to blackmailing or making a person look bad. There are several existing efforts to detect fake images, but there have been very few efforts developed for video content on social media.
Secure Multi-Party Computation with Robust Reversible Watermarking in Encrypted Images Using AES Cryptosystem

Reversible watermarking research has increasingly concentrated on enhancing its robustness to resist attacks during digital media transmission because of the increasing development of network media. In reversible watermarking, the important components are the watermark's durability and the cover image's ability to maintain secrecy. In this paper, a secure multi-party computation (MPC) with robust reversible watermarking in encrypted images is proposed. We can share any text information embedded in an image by encrypting the image with a secret key, and then only the authorized user can access the data. The Pixel Repetition Method is used to perform pre-processing on the image after it has been taken as input. Payload capacity, visual quality, and attack protection are increased by the LSB information hiding algorithm of images using a secret key. It combines information hiding and cryptography to increase security and identity authentication based on digital signature and encryption technology. Its premise is the receiver's extraction of concealed data using the reverse process of System. Finally, through the safety and PSNR (peak signal-to-noise ratio) comparison experiment, the enhanced LSB image steganography algorithm makes use of encryption technology and is better than the standard LSB image steganography method in terms of security and PSNR.
Designing of image enhancement technique for contrast and color improvement based on haze removal of underwater images

This work presents and analyses a revolutionary quality upgrade of an underwater image by haze removal and colour contrast enhancement utilizing a multi-image fusion based on fuzzy logic technique. Haze can lower vision and make extracting usable information from underwater photos challenging, which often degrades the images. Haze removal can enhance the visual quality of the photos and make additional analysis easier. Thus, it is recognized as a crucial and challenging task in the field of underwater image processing. In the presented approach first, white balancing and contrast enhancement is achieved parallelly using the Contrast Limited Adaptive histogram equalization (CLAHE) technique to generate two inputs from the given-colored image for further weight mapping and fusion. Weight mapping of Luminance Chromatic and Saliency components of the contrast-enhanced image and white balanced is achieved separately. In the second step fusion of all six components is done using Gaussian and Laplacian pyramids to produce high-quality haze-free underwater images. The success of our method in enhancing the contrast, color, and overall visual quality of underwater photos is shown by the experimental findings.
Visual Modeling Technology for Computer to Modal Numerical Simulation

Computer technology has brought tremendous changes to our lives. The introduction of visualization technology in the computer system can visually display the data analysis results. In this paper, a modal numerical simulation analysis system is built on the basis of visual modeling. The system is developed using visual modeling software UG. By studying the steps of structural modal analysis and the idea of modal decomposition, the system is applied to identify modal parameters in experiments, and the impulse response function of the system is calculated using natural excitation technology. Compared with the theoretical modal value, the results show that the difference between the theoretical value and the calculated value of the impulse response is very small, and the variation curve is highly coincident, which shows that the system in this paper is very effective in modal numerical simulation.
Research on ship detection technology based on improved YOLOv5

Ocean scene perception is the premise for unmanned ships to effectively complete all kinds of established tasks, and ship detection is the basic task of perception. Improving the accuracy of marine ship detection algorithms is of great importance to improve the working ability of unmanned ships. Due to the complexity of the marine environment, the data set that can be used to detect ships on the sea is small. On behalf of solving the mentioned problems, this paper suggests an algorithm based on YOLOv5 according to the characteristics of visible image ship detection in the unmanned ship perception system, optimizes the input end, loss function and detection box of the depth learning network model, and uses the migration learning strategy to train the network model. The experimental results manifest that the average precision (AP) of the algorithm for ship detection in the sea surface visible image reaches 98.6%, 1.69 percentage points higher than YOLOv5, and the average detection time per picture is about 45ms, which can meet the demands of ship detection in different situations.
Education of the Affective Ideal in Contexts of Cultural Diversity: BIBLIOMETRIC and Thematic Analysis (2006-2021)

Emotional education has grown exponentially in the last decade, although outlining an emotional regulation from parameters of majority cultural groups that leaves aside the socialization of emotion from an affective ideal- or the expected behaviour on the emotional plane that considers cultural differences and education minority group family members. This study is to analyse research about the affective ideal in contexts of cultural diversity to contribute to it understanding in education, through a review of documents published in the Web of Sciences, Scopus, and APA PsycNet databases, in the diachronic period 2006-2021. The need for an emotional education that recognizes the affective ideal of the cultures shared in a territory is discussed. It is concluded that there is an incipient investigation of cultural mediatization on the affective ideal in contexts of sociocultural diversity, and little research in the educational plane. This should be preferred in Latin America, marked by models of monocultural emotional education models, which in culturally diverse educational settings, translates into discrimination and racism.
A Lightweight CP-ABE Scheme with Direct Attribute Revocation for Vehicular Ad Hoc Network

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) technology provides a new solution to address the security and fine-grained access control of traffic information in vehicular ad hoc networks (VANETs). However, in most CP-ABE schemes for VANETs, attribute revocation suffers from high system consumption and complex revocation operations, as well as from high computational overhead and low efficiency due to the use of bilinear pairwise operations. Based on this, this paper proposes a lightweight CP-ABE scheme that supports direct attribute revocation in VANETs. The scheme implements an agent-based direct attribute revocation mechanism by separating dynamic and static attributes of vehicle terminals, which reduces system consumption and simplifies the revocation operation process. The scheme uses scalar multiplication on elliptic curves instead of bilinear pairing operations and uses computational outsourcing techniques to reduce the terminal decryption cost and improve the efficiency of the scheme. The security and performance analysis shows that the overall efficiency of our scheme is better than the existing schemes under the premise of ensuring data confidentiality and integrity.
Strategies and Efforts to Increase Children's Interest in Reading in Gampong Mamplam

The purpose of this study is to find alternatives to increase interest in reading among children, as an effort to overcome low interest in reading. Good reading interest is an indicator of literacy progress as well as an indicator of the nation's progress. The steps taken in this service are by having a mini library filled with interesting story books where every 5 days a week before starting the activity there is a reading together. This strategy is created and implemented by students with the aim of increasing reading interest in children in Mamplam village, with frequent reading every day makes children accustomed to reading activities. These strategies and efforts show the result that having a mini library is able to develop an interest in reading in children in Gampong Mamplam, which becomes a change in the children and makes them more enthusiastic about reading.
USER USER EXPERIENCE TEST MARKETPLACE E-COMMERCE KOPI DENGAN MENGGUNAKAN ISO METRICS PADA UMKM DI ACEH TENGAH

E-commerce sites have become popular for sellers in promoting their business during the covid-19 pandemic. One of the e-commerce that is used in promoting business is shopee. . This e-commerce website is very often used by Indonesian people in selling goods. In order to make it easier for middle-range businesss to market their products on e-commerce shopee, this website needs to be tested by users to provide feedback on how the website feels after use. The purpose of this research is to test the user experience (UX) on e-commerce shopee as a seller. The UX test was carried out in a room set-up like in a laboratory with 10 users who have never used the Shopee website as sellers. During testing, users are given several tasks related to the website while the facilitator makes observations to analyze usability metrics. The data collected will reveal the weaknesses and strengths of the website. Usability score is measured based on ISO usability metrics including effectiveness, efficiency and satisfaction. The results of this UX test show that the website is quite user-friendly as evidenced by a usability score of around 84% or above average. It is expected that this research can be adopted by software engineers or usability testers to produce user-friendly e-commerce that makes users comfortable and easy to use the website.
PELATIHAN PEMBUATAN MEDIA PEMBELAJARAN BERBASIS E-LEARNING BAGI GURU SMA NEGARI SATU ATAP WASUR MERAUKE

The Educators are required to always improve their abilities so that they are more competent in terms of mastery communication and information technology. One factor that greatly influences the quality of education is the use of instructional media. Good learning media will help the teachers in organizing a good learning process well and help to improve the student learning outcomes. In an effort to improve teacher professionalism, especially in terms of mastery of technology and information, training is held using computer-based learning applications. The process of activities carried out aims to develop the ability of teachers to utilize existing software in the learning process, improving the abilities of teachers to operate computer usage, and show quantity and quality in various learning process inside and outside the classroom. Service methods used are training in the form of talks, discussions and questions and answers. The training conducted at Satu Atap High School run well and smoothly so that further assistance and development of e-learning media.
Grouping of Toddlers with Malnutrition Based on Provinces in Indonesia Using K-Medoids Algorithm

Malnutrition is a poor health condition in infants and toddlers caused by a lack of nutritional intake. Babies and toddlers who suffer from malnutrition will experience conditions of slowness in development, slowness in thinking, underweight and so on. Malnutrition can be prevented by complete immunization from birth, providing good nutrition for their development, and so on. The purpose of this study was to determine the results of the grouping of provinces with the highest malnutrition sufferers using the K-Medoids method which is part of Data Mining. The K-Medoids method is a clustering method that can break the dataset into several groups. In this study, the data used were sourced from the Central Statistics Agency in 2016 – 2018. The results of this clustering will later show the province which is the toddler with the highest malnutrition. This research is expected to provide information for the government regarding the grouping of children under five with malnutrition in Indonesia.
MENINGKATKAN KETERAMPILAN MENULIS KARANGAN NARASI MELALUI PENERAPAN STRATEGI GUIDED NOTE TAKING PADA SISWA KELAS IV DI SD NEGERI 1 KAOBULA

This study aims to determine the improvement of narrative essay writing skills through guide note taking strategies for fourth grade students of SD Negeri 1 Kaobula. The type of research conducted by the researcher is classroom action research (CAR). The results showed that the average value of each of these aspects can be seen that the narrative essay writing skill has not met the target of achieving a score of 75.00 in the class average. The average score of 50.3 comes from the total score of each narrative essay writing skill. The average value of narrative essay writing skills using the Application of Guided Note Taking Strategy is 63.79 and is included in the sufficient category. 86% of students also have not reached the KKM, which is 70. So, the target for a class average of 75 with good categories still cannot be achieved. For this reason, researchers will follow up with learning in cycle II. The average value achieved in the second cycle is 78. The average value has met the classical average value to be achieved, which is 78. Then the percentage results that reach a value &gt; 90 in the very good category are 1 student or 4, 17%, then the value of students who reach a value of 80-90 with a good category of 12 students or 50%, then the value of students who get a value of 60-70 with a sufficient category of 11 respondents or 45.84% and the value of students who get a value of 40-50 with the less category, no students get that value.
KLASIFIKASI TINGKAT KEPUASAN MAHASISWA TERHADAP FASILITAS PADA FTIK UNIVERSITAS DHARMAWANGSA MEDAN DENGAN ALGORITMA NAIVE BAYES

ABSTRACT Facilities are a support for the implementation of a process in a business in this case the Dharmawangsa University campus, by increasing the level of student satisfaction with the facilities available, student comfort in learning will be achieved. This study used questionnaire data on 70 respondents who were students of Dharmawangsa University. Previously, the questionnaire consisted of 42 questions. After being tested for validity and reliability, 20 questions were obtained. Then from the results of the questionnaire data, a classification of student satisfaction levels will be carried out using one of the algorithms in data mining, namely Naive Bayes. The results obtained by using the rapid miner application with 50 training data and 19 data testing data, the results obtained are a classification accuracy of 73.68% with a recall value of 83.33% and a precision of 83.33%, then there are 9 attributes that have a value dissatisfaction is higher than the satisfaction score given by respondents, this can be a concern of the leadership to improve these facilities so as to increase the level of satisfaction with the facilities provided by Dharmawangsa University.Keywords: Student Satisfaction, Data Mining, Naive Bayes
HANDWRITTEN KANNADA CHARACTER RECOGNITION INAN UNCONSTRAINED ENVIRONMENT USING CONVOLUTIONAL NEURAL NETWORK TECHNIQUE : A SURVEY

Due to the wide variety of writing styles, the recognition of handwritten characters, particularly in Kannada, is a challenging area of study.In order to convert the handwritten text into an electronic format, this work focuses on deciphering handwritten Kannada characters utilizing deep learning algorithms and optical character recognition (OCR).Deep learning methods like recurrent neural networks (RNNs) and convolutional neural networks are the most renowned and widely used methods for handwriting recognition (CNNs).The paper examines the key algorithms for identifying and categorizing handwritten characters as well as the numerous approaches utilized for deciphering handwritten material.Towards the conclusion, the accuracy offered by various systems is contrasted.In general, technological developments have made life simpler, and the expanding interest in handwritten recognition in computer science follows this trend.
Implementasi Cloudflare Zero Trust Dalam Mendeteksi Aktivitas Cryptojcking Pada Jaringan Komputer

With the increasing popularity of cryptocurrency (cryptocurrency) causing high crypto mining activity, crypto mining requires large resources so that many cybercriminals carry out illegal activities by stealing access to other people's computers to be used to mine cryptocurrencies (cryptojacking), this can interfere with the performance of a computer if the computer is used illegally without the knowledge of the computer owner, apart from that crypto mining also runs by utilizing the internet network, CPU usage and the power source from the victim's computer, for this reason this research will implement software as service (Saas) services from Cloudflare zero trust that can detect and anticipate cryptojacking activities on the internet network in order to anticipate attacks, Cloudflare zero trust will be installed on the network using the MikroTik routerboard intermediary, besides that the author will conduct an attack trial (penetrator si testing) by carrying out crypto mining activities on computers in a network and analyzing whether Cloudflare Zero Trust is able to detect and anticipate these attacks.
Improving the Utility of Web Surfing Using AI Techniques

This paper aims to improve web surfing security and utility by leveraging artificial intelligence (AI) and machine learning (ML) algorithms. A more personalized, efficient, and secure online experience is achieved through improving the utility and security of web browsing. Web browsing has become an integral part of our daily lives in the digital age. Users often face information overload, irrelevant content, security threats, and privacy concerns. AI and machine learning are used in the proposed system to refine web browsing. A web browsing utility that uses user preferences, interests, and browsing behavior to provide personalized recommendations, filter out irrelevant content, and enhance overall utility is the objective of this paper.