Reviewerly/exHarmony huggingface dataset:
Toward Auto-Evaluation With Confidence-Based Category Relation-Aware Regression

Auto-evaluation aims to automatically evaluate a trained model on any test dataset without human annotations. Most existing methods utilize global statistics of features extracted by the model as the representation of a dataset. This ignores the influence of the classification head and loses category-wise confusion information of the model. However, ratios of instances assigned to different categories together with their confidence scores reflect how many instances in which categories are difficult for the model to classify, which contain significant indicators for both overall and category-wise performances. In this paper, we propose a Confidence-based Category Relation-aware Regression (C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) method. C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> divides all instances in a meta-set into different categories according to their confidence scores and extracts the global representation from them. For each category, C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> encodes its local confusion relations to other categories into a local representation. The overall and category-wise performances are regressed from global and local representations, respectively. Extensive experiments show the effectiveness of our method.
The chatbots’ challenge in education: Disruption or destruction? (Reflections based on conversations with ChatGPT)

The article addresses the positive and negative implications on instruction, learning, and assessment of the growing spread of chatbots based on large language models (LLMs) in education. It is based on extensive conversations with ChatGPT regarding pedagogy-related issues and relevant documents. Discussed are the challenges of chatbots like ChatGPT to educators – their potential to advance deep learning and the roles of the instructor and the school context in causing it to happen on the one hand. On the other hand, it underscores the pedagogical drawbacks of improper usage of such chatbots and the instructional practices and school contexts that could escalate learning. Three school- culture components, namely, classroom learning, teacher professional learning, and school leadership, are the essential aspects of pedagogical approaches that, in a particular constellation, could enhance and, in another, impede the chatbot’s potential to advance deep learning. The underlying theoretical framework is assessment-driven, contrasting assessment for learning (AfL) and assessment for grading, distinguishing assessment cultures from testing cultures. Patterns of chatbot usage that align with the principles of each culture are discussed. A sample of quotes from the conversations with ChatGPT is presented to support the insights gained from the chatting experience and the conclusions drawn.
DEFD: Adapted Decision Tree Ensemble for Financial Fraud Detection

Financial fraud activities are increasing. Financial institutions must improve their systems, judged not efficient enough. Our work is to provide a solution to analyse transactions and explain the transactions blocked by the systems. It is crucial to inform the experts who will review the blocked transactions to help them make the best decision. Our approach is based on an ensemble of decision trees to compute a fraudulent score. Any transaction above a certain threshold will be considered fraudulent. With this approach, we chose the decision tree algorithm for its interpretability to help the investigation of experts assigned to review fraudulent transactions. Experiments are done with 1M transactions, and we obtain a f1-score of 0.89 with our method.
Detecting Malicious Browser Extensions by Combining Machine Learning and Feature Engineering

As the popularity of the internet continues to grow, along with the use of web browsers and browser extensions, the threat of malicious browser extensions has increased and therefore demands an effective way to detect and in turn prevent the installation of these malicious extensions. These extensions compromise private user information (including usernames and passwords) and are also able to compromise the user’s computer in the form of Trojans and other malicious software. This paper presents a method which combines machine learning and feature engineering to detect malicious browser extensions. By analyzing the static code of browser extensions and looking for features in the static code, the method predicts whether a browser extension is malicious or benign with a machine learning algorithm. Four machine learning algorithms (SVM, RF, KNN, and XGBoost) were tested with a dataset collected by ourselves in this study. Their detection performance in terms of different performance metrics are discussed.
Information Extraction and Ontology Population Using Car Insurance Reports

In the automotive field, the management of vehicle transportation can generate different types of damage to the bodywork during transportation. The assessment of these damages is one of the most difficult tasks. This is because there are many criteria for damage assessment, depending on the type and the severity of the damage, taking into account the damaged part. The reports describing the damage are not used to perform analyses or to create predictive models, or at least stored in a database. The non-use of this data is due to the lack of an ontology to model insurance reports and systems to extract useful information. In this paper, we define an ontology for modeling car damage based on the knowledge of insurance experts and their description reports. Then, we populate it using information extracted with information extraction system (IE) based on named entity recognition (NER). The experimentation was performed on a real dataset of insurance reports, and we compare between four NER models (BERT, BiLSTM-CRF and CRF). The reported results show that CRF outperforms all other NER algorithms, achieving the highest scores for all metrics in most cases.
FlashGAN: Generating Ambient Images from Flash Photographs

Mobile Cameras capture images deftly in scenarios with ample light and can meticulously highlight even the finest detail from the visible spectrum. However, they perform poorly in low-light setups owing to their sensor size, and so, a flash gets triggered to capture the image better. Photographs taken using a flashlight have artefacts like atypical skin tone, sharp shadow, non-uniform illumination, and specular highlights. This work proposes a conditional generative adversarial network (cGAN) to generate ambient images with uniform illumination from the flash photographs and mitigate other artefacts introduced by the triggered flash. The proposed architecture’s generator has a VGG-16 inspired encoder at its core, pipelined with a decoder. A discriminator is employed to classify patches from each image as real or generated and penalize the network accordingly. Experimental results demonstrate that the proposed architecture significantly outperforms the current state-of-the-art, performing even better on facial images with homogenous backgrounds.
Multi-generator MD-GAN with Reset Discriminator: A Framework to Handle Non-IID Data

Federated Learning is a machine learning concept that allows the training of machine learning models in a collaborative fashion by many users or devices, keeping the user data decentralized, thus providing data security and privacy. It focuses on optimal utilization of resources to train a machine learning model without accessing the user’s private data. One of the main challenges in federated learning is to train a federated machine learning model with Non-IID (Non-Identically Independently Distributed) client/User Data. In this paper, we introduce a novel federated architecture, Multi-Generator MD-GAN (Multi-Discriminator Generative Adversarial Networks) with Reset Discriminator (MG-GAN-RD), to tackle one of the major challenges in federated learning, i.e., to train a federated model with Non-IID client image data. Our model learns the joint distribution of the Non-IID client image dataset and trains a global classifier without accessing the client’s private dataset. Our approach uses label-specific generators at the server, and each client consists of a single discriminator with a reset property. We will show that our global classifier trained using our approach performs consistently well and provides accuracy in competing with the state-of-the-art federated learning algorithms.
Credit Card Fraud Detection Using Random Forest and Cart Algorithm

The project is primarily concerned with detecting credit card fraud in the real world. The phenomenal increase in the number of credit card transactions has recently resulted in a significant increase in fraudulent activities. The goal is to obtain goods without paying for them or to withdraw unauthorized funds from an account. In order to minimize losses, all credit card issuing banks must implement effective fraud detection systems. One of the most difficult challenges in starting the business is that neither the card nor the cardholder must be present at the time of purchase. This makes it impossible for the merchant to determine whether or not the customer making a purchase is the legitimate cardholder. Using the proposed scheme and the random forest algorithm, the accuracy of detecting fraud can be improved. Random forest algorithm classification process to analyze data set and user current dataset. Finally, improve the accuracy of the output data. The techniques' performance is measured using accuracy, sensitivity, specificity, and precision. Then, by processing some of the provided attributes, the fraud detection is identified and the graphical model visualization is provided. The techniques' performance is measured using accuracy, sensitivity, specificity, and precision.
Effects of Information Awareness, Search Purpose, and Search Expectation on Information Avoidance

The purpose of this study is to identify the effects of information awareness, search purpose, search expectation, and search subject type on information avoidance. An online survey was conducted targeting the general public by applying the information seeking-avoidance measurement tool. As a result of the study, first, the effect of information recognition on the personal factors of information avoidance was positively significant. Second, the effect of search expectation on the information factor of information avoidance was negatively significant. Third, the effect of health information on personal factors of information avoidance was positively significant in search subject types. Fourth, the effect of health information on system factors of information avoidance was negatively significant. In information avoidance, not only the searcher's psychological, emotional and affective problems, but also the seeker's search aspects, information awareness, and search expectations should be considered.
A Study on the Quality of Journalism Content on Outstanding Fact Check News

The studied of journalistic content of outstanding fact-check news to identify formal characteristics in order to improve the quality of journalism in this study. To this end, 60 outstanding works that have been evaluated by journalists and industry judges were analyzed within the SNU Fact Check, which provides fact-check content in partnership with 34 domestic media companies. As a result of the study, 60 fact-check news averaged 2,196 characters, which was more textual than general news. It also used more than four average multimedia sources, including photos and video charts, to help understand the fact check news. The average number of real-name reporters was 1.4 and the average number of anonymous reporters was 0.5, so they tried to use real-name reporters. The most important part of the evidence used an average of 5.8 cases to increase the objectivity and reliability of the article. The results of the judgment mainly focused on correcting the wrong facts, and it was evaluated to increase transparency and perfection through correction of small contents.
A Study on the Carrying Capacity Evaluation Model for Sustainable Island Developments

Sustainable development is based on the coexistence of man and nature. This means that the natural environment has limits to its capacity to accommodate humanity, so human activities that use it must be controlled to stay within the natural environment's capacity range. Since the island area is remote from the mainland and has a limited supply of water and electricity, access to the island should be considered due to environmental damage and isolation, and development land and carryin capacity should be considered, especially due to the island's narrowness. Because the island, an isolated space surrounded by water, communicates with the inland around the port, the flow and boundaries of people and goods are strictly defined. With these characteristics, the concept of capacity is more easily applied to islands than to other regions, has more meaning, and can be a useful tool for assessing sustainability. As a result, this study proposed the introduction of the concept of island carrying capacity as a planned management means for maintaining a certain level of quality of life and the long-term development of islanders, as well as a theoretical discussion of the island carrying capacity evaluation system. This aids in understanding the traces of human activity, the relationship between carrying capacity and sustainable development, and can eventually provide policymakers and planners with hints for developing appropriate strategies and action plans to manage the island area in a planned manner.
Confidentiality Enhancement Using Spread Spectrum Modulation Technique for Aggregated Data in Wireless Sensor Networks

Wireless Sensor Networks (WSN) play a crucial role in transmitting bulk data remotely on Internet of Things (IoT). The data transmitted by the tiny sensor nodes of the network are basically the physical parameters of the environment like temperature, vibration, pressure etc. There exists a huge co-relation in the data sent by the nodes. This bulk data needs to be aggregated in order to conserve the energy of the network and thereby enhance the network lifetime. Although the aggregation might lead to loss of actual data, aggregating helps in reducing energy requirement during transmission which is of primary importance and hence is considered as a suitable method in WSN. Adding confidentiality to aggregated data helps in sending the data more securely. Spread spectrum modulation is a widely used technique to provide confidentiality in communication systems. In this research work, we implement a data aggregation technique and apply the spread spectrum modulation technique to provide confidentiality to the aggregated data that needs to be transmitted from the cluster head node to the gateway. Here, data aggregation process consists of averaging the number of data that are sensed by a sensor node and transmitting only its average value to the gateway. This reduces the amount of data transmission and helps in conserving energy. Further, the spread spectrum technique implements Binary Phase Shift Keying (BPSK) method with Frequency Hopping Spread Spectrum (FHSS) carried out using six different frequencies on MATLAB 2020a. The simulation results evaluate the performance of the system. The graphs are plotted for modulated and demodulated signals, spread and de-spread sequences, Bit Error Rate (BER) of BPSK/ FHSS over Rayleigh flat fading channel, Power Spectral Density (PSD) and Fast Fourier Transform (FFT) of frequency hopped signal. The results show that BER value decreases with increase in Signal to Noise Ratio (SNR). The maximum power consumption in the network is 7.518 mW at 5MHz frequency, for adding confidentiality to the aggregated data to be transmitted. Thus, the proposed work promises efficient energy consumption, longer network lifetimes with added confidentiality.
Generalization of the shortest path approach for superpixel segmentation of omnidirectional images

With the growing use of image capture devices using wide angles and the need for fast and accurate image analysis in computer vision, there is a demand for dedicated under-representation methods. Most decomposition methods segment an image into a small number of irregular homogeneous regions, called superpixels. Nevertheless, these approaches are generally designed to process natural 2D planar images, i.e., captured with a 90o angle view without distortion. In this work, we present SphSPS, a new general decomposition method (for Spherical Shortest Path-based Superpixels)1, that is dedicated to wide 360o omnidirectional or spherical images. The produced superpixels respect both the geometry of the 3D spherical acquisition space, and the boundaries of image objects. To fastly extract relevant clustering features, we generalize the shortest path approach between a pixel and a superpixel center. We demonstrate that considering the geometry of the acquisition space to compute the shortest path enables to jointly improve the segmentation accuracy and the shape regularity of superpixels. To evaluate this regularity property, we propose a generalization of a standard 2D regularity metric to the spherical space, addressing the limitations of the only existing spherical compactness measure. Finally, SphSPS is validated on reference 360o images from the PSD (Panorama Segmentation Dataset) and also on synthetic road omnidirectional images. Our method significantly outperforms both planar and spherical state-of-the-art approaches in terms of segmentation accuracy, robustness to noise and regularity, providing a very interesting tool for superpixel-based applications on 360o images.
A Study on the Applicability of eXplainable Artificial Intelligence(XAI) Methodology by Industrial District

The learning performance of artificial intelligence (AI) technologies such as machine learning and deep learning is approaching or surpassing that of humans, and humans are gaining new insights through hidden patterns and rules discovered by AI, but their delivery and explanatory power is in short supply. As the use of AI technology expands by industry, values such as transparency, fairness, and accountability are continuously required in addition to accuracy. Accordingly, the demand and necessity for eXplainable Artificial Intelligence (XAI) has recently been emphasized. XAI is an analysis model, process, and service for determining the reliability of AI results by explaining what the reason is if there is an error in the output produced by the AI model. This study reviewed SHAP, LIME, and LRP, which are XAI methodologies, and explored the applicability of these methodologies to each industry. In the financial field, algorithms such as SHAP, LIME, and LRP can be applied to support credit rating prediction, loan decision-making, and investment decision-making, and to ensure their explainability and interpretability. This can increase fairness and reliability of AI results between financial consumers and financial institutions. In the defense and military fields, AI staff is being used as an auxiliary tool for key decision makers. If the explainability and interpretability of the results are guaranteed by applying XAI, the role of AI staff will change to active utilization. In the field of industrial accidents, SHAP can be used in a model that predicts industrial accidents to determine the degree of influence of variables that affect the occurrence of industrial accidents. As such, the XAI methodology can contribute to increasing the transparency and interpretability of models in various AI-based prediction models.
A Qualitative Study on the Experience of Parents in Community-Based Community Child Centers

The purpose of this study is to explore the meaning of parents’ experiences of community-based Community Child Centers. The participants in this study were 9 parents who had more than 3 years of parenting experience as parents of Community Child Centers, participating in parentship activities or village activities, and data were collected through individual in-depth interviews. The collected data were subjected to inductive thematic analysis of general qualitative research methods. The results of the study are divided into 3 categories and 11 subcategories: ‘Members participating as caring subjects’, ‘Relationship networks formed through communication and exchange between parents’, ‘A safe village that cares together’. Based on the research results, the policy suggestions for Community Child Centers to realize community care are as follows. First, Community Child Centers are community-oriented, creating structures for parents to participate and interacting with different units in the village. Second, For communal care with the village, the government’s continuous support is needed to activate village communities, village meetings, and village projects in the region. Third, it is necessary to create an environment in which parents of Community Child Centers can participate in center activities. Fourth, the government needs to establish and support standards for Community Child Centers to operate socially integrate.
Determination of Hazardous Residues in Livestock Products using LC-MS/MS in Gyeongsang Province

After collecting livestock meat from cattle, pigs, chickens, ducks, sheep, and horses in the Gyeongnam and Gyeongbuk porvinde regions for three years from 2020 to 2022, hazardous residues were examined using LC-MS/MS. In the first qualitative test, a total of 45 cases (0.5%), 53 cases (0.58%), and 33 cases (0.47%) were positive for the three years from 2020 to 2022, respectively. In the second quantitative test, a total of 23 cases(1.58%), 26 cases (1.39%), and 25 cases (0.58%) of hazardous residues were found to be positive in 2020. Overall, the positive rate was high in beef, but there was not a single positive reaction in duck, lamb, and horse meat did not show a positive reaction for 3years. Therefore, it is necessary to further strengthen the inspection of harmful residues on beef in the future. To this end, the government will have to invest heavily in manpower and equipment in the livestock sanitation laboratory.
A Multidimensional Adaptive Assessment Model for Innovative Technologies in New-Type Power System

With the continuous development of new-type power systems, all kinds of innovative technologies have come into being. However, due to the scattered development of various technologies and the lack of co-ordinated planning, it makes some of the innovative technologies ignore the optimisation of investment in order to achieve the expected construction goals. Therefore, in order to guide grid enterprises and promote technology application and planning, the construction conditions of innovative technologies for new power systems are analysed and a three-dimensional spatial three-dimensional evaluation system is established, taking into account the spatial and temporal resources required for technology construction. The continuous interval ordered weighted average operator is used to improve the subjective and objective comprehensive assignment method, reducing the subjectivity of the evaluation and improving the compatibility of the evaluation system. Finally, the fuzzy comprehensive evaluation method is used to determine the affiliation degree of each index, and then the evaluation level of the new energy network innovation technology is obtained. The results show that the proposed method can identify the adaptability of innovative technologies to different time and space and the suitability of different time and space for a certain technology application in both directions.