Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Algorithms: Categorization and Structural Properties

Over the last years, the field of artificial intelligence (AI) has continuously evolved to great success. As a subset of AI, Reinforcement Learning (RL) has gained significant popularity as well and a variety of RL algorithms and extensions have been developed for various use cases. Although RL is applicable to a wide range of problems today, the amount of options is overwhelming and identifying the advantages and disadvantages of methods for selecting the most suitable algorithms is difficult. Sources use conflicting terminology, imply improvements to alternative algorithms without mathematical or empirical proof, or provide incomplete information. As a result, there is the chance for engineers and researchers to miss alternatives or perfect-fit algorithms for their specific problems. In this paper, we identify and explain essential properties of RL problems and algorithms. Our discussion of these concepts can be used to select, optimize, and compare RL algorithms and their extensions with respect to particular problems, as well as reason about their performance.
Creating Interactive Visualizations with Plotly

This lesson demonstrates how to create interactive data visualizations in Python with Plotly's open-source graphing libraries using materials from the Historical Violence Database.
Sentiment Analyzer

Abstract: This project is an exploration into sentiment analysis, aiming to construct a resilient sentiment analyzer through natural language processing (NLP). The primary objective lies in identifying emotions—positive, negative, or neutral—in varied textual content. Methodologically, it involves the creation of meticulously curated datasets, employing advanced pre-processing techniques, and delving into diverse model explorations. Despite challenges encountered, such as deciphering sarcasm and navigating contextual nuances, the project achieves the development of a high-performing sentiment analyzer. Emphasizing continual enhancement, the project underscores the need for ongoing training on evolving datasets and the integration of advanced NLP models to elevate accuracy levels, acknowledging the significance of these advancements in sentiment analysis.
Using the spirit of the Dabie Mountains to educate contemporary young students

The spirit of Dabie Mountain plays an important role in the Chinese Revolution and socialist modernization, and it is an important part of the Chinese revolutionary spirit. As an educator, we should take the era value of the Dabie spirit as the guidance, take the implementation of core literacy education as the means, and start from the training standard of talents, to promote the comprehensive and healthy development of contemporary young students’ body and mind.
Research on the Cultivating Mechanism of Pokémon Franchise Core Games

Since the birth of Pokémon in 1996, after 27 years of development, it has become the world’s second hottest-selling series of video games and is highly influential worldwide. As a role-playing game, the game encourages players to keep exploring, meet more Pokémon, and become league champions through Pokémon battles. The game revolves around four core elements: col-lecting, cultivating, exchanging and battling. This study will focus on the cultivating mechanism of the Pokémon game and explore the specific mech-anism and implementation of the Pokémon cultivating gameplay in the game. As a benchmark in the industry, the cultivating gameplay of Pokémon games has been very mature, and the study of its specific mechanics is in-dicative of related games. This study observes and analyzes the Pokémon ability cultivation, Pokémon offspring cultivation, and Pokémon interaction in three directions: the way players participate in Pokémon cultivation, the results of cultivation, and the experience players get. After analysis, this study specifically divides the Pokémon cultivating gameplay into three as-pects: ability training, offspring nurturing and emotional cultivation, and summarizes the specific mechanisms and related impacts included in each aspect and gives opinions on development.
Research on Higher Vocational Teaching Mode Based on Competitive Mechanism

How to arouse students’ interest in learning so as to improve the quality of teaching has always been an important subject for higher vocational education. Practice has proved that the competition method proposed in this study is an effective way to arouse student’s learning interest. Taking programming language courses as an example, it is proposed to introduce a competition mechanism in the teaching process, practicing follow teaching, and by treating each practicing topic as a competition, record the results according to the completion speed and quality, and rank them. For the top-ranked students, points will be added according to certain rules. Collecting personal grades for each class as a process evaluation which is an important part of students’ grade. By introducing the competition mechanism, students’ interest in learning can be greatly stimulated and the quality of teaching can be greatly improved.
Application of Naive Bayes Algorithm for Physical Fitness Level Classification

The implementation of physical fitness tests requires adequate facilities, so technology is needed to make it easier without having to provide facilities. The purpose of this study is to make it easier to get the results of a person's physical fitness level using age, gender, height and weight data through an intelligent system using the naïve Bayes algorithm without having to do a physical fitness test. This research is included in the Experimental research. The method used in this study used machine learning and classification with the naïve Bayes algorithm. Data analysis techniques use probability by using data tests and evaluations. The evaluation used uses accuracy. The population in this study was 100 college students. Training model scheme 98 and test 2 get an accuracy value when training is 100%, on testing an accuracy value of 50%. The best model is used as a reference in predicting new data, using 5 new data where 3 data already know the VO2Max value with the same prediction value and actual value, then 2 new data are not yet known VO2Max value, the 4th data gets a value of 44.2 and the 5th data gets a value of 33.2. The results of VO2Max testing using the naïve Bayes algorithm are declared accountable. Contribution to future research is to multiply research datasets to improve accuracy and improve user interface quality through development research.
ENHANCED HYBRID PATH PLANNING ALGORITHM BASED ON APF AND A-STAR

Abstract. The use of robots had increased widely in various fields, such as air transport systems, search and rescue, and agriculture, necessitating the need for path planning and obstacle avoidance systems to ensure safe and autonomous navigation toward the intended goal. Many Path-planning techniques are used to guide the mobile robot toward its goal with an optimized path and time. Among these techniques, the Artificial Potential Field (APF) algorithm stands out as one of the effective approaches, capable of operating in both static and dynamic environments to achieve optimal path planning. The APF is built by fusing two forces that attract the robot toward a goal location and repulsive forces that repel the robot away from the obstacle. Despite its effectiveness, the APF algorithm faces a major challenge, which is falling into a local-minimum issue. This paper presents a hybrid approach that combines the modified APF algorithm global optimization capabilities with the A-Star path-planning technique's real-time adaptability to overcome traditional APF local minimum issues. The transition back and forth between the two algorithms where carried out by a manager that can determine the adequate algorithm to be used instantaneously. Several experiments are presented to demonstrate the hybrid algorithm's effectiveness in various environments. The results show that the enhanced APF reach an optimal path to goal 50% faster compared to A-star, and managed to get out of local minimum compared to traditional APF and find a path shorter than A-star.
DeepTD: Diversity-Guided Deep Neural Network Test Generation

Coverage-guided Fuzz Testing (CGF) techniques have been applied to deep neural network (DNN) testing in recent years, generating a significant number of test samples to uncover inherent defects in DNN models. However, the effectiveness of CGF techniques that utilize structured coverage metrics as coverage criteria is currently being questioned. A few unstructured coverage metrics, such as surprise adequacy, only take into account the diversity of the test samples against the training set, while ignoring the diversity of the test samples themselves. In addition to this, the existing surprise adequacy metrics have some limitations in their applications. Therefore, this paper proposes DeepTD, a diversity-guided deep neural networks test generation method. Firstly, DeepTD selects high-loss test samples from each class on average, ensuring these test seeds possess a strong ability to reveal model errors. Then, DeepTD transforms these test seeds to enhance the diversity of the generated samples. Finally, Cluster-based Surprise Adequacy is designed to guide the generation of test samples. To evaluate the effectiveness of DeepTD, six DNN models are selected as subjects, covering two well-known image datasets. Experimental results demonstrate that the Cluster-based Surprise Adequacy outperforms the two existing metrics not only in computational efficiency but also in discovering more model defects. What’s more, the test samples generated by DeepTD are on average 6.04% and 3.24% more effective for model retraining in MNIST and CIFAR10 compared to baseline methods, respectively.
Professional'naia deiatel'nost' kak nepreryvnoe pedagogicheskoe samoobrazovanie

The chapter summarizes the experience of creative self-embodiment, including pre-university, pre-teaching experience, its determination in the professional-structural and time continuum as a result of external guidelines (pedagogical environment) and internal factors of personal self-development. The study uses theoretical methods: retrospective analysis; analysis and synthesis; abstraction and concretization; empirical methods (biographical method); examples of experimental tasks.
Software Vulnerability Detection Using an Enhanced Generalization Strategy

Detecting vulnerabilities in software is crucial for preventing cybersecurity attacks, and current machine learning-based methods rely on large amounts of labeled data to train detection models. On the one hand, a major assumption is that the training and test data follow an identical distribution. However, vulnerabilities in different software projects may exhibit various distributions due to their application scenarios, coding habits, and other factors. On the other hand, when detecting vulnerabilities in new projects, it is time-consuming to retrain and test the models. Especially for new projects being developed, it has few or no instances of vulnerabilities. Therefore, how to leverage previous learning experience to learn new projects faster is important. To address these issues, we propose VulGML, a vulnerability detection approach using graph embedding and meta-learning. The goal is to establish a model with enhanced generalization, so that the model trained on multiple known projects can detect vulnerabilities in new projects. To further illustrate the strong generalization of VulGML, we also choose multiple known vulnerability types to train the meta-learning model and a new vulnerability type for vulnerability detection. Experimental results show that VulGML outperforms the state-of-the-art methods by 6.44–39.61% in detecting new projects, achieves an accuracy higher than 77.80% when detecting vulnerabilities in new vulnerability types, and its modules have greatly improved detection performance, demonstrating that VulGML is potentially valuable in practical usage.
Application of Artificial Intelligence Methods and Evaluation Models for Breast Cancer Diagnosis and Prognosis

Cancer is a primary global health concern, and accurate diagnosis and prognosis are crucial in patient care. This paper applies the deep learning algorithm CNN to detect and prognosis breast cancer. The results of the conducted experiment are assessed through rigorous evaluation frameworks, ensuring clinical relevance and applicability. The performance of the developed CNN model enhanced the accuracy, efficiency, and reliability of the breast cancer diagnosis and prognosis.
Performance Analysis of Authentication System: A Systematic Literature Review

Background: Data authentication is vital nowadays, as the development of the internet and its applications allow users to have all-time data availability, attracting attention towards security and privacy and leading to authenticating legitimate users. Methods: We have diversified means to gain access to our accounts, like passwords, biometrics, and smartcards, even by merging two or more techniques or various factors of authentication. This paper presents a systematic literature review of papers published from 2010 to 2022 and gives an overview of all authentication techniques available in the market. Results: Our study provides a comprehensive overview of all three authentication techniques with all performance metrics (Accuracy, Equal Error Rate (EER), False Acceptance Rate (FAR)), security, privacy, memory requirements, and usability (Acceptability by user)) that will help one choose a perfect authentication technique for an application. Conclusion: In addition, the study also explores the performance of multimodal and multifactor authentication and the application areas of authentication.
HeatC: A Variable-Grained Coverage Criterion for Deep Learning Systems

Deep learning (DL) systems have achieved significant success in numerous cutting-edge fields. However, the deployment of DL systems in safety-critical areas has raised public concerns about their correctness and robustness. To provide testing evidence for the dependable behavior of Deep Neural Networks (DNNs), various DL coverage criteria have been proposed. These coverage criteria are often “ad-hoc” in terms of granularity for different tasks, but designing appropriate criteria for every possible usage scenario is infeasible and will make the coverage testing lack of uniform standards. In this paper, we proposes a variable-grained DL coverage criterion named HeatC as a common solution for different coverage testing tasks. HeatC leverages class-activation-map-based features from neural networks and clusters these features to generate test targets. Experiments demonstrate that HeatC outperforms existing mainstream coverage criteria in assessing the adequacy of test suites and selecting high-value test samples from unlabeled datasets.
Enhancing Branch and Bound for Robustness Verification of Neural Networks via an Effective Branching Strategy

The existence of adversarial examples highlights the vulnerability of neural networks and brings much interest to the formal verification of neural network robustness. To improve the scalability of neural network verification while approaching completeness, researchers have adopted the branch-and-bound (BaB) framework. Better branching can reduce the number of branches to explore and plays an important role in BaB verification methods. In this paper, we propose a new branching strategy. It utilizes a low-cost metric to make splitting decisions and supports branching on ReLU activation functions. We conduct experiments on widely used benchmarks to evaluate its performance. Simulation results demonstrate that this branching strategy effectively improves the verification efficiency and is better than the state-of-the-art strategies in overall performance.
Formalization of Lambda Calculus with Explicit Names as a Nominal Reasoning Framework

We formalize the metatheory of lambda calculus in Coq, in its classic form with explicit names. The formalization is founded upon an intuitive $$\alpha $$ -equivalence definition without substitution or name swapping. Furthermore, we provide structural and rule induction principles that encapsulate the Barendregt Variable Convention, enabling formal proofs mirroring informally-styled ones. These principles are leveraged to establish foundational results such as the Church-Rosser theorem. Demonstrating the framework’s utility, we extend first-order logic with predicate definitions, ensuring its soundness through properties obtained from the metatheory by encoding propositions as lambda terms.
A SUPERPOINT NEURAL NETWORK IMPLEMENTATION FOR ACCURATE FEATURE EXTRACTION IN UNSTRUCTURED ENVIRONMENTS

Abstract. Feature extraction plays a crucial role in visual localization, SLAM (Simultaneous Localization and Mapping) and autonomous navigation, by enabling the extraction and tracking of distinctive visual features for both mapping and localization tasks. However, most of the studies, investigate the efficiency and performance of the algorithms in urban, vegetated or indoor environments and not in unstructured environments which suffers by poor information in visual cues where a feature extraction algorithm or architecture could base on. In this study, an investigation of SuperPoint architecture’s efficiency in keypoint detection and description applied to unstructured and planetary scenes was conducted, producing three different models: (a) an original SuperPoint model trained from scratch, (b) an original fine-tuned SuperPoint model, (c) an optimized SuperPoint model, trained from scratch with the same parametarization as the corresponding original model. For the training process, a dataset of 48 000 images was utilized representing unstructured scenes from Earth, Moon and Mars while a benchmark dataset was used aiming to evaluate the model in illumination and viewpoint changes. The experimentation proved that the optimized SuperPoint model provides superior performance using repeatability and homography estimation metrics, compared with the original SuperPoint models, and handcrafted keypoint detectors and descriptors.
A Linear Differentiation Scheme for Camouflaged Target Detection using Convolution Neural Networks

Camouflaged objects are masked within an existing image or video under similar patterns. This makes it tedious to detect target objects post classification. The pattern distributions are monotonous due to similar pixels and non-contrast regions. In this paper, a distribution-differentiated target detection scheme (DDTDS) is proposed for segregating and identifying camouflaged objects. First, the image is segmented using textural pixel patterns for which the linear differentiation is performed. Convolutional neural learning is used for training the regions across pixel distribution and pattern formations. The neural network employs two layers for linear training and pattern differentiation. The differentiated region is trained for its positive rate in identifying the region around the target. Non-uniform patterns are used for training the second layer of the neural network. The proposed scheme pursues a recurrent iteration until the maximum segmentation is achieved. The metrics of positive rate, detection time, and false negatives are used for assessing the proposed scheme’s performance.
The Level Cardinality of Fuzzy Module Under ℤ-Module Homomorphism on ℤn into ℤm Where gcd(n, m) Is Product of Primes

The homomorphic image of a fuzzy module of an R-module is a fuzzy module [1]. In one of our papers [2] we have proved that if f : ℤn → ℤm is a ℤ-module homomorphism with gcd(n, m) = p, a prime and λ is any fuzzy module on ℤn with any level cardinality then the fuzzy module f(λ) on ℤm has level cardinality atmost 3. In this paper, we are considering the fuzzy module homomorphism between the ℤ-modules ℤn and ℤm where n,m ∈ ℤ+ with gcd(n, m) = pq, where p and q are primes and trying to find the level cardinality of the fuzzy module f(λ) on ℤm when λ is a fuzzy module on ℤn.
The Role of Data Stream Mining in Improving Self-Medication Practices in the Syrian Arab Republic

Self-medication is a widespread practice throughout the world, especially in the Syrian Arab Republic. However, if done incorrectly, this method might be harmful. Data stream mining, which involves analyzing vast amounts of data from multiple sources, has proven to be an effective technique for enhancing self-medication practices. The role of data stream mining, and how it may evaluate information from various sources like social media, electronic health records, pharmacy sales data, sensor-based medical devices, etc. have been explored for the research. Healthcare professionals in Syria can improve patient outcomes and safety related to self medication by employing data stream mining to uncover important information about patient behaviors, drug effectiveness, and adverse events. In this paper, a strategy for generating evidence-based medical data utilizing data stream mining techniques is suggested. Here, methods including association rule mining, categorization, and data clustering have been described. For future progression of this research, the information gap and other data collection-related problems need to be resolved. The implementation demonstrates that healthcare professionals can use a variety of data stream mining techniques to better understand drug usage patterns and spot opportunities while also learning about the prevalence of self-medication in different parts of the Syrian Arab Republic.
Two Count Sketch Kaczmarz Algorithms for Linear Systems

In this paper, combining count sketch, we construct a count sketch two greedy subspace Kaczmarz (CS-2GSK) algorithm. In addition, the block count sketch Kaczmarz (BCSK) algorithm is also given, which selects multiple different rows simultaneously at each iteration. We prove that our methods converge to the unique solution of the linear systems. Finally, the numerical experiments show the high efficiency and robustness of our methods.
ANXIETY AND UNCERTAINTY MANAGEMENT: LIGHTS AND SHADOWS OF INTERNATIONAL STUDENTS IN INTERCULTURAL COMMUNICATION AT DIPONEGORO UNIVERSITY IN SEMARANG

The aim of this study is to explore and interpret the significant challenges experienced by international students and the strategies used to cope with them in managing their anxiety and uncertainty in intercultural communication settings at Diponegoro University. A sample size of five students was purposively selected and participated in the data collection using in-depth and semi-structured interviews. The data were analyzed qualitatively using Interpretative Phenomenological Analysis. Through their lived experiences, we realized that majority of participants were clueless about the Indonesian language and Indonesia/ Javanese culture in which some of them faced anxiety and uncertainty challenges in their arrival et during their initial interaction, and adaptation process challenges based chiefly on language anxiety and uncertainty; new culture and environment in communication and interaction with the inside and outside the campuses. They coped with their anxiety and uncertainty by accepting host culture’s lifestyle, friendships, learning the Indonesian language, attending prayers at Mosques, hanging out with friends, and self-learning. Additional learning materials such as dictionaries, google translate, watching the news, books, family assistance, and listening to music were added to overcome their linguistic anxiety and uncertainty. The motivation from lecturers, classmates, and local people to learn a new language was valuable.