Reviewerly/exHarmony huggingface dataset:
A weighted-sum chaotic sparrow search algorithm for interdisciplinary feature selection and data classification

Abstract In today’s data-driven digital culture, there is a critical demand for optimized solutions that essentially reduce operating expenses while attempting to increase productivity. The amount of memory and processing time that can be used to process enormous volumes of data are subject to a number of limitations. This would undoubtedly be more of a problem if a dataset contained redundant and uninteresting information. For instance, many datasets contain a number of non-informative features that primarily deceive a given classification algorithm. In order to tackle this, researchers have been developing a variety of feature selection (FS) techniques that aim to eliminate unnecessary information from the raw datasets before putting them in front of a machine learning (ML) algorithm. Meta-heuristic optimization algorithms are often a solid choice to solve NP-hard problems like FS. In this study, we present a wrapper FS technique based on the sparrow search algorithm (SSA), a type of meta-heuristic. SSA is a swarm intelligence (SI) method that stands out because of its quick convergence and improved stability. SSA does have some drawbacks, like lower swarm diversity and weak exploration ability in late iterations, like the majority of SI algorithms. So, using ten chaotic maps, we try to ameliorate SSA in three ways: (i) the initial swarm generation; (ii) the substitution of two random variables in SSA; and (iii) clamping the sparrows crossing the search range. As a result, we get CSSA, a chaotic form of SSA. Extensive comparisons show CSSA to be superior in terms of swarm diversity and convergence speed in solving various representative functions from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) benchmark set. Furthermore, experimental analysis of CSSA on eighteen interdisciplinary, multi-scale ML datasets from the University of California Irvine (UCI) data repository, as well as three high-dimensional microarray datasets, demonstrates that CSSA outperforms twelve state-of-the-art algorithms in a classification task based on FS discipline. Finally, a 5%-significance-level statistical post-hoc analysis based on Wilcoxon’s signed-rank test, Friedman’s rank test, and Nemenyi’s test confirms CSSA’s significance in terms of overall fitness, classification accuracy, selected feature size, computational time, convergence trace, and stability.
ELion: An Intelligent Chinese Composition Tutoring System Based on Large Language Models

For a long time, Chinese language teachers in primary and secondary schools have been confronting challenges of heavy workload, low efficiency, and difficulty in improving the quality of composition evaluations. This article introduces “ELion”, an intelligent Chinese composition tutoring system based on large language models. The system utilizes deep linguistic features to evaluate the quality of compositions and provide interpretable feedback. By discussing the overall design, evaluation framework structure, and scoring algorithm principles of ELion, this paper addresses the theoretical, technical, and engineering issues of intelligent evaluation of Chinese compositions in the educational context. Small-scale experiments conducted in schools demonstrate that ELion performs well in language error detection, rhetorical techniques, and the expression of actions and emotions. It can basically meet the needs of Chinese language teaching in primary and secondary schools. In the future, ELion will further develop algorithms for ”instruction-learning-evaluation” alignment assessment, and personalized precise feedback generation, based on the GPT model. This will improve the evaluation effectiveness in topic analysis, text structure, and genuine emotional expression. Additionally, systematic field experiments for the system will be conducted to explore the application of artificial intelligence in education.
Deep Reinforcement Learning Using Optimized Monte Carlo Tree Search in EWN

EinStein würfelt nicht! is a perfect information stochastic game in which randomness influences the game process enormously. In this paper, we propose an optimized algorithm named Quick Neural Network Tree Search (QNNTS) based on deep reinforcement learning and Monte Carlo tree search (MCTS) to construct the artificial intelligence agent of EWN. Meanwhile the lightness of the model makes it possible to train with much less computing resources. The optimization structure of the algorithm based on MCTS is named Optimized UCT with Heuristic Search (UCTH), which introduces the expectation valuation strategy into the Monte Carlo tree search. As the prerequisite product of QNNTS, it performs with an improvement of the winning rate. Ultimately, the Attention-ResNet structure combined with domain knowledge is used to obtain the proposed algorithm. Compared with several conventional algorithms, it gains high winning rates of at least 68%.
Free Moving Gaze-related Electroencephalography in Mobile Virtual Environments

In realistic contexts involving natural, unconstrained head and body movement, methods for parsing eye movements and using these segmentations to analyze other modalities of synchronously recorded physiological data are still in their infancy. In this study, we recorded eye and head movements, along with simultaneous electroencephalography (EEG), during a 3D visual oddball task in a virtual environment. Healthy adults evaluated standards and deviants presented to their near or far peripheral field of view either by moving their eyes only (low head movement (HM)) or turning their heads (high HM). Compensatory eye movements – likely related to vestibulo-ocular reflex – were found to accompany high HM, suggesting the possible inadequacy of algorithms based purely on the angular velocity of pupil movement for parsing fixations and saccades during head movement – as visual processing likely begins before compensatory eye movement attenuates. To assess the validity of a velocity-based parsing approach, we compared three approaches to computing fixation-related potentials (FRPs) during either low or high HM – stimulus-locking, gaze-related fixation-locking, and simple gaze-locking. Under low HM conditions, both gaze-related fixation-locking and simple gaze-locking yielded classic oddball effects within the expected time window of the P300. Further, evidence of sensitivity to the relative frequency of standards versus deviants was detectable from about 300 ms before fixation onset. On high HM trials, gaze-related fixation-locking yielded a more robust P300 effect than simple gaze-locking. This outcome reveals that despite uncertainty in fixation onset times during head turns, fixation-locking approaches incorporating gaze information are viable in paradigms involving head movement.
A Testing Framework for AI Linguistic Systems (testFAILS)

This paper introduces testFAILS, an innovative testing framework designed for the rigorous evaluation of AI Linguistic Systems, with a particular emphasis on various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, “How should we evaluate AI?” While the Turing test has traditionally been the benchmark for AI evaluation, we argue that current publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Our research, which is ongoing, has already validated several versions of ChatGPT, and we are currently conducting comprehensive testing on the latest models, including ChatGPT-4, Bard, Bing Bot, and the LLaMA model. The testFAILS framework is designed to be adaptable, ready to evaluate new bot versions as they are released. Additionally, we have tested available chatbot APIs and developed our own application, AIDoctor, utilizing the ChatGPT-4 model and Microsoft Azure AI technologies.
Perancangan Website Profil Program Studi Menggunakan Content Management System Wordpress

This article discusses designing a study program profile website using the WordPress Content Management System (CMS). Higher education has a need to present relevant and up-to-date information to prospective students, students, lecturers, and the general public. A study program profile website can be an effective tool to meet these needs. The design method used in this study involves the stages of needs analysis, interface design, selecting the appropriate WordPress theme, developing special features relevant to the study program, and testing functionality. Using the WordPress CMS as a content management platform makes it easy to manage and update content independently without requiring advanced programming skills. The result of this research is a responsive, attractive, and easy to navigate study program profile website. The features implemented include information about study programs, curriculum, lecturers, facilities, and the latest news. In addition, it is equipped with a search feature to make it easier for users to find the specific information they want. Implementing the WordPress CMS as a content management platform provides time and cost efficiency benefits in managing websites. In addition, the flexibility and ease of adding or changing content ensures that the information presented is always up-to-date relevant. In conclusion, designing a study program profile website using the WordPress CMS is an effective solution for presenting up-to-date and relevant information to stakeholders in the academic environment. This research contributes to the field of information technology, especially in the use of CMS platforms for efficient content management in the context of higher education.
Analysis Of Information Security It Governance With Cobit 5 Framework At PT. Indonesia Comnets Plus (ICON+) SUMBAGUT

Currently, information technology is an important part for companies to meet their needs and support the achievement of corporate strategy. Providing a competitive advantage, increasing effectiveness, time and reducing costs is the role of information technology in today's business area. As well as information is an important asset for the company that helps in the development of the company. Information really needs to be guarded and managed securely. PT. ICON+ prioritizes information technology in daily activities, so PT. ICON+ also needs to secure the information assets that exist in the company. Information security must be maintained because it is an important asset owned by the company. Therefore, examining information technology governance and information security using COBIT 5 in a company is really needed. This research can find out whether the company has fulfilled the indicator requirements or not otherwise. The framework and maintenance governance audit was carried out at PT. ICON+ is used to find out indicators of ability. The results were obtained using a questionnaire distributed to companies. In this study, it was found that PT. ICON+ achieved the expected result.
Data Exploration Based on Local Attribution Explanation: A Medical Use Case

Exploratory data analysis allows to discover knowledge and patterns and to test hypotheses. Modelling predictive tools associated with explainability made it possible to explore more and more complex relationships between attributes. This study presents a method to use local explanations as a new data space to retrieve precise and pertinent information. We aim to apply this method to a medical dataset and underline the benefit of using explanations to gain knowledge. In particular, we show that clusters based on local explanations, combined with decision rules, allow to better characterise patient subgroups.
Towards Reliable Machine Learning

The increasing interest in machine and deep learning applications has highlighted how much powerful is to have notable amounts of data and complex models which can leverage them to solve hard tasks. The reverse of the medal of this complexity is a lack of interpretability for obtained results and an impossibility to inspect data to prevent possible issues like biases or to find new knowledge. This paper focuses on the Explainable Artificial Intelligence topic, aimed to overcome the highlighted critical issues by providing users and practitioners with the instruments for a more aware use of models and data, giving an overview of some of the open problems and describing some methodologies proposed to face them.
UAV-Assisted Intelligent Traffic Diagnosis System Design

Traffic flow is one of the most important information for traffic management. Traditionally, the data were obtained only from intersection monitors, which lack a macroscopic view of the entire roadway. Recently, the applications of unmanned aerial vehicles (UAV) have been widely applied to many fields and have become popular. Due to the programmable path planning and 3D movement characteristics of UAVs, we integrate edge computing for image recognition processing with UAVs to perform traffic flow analysis. This study successfully developed a prototype system to analyze the road segment video that is recorded from UAV-mounted cameras. A deep learning technique will be used to perform vehicle identification and tracking tasks. The average vehicle speed and vehicle flow can then be determined. In addition, violation event detection (including speeding, illegal parking, etc.) can also be reported. The system will automatically produce the diagnosis report. It can greatly reduce the burden of traditional manual image viewing, and the analyzed results can be used for traffic management units to improve traffic strategies.
The Teachers Strategy in Teaching Reading Comprehension at SMP Swasta Mardi Wiyata Utama Gunung Bayu

The aim of this research was to find the teacher’s strategy in teaching reading comprehension at SMP Mardi Wiyata Utama Gunung Bayu in the academic year 2022/2023. This research used qualitative approach and the research used obseravation, questionnaire, recording, and interview as the method of collecting data. The objective of this research is to know the the English teacher’s strategies in teaching reading. the result of research is the english teacher impementing three strategies in teaching reading. They are scaffolding, and qars, (Question and Answer Relationship). Based on the observation, recording, interview, and the data of questionnaire show that the english teacher implement all the strategies in teaching reading. Moreover, the data from questionnaire revealed that most of the students agree that the english teacher has good ability in teaching reading by using those strategy.&#x0D; Keywords: Teachers Strategy, Reading Comprehension, Teaching Reading
Examining the Potential of Generative Language Models for Aviation Safety Analysis: Case Study and Insights Using the Aviation Safety Reporting System (ASRS)

This research investigates the potential application of generative language models, especially ChatGPT, in aviation safety analysis as a means to enhance the efficiency of safety analyses and accelerate the time it takes to process incident reports. In particular, ChatGPT was leveraged to generate incident synopses from narratives, which were subsequently compared with ground-truth synopses from the Aviation Safety Reporting System (ASRS) dataset. The comparison was facilitated by using embeddings from Large Language Models (LLMs), with aeroBERT demonstrating the highest similarity due to its aerospace-specific fine-tuning. A positive correlation was observed between the synopsis length and its cosine similarity. In a subsequent phase, human factors issues involved in incidents, as identified by ChatGPT, were compared to human factors issues identified by safety analysts. The precision was found to be 0.61, with ChatGPT demonstrating a cautious approach toward attributing human factors issues. Finally, the model was utilized to execute an evaluation of accountability. As no dedicated ground-truth column existed for this task, a manual evaluation was conducted to compare the quality of outputs provided by ChatGPT to the ground truths provided by safety analysts. This study discusses the advantages and pitfalls of generative language models in the context of aviation safety analysis and proposes a human-in-the-loop system to ensure responsible and effective utilization of such models, leading to continuous improvement and fostering a collaborative approach in the aviation safety domain.
Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset

Automatic text simplification can help patients to better understand their own clinical notes. A major hurdle for the development of clinical text simplification methods is the lack of high quality resources. We report ongoing efforts in creating a parallel dataset of professionally simplified clinical notes. Currently, this corpus consists of 851 document-level simplifications of German pathology reports. We highlight characteristics of this dataset and establish first baselines for paragraph-level simplification.
ELectronic Archimedes spiral Neural Network (ELANN)

The Archimedes spiral is a clinical tool that aids in the diagnosis and monitoring of essential tremor. However, spiral ratings may vary based on experience and training of the rating physician. This study sought to generate an objective standard model for tremor evaluation using convolutional neural networks. One senior movement disorders neurologist (Neurologist 1) with over 30 years of clinical experience used the Bain and Findley Spirography Rating Scale to rate 1653 Archimedes spiral images from 46 essential tremor patients (mild to severe tremor) and 75 control subjects (no to mild tremor). Neurologist 1's labels were used as the reference standard to train the model. After training the model, a randomly selected subset of spiral testing data was re-evaluated by Neurologist 1, by a second senior movement disorders neurologist (Neurologist 2) with over 27 years of clinical experience, and by our model. Cohen's Weighted Kappa 95% confidence intervals were calculated from all rater comparisons to determine if our model performs with the same proficiency as two senior movement disorders neurologists. The Cohen's Weighted Kappa 95% confidence intervals for the agreement between the reference standard scores and Neurologist 1's rerated scores, for the agreement between the reference standard scores and Neurologist 2's scores, and for the agreement between the reference standard scores and our model's scores were 0.93-0.98, 0.86-0.94, and 0.89-0.96, respectively. With overlapping Cohen's Weighted Kappa 95% confidence intervals for all agreement comparisons, we demonstrate that our model evaluates spirals with the same proficiency as two senior movement disorders neurologists.
Deep Learning Based Localisation and Segmentation of Prostate Cancer from mp-MRI Images

Prostate Cancer (PCa) is one of the most common diseases in adult males. Currently, mp-MRI imaging represents the most promising technique for screening, diagnosing, and managing this cancer. However, the multiple mp-MRI sequences' visual interpretation is not straightforward and may present crucial inter-reader variability in the diagnosis, especially when the images contradict each other. In this work, we propose a computer-aided diagnostic system to assist the radiologist inlocating and segmenting prostate lesions. As fully convolutional neural networks (UNet) have proved themselves the leading algorithm for biomedical image segmentation, we investigate their use to find PCa lesions and segment for accurate lesions contours jointly. We offer a fully automatic system via MultiResUNet, initially proposed to segment skin cancer. We trained and validated an altered version of the MultiResUnet model using an augmented Radboudumc prostate cancer dataset and obtained encouraging results. An accuracy of 98.34\% is achieved, outperforming the concurrent system based on deep architecture.
Parasitic egg recognition using convolution and attention network

Abstract Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.
Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP). The model is trained and tested on a dataset of text samples collected from various authors, and the performance is evaluated using accuracy, precision, recall, and F1 score. We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model. Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%. Furthermore, we demonstrate that hyperparameter tuning can help achieve significant performance improvements, even using a relatively simple model architecture like CNNs. Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice. Overall, this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations. Our findings have important implications for developing accurate and reliable authorship verification systems, which are crucial for various applications in digital forensics, such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.