Reviewerly/exHarmony huggingface dataset:
Classifying Tweets with Keras and TensorFlow using RNN (Bi-LSTM)

Understanding public opinion, sentiment analysis, and subject recognition have all become more and more important as social media platforms have grown exponentially. The methodology for categorizing tweets using Keras and TensorFlow with a Recurrent Neural Network (RNN) architecture, specifically Long Short-Term Memory (LSTM) units, is presented in this research article. The method uses word embeddings and other properties to improve tweet representation, allowing the model to reliably identify specified categories and capture contextual connections. Our RNN-LSTM model beats baseline methods after extensive testing and evaluation, proving its suitability for tweet classification applications. The model's comprehension of tweet content is further improved by the incorporation of pre-trained word embeddings as well as features like emotion scores and hashtags. The approach offers a thorough framework for using deep learning methods in tweet classification, opening the door for uses cases including sentiment analysis, topic recognition, and opinion mining. By providing knowledge on the possibilities of RNN-LSTM models and their use in comprehending and analysing social media data, this research makes a contribution to the area. The results emphasise how crucial it is to take temporal dynamics and contextual factors into account while handling tweet classification jobs. Future research may concentrate on researching other pre-trained embeddings, investigating advanced RNN architectures, and solving issues with noisy and biassed twitter data. Overall, the large volume of information published on social networking sites like Twitter may now be better understood and analysed thanks to this research.&#x0D;
Writing Knowledge and Writing Performance of Undergraduate Students

This paper describes an investigation about the relationship between students' writing knowledge and second language writing performance. Metacognitive and metalinguistic knowledge contribute significantly to writing performance. As a foreign language, 78 undergraduate students took a writing knowledge class and a writing proficiency test. The knowledge process has a strong influence on writing performance. Except for vocabulary knowledge, other aspects and sub-aspects had no significant effect on writing performance. Vocabulary knowledge had a negative impact on writing performance. In other words, while some aspects and sub-aspects of writing knowledge did not significantly influence writing performance, others did. The pedagogical implication is that all aspects of writing knowledge must be explicitly taught in writing instruction in order to improve students' writing quality. However, there must be a priority of aspect as the core of the material to be taught, with the other aspects and sub-aspects serving as complementary materials. This could result in a moderate relationship finding and a low percentage of contribution. Undergraduate English as a Foreign Language students from various institutions and provinces may have varying levels of language proficiency and writing skills.
A Socio-Psychological Analysis of Gigoo’s The Lion of Kashmir

In the current study the socio-psychological analysis of The Lion of Kashmir (2020) by Siddhartha Gigoo's is done by using the prism of socio-psychological theory and the theoretical notion of psychoanalytical theory. According to the researcher, the novel's protagonist has a disturbed mental state brought on by violence. There many elements which can induce mental disturbances. These elements include frequent bombardment, a shortage of basic amenities in contested regions, homelessness, the murder and kidnapping of family members, and others. The study also looks at how the victims of this mental disorder suffered from inferiority complexes and self-shattering. The novel's author provides a window into the protagonist's mind and paints a vivid picture of his anxieties. The narration's phantasmagoria offers us several perspectives on the story. This analysis supports the idea that the selected novel of Gigoo emphasizes life is difficult for Kashmiris.
Face recognition using local binary pattern and Gabor-Kernel Fisher analysis

Face recognition technology is one of the everyday tasks in our daily life. But, recognising the correct face with high accuracy from large databases is a challenging task. To overcome this challenge, feature fusion of local binary pattern (LBP) with Gabor-Kernel Fisher analysis (Gabor-KFA) has proposed for face recognition. In this method, by using Gabor filter, extract Gabor features from a face image, on the other hand, extract features from LBP coded face image, then combined these extracted features generate high dimensional feature space. With this high dimensionality features, the complexity of training time and identification time may increase. To avoid this complexity, the Kernel Fisher analysis algorithm was adopted to reduce the feature vector size. Experiments were conducted separately on Gabor features and also on fused features. To test the performance of the proposed approach, the experiments were performed on the IIT Delhi database, ORL database, and FR database.
EVALUASI KINERJA LAYANAN PERPUSTAKAAN UIN SUNAN AMPEL SURABAYA BERBASIS STANDAR NASIONAL DAN INTERNASIONAL

The performance of the university library must always be evaluated and assessed. Assessment or evaluation activities usually have to use standard measuring tools, so that the results can be accounted for. The results of the evaluation carried out correctly can be used as a reference and basis for library development in the future. This research is an evaluated study of the library service performance evaluation of UIN Sunan Ampel Surabaya (UINSA). The standards used as evaluation tools are the National Standards for College Libraries (SNP 13 of 2017) and the Standards for libraries in higher education issued by the Association of College and Research Libraries (ACRL), a division of the American Library Association (ALA). 2018. Using both instruments of national and international standards, the researcher wants to know the level of achievement of UINSA's library service performance from these two levels. This study uses the collecting data methods, namely documentation, questionnaires, and observations. Documentation is used to evaluate performance based on SNP 13 in 2017 while a questionnaire is used to evaluate performance based on the Standard for libraries in higher education. Documentation guide using the university library accreditation instrument. Meanwhile, the questionnaire was compiled based on the performance indicators of the 3 (three) principles of Standard for libraries in higher education, namely Professional Values, Education role, and Discovery. The results showed that the performance of UINSA Library services based on SNP 13 of 2017 had reached the standard, with a value of 4.6. The Evaluation based on the Standard for libraries in higher education, it also exceeds the standard with a value of 4.2. The results of this study became the basis for the preparation of a long-term library development policy document as part of the quality manual document. A document that will form the basis of library development for the next 5 years.
LIBRARY ANXIETY MAHASISWA ASING DI PERPUSTAKAAN

Library anxiety is a unique phenomenon. Anxiety often arises when someone is in a new place. Padang State University has some students from several countries. Environmental and cultural differences force the students to adapt to their new environment. Anxiety could arise due to the lack of exposure in the library environment or lack of knowledge of the library. One of the anxious attitudes can be seen in the reluctance of students to come to the library. This study aims to determine library anxiety and the factors of library anxiety towards foreign students at the Padang State University Library using a descriptive method with a case study approach, Data collection was carried out using structured interview techniques, observation, and documentation. The informants in this study were five foreign students that selected based on purposive sampling. Research result shows that foreign students at Padang State University Library do not experience library anxiety. This can be seen from the results that from nine questions given, six questions were answered positively by the informants and just three questions confirmed negative: lack of knowledge about where, how to start, and library knowledge barriers.
Pemanfaatan Teknik Simpul untuk Pembuatan Tas Berbahan Denim

Denim is one of the oldest materials in the world, and until now the material is still known to everyone. The use of denim fabric for textile products is a phenomenon that will never die from time to time because this material is always used in creating a fashion product. Bags are one of the things that can support the appearance made of various textile materials, one of which is denim. The use of the knot technique in the manufacture of women's bags aims to provide a new alternative in making bags without sewing techniques. The knot technique is able to provide its own value in a work even if only by using a simple knot. The result of the design is a denim bag tote bag and a drawstring sling with a knot technique. Using the design method according to SP Gustami which consists of three stages, namely exploration, design, and realization.Keywords: denim, knots, woman’s bag.
Evaluation of Undergraduates and Postgraduates’ Utilisation of Internet for Academic Purpose in STREAM Fields

This study examines undergraduates and postgraduates use of the internet for academic purposes in STREAM fields. A mixed method research which sampled 100 respondents selected through snowballing sampling technique. Four research questions and one hypothesis guided this study. Two instruments namely Internet Usage Questionnaire (IUQ) and semi-structured interview on challenges experienced in the utilisation of internet (IUIT) were employed for data collection. Standard deviation and independent sample t-test analysed the descriptive (quantitative) aspect of questions and hypothesis raised respectively, while thematic analysis was employed in the narrative (qualitative) section. The study found that mobile apps, software, and website were among the media through which students access academic resources on the internet. No significant difference was found in the level of usage of the internet for academic purposes between undergraduates and postgraduates in STREAM fields. Poor internet facility, high cost of internet subscription, wide search for relevant academic resources due to inaccessibility to paid resources and many ad pop-ups owing to unlicensed software were among the challenges experienced by the respondents. Furthermore, stakeholders are enjoined to make available fast and affordable internet to ease the concerns raised.
Sistem Prakiraan Cuaca Berbasis Android dengan Metode Jaringan Syaraf Tiruan Backpropagation

This research aims to develop a weather forecasting system using an Android-based mobile device combined with a microcontroller platform. The device built consists of a DHT11 sensor, Arduino UNO microcontroller, HC-05 Bluetooth module, and an Android phone that is used by users to view weather information. The Neural Network Backpropagation method is useful as a learning algorithm in obtaining appropriate weights so that the system is able to make weather forecasts correctly. Temperature and humidity data from the Padang City BMKG are used as input data to the Matlab application to be trained to find accurate patterns and weights. This appropriate weight is useful as a weighing factor in the Backpropagation testing process on Arduino Uno. In the process, the system works to measure temperature and humidity with the DHT11 sensor, then Arduino processes the input from the sensor to then send the results to the Android application. The test results with 14 trial data revealed that the system with the Neural Network Backpropagation learning method had a success rate of 78.6% for measuring and forecasting the weather.
Neural Network Models

The most recent development in distributional semantics is represented by models based on artificial neural networks. In this chapter, we focus on the use of neural networks to build static embeddings. Like random encoding models, neural networks incrementally learn embeddings by reducing the high dimensionality of distributional data without building an explicit co-occurrence matrix. Differing from the first generation of distributional semantic models (DSMs), also termed count models, the distributional representations produced by neural DSMs are the by-product of training the network to predict neighboring words, hence the name of predict models. Since semantically similar words tend to co-occur with similar contexts, the network learns to encode similar lexemes with similar distributional vectors. After introducing the basic concepts of neural computation, we illustrate neural language models and their use to learn distributional representations. Then we pass to describe the most popular static neural DSMs, CBOW, and Skip-Gram. We conclude the chapter with a comparison between count and predict models.
Error Generic Data Poisoning Defense

Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.
Test-Time Evasion Attacks (Adversarial Inputs)

Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.
Penyandian Pesan Hybrid dengan Myszkowski Cipher dan Rivest, Shamir, Adleman (RSA)

Classical and modern cryptography have advantages and disadvantages in securing text messages. Hybrid algorithms can combine two algorithms to produce a higher level of message security when compared to one type of algorithm. This study discusses a hybrid algorithm that combines Myszkowski Cipher and RSA. The purpose of this research is to obtain ciphertext that is more difficult to crack by cryptanalysis. The hybrid algorithm process is done by encrypting text messages with the Myszkowski Cipher algorithm then the key used in the Myszkowski Cipher algorithm are encrypted using the RSA algorithm with the equation . Then the ciphertext and cipherkey will be obtained in numerical. The decryption process is done by decrypting the cipherkey by specifying the value of as the private key so that using the equation . Next is by trying the value of until a value is obtained, after which decryption to the equation , then the process of decrypting ciphertext using Myszkowski Cipher can be done. The benefit of this research is can improve the security of messages because it applies the advantages of each algorithm in its encoding.
Penggabungan Metode Vigènere Cipher dan ElGamal Pada Pengamanan Pesan Rahasia

Vigènere Cipher is a symmetric cryptographic algorithm that uses the same type of key in the encryption and decryption process. The security of the Vigènere Cipher method lies in the modulo calculation used. ElGamal is an asymmetric cryptographic algorithm that uses two different types of keys in the encryption and decryption process. The security of the ElGamal algorithm lies in the complexity of calculating large prime numbers. The Vigènere Cipher and ElGamal have their advantages and disadvantages. The researchers are interested in combining the advantages of the two methods. In this study, two locks were carried out in the encryption and decryption process. The encryption process uses a public key and the decryption process uses a public key and a secret key . The key used comes from key formation using the ElGamal algorithm. The key security formed from the ElGamal algorithm lies in the large prime , the primitive root of the prime number , and the random integer which comes from the last three digits of Student Number. The conclusion of this study is that the combination of the Vigènere Cipher and ElGamal methods can increase the security of secret messages because it produces a ciphertext with twice the size ) of the original message.
Incarcerated Adult Learners’ English Reading for General Information Through the Skimming Strategy and Graphic Organizers

The present action research study aimed to explore how the explicit teaching of the skimming reading strategy using graphic organizers can support six adult learners’ reading for general information in a Chilean EFL incarcerated context and to describe their perceptions of the skimming strategy intervention. Based on an intervention of four sessions utilizing the Cognitive Academic Language Learning Approach (CALLA), the data was collected using pre and post intervention tests and a focus group discussion. The findings revealed that the explicit teaching of skimming strategy and the use of graphic organizers helped incarcerated learners to improve their reading skills to find general information in a text. Moreover, the findings showed positive perceptions from learners towards the teaching of the strategy and the use of graphic organizers.
$K$-Relations-Based Consensus Clustering With Entropy-Norm Regularizers

Consensus clustering is to find a high quality and robust partition that is in agreement with multiple existing base clusterings. However, its computational cost is often very expensive and the quality of the final clustering is easily affected by uncertain consensus relations between clusters. In order to solve these problems, we develop a new <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -type algorithm, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -relations-based consensus clustering with double entropy-norm regularizers (KRCC-DE). In this algorithm, we build an optimization model to learn a consensus-relation matrix between final and base clusters and employ double entropy-norm regularizers to control the distribution of these consensus relations, which can reduce the impact of the uncertain consensus relations. The proposed algorithm uses an iterative strategy with strict updating formulas to get the optimal solution. Since its computation complexity is linear with the number of objects, base clusters, or final clusters, it can take low computational costs to effectively solve the consensus clustering problem. In experimental analysis, we compared the proposed algorithm with other <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -type-based and global-search consensus clustering algorithms on benchmark datasets. The experimental results illustrate that the proposed algorithm can balance the quality of the final clustering and its computational cost well.
NVIF: Neighboring Variational Information Flow for Cooperative Large-Scale Multiagent Reinforcement Learning

Communication-based multiagent reinforcement learning (MARL) has shown promising results in promoting cooperation by enabling agents to exchange information. However, the existing methods have limitations in large-scale multiagent systems due to high information redundancy, and they tend to overlook the unstable training process caused by the online-trained communication protocol. In this work, we propose a novel method called neighboring variational information flow (NVIF), which enhances communication among neighboring agents by providing them with the maximum information set (MIS) containing more information than the existing methods. NVIF compresses the MIS into a compact latent state while adopting neighboring communication. To stabilize the overall training process, we introduce a two-stage training mechanism. We first pretrain the NVIF module using a randomly sampled offline dataset to create a task-agnostic and stable communication protocol, and then use the pretrained protocol to perform online policy training with RL algorithms. Our theoretical analysis indicates that NVIF-proximal policy optimization (PPO), which combines NVIF with PPO, has the potential to promote cooperation with agent-specific rewards. Experiment results demonstrate the superiority of our method in both heterogeneous and homogeneous settings. Additional experiment results also demonstrate the potential of our method for multitask learning.
Randomness Assessment Technique for Quantum-Safe Security Systems Based on Ternary Key Distribution Protocols

In recent decades, quantum cryptography has made its way from scientific researches to practical implementations and commercial use. The absolute advantage of using quantum methods of information protection is independence from the computing capabilities of an attacker. In addition, due to the use of unique properties of quantum methods, it becomes possible to achieve an unprecedented level of security with the possibility of detecting the violator during the operation of some protocols in online mode. However, although many questions and problems in quantum cryptography have already been solved, there are still many unresolved ones. The use of ternary pseudorandom sequences raised the question of the statistical reliability of these sequences. And although today there are a large number of different methods for evaluating the randomness of a sequence, however, they are mostly aimed at evaluating standard binary sequences. This work is devoted to the method of evaluating ternary sequences, one of the stages and an experimental study are presented.