Reviewerly/exHarmony huggingface dataset:
Automatic Image Contrast Enhancement Based on Reinfrocement Learning

Image contrast enhancement is a subjective problem depending on personal preference and subject field property. Every person has different perception on the assessment of an enhanced image quality. Thus, it is difficult to have one ideal outcome that satisfies every person with the existing conventional image enhancement techniques. In this paper, we proposed a simple and efficient reinforcement learning based image contrast enhancement method for personal preference. Our method consists of state, action, reward or punishment definition, and policy learning. We have implemented Q-learning and State Action Reward State Action (SARSA) algorithms. The training process is easy for any user by clicking some buttons in our developed graphical user interface (GUI). The experimental results demonstrate good performance of our proposed method in this paper.
Revolutionising Data Handling

In-memory computing is proving a smarter way to access and process data.
Blockchain-Based Secure Medical Data Management with Enabling IoT

Nowadays, the Electronic Health Care System is rapidly expanding. The management of Electronic Health Records (EHRs) on cloud resources has undergone a paradigm change in recent years. The interconnection of connected devices for data collection and intelligent decision-making is known as the Internet of Things (IoT). However, IoT is vulnerable to privacy and security breaches due to a lack of inherent security controls. Data retrieved from IoT devices are mainly stored in a database. For a certain patient, this information is crucial. If the evidence tampers, the doctor will be unable to determine the true nature of the patient's condition and therefore will be unable to provide appropriate care, causing significant damage to the patient. As a result, it is important to ensure a higher level of security in the health sector. For this sake, different security systems have been introduced for the welfare of mankind. Such an emerging technology might be Blockchain. In this case, blockchain technologies would be extremely beneficial. We have created a framework that employs Blockchain technologies to eliminate anonymous data access in the cloud while also ensuring data security. Here, in this paper, we have demonstrated a system prototype using IoT, blockchain, and a user-friendly website.
Crime-Finder: A System for Extraction and Visualization of Crime Data from Bengali Online Newspaper Articles

Information extraction is the task of automatic extraction of relevant information from unstructured or semi-structured documents and other electronically represented sources. In this paper, we present a system to extract and visualize crime information from Bengali online newspaper articles. Although law enforcement agencies can possess this kind of information, they do not usually make them publicly accessible. Apart from this, newspaper articles can also be a good source of such information in an unstructured form. Therefore, this paper focuses on the automatic extraction of publicly available yet unstructured crime information and visualization to make them readily available to the general public. To demonstrate the feasibility of our approach, we focus on crime location data in Bengali newspaper articles. A news article contains many location data as part of the sentences in it. We first employ four different machine learning algorithms to classify the crime location sentences, compare their accuracy and deploy Named Entity Recognizer (NER) to extract the actual address from these location sentences. We then use geocoder to get geo-coordinates. Finally, we built a data visualization tool that utilizes the end result of our approach to further demonstrate that our results can be used for data visualization purposes.
Multilevel Voting Models in Cyber Aggression Detection for Bangla Texts

A cyber aggression over social interactive platforms has increased extensively in the past few years. People, especially teenagers, of all over the world, are continuously becoming the victim of bullying, harassment, racism, sexism and toxicity irrespective of the language they speak. Motivating by the need for cyber aggression detection, we have analyzed a number of individual machine learning classifiers and ensemble-based techniques for detecting cyber bullying in the Bangla Language. Ambiguity in the dialects and miscellaneous vocabularies and sentence structures in Bangla texts have been the main obstacles to achieve satisfactory classification performance so far. However, this research work aims to build multilevel-based voting models to automatically classify Bangla texts into two groups: ‘toxic’ and ‘non-toxic’. Combining with various feature generation techniques and cross-validation techniques, we've achieved high accuracy individually for Logistic Regression and Random forest ensemble technique which is surpassed by our proposed double-level ensemble (DLE) and triple-level ensemble (TLE) voting models. TLE also outperforms DLE yielding the maximum accuracy of 89%.
Deep Learning

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Basic Concepts

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Evaluation of 2D and 3D posture for human activity recognition

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Md Amran Hossen, Abdul Ghani Naim, Pg Emeroylariffion Abas; Evaluation of 2D and 3D posture for human activity recognition. AIP Conference Proceedings 10 January 2023; 2643 (1): 040013. https://doi.org/10.1063/5.0111224 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Hand Gesture Identification and Voice Command Based Hardware Reduction

The use of physical devices like mouse and keyboard to communicate to our computer has hindered the natural interface between the computer and its user. To eradicate this barrier, we have designed a virtual mouse which will act upon the various hand gestures the user provides and will help them interact with the computer in a better way. The virtual mouse control of our system will keep a track of the movements of the fingers and palm, thus providing us with a specific output based on the recognized action of our hand. The proposed model will recognize both static as well as dynamic hand gestures and the results will show that the model that has been proposed and built will intuitively interact with the computer and it can be achieved the minimum usage of any hardware requirements. Our model will also have a major advantage over the current existing models, which is, it also has the feature of voice command recognition, meaning that even if the user if not able to provide appropriate gestures using their hand due to some medical condition, they will still be able to interact with the computer via voice commands.
A Present-Charitable App Design &amp; Development Based On Harmonyos

Currently, fraudulent donations are frequent, leading to a gradual decline in the credibility of charitable organizations and internet charity fundraising information platforms. To solve this problem, an all-in-one APP application based on Harmony OS 2.0 was built to allow the public to participate in charities, follow charities, and monitor charities conveniently. The donation information management APP application development system adopts a modular R&D idea, mainly using DevEco Studio 3.0 and IntelliJ IDEA 2021 as the R&D platform, combines Spring Boot architecture and MYSQL database, etc., and completes the creation of critical functional templates after system design. The system provides users with sufficient information and quick means of inquiry, enabling people to participate easily in charity monitoring, increasing their enthusiasm for charity, and promoting the development of charity in China.
Evaluation of Cultural Heritage Utilization Policy : Focusing on Local Cultural Heritage Utilization Project

The purpose of this study is to evaluate the performance of the Local cultural heritage utilization project and to seek future improvement plans. this study applied Vedung (2010)'s policy evaluation system model to empirically evaluate the local cultural heritage utilization project, which is a representative project of cultural heritage utilization policy. First of all, the evaluation model was set up in the steps of input-implementation-result. The results were more specified as outputs and impacts. The evaluation was based on project statistical data, related reports, work plans or performance reports of the Cultural Heritage Administration. Finally, through the evaluation results, this study suggested improvement directions in terms of business continuity, institutional arrangements, and program planning and capacity building.
A Study on the Improvement of Writing Feedback System and Its Use : Focusing on Using in University's Writing Curriculums and Extracurriculums

In writing education as a liberal arts education, an online education system was established to facilitate communication between teachers and students without time and space restrictions so that effective writing education could be achieved. writing feedback system supports each university to directly utilize the writing feedback system, and allows research on writing feedback to be actively conducted in various fields. In order to carry out the project step by step according to the system construction procedure and to increase utilization in universities, we intend to support education training and workshops for university officials and teachers.
A Study on the Needs Analysis for the Development of Liberal Arts Curriculum for International Students for Academic Purpose: Based on Focus Group Interview of International Students and Teachers at K University

The purpose of this study is to develop a high-quality liberal arts curriculum for International Students of Academic Purpose. The direction of development was proposed by investigating and analyzing the in-depth needs of undergraduate international students and instructors. The subjects of the study were International Students and instructors at ‘K’ University, and the results of the demand survey were analyzed and presented by conducting a focus group interview. The contents discussed based on the analysis results of this study are as follows. In order to increase the satisfaction of undergraduate students in the liberal arts curriculum, it is essential to develop specialized courses for international students separately from Korean students. In addition to the currently opened Korean language classes, various subjects based on core competencies should be opened. In particular, it should not be limited to classes centered on Korean language and Korean culture. Furthermore, when opening a new subject, not only the level of learners should be considered, but also the learning content and educational environment where local learning is conducted to learn the region should be prepared separately. In addition, it is necessary to provide international students with subjects on convergence topics that have recently been emphasized, and to try various ways of teaching through active support from instructors.
Clustering

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Further Explorations

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Classification 2

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
Look, Tell and Match: Refining Video-Text Retrieval with Semantic Information

Video-text retrieval aims to search videos from the database by a natural sentence. However, the core problem of this challenging task is the modality gap between video and text data. As videos are made of abundant visual patterns and texts depict abstract and general concepts. When considering videotext matching, the extracted features from videos and texts remain an asymmetric scale information problem. In this paper, a novel Semantic Enhanced Cross-Modal Retrieval (SECMR) method is proposed to align videos and texts at a symmetric scale. Specifically, we utilize a generative model to predict semantic information from given videos and refine the visual feature with enhanced semantic information. Then we utilize a contrastive learning method to align refined videos and texts in a common space. Extensive experiments demonstrate that our proposed SECMR outperforms the-state-of-the-art counterparts by 1.3%, 3.6%, 4.1% in the metric R@1, R@5, R@10, respectively. Our codes and trained models are released at: https://anonymous.4open.science/r/SECMR-2104.
A Study on the Institutional Change of National R&amp;D Sanctions : From the Perspective of New Institutionalism

The sanctions imposed on national R&amp;D projects are strong administrative measures that can cause researchers to break their research career.&#x0D; Nevertheless, the national R&amp;D sanctions have not been analyzed in depth.&#x0D; Since national R&amp;D sanctions are administrative measures that limit researchers to perform national R&amp;D for a certain period of time, it is important to consider the process of existing institutional changes and the effects of these gradual or radical institutional changes. Therefore, in this study, changes in national R&amp;D sanctions were analyzed from the perspective of new institutionalism, focusing on the 『Framework Act on Science and Technology』, the『Presidential Decree on National R&amp;D Management』, the『National R&amp;D Innovation Act』and subordinate ordinances that are common, applied, and standard in national R&amp;D projects. Gradual institutional changes mainly occur to clarify the ambiguity of the existing institutions, and mainly occur in institutional elements related to procedures and methods. On the other hand, radical institutional changes were the result of external variables such as changes in the external environment, and mainly occurred in institutional factors related to reasons and subjects.&#x0D; Radical institutional changes have the possibility of generating institutional contradictions in the process. In the radical change of the sanctions institution that may infringe on researchers' rights, it is necessary to carefully examine whether there is no internal contradiction in the institution, whether there is room for violation of the principle of proportionality in the process of pursuing equity. As the newly established sanctions institution is changed according to the『National R&amp;D Innovation Act』, the history and context of past institutional changes should be considered.
Psychological Concept of the Scientific Rationality Types. Part 2. Development of Scientific Knowledge

In the first part of the article, it was shown that the type of scientific rationality of a particular theory is determined by the substantial features of the ideal ob­jects used. Depending on the contribution of two components to their content – elements of sensory experience and “free fiction” of the researcher – it is pro­posed to distinguish eight types of rationality. In the second part of the article, an attempt is made to trace the main trends of the historical change of these types of rationality in the history of science, as well as to identify the psycho­logical regulators of the creation of a new scientific knowledge by a particular researcher, specifically involving a transition to a different type of scientific rationality. The provisions of the psychological concept of the scientific ra­tionality types based on the constructivist version of the Self-Determination Theory are developed. A brief analysis of the changes of the rationality types in the history of natural sciences and socio-humanitarian knowledge is given. It is shown that the change of rationality types is nonlinear and assumes the simultaneous coexistence of at least several types within the same branch of science. At the same time, the type of rationality shared by a particular re­searcher determines his individual views on the permissibility of such coexist­ence. Within the framework of the constructivist version of the Self-Determin­ation Theory, the psychological regulators of the formation and competition of scientific knowledge (which are various types of emotionally charged im­ages created by the human psyche) are also analyzed. Psychological regulators of the researcher’s transition to a new type of scientific rationality for a spe­cific scientific field are identified.
A Classification Method for Imbalanced Data Based on Ant Lion Optimizer

Imbalanced data will bring difficulties in data processing, which is very common in data engineering. These data usually have sophisticated distributions. Different resampling methods are required for dealing with data with different distributions, while fixed ones are adopted traditionally. Therefore, to select appropriate resampling methods for data with such characteristics, we propose a novel classification method for Imbalanced Data based on Ant Lion Optimizer, called ALOID. It combines adaptive resampling strategies, feature selection, and ensemble classifiers. The adaptive resampling strategy refers to utilizing roulette wheel selection to choose the most suitable resampling method with a greater probability for each dataset according to the variable probabilities of resampling methods. Then a two-stage approach is further used in feature selection: preprocessing and enhancing. In addition, we adopt an ensemble classifier with dynamic weights. The variable probabilities of resampling methods, features, and the weights of base classifiers are coded in individual solutions. A large number of comprehensive experiments have been carried out in this paper. ALOID is compared with 8 state-of-the-art algorithms on 33 publicly available imbalanced datasets. Using K-nearest neighbor as the base classifier, we have found ALOID outperforms other methods in most cases, especially on high-dimensional imbalanced datasets. Experiment results demonstrate the performance advantage of ALOID over other comparable algorithms.
An Interpretable Conditional Augmentation Classification Approach for Imbalanced EHRs Mortality Prediction

One of the most crucial tasks in the ICU is mortality prediction. The number of deceased patients is significantly lower than the number of survivors, and it is simple to over-identify the survivors. Additionally, the clinical use of present machine learning and deep learning models is challenging due to their lack of interpretability. To address the aforementioned issues, we innovatively propose the Interpretable Conditional Augmentation Classification (ICAC) method. By using CWGAN to create balanced samples, ICAC learns the distribution of minor samples. In order to make better clinical suggestions, the Shapley value is utilized to examine the marginal contribution of patient characteristics to the prediction model. We test the model on the latest released MIMIC-IV, and the experimental results show that the AUC index of our model is superior than that of the basic model. Our proposed method can successfully address the class imbalance issue in EHRs, clarify how features affect model outcomes, and offer useful recommendations for clinical practice.