Reviewerly/exHarmony huggingface dataset:
A dichotomy color quantization algorithm for the HSI color space

Abstract Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color quantization algorithms for the Hue Saturation Intensity (HSI) color space with a simple uniform quantization algorithm. In this paper, we propose a dichotomy color quantization algorithm for the HSI color space. The proposed color quantization algorithm can display images with a smaller number of colors than other quantization methods of RGB color space. The proposed algorithm has three main steps as follows: first, a single-valued monotonic function of the Hue (H) component in the from RGB color space to HSI color space (RGB-HSI) color space conversion is constructed, which can avoid the partition calculation of the H component in the RGB-HSI color space; second, an iterative quantization algorithm based on the single-valued monotonic function is proposed; and third, a dichotomy quantization algorithm is proposed to improve the iterative quantization algorithm. Both visual and numerical evaluations reveal that the proposed method presents promising quantization results.
Implementasi Aplikasi Duolingo dalam Meningkatkan Kosa Kata Bahasa Inggris

Since English is a subject that all students in Indonesian schools are required to study, schools are under pressure to make studying the language enjoyable for the pupils. The use of the Duolingo app to increase the English vocabulary of Grade 3 A MI Tarbiyatul Islam pupils is discussed in this study. memorizing a language involves memorizing a lot of vocabulary. This study employs a qualitative case study methodology. Four people the head of the madrasa, an English teacher, and two Class 3 A students from MI Tarbiyatul Islam served as the study's informants. Data gathering, data reduction, data display, and conclusions are all used in data analysis. According to the research findings, the Duolingo application can increase students' vocabulary acquisition because of its supporting elements, which include an initial ability analysis, engaging the students' attention, repetition, and practice questions.
Text Generation with GAN Networks using Feedback Score

Text generation using GAN networks is becoming more effective but still requires new approaches to achieve stable training and controlled output. Applying feedback score to control text generation is one of the most important topics in NLP nowadays. Feedback or response is a natural part of conversations and not only consists of words, but also can take other shapes such as emotions, or other reactions. In dialogue processes feedback is a factor influencing the next phrase or reaction. Depending on this feedback or response we correct our possible answers by trying to change the tone, context, or even structure of the sentences. Applying feedback as part of the GAN model structure will give us new ways to apply feedback and generate well-controlled outputs with defined scores which is very important in realworld applications and systems. With GAN networks and their instability in training and unique architecture, it becomes trickier and requires new ways of solving this problem. The matter of feedback usages for text generation task using GAN networks we will review in this paper and experiment with integrating score values into GAN's generator model layers.
GAWAD LAGUNA (GFTSL): IMPLICATION IN DEVELOPING SOCIAL AWARENESS AND LINGUISTIC AND CULTURAL COMPETENCIES OF THE OUTSTANDING YOUTH OF LAGUNA

This extensive study's major goal is to ascertain the effect that the Gawad Laguna program-previously known as GFTSL or Gawad Felicisimo T. San Luis-has had on the outstanding youth of Laguna's development of social awareness and linguistic and cultural competence.The following statements listed below will be answered through this study.Second, the researcher also employed Mean and Standard Deviation to gauge participants' levels of agreement with Program Benefits including Social Awareness and Linguistic and Cultural Competencies.The Pearson Correlation Coefficient was lastly used by the researcher to determine the significant effects of Immersion and Edification as components of the Whole Process of Gawad Laguna (GFTSL) on the level of Social Awareness and Linguistic and Cultural Competencies of the Outstanding Youth of Laguna.
An Efficient Approach for Data Encryption and Decryption

Abstract: Data security is an essential component of an organization in order to keep the information safe from various competitors. Cryptography is a technique used to avoid unauthorized access of data. It has two main components- Encryption algorithm and Key. Sometime, multiple keys can also be used for encryption. A number of cryptographic algorithms are available in market such as DES, AES, TDES and RSA. The strength of these encryption algorithms depends upon their key strength. The long key length takes more computing time to crack the code and it becomes difficult for the hacker to detect the cryptographic model. In this paper we suggest an innovation in the age-old conventional cryptographic technique of HILLCIPHER using the concept of self repetitive matrix. A numerical method has been stated, mathematically proved and later implemented in generating a random matrix of given periodicity. The method of self-repetitive matrix has then been used to simulate a communication channel with proper decompression techniques to facilitate bit saving.
Suspicious Activity Detection Using Machine Learning

Abstract: Various technologies have been utilized to implement the safety of life and property byinstalling high quality CCTV cameras. It is not possible to manually monitor each and every moment activity. Furthermore, in practical scenario the most unpredictable one is human behaviour and it is very difficult to find whether it is suspicious or normal. In this work the notion of CNN is used to detect suspicious or normal activity in an environment, and a systemis proposed that sends an alert message to the similarity authority, in case of predicting a suspicious activity. It's worth noting that the effectiveness of a suspicious activity detection system relies on the quality of the training data, the architecture of the Machine Learning model, and the deployment environment. Ongoing monitoring, regular updates, and continuousimprovement are important for maintaining the system's accuracy and adapting it to new and emerging types of suspicious activities
Udah Cebol, Bantet Pula Aku: Lexical Cohesion on Body Shaming in IbuHamil.com Discussion Forum

Women contribute almost half of the total internet users in Indonesia and millennial mothers are a significant group of internet users, especially social media. This makes the millennial mother group vulnerable to crime in the digital space. One form of crime is body shaming which is part of mom-shaming. In this study, we investigate body shaming from the perspective of Discourse Analysis using Halliday and Hasan's classification on lexical cohesion. We focus on three categories in lexical cohesion, which are repetition, synonymy, and antonymy to illustrate lexical cohesion in IbuHamil.com’s discussion forum. Body shaming discussed in the article sources from a thread in Ngobrol Apa Saja forum in IbuHamil.com. The thread discussing body shaming experience receives 37 responses in 18 days. In terms of lexical cohesion, the data display 300 words in repetition with 28 inflected words, 38 words in synonymy, and 16 words in antonymy. There are some typical words in illustrating physical features in body shaming, which mainly highlight weight, height, and attempts to achieve an ideal body mass index. The discussion forum also demonstrates the frequent use of contraction which characterizes the internet chat room communication. Additionally, users' responses express empathy and support to the topic starter. The users also propose advice on handling body shaming and how to avoid it in the future.
Proses Cut and Fill Pada Lahan Berlereng Menggunakan Data Spasial Pada Lembah Barbate

Abstrak. Galian dan timbunan atau yang biasa disebut dengan cut and fill adalah pekerjaan yang sangat penting untuk memperbaiki suatu lahan yang berlereng sehingga lahan tersebut bisa menjadi datar. Pada Lembah Barbate memiliki kelerengan 30% untuk budidaya tanaman kurma yang mana apabila lahan yang berlereng tidak dilakukan konservasi tanah maka pertumbuhan tanah tidak produktif, maka dari itu proses galian dan timbunan sangat disarankan untuk lahan berlereng pada Lembah Barbate. Tujuan dari galian dan timbunan ini untuk menghitung volume galian dan timbunan di Lembah Barbate dengan bantuan data-data spasial. Metode yang dipakai pada penelitian ini yaitu metode composite volume. Metode ini memerlukan dua permukaan tanah yang berbeda yaitu permukaan tanah asli (originial surface) dan permukaan tanah rencana (design surface). Hasil dari perhitungan cut and fill diperoleh galian (cut) sebanyak 2.183.255 m³ dan hasil dari volume timbunan (fill) sebanyak 340.877 m³.Cut and Fill Process on Sloping Land Using Spatial Data In The Barbate ValleyAbstract. cut and fill or what is commonly referred to as cut and fill is a very important job to improve a sloping land so that the land can be flat. The Barbate Valley has a slope of 30% for the cultivation of date palms, which if the sloped land is not conserved, the soil growth is not productive, therefore the excavation and embankment process is highly recommended for sloping land in the Barbate Valley. The purpose of this cut and fill is to calculate the volume of cut and fill carried out in the Barbate Valley with the help of spatial data. The method used in this research is the composite volume method. This method requires two different soil surfaces, namely the original surface and the design surface. The results of the cut and fill calculations are obtained as much as 2,183,255 m³ of excavation and the results of the fill volume as much as 340,877 m³.
Improving methods for generating encryption keys using strange attractors

The urgency of the work is determined by the need to transfer confidential information through open communication channels. Such information can be of two types: symmetric encryption keys and directly informational messages that are encrypted with encryption keys. The article deals with the problem of improving the transmission of closed information over open channels using the Diffie- Hellman algorithm. The improvement is due to the introduction of a new type of one-sided function based on the numerical solution of the system of ordinary differential equations describing the dynamics of the phase coordinate movement of the strange attractor. For this purpose, the classic Diffie-Hellman algorithm based on the one-sided function of the discrete logarithm was considered. The required properties of one-sided functions in the general case were considered. Next, the peculiarities of algorithm modification in the case of transition to a one-sided function based on the use of a strange attractor were considered. It is assumed that at the beginning of the operation of the modified algorithm, through a secret channel, the exchange parties (agents) exchange information regarding the properties of the strange attractor to be used, namely, the definition of the differential equations describing the dynamics of a strange attractor, the values of the parameters of the equations, the initial integration conditions and the integration step (for methods with a constant step of integration). After that, all exchanges are conducted exclusively through open channels. The paper also considers the case of information exchange between more than two agents, in particular, the approach of hiding the number of agents participating in the exchange. Approbation of the method is carried out and intermediate and final results of the one-sided function based on strange attractors are given. Possibilities regarding partial disclosure by agents of certain parameters of the use of one-way functions are discussed. But at the same time, the safety of revealing such information is justified in the general case (both in the classical and in the modified Diffie-Hellman method). It was determined that depending on the needs of users, the complexity of the encryption keys can be increased by changing the initial parameters of the attractor, which will also allow controlling speed of key generation and encryption in general. The software that implements the proposed modified algorithm is implemented in three programming languages, C#, Python, and MatLab. This made it possible to perform a comparative analysis of the results and consciously choose the programming language of individual parts of the software to optimize the encryption key generation process for specific conditions.
Construction of English teaching quality diagnosis and evaluation model combined with improved EM algorithm

With the continuous improvement of teaching methods, people put forward higher requirements for the quality of English teaching. Traditional approaches for assessing instructional quality are less sensitive to the initial data in the evaluation process, and cannot achieve the optimal convergence effect. To better evaluate the level of instruction in English, the research uses the improved EM algorithm to create a diagnostic evaluation model for English teaching quality. The performance of the model is tested by using the English scores of college students. The outcomes demonstrate that the accuracy of enhanced model is 99%. The trained model is used for data testing, and the test error obtained is 0.8%. The error is 0.4% and 0.1% lower than EM model and FCM-EM model respectively. Therefore, the IFCM-EM evaluation method for teaching English proposed in the study has good accuracy and efficiency. It can effectively diagnose and evaluate the effectiveness of English instruction in schools, as well as offer assistance with improve English teaching evaluation methods and teaching levels.
An Optimised BERT Pretraining Approach for Identification of Targeted Offensive Language: Data Imbalance and Potential Solutions

Targeted offensive comments and hate speech on online media platforms are on the rise, with evidential mental health consequences including suicide. Several NLP techniques have been proposed and in use. However, data imbalance in the training dataset is stopping them from performing at full potential. Solutions include under-sampling of the majority class, oversampling of the minority class or introducing synthetic samples. These approaches present with their own unique problems - that of critical information loss, overfitting and non-generalised models. The presented research explores these approaches for addressing the data imbalance problem, by varying the under/over/synthetic sampling rate and studying the performance as well as the generalisability of the models.
An action recognition method based on neural architecture search

Facing complex and diverse action recognition scenarios, artificially designed neural networks show poor generalization performance. Therefore, an automatic designing method of 3D convolutional neural networks based on neural architecture search is proposed. Firstly, a variety of human behaviors are extracted to construct training sets and validation sets. Additionally, the weights of neuron connections are updated using the loss on the training set, the discrete network architecture search space is continuous through continuous relaxation, and the search space is reduced by using the hierarchical idea. What’s more, the objective function is optimized by combining gradient descent, realizing fast search, and stacking the obtained computing units to form an overall network at the same time. Evaluations based on public data sets show that the designed neural network model achieves comparable performance to the artificially designed network model in the task of human action recognition, solving the difficulty of deep learning method migration between working at different scenarios by automatically customizing the network.
Precise On-Chip Spectral and Temporal Control of Single-Photon-Level Optical Pulses

The constantly growing need for fast and secure optical communication systems resulted in the rapid development of classical and quantum photonic technologies. Continuous research on improving the capacities of communication channels and the efficiency of optical information processing systems were naturally followed by miniaturization. Photonic integration combined with spectral-temporal multiplexing has led to improvements in communications. In this paper, we present accurate control of temporal and spectral profiles of optical pulses generated at the telecom wavelengths using an indium phosphide-based photonic integrated circuit. The device enables simultaneous temporally and spectrally resolved measurements using single photon counting. We discuss its applications in multidimensional time-frequency quantum key distribution, where security relies on time-frequency uncertainty, and other applications.
A recall model for programming map nearest neighbor recommendation

The programming map is a tree representation method of the abstract syntax structure of the source code. Recommending the programming map is of great significance for code completion, code recommendation and improving the cognition of code construction. This paper proposes a recall model for programming map nearest neighbor recommendation-PMRM. The model first proposes a programming map node coverage analysis algorithm to shard each node of the programming map and extract the node level coverage information, and then perform semantic clustering of the corresponding level nodes based on the hierarchical information. Replace other nodes in the cluster according to the clustering results, and finally calculate the edit distance of the programming map structure as a measure of similarity, which solves the recommendation problem based on the semantic and hierarchical structure information of programming map nodes. The experimental results show that the recall model PMRM proposed in this paper is better than other methods have obvious advantages.
Communication jamming signal recognition method based on fractal dimension and information entropy

Aiming at the problem of low recognition accuracy of traditional communication jamming signals, a communication jamming signal recognition method combining fractal dimension and information entropy is proposed. Firstly, Fourier transform, singular value decomposition and wavelet packet decomposition are performed on the jamming signal in turn; Then, the fractal dimension and information entropy are used to extract the spectrum box dimension, time-frequency image singular value entropy and wavelet packet energy entropy of the jamming signal respectively, and the three-dimensional feature vector is constructed; Finally, the feature vector is input into multi classification support vector machine for classification and recognition. The simulation results show that the proposed method can accurately identify five types of typical communication jamming signals under the condition of low jamming noise ratio, and the recognition rate can reach more than 90% when the jamming noise ratio is -10dB. It is a strong anti noise communication jamming recognition method with practical application value.
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Benchmarking a high-precision quantum operation is a big challenge for many quantum systems in the presence of various noises as well as control errors. Here we propose an $O(1)$ benchmarking of a dynamically corrected rotation by taking quantum advantage of a squeezed spin state in a spin-1 Bose-Einstein condensate (BEC). Our analytical and numerical results show that tiny rotation infidelity, defined by $1\ensuremath{-}F$ with $F$ the rotation fidelity, can be calibrated in the order of $1/{N}^{2}$ by only several measurements of the rotation error for $N$ atoms in an optimally squeezed spin state. Such an $O(1)$ benchmarking is possible not only in a spin-1 BEC but also in other many-spin or many-qubit systems if a squeezed or entangled state is available.
Fundamental Concepts in Graph Attention Networks

Graph attention networks, also known as GATs, are a specific kind of neural network design that can function on input that is arranged as a graph. These networks make use of masked self-attentional layers in order to compensate for the shortcomings that were present in prior approaches that were based on graph convolutions. The main advantage of GAT is its ability to model the dependencies between nodes in a graph, while also allowing for different weights to be assigned to different edges in the graph. GAT is able to capture both local and global information in a graph. Local information refers to the information surrounding each node, while global information refers to the information about the entire graph. This is achieved through the use of attention mechanisms, which allow the network to selectively focus on certain nodes and edges while ignoring others. It also has scalability, interpretability, flexibility characteristics. This chapter discusses the fundamental concepts in graph attention networks.
Authentication Based on Image Password

Abstract: In the modern day, graphical password authentication that relies on memory or recognition is a different and still developing sort of authentication. The user copies the picture or is able to identify the image that was used or made during the registration process. Only authorised users are able to access resources and information thanks to passwords. The newest fashion is graphic passwords, which appear to be a highly promising authentication technique. It is available as a substitute for text passwords. The most popular method is using usernames and passwords that are alphanumeric. While this method has certain benefits over plain text passwords, it also has some drawbacks. Users strive to memorise short, strong passwords that can be broken when using alphanumeric passwords, but doing so becomes cumbersome. Users that use graphical passwords save a picture or certain areas of an image as their password. Biometrics is now the most sophisticated and secure form of authentication, yet it is too costly for widespread usage. Graphical passwords are a fantastic alternative authentication mechanism since they are less expensive, more secure, and simpler for everyone to use. [1]
A Novel Routing Protocol-Based Data Transmission to Enhance the Quality of Service for Internet of Medical Things Using 5G

When it comes to improving people's health, various forms of smart city applications are put to use in the area of smart healthcare. Health care providers are increasingly using the internet of medical things (IoMT) as an environment of linked healthcare systems, computational capabilities, and health monitors to improve the quality of service (QoS). Smart healthcare devices transmit data to IoMT and it requires an efficient solution. IoMT's and 5G networks' importance necessitates the development and testing of a novel multi-hop reinforced clustering-based routing protocol (MRCRP) to improve health data transmission's QoS. Cluster head selection is carried out using an energy-efficient approach to enhance resource usage. Min-max normalization is used to transform original data into meaningful data for more analysis. Using principal component analysis (PCA), relevant features can be extracted. The proposed routing technique is then employed to effectively transmit the data. The suggested approach's performance measures, such as delay, throughput, energy consumption, and packet drop ratio are studied, as is a comparative analysis with standard routing protocols..