Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF BIORHYTHM GRAPH WITH TRIGONOMETRIC FUNCTIONS USING PYTHON

This study aimed to implement the researcher's biorhythm graph using a programing language called Python in relation to trigonometric functions. The etymology of biorhythm is a combination of two Greek words, Bio, which means life, and Rhythm, which means regular and accurate rhythm, and means the rules of human life rhythm. In other words, the biorhythm is a theory that everyone is governed by three rhythm curves called physical rhythm, emotional rhythm, and intellectual rhythm that start inside the body from birth to death. During this study, biorhythm and trigonometric functions were studied, and biorhythm graphs were implemented with trigonometric functions when the researcher's date of birth was entered through Python. A biorhythm graph was implemented based on Python, the most used programming language. The Python syntax used in the algorithm is typically a sympy function, a datetime function, arrange function, and a function. The value obtained after implementing the algorithm using this grammar is as follows. This is the result of implementing a biorhythm graph using the algorithm implemented by the two researchers. This paper can be used in various fields related to the relationship between biorhythms and trigonometric functions.
Breast Cancer Prediction Feature Selection Using ML Algorithms

Abstract Breast cancer is a disease that breast epithelial cells uncontrolled hyperplasia under the effects of several different carcinogens. The early symptoms of breast cancer are breast lesions, nipple discharge and axillary fossa lymphadenectasis. Breast cancer can directly threat the life of patient by casing various organ lesions and metastasis of cancer cells in the late stage of suffering from this disease. The method of classifying tumors into malignant or benign is the main challenge that needed to be dealt with to treat patient in a correct way. This paper shows the different classification models to predict the kind of breast cancer. All data used to distinguish the type of breast cancer in the research comes from UCI repository. Some simple classifiers are employed in the research, for instance, Decision Tree Classifier, Extreme Gradient Boosting, Naïve Bayes Classifier, Gradient Boosting, Support Vector Machine. The results show that using Support Vector Machine algorithm the prediction accuracy of breast cancer reaches 99.7%.
A study on the Differences in Willingness to Work, Job-Seeking Efficacy and Job Preparation Behaviors based on Adult Learners' Individual Characteristics

This study aimed to examine the perception differences in willingness to work, job-seeking self-efficacy, and job preparation behaviors among 217 learners participating in SW vocational education, based on their individual characteristics such as major, work experience, and gender. Using SPSS 28.0, t-tests and multiple regression analysis were conducted. The results revealed no significant perception differences in willingness to work, job-seeking self-efficacy, and job preparation behaviors based on major and work experience. However, differences in perception were observed based on gender, specifically in job-seeking self-efficacy and job preparation behaviors. The regression models examining the impact of willingness to work and job-seeking self-efficacy on job preparation behaviors were found to be suitable. It was found that willingness to work does not influence job preparation behaviors for individuals with a major and no work experience, as well as male learners. On the other hand, for non-major learners, individuals with work experience, and female learners, both willingness to work and job-seeking self-efficacy were found to influence job preparation behaviors. This study identified differences based on individual characteristics and highlights the need for training institutions to enhance learners' job preparation behaviors by fostering willingness to work and job-seeking self-efficacy, particularly in the context of SW vocational education.
The Current Situation of Teaching Plan of Schools in Japan and System Development to Improve the Problem

In Japanese educational culture, it is important for teachers to share information obtained through planning and practicing teaching and learning in order to manage classes. However, in the Japanese educational field, teachers are not able to have enough time for lesson preparation. Therefore, they partially extract necessary information from the uniformed teaching plan took over in the same format or from staggering amount of unsorted information on the internet. This causes many problems. For example, the teaching plan is not optimized for the students, who are in his/her classroom at the time. In addition, teachers are overburdened with planning and preparing lessons. In view of the foregoing, In this research, we focused on the process of creating teaching plans in the Japanese educational field. We will identify current problems and develop a teaching plan database that can be easily accessed by anyone. In short, the system can equally provide all teachers with the know-how of the educational practices that many teachers have developed as experiences. In this way, we aim to improve the quality of education.
Situational tasks in the study of chemistry by students of nonchemical specialties

The article presents situational tasks in the discipline of higher educational institutions «Chemistry» and substantiates the advantages and importance of using this type of task in practical classes. Thus, a specific example of work to reduce the gap between the knowledge and skills of a university graduate and the needs of the employer is given. Situational tasks are presented as learning tools that are important to use, on the one hand, to ensure that the teacher develops students’ knowledge, skills and abilities provided for by regulatory documentation and, on the other hand, to make students aware of the purpose of studying a particular topic of the discipline, ways to search and find solutions concerning their professional activities, explanation and competent solution of everyday issues. For example, tasks and assignments on some topics of the course are presented: the basic concepts and laws of chemistry, the periodic system of elements, the energetics of chemical reactions, solutions and the basics of electrochemistry for students of non-chemical specialties of the university studying chemistry as a general educational discipline, which is a prerequisite for basic and specialized disciplines of the specialty. Such work in practical classes makes it possible to observe the expansion of knowledge and skills of students, an increase in their interest in the chosen profession and in the discipline «Chemistry», socio-psychological satisfaction from their own ability to apply the acquired knowledge in practice and, as a result, an easier inclusion of a young specialist in professional activity and the team.
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning

The proliferation of global censorship has led to the development of a plethora of measurement platforms to monitor and expose it. Censorship of the domain name system (DNS) is a key mechanism used across different countries. It is currently detected by applying heuristics to samples of DNS queries and responses (probes) for specific destinations. These heuristics, however, are both platform-specific and have been found to be brittle when censors change their blocking behavior, necessitating a more reliable automated process for detecting censorship.
Trustworthy Recommender Systems: Foundations and Frontiers

Recommender systems aim to provide personalized suggestions to users, helping them make effective decisions. However, recent evidence has revealed the untrustworthy aspects of advanced recommender systems, leading to harmful effects in safety-critical areas like finance and healthcare. This tutorial will offer a comprehensive overview of achieving trustworthy recommender systems. It will cover six important aspects: Safety & Robustness, Non-discrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. Each aspect will be defined and categorized, followed by a discussion of the latest research progress and notable works. Additionally, potential interactions among these aspects and future research directions for trustworthy recommender systems will be explored.
Efficient Bi-Level Optimization for Recommendation Denoising

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable.
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou

Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction systems. Conventional algorithms mostly follow two cascading stages: a simple General Search Unit (GSU) for fast and coarse search over tens of thousands of long-term behaviors and an Exact Search Unit (ESU) for effective Target Attention (TA) over the small number of finalists from GSU. Although efficient, existing algorithms mostly suffer from a crucial limitation: the inconsistent target-behavior relevance metrics between GSU and ESU. As a result, their GSU usually misses highly relevant behaviors but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU, no matter how attention is allocated, mostly deviates from the real user interests and thus degrades the overall CTR prediction accuracy. To address such inconsistency, we propose TWo-stage Interest Network (TWIN), where our Consistency-Preserved GSU (CP-GSU) adopts the identical target-behavior relevance metric as the TA in ESU, making the two stages twins. Specifically, to break TA's computational bottleneck and extend it from ESU to GSU, or namely from behavior length 102 to length 104 - 105, we build a novel attention mechanism by behavior feature splitting. For the video inherent features of a behavior, we calculate their linear projection by efficient pre-computing & caching strategies. And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost. The consistency between two stages, together with the effective TA-based relevance metric in CP-GSU, contributes to significant performance gain in CTR prediction. Offline experiments on a 46 billion scale real production dataset from Kuaishou and an Online A/B test show that TWIN outperforms all compared SOTA algorithms. With optimized online infrastructure, we reduce the computational bottleneck by 99.3%, which contributes to the successful deployment of TWIN on Kuaishou, serving the main traffic of hundreds of millions of active users everyday.
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop

Industrial recommender systems face the challenge of operating in non-stationary environments, where data distribution shifts arise from evolving user behaviors over time. To tackle this challenge, a common approach is to periodically re-train or incrementally update deployed deep models with newly observed data, resulting in a continual learning process. However, the conventional learning paradigm of neural networks relies on iterative gradient-based updates with a small learning rate, making it slow for large recommendation models to adapt. In this paper, we introduce ReLoop2, a self-correcting learning loop that facilitates fast model adaptation in online recommender systems through responsive error compensation. Inspired by the slow-fast complementary learning system observed in human brains, we propose an error memory module that directly stores error samples from incoming data streams. These stored samples are subsequently leveraged to compensate for model prediction errors during testing, particularly under distribution shifts. The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation. We evaluate the effectiveness of ReLoop2 on three open benchmark datasets as well as a real-world production dataset. The results demonstrate the potential of ReLoop2 in enhancing the responsiveness and adaptiveness of recommender systems operating in non-stationary environments.
MicroscopeSketch: Accurate Sliding Estimation Using Adaptive Zooming

High-accuracy real-time data stream estimations are critical for various applications, and sliding-window-based techniques have attracted wide attention. However, existing solutions struggle to achieve high accuracy, generality, and low memory usage simultaneously. To overcome these limitations, we present MicroscopeSketch, a high-accuracy sketch framework. Our key technique, called adaptive zooming, dynamically adjusts the granularity of counters to maximize accuracy while minimizing memory usage. By applying MicroscopeSketch to three specific tasks---frequency estimation, top-k frequent items discovery, and top-k heavy changes identification-we demonstrate substantial improvements over existing methods, reducing errors by roughly 4 times for frequency estimation and 3 times for identifying top-k items. The relevant source code is available in a GitHub repository.
Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog

Retrieving proper domain knowledge from an external database lies at the heart of end-toend task-oriented dialog systems to generate informative responses.Most existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses, leading to suboptimal retrieval performance when the knowledge base becomes large-scale.To address this, we propose to decouple knowledge retrieval from response generation and introduce a multigrained knowledge retriever (MAKER) that includes an entity selector to search for relevant entities and an attribute selector to filter out irrelevant attributes.To train the retriever, we propose a novel distillation objective that derives supervision signals from the response generator.Experiments conducted on three standard benchmarks with both small and largescale knowledge bases demonstrate that our retriever performs knowledge retrieval more effectively than existing methods.Our code has been made publicly available.
Boosting Event Extraction with Denoised Structure-to-Text Augmentation

Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations.In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity.However, when applying to the task of event extraction, recent data augmentation methods often neglect the problem of grammatical incorrectness, structure misalignment, and semantic drifting, leading to unsatisfactory performances.In order to solve these problems, we propose a denoised structure-to-text augmentation framework for event extraction (DAEE), which generates additional training data through the knowledge-based structure-totext generation model and selects the effective subset from the generated data iteratively with a deep reinforcement learning agent.Experimental results on several datasets demonstrate that the proposed method generates more diverse text representations for event extraction and achieves comparable results with the stateof-the-art.
Songs Across Borders: Singable and Controllable Neural Lyric Translation

The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations.This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation problem, converting theoretical guidance and practical techniques from translatology literature to promptdriven NMT approaches, exploring better adaptation methods, and instantiating them to an English-Chinese lyric translation system.Our model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall.In our subjective evaluation, our model shows a 75% relative enhancement on overall quality, compared against naive finetuning 1 .
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection

Classic approaches to content moderation typically apply a rule-based heuristic approach to flag content.While rules are easily customizable and intuitive for humans to interpret, they are inherently fragile and lack the flexibility or robustness needed to moderate the vast amount of undesirable content found online today.Recent advances in deep learning have demonstrated the promise of using highly effective deep neural models to overcome these challenges.However, despite the improved performance, these data-driven models lack transparency and explainability, often leading to mistrust from everyday users and a lack of adoption by many platforms.In this paper, we present Rule By Example (RBE): a novel exemplarbased contrastive learning approach for learning from logical rules for the task of textual content moderation.RBE is capable of providing rule-grounded predictions, allowing for more explainable and customizable predictions compared to typical deep learning-based approaches.We demonstrate that our approach is capable of learning rich rule embedding representations using only a few data examples.Experimental results on 3 popular hate speech classification datasets show that RBE is able to outperform state-of-the-art deep learning classifiers as well as the use of rules in both supervised and unsupervised settings while providing explainable model predictions via rulegrounding.
SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposes

We present a neural Sanskrit Natural Language Processing (NLP) toolkit named SanskritShala 1 to facilitate computational linguistic analyses for several tasks such as word segmentation, morphological tagging, dependency parsing, and compound type identification.Our systems currently report state-of-the-art performance on available benchmark datasets for all tasks.San-skritShala is deployed as a web-based application, which allows a user to get real-time analysis for the given input.It is built with easy-touse interactive data annotation features that allow annotators to correct the system predictions when it makes mistakes.We publicly release the source codes of the 4 modules included in the toolkit, 7 word embedding models that have been trained on publicly available Sanskrit corpora and multiple annotated datasets such as word similarity, relatedness, categorization, analogy prediction to assess intrinsic properties of word embeddings.So far as we know, this is the first neural-based Sanskrit NLP toolkit that has a web-based interface and a number of NLP modules.We are sure that the people who are willing to work with Sanskrit will find it useful for pedagogical and annotative purposes.SanskritShala is available at: https:// cnerg.iitkgp.ac.in/sanskritshala.
Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term

Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization term. We prove its generalization bound through the combination of PAC and Bayes-PAC techniques, and evaluate its performance on various public datasets. The results demonstrate that WSAM achieves improved generalization, or is at least highly competitive, compared to the vanilla optimizer, SAM and its variants. The code is available at this link https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers.
A Hyperparameter Optimization Toolkit for Neural Machine Translation Research

Hyperparameter optimization is an important but often overlooked process in the research of deep learning technologies.To obtain a good model, one must carefully tune hyperparameters that determine the architecture and training algorithm.Insufficient tuning may result in poor results, while inequitable tuning may lead to exaggerated differences between models.We present a hyperparameter optimization toolkit for neural machine translation (NMT) to help researchers focus their time on the creative rather than the mundane.The toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software.Using the Asynchronous Successive Halving Algorithm (ASHA), we demonstrate that it is possible to discover near-optimal models under a computational budget with little effort. 1
ShapleyFL: Robust Federated Learning Based on Shapley Value

Federated Learning (FL) allows clients to form a consortium to train a global model under the orchestration of a central server while keeping data on the local client without sharing it, thus mitigating data privacy issues. However, training a robust global model is challenging since the local data is invisible to the server. The local data of clients are naturally heterogeneous, while some clients can use corrupted data or send malicious updates to interfere with the training process artificially. Meanwhile, communication and computation costs are inevitable challenges in designing a practical FL algorithm. In this paper, to improve the robustness of FL, we propose a Shapley value-inspired adaptive weighting mechanism, which regards the FL training as sequential cooperative games and adjusts clients' weights according to their contributions. We also develop a client sampling strategy based on importance sampling, which can reduce the communication cost by optimizing the variance of the global updates according to the weights of clients. Furthermore, to diminish the computation cost of the server, we propose a weight calculation method by estimating differences between the Shapley value of clients. Our experimental results on several real data sets demonstrate the effectiveness of our approaches.