Reviewerly/exHarmony huggingface dataset:
Recent Advances in Class-Incremental Learning

A large number of deep learning models have been applied in a wide range of fields nowadays. However, most existing models can only generalize to the categories in the training set and are unable to learn new categories incrementally. In practical applications, new categories or tasks will constantly emerge, which requires models to continuously learn new category knowledge like humans while maintaining existing category knowledge. Such the learning process, i.e., class-incremental learning (CIL), abstracts more attention from the research community. CIL faces several challenges, such as imbalanced data distribution, limited model memory capacity, and the catastrophic forgetting of category representation. Therefore, we provide an up-to-date and detailed overview of CIL methods in this survey, including data-based, model-based, and representation-based approaches. We also discuss the impact of pre-trained models on CIL and compare the latest methods on widely-used benchmarks. Finally, we summarize the challenges and future directions of CIL.
Combining Contrastive Learning and Knowledge Graph Embeddings to Develop Medical Word Embeddings for the Italian Language

Word embeddings play a significant role in today’s Natural Language Processing tasks and applications. However, there is a significant gap in the availability of high quality-word embeddings specific to the Italian medical domain. This study aims to address this gap by proposing a tailored solution that combines Contrastive Learning (CL) methods and Knowledge Graph Embedding (KGE), introducing a new variant of the loss function. Given the limited availability of medical texts and controlled vocabularies in the Italian language, traditional approaches for word embedding generation may not yield adequate results. To overcome this challenge, our approach leverages the synergistic benefits of CL and KGE techniques. We achieve a significant performance boost compared to the initial model, while using a considerably smaller amount of data. This work establishes a solid foundation for further investigations aimed at improving the accuracy and coverage of word embeddings in low-resource languages and specialized domains.
Toward Novel Optimizers: A Moreau-Yosida View of Gradient-Based Learning

Machine Learning (ML) strongly relies on optimization procedures that are based on gradient descent. Several gradient-based update schemes have been proposed in the scientific literature, especially in the context of neural networks, that have become common optimizers in software libraries for ML. In this paper, we re-frame gradient-based update strategies under the unifying lens of a Moreau-Yosida (MY) approximation of the loss function. By means of a first-order Taylor expansion, we make the MY approximation concretely exploitable to generalize the model update. In turn, this makes it easy to evaluate and compare the regularization properties that underlie the most common optimizers, such as gradient descent with momentum, ADAGRAD, RMSprop, and ADAM. The MY-based unifying view opens to the possibility of designing novel update schemes with customizable regularization properties. As case-study we propose to use the network outputs to deform the notion of closeness in the parameter space.
THEORETICAL ASPECTS OF THE FORMATION OF ENVIRONMENTAL CULTURE OF YOUNG SCHOOLCHILDREN IN EXTRACURRICULAR ACTIVITIES

The article emphasizes the characteristic features of the process of formation of environmental culture among young students in extracurricular activities. Elementary school is the most favorable period for environmental education, and the ways of forming a positive and correct attitude towards nature in the majority of children at the elementary school age are analyzed. In extracurricular activities, the tasks of forming the environmental culture of elementary school students are indicated. The main components of the ecological culture formation system are determined. The basic conditions for the successful formation of ecological culture are described. The theoretical aspects of the formation of a protective, careful and responsible attitude towards inanimate and living nature objects in students are analyzed. It is during the elementary school age that the child has an emotional impression of nature, the formation of ideas about the various forms of life on the Earth, and as a result, directions for the formation of fundamental principles of ecological consciousness, thinking, and culture are shown. The main content of the effectiveness of extracurricular activities in the formation of environmental culture of young students is analyzed.
Research on CBRN Practical Assessment Technology Based on Artificial Intelligence Technology

Combined with the common practical assessment problems in the field of CBRN, which have the problems of high subjective factors and inconsistent assessment standards, this paper proposes the feasibility design scheme of the assessment system based on intelligent identification technology, and analyzes the key technologies such as target recognition technology and expert system involved in the system. While verifying the feasibility of the assessment system design scheme based on intelligent identification technology, it provides ideas for the construction of the assessment system based on intelligent identification technology. It also provides a good solution to improve the quality of assessment.
Learn to Enhance the Negative Information in Convolutional Neural Network

This paper proposes a learnable nonlinear activation mechanism specifically for convolutional neural network (CNN) termed as LENI, which learns to enhance the negative information in CNNs. In sharp contrast to ReLU which cuts off the negative neurons and suffers from the issue of “dying ReLU”, LENI enjoys the capacity to reconstruct the dead neurons and reduce the information loss. Compared to improved ReLUs, LENI introduces a learnable approach to process the negative phase information more properly. In this way, LENI can enhance the model representational capacity significantly while maintaining the original advantages of ReLU. As a generic activation mechanism, LENI possesses the property of portability and can be easily utilized in any CNN models through simply replacing the activation layers with LENI block. Extensive experiments validate that LENI can improve the performance of various baseline models on various benchmark datasets by a clear margin (up to 1.24% higher top-1 accuracy on ImageNet-1k) with negligible extra parameters. Further experiments show that LENI can act as a channel compensation mechanism, offering competitive or even better performance but with fewer learned parameters than baseline models. In addition, LENI introduces the asymmetry to the model structure which contributes to the enhancement of representational capacity. Through visualization experiments, we validate that LENI can retain more information and learn more representations.
Supply Chain Tracing and Anti-Counterfeiting with Distributed Ledger Technology

In recent times, there has been a rampant proliferation of counterfeit products that has left a trail of devastation in the manufacturing sectors. The repercussions of this extend to companies, impacting their brand reputation, revenue streams and overall profitability. Industries like agriculture, banking, electronics, and high-value deliveries uses the emergence of blockchain technology as a powerful tool to discern between authentic and counterfeit items. Its potential as a means to curtail the influx of fake products in the market is substantial. Blockchain technology, at its core, operates as a decentralized and distributed digital ledger system, meticulously recording transactions within interconnected blocks across multiple databases. The inherent security of this technology ensures the immutability of these blocks, rendering them invulnerable to alteration or hacking. By leveraging blockchain technology, consumers can independently verify the authenticity of a product, eliminating the need for reliance on third-party intermediaries. Incorporating recent technological advancements, the utilization of Quick Response (QR) codes offers a robust approach to combat the proliferation of counterfeit goods. The integration of blockchain technology with QR codes serves as a means to uphold the integrity of products. This innovative system securely stores product details and unique codes in the form of blocks, where QR codes play a pivotal role in collecting and matching these unique codes with entries in the blockchain database. If the QR code matches with entries in the database, the user receives a confirmation of the product's authenticity; otherwise, an alert is triggered, signaling the presence of a counterfeit product.
Entanglement Classification for Three-qubit Pure Quantum System using Special Linear Group under the SLOCC Protocol

Quantum technology has been introduced in the IR 4.0, breeding a new era of advanced technology revolutionizing the future. Hence, understanding the key resources of quantum technology, quantum entanglement is vital. Growing interests in quantum technologies has raised comprehensive studies of quantum entanglement, especially on the entanglement classification. Special Linear group, SL(n) of multipartite entanglement classification under the SLOCC protocol is not widely studied due to its complex structure, creating a curb in developing its classification method. Therefore, this paper developed and delivered a classification method of pure multipartite, three-qubit quantum state using a combination of Special Linear group, SL(2) x SL(2) x SL(2) model operator under the SLOCC, classifying entanglement using the model operator with certain selected parameters. Further analysis was done resulting in the determination of the six subgroups, namely fully separable (A-B-C), bi-separable (A-BC, B-AC and C-AB) and genuinely entangled (W and GHZ).
Penerapan Metode Fuzzy Tsukamoto Dalam Memprediksi Permintaan Barang

Azthana Digital Media is a digital agency that works with a number of Indonesian vendors, including Pempek Mola, Pempek Kalani, and Eightsy Bag. Azthana oversees distribution and maintains warehousing for all three brands simultaneously. However, due to fluctuations in product demand, there are sometimes gaps in the supply of goods across all brands. So we need a method that can be implemented in inventory management and product development in the future. Forecasting strategies are useful tools for maintaining and expanding stock levels. In this study, researchers used the Fuzzy Tsukamoto technique built into a web-based inventory system to estimate consumer demand. Sales data, product purchases and goods production data are the three main inputs used to formulate demand forecasts. The results of this study can be concluded that the inventory system created can help the related team to find out the exact amount of stock available and to implement the Tsukamoto fuzzy calculations in predicting the demand for goods in the system that is running well as evidenced by the results of manual calculations and the system if the demand value is input equal to 355 with four fuzzy rules that are set together to produce a production value of requests for goods that must be ordered to suppliers of 522 packs.
Enhancement of Text Recognizing Exploitation in Phishing Websites using LSTM in Comparison with CNN based on Improving the Accuracy Rate

The objective of the work is to predict the accuracy of phishing websites based on exploitation of text recognition using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). To achieve accuracy, a novel np.random function was used. Method and Materials : Accuracy and Loss are performed with DATA dataset from the keras library. The total sample size is 20. The two groups Convolutional Neural Network (N=10) and Long Short Memory (N=10). Result : The result proved that Convolutional Neural Network (CNN) with better accuracy of 97.3% than Long Short Term Memory (LSTM) accuracy of 93.2%. Finally CNN appears significantly better than LSTM. The two algorithms CNN and LSTM are statistically satisfied with the independent sample T-Test value (p&lt;0.001) with a confidence level of 95%. Conclusion : Detecting the phishing website significantly seems to be better in CNN (Std.Error Mean = .0632) than LSTM (Std.Error Mean = .0678).
Dynamic Weighted Neural Bellman-Ford Network for Knowledge Graph Reasoning

Recent studies have shown that subgraphs of the head entity, such as related relations and neighborhoods, are helpful for Knowledge Graph Reasoning (KGR). However, prior studies tend to focus solely on enhancing entity representations using related relations, with little attention paid to the impact of different relations on different entities and their importance in various reasoning paths. Meanwhile, conventional Graph Neural Networks (GNNs) utilized for KGR consider simultaneously neighboring nodes and connected relations of the head entity but typically use a standard message-passing paradigm over the entire Knowledge Graph (KG). This results in over-smoothed representations and limits efficiency. To address the above-mentioned limitations of existing methods, we propose a Dynamic Weighted Neural Bellman-Ford Network (DyNBF) for KGR, which utilizes relation weights generated from subgraphs to compute only the most relevant relations and entities. This way, we can integrate multiple reasoning paths more flexibly to achieve better interpretable reasoning, while scaling more easily to more complex and larger KGs. DyNBF consists of two key modules: 1) a transformer-based relation weights generator module, which computes the weights of different relations on the path with a sequence-to-sequence model, and 2) an NBFNet-based logic reasoner module, which obtains entity representations and conducts fact prediction with dynamic weights from the previous module. Empirical results on three standard KGR datasets demonstrate that the proposed approach can generate explainable reasoning paths and obtain competitive performance.
Evaluation and Solution for SAP Implementation Using Technology Acceptance Model: A Case Study in an Indonesian Company

&#x0D; &#x0D; &#x0D; &#x0D; Several companies have implemented an end-to-end SAP system, but are experiencing problems. To find the root cause of this problem, this study evaluates employee user acceptance of SAP in one company in Indonesia. The variables used are taken from the Technology Acceptance Model which includes Perceived Usefulness, Perceived Ease of Use, Attitude Toward Use, and also Behavioral Intention to Use. Furthermore, it was also found that the training did not affect Cooperation and the Perceived Ease of Use of the SAP System. The training was chosen as the root cause of the constraints on SAP implementation in this company. After the root cause of the problem is found, a solution is designed in the form of a website that aims to improve SAP training at this company.&#x0D; &#x0D; &#x0D; &#x0D;
Exploring Anaphoric and Cataphoric References Used in “Bright, an English Textbook”

This study aims to investigate the use of anaphoric and cataphoric references in narrative text in “Bright An English” Textbook for 8th-grade junior high school students, and to identify the most commonly used in the texts. This study examined the data that contained the anaphoric and cataphoric by the theory of Halliday and Hasan (1976). The study used a qualitative descriptive approach because it dealt with descriptive. The main data for this study was “Bright, an English textbook” for 8th-grade junior high school students, written by Nur Zaida and published by Erlangga. The results showed those anaphoric and cataphoric references contained in the English textbook; 22 sentences had anaphoric and cataphoric references in detail of which fifteen sentences were anaphoric, and seven sentences were cataphoric. “Bright, an English textbook” for eighth-grade junior high school students frequently uses pronominal anaphora because it addresses the referent of the anaphora with a pronoun. Pronouns were important because they linked sentences, which helped to avoid the nouns being used repeatedly. Besides, the cataphoric reference was rarely used, which is considered more difficult for 8th graders to understand.
The Efficacy of Using Guided Questions in Teaching Writing Descriptive Text to Indonesian English as Foreign Language (EFL) Students

This research aims at finding out the efficacy of using Guided Questions in teaching writing especially descriptive text to Indonesian English as Foreign Language (EFL) students. The main question of this research: Is the use of Guided Questions effective in teaching writing to Indonesian EFL students? The research can be an addition to the persisting teaching writing in English particularly the descriptive text through the use of guided questions. This research was conducted at the tenth-grade students at SMA Kristen Mercusuar Kupang, an Indonesian High School. The total number of samples was 25 students. The research used a pre-experimental method with a pre-test and post-test design. The pre-test was given to find out the basic ability of the students in mastering writing descriptive text and the post-test given to find out the students’ improvement in mastering writing descriptive text after giving the treatment by using guided questions. The findings show that using guided questions is effective in teaching writing descriptive text to Indonesian EFL students. It is supported by the result of the significance test through SPSS 20 program that the P was 0.00. Therefore, it is recommended to use the guided questions in developing students’ writing descriptive of Indonesian EFL students.
STRATEGY FOR INCREASING THE ROLE OF EDUCATIONAL STAKEHOLDERS IN OVERCOMING FINANCING PROBLEMS

The financing problem is a classic problem that is still the main enemy of educational institutions. Funding that is not managed well will give rise to new problems and is vulnerable to hampering educational programs that have been created. This research aims to discuss qualitative research methods for literature study. The method used is library research, collecting data by searching for sources and reconstructing them from various sources such as books, journals and existing research.This article presents the concept of managing and raising educational funds (fundraising) which can be applied by educational institutions as an alternative to exploring financial resources in addition to using financial resources from the government and school/madrasah committees.
Performance Level of Grade 10 learners in Araling Panlipunan: Basis for the Development of Strategic Intervention Package ( SIP)

Abstract This quantitative study aimed to describe the Performance Level of Baganian National High School Grade10 learners in Araling Panlipunan. It made use of the results of the different performance assessments of the Grade 10 learners in Quarter I to be able to identify the least mastered competencies and used as a baseline in the development of the Strategic Intervention Package (SIP) in the school year 2023-2024. The academic performance of Grade 10 learners in Araling Panlipunan was assessed in several studies. Strategic Intervention Package significantly, improved the performance of Grade 10 learners.
Production-education integrated person-education mode on the psychological stress of college students

Background Many vocational schools adopt the mode of integrating production and education and take the middle school and factory as the teaching background. The purpose of this study is to explore the influence of this educational strategy on students’ psychological stress. Subjects and Methods This study selected 200 students from a vocational college as the research object and randomly divided them into a control group and a study group. The control group adopted the traditional teaching mode. The research group adopted the teaching mode of integration production and education, and integration of school and enterprise. Hamilton Rating Scale for Anxiety (HAM-A) was used as a control for analysis, and statistical software SPSS 22.0 was used for data analysis. Results After six months of experimental research, the anxiety scale scores of students in the control group dropped from 22 to 18 points, and those in the study group dropped from 23 to 11 points. The psychological stress of the students in the study group was significantly different before and after the experiment ( P &lt;0.05). The experimental results show that the teaching mode combined with production and education can alleviate the psychological pressure of vocational college students. Conclusions In this study, the new teaching method combined with middle-class teaching can obviously relieve students’ psychological pressure. At the same time, it can also provide a reference for the administrators of other vocational colleges to promote the healthy development of education and the prosperity of the local economy. Acknowledgement Guangdong Province Education Science planning project (No. 2021GXJK658).
FACIAL EMOTION RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK BASED ON THE VISUAL GEOMETRY GROUP-19

Facial recognition is one of many popular and difficult tasks in computer vision. A variety of research have been conducted on this subject, each of which suggests a stand-alone approach. While many studies strive for more accuracy, this study research aims to increase the efficiency of human computers by classifying emotions based on human faces using a self-based neural network. The usage of a Convolutional Neural Network (CNN) based on the Visual Geometry Group - 19 (VGG-19) classification model, which has been employed in ImageNet data sets and improved for emotion classification, is proposed in this paper. The classification process was conducted using FER-2013 dataset, which consists of over 35,000 facial images captured in various settings and contains 7 different emotions. The dataset was divided into three subsets, with 80% allocated for training, 10% for validation, and 10% for testing. With an accuracy of 71.80%, the proposed technique surpasses most self-based models.
Transitivity Analysis of AJ Hoge’s Short Story “Day of the Dead”

This study analyzes a short story created by AJ Hoge, a well-known English Materials creator of effortless English learning with his short story. It aims to reveal how meanings are constructed and what characterizes the story. Transitivity system, a framework of Halliday's Systemic Functional Linguistics (SFL), is used as an analytical tool. The data are gathered from the short story entitled “Day of the Dead”. This study employs qualitative descriptive analysis incorporating SFL into the methodology and Transitivity system analysis. This study finds that the creator employs all six Process types of Transitivity i.e., Material, Mental, Relational, Behavioral, Existential, and Verbal Processes. In constructing meanings, the six Processes are manifested in the three elements of the story: orientation, sequence of events, and reorientation. The employment of the six Processes is as an effort to expose learner to the clause as representation completely, that is to provide learners with all possible represented experiences through Processes that may happen in the world. The dominant Process of Material becomes the characteristic of the text and meets the linguistic features of recount text.