Reviewerly/exHarmony huggingface dataset:
Enhanced Credit Card Fraud Detection: A Novel Approach Integrating Bayesian Optimized Random Forest Classifier with Advanced Feature Analysis and Real-time Data Adaptation

In the financial industry, credit card fraud is a widespread issue that costs both individuals and businesses a lot of money. Using their capacity to spot patterns and abnormalities in huge datasets, machine learning algorithms have demonstrated their effectiveness as tools for fraud detection. This paper suggests a cutting-edge method, specifically an enhanced Bayesian random forest classifier, to improve the detection of credit card fraud. We solve the shortcomings and difficulties of conventional random forest classifiers by applying Bayesian optimization to optimize the model's hyperparameters. Using a publicly available dataset on credit card fraud, we ran experiments to assess the efficacy of the suggested strategy. The effectiveness of our improved Bayesian random forest classifier was evaluated in comparison to cutting-edge methods. The findings demonstrate a fantastic area under the curve (AUC) of 0.99 and a remarkable accuracy of 99.6%, demonstrating the superiority of our proposed classifier above traditional random forest and benchmark techniques. We also investigate the model's interpretability by looking at the importance of characteristics in fraud detection. This research improves the proposed approach's openness and interpretability by offering useful insights into the underlying components that contribute to fraud detection. Our work shows the effectiveness of the optimized Bayesian random forest classifier, but it's crucial to recognize its limitations and the room for improvement in the future.
The Representation of Refugees and Migrants in the Italian Media Discourse

The ‘migration discourse’, including the ‘refugee crisis’, has been the subject of different works analyzing how the public images and identities of refugees are constructed. Over the last decade the use of corpus linguistics methods in discourse analyses have provided the opportunity to focus on typical language usage with the help of corpus linguistic tools, since the choice of a collocation can create a reference to a complex framework of ideological-political understanding.
Sample Viability Assessment from H &amp;E Whole Slide Images for Lung Cancer Molecular Testing

Molecular testing has become an essential tool in precision oncology as targeted therapies have shown to increase the survival rate of patients. However, for molecular test results to be reliable, some requirements must be met, such as the presence of a minimum percentage of tumor cells in a minimum area of the sample. Currently, this analysis is performed by viewing the histopathological slides under the microscope and manually quantifying and highlighting the areas with the highest tumor cellularity. This results in low reproducibility and high subjectivity. To address these problems, we propose a deep learning framework to assist pathologists in the evaluation of the viability of a sample for molecular testing. The developed approach highlights viable sample regions, as well as areas that require further processing. To this aim, we implement a 3-step methodology to analyze Whole Slide Images (WSI): 1) stain normalization of WSI tiles, 2) classification of tiles by a cascade approach, 3) heatmap generation to determine the area of the WSI to perform molecular testing. Moreover, we use three lung cancer subtypes and compare the performance when the models are trained separately for each type or jointly. We achieve a F1-score of 0.63 at tile-level, while at the WSI-level the F1-scores were 0.71 and 0.96.
On Sensitivity of Compact Directed Acyclic Word Graphs

Compact directed acyclic word graphs (CDAWGs) [Blumer et al. 1987] are a fundamental data structure on strings with applications in text pattern searching, data compression, and pattern discovery. Intuitively, the CDAWG of a string T is obtained by merging isomorphic subtrees of the suffix tree [Weiner 1973] of the same string T, thus CDAWGs are a compact indexing structure. In this paper, we investigate the sensitivity of CDAWGs when a single character edit operation (insertion, deletion, or substitution) is performed at the left-end of the input string T, namely, we are interested in the worst-case increase in the size of the CDAWG after a left-end edit operation. We prove that if $$\textsf{e}$$ is the number of edges of the CDAWG for string T, then the number of new edges added to the CDAWG after a left-end edit operation on T is less than $$\textsf{e}$$ . Further, we present almost matching lower bounds on the sensitivity of CDAWGs for all cases of insertion, deletion, and substitution.
Robust Zero-Watermarking Algorithm for Medical Images Based on SUSAN-DCT

With the digitization of medical systems, the information security of medical images faces great risks. In order to protect the personal information of patients contained in medical images, this paper proposes a robust medical image zero watermarking algorithm based on SUSAN-DCT. Firstly, SUSAN transform and block discrete cosine transform are processed on the original medical image. Then, calculate the Hu moment in blocks to obtain the feature matrix, and XOR it with the watermark image to construct a zero watermark. Before constructing the zero watermark, the original medical image is pre-processed using a logical encryption algorithm, and the reliability of the algorithm is improved. According to the simulation results, it can be seen that the algorithm has strong robustness under conventional and geometric attacks, and is a reliable and safe zero-watermarking algorithm.
Robust Zero Watermarking Algorithm for Medical Images Based on BRISK-FREAK and DCT

With the development of mobile Internet technology, the technology in the field of medical image processing is constantly updated and iterated. Digital watermarking technology plays an important role in the field of medical image processing, providing guarantee for the security of medical image information. Aiming at the poor robustness of medical image watermarking algorithms to geometric attacks, a robust zero-watermarking algorithm for medical images based on BRISK-FREAK and discrete cosine transform (DCT) is proposed. First, Binary Robust Invariant Scalable Kepoints (BRISK) was applied to extract the feature points of the medical image. Then, Fast Retina Keypoint (FREAK) was used to describe the extracted feature points and generate a feature descriptor matrix. Further, DCT was performed on the FREAK descriptor matrix, and the low-frequency part was processed via the perceptual hash algorithm to obtain a 32-bit binary feature vector. In order to enhance the security of the watermark information, the logistic mapping algorithm is utilized to encrypt the watermark before embedding the watermark. Finally, with the help of the zero watermarking technology, the proposed algorithm can be embedded and extract the watermark without changing any of the medical image information. Experimental results show that the algorithm has good robustness under some conventional attacks and geometric attacks. Especially under geometric attacks, the performance of the algorithm is excellent.
Fine-Grained Image Retrieval via Object Localization

In this paper, a network consisting of an object localization module and a discriminative feature extraction module is designed for fine-grained image retrieval (FGIR). In order to reduce the interference of complex backgrounds, the object localization module is introduced into the network before feature extraction. By selecting the convolutional feature descriptors, the main object is separated from the background, and thus, most of the interference is filtered out. Further, in order to improve the overall performance of the network, a discriminative filter bank is introduced into the network as the local feature detector. Hence, the local discriminative features can be extracted directly from the original feature map. The experimental results based on the CUB-200-2011 and Cars-196 datasets demonstrate that the proposed method can improve the performance of FGIR.
Design and Implementation of a Human Flow Visual Analysis System Based on RT-Thread System

The most effective measure to control and prevent COVID-19 is to maintain a safe social distance. The Chinese government has previously adopted a physical social distance of 1m as a standard for safe social distance in public places such as campuses and factories and has achieved good results. Social distance has also been widely recognized as an implementation program by the World Health Organization to minimize the spread of COVID-19 in public places. Therefore, for the social safety distance risk of pedestrians during the COVID-19 epidemic, this study proposes an RT-Thread-based social safety distance risk assessment under the COVID-19 epidemic. Firstly, the pedestrian is trained with fine-tuned YOLO-Fastest for target extraction, and the pedestrian key points are extracted to treat the continuous pedestrian motion as a continuous motion of prime points to achieve tracking processing of pedestrians; secondly, the trained weight files are deployed to the RT-Thread OS engineering project through STM32CubeMX AI and RT-AK toolkit to identify the face position and detect whether a mask is worn or not. The recognition rate is 30.71% for unmasked faces and 78.84% for masked faces on the ART-Pi with a maximum main frequency of 480Mhz. It has good performance in human flow calculation and spacing calculation by pixel position as a difference. This can provide ideas for handling safe social distancing of pedestrians in places such as campuses for epidemic prevention and control.
Efficient Skip Connections Realization for Secure Inference on Encrypted Data

Homomorphic Encryption (HE) is a cryptographic tool that allows performing computation under encryption, which is used by many privacy-preserving machine learning solutions, for example, to perform secure classification. Modern deep learning applications yield good performance for example in image processing tasks benchmarks by including many skip connections. The latter appears to be very costly when attempting to execute model inference under HE. In this paper, we show that by replacing (mid-term) skip connections with (short-term) Dirac parameterization and (long-term) shared-source skip connection we were able to reduce the skip connections burden for HE-based solutions, achieving $$\times 1.3$$ computing power improvement for the sameaccuracy.
Existence of primitive pairs with two prescribed traces over finite fields

Given [Formula: see text], a field with [Formula: see text] elements, where [Formula: see text] is a prime power and [Formula: see text] is a positive integer. Let [Formula: see text] and [Formula: see text] is a rational function, where [Formula: see text] and [Formula: see text] are distinct irreducible polynomials with [Formula: see text] in [Formula: see text]. We construct a sufficient condition on [Formula: see text] which guarantees primitive pairing [Formula: see text] exists in [Formula: see text] such that [Formula: see text] and [Formula: see text] for any prescribed [Formula: see text]. Further, we demonstrate for any positive integer [Formula: see text], such a pair definitely exists for large [Formula: see text]. The scenario when [Formula: see text] is handled separately and we verified that such a pair exists for all [Formula: see text] except from possible 71 values of [Formula: see text]. A result for the case [Formula: see text] is given as well.
Effective News Text Summarization Techniques

n proposed work, we successfully implemented a news text summarization system using Natural Language Processing (NLP) techniques and the Latent Semantic Analysis (LSA) algorithm. The purpose of our project was to extract important information from a large volume of news articles and present it in a concise and easily understandable manner. To achieve this, we utilized the LSA algorithm, which is known for its ability to capture the underlying semantic structure of text. LSA employs a mathematical model to analyse relationships between words in a document, creating a semantic representation where words with similar contexts are grouped together in a vector space. The LSA-based summarization process involved several steps. First, we pre-processed the news articles by removing stop words, punctuation, and other non-relevant elements. Then, we constructed a term-document matrix, where rows represented words and columns represented documents, with matrix values representing word frequencies. Next, we applied Singular Value Decomposition (SVD) to the term-document matrix. SVD helped reduce the matrix's dimensionality by identifying the most important latent semantic concepts. This resulted in a lower-dimensional representation that captured the essential information. Finally, we identified the most important sentences in the news articles by measuring the cosine similarity between each sentence and the summary. Sentences with the highest cosine similarity scores were selected as summary sentences. The proposed system demonstrated the effectiveness of the LSA algorithm for news text summarization. By capturing the semantic structure of the text, it generated summaries that allowed users to understand the key points of a news article quickly and easily. Our implementation had practical applications for content recommendation systems, news aggregation platforms, and personalized news feeds. However, it is important to acknowledge the limitations of the LSA algorithm. It may struggle with handling idiomatic expressions and can be sensitive to the quality of the input data. These considerations highlight the need for ongoing research and development to enhance the performance and robustness of news text summarization systems.
Fine-Grained Sentiment Analysis of Covid-19 Quarantine Hotels through Text Mining

The Covid-19 pandemic compelled countries to conduct mandatory quarantine for incoming travelers.Due to shortages in government facilities, hotels have become alternative quarantine sites.The quarantine procedures adopted by hotels have overlooked the guests' comfort and well-being.The objective of this study is to discover critical quarantine service attributes and understand quarantined guest sentiments.Fine-grained sentiment analysis was conducted by tokenizing and analyzing each word in each review sentence to categorize the hotel service attributes and detect the valence of guests' sentiments.A database was formed using extracted data from English and Chinese textual reviews from TripAdvisor and Trip.Com on 23 designated quarantine sites in the Hong Kong region.A total of 14,023 English review sentences and 17,226 Chinese review sentences were extracted from the period of December 2019 to November 2022.Natural Languages Processes (NLP) such as VADER Sentiment Analysis, Latent Dirichlet Allocation (LDA), and Skip-Grams were utilized to categorize the quarantine service attributes and gather key sentiments and insights.A comparative analysis of Quarantine Hotels and General Hotels was conducted using the Jaccard similarity index and results showed an overall similarity of only 15.79%.Managerial implications were suggested to improve quarantine services in the hotel industry.It is found that Chinese reviews emphasized staff, service, and facilities.While English reviews emphasized the overall quarantine stay, food, and check-in procedures.Negative review sentiments confirmed the disregard for guests' mental health while positive sentiments emphasized the importance of guest-staff interaction.The direct online interaction with staff through an app and conducting online group activities reflected high positive sentiment valence.Applying insights from this study to existing hotel service procedures may deem useful should another crisis hit the hospitality industry.
Service Quality Assessment of a Local Government Unit’s Disaster Response using Factor Analysis

The Philippines is one of the Asian countries most vulnerable to natural disasters according to Climate Change Vulnerability Index, and has been under the Pacific Typhoon Belt due to its location.As a result, the Philippines is highly vulnerable to typhoons and flooding.Tarlac, a province in the Philippines, is a frequently flooded location due to its geographic features.With this, this study aimed to assess the service quality of La Paz Tarlac LGU's disaster response by analyzing the underlying factors that explain the correlational pattern between a set of measured variables using SPSS software .The researchers evaluated this by getting the points of view of both LGU employees and residents of the area.Through Analytic Hierarchy Process (AHP), the complex information in multi-criterion decisionmaking was dealt with.As a result, the alternative solutions with the most significant weight among others for each criterion are as follows: "Standard Operating Procedure on Disaster Management," "Drill and Trainings," and "New and Improved Disaster Reduction Technologies," respectively.By clustering variables, factor 1 is linked with the Efficiency of LGU's Disaster Response.The second aspect relates to the Readiness of LGU Personnel for Disaster Operations.Factor 3 is connected with Effective Disaster Plan Dissemination.
Analysis of Customers’ Expectations and Perceptions on the EDSA Carousel through Factor Analysis and Analytical Hierarchy Process

The Bus Carousel is one of the primary modes of transportation in EDSA.The factors identified were affordability, reliability, accessibility, environment, tangibility, comfortability, and safety.This study examines the relationship between variables identified to improve the public transportation service and establishes a recommendation for the problems to be discovered from the data collected via an online survey among passengers within a year.Through Factor Analysis, it was identified that the sub-factors that are relevant to each are pricing and budgeting for affordability, journey satisfaction for reliability, ease of access for accessibility, pollution exposure for the environment, customer service for tangibility, bus stop and bus conditions for comfortability, and personal safety and security for safety.2-3 alternatives were determined for each sub-factor/criterion.Using AHP, the best decisions are to implement an automated payment system, create an incentive platform for e-payment, implement a real-time estimated time of journey from one station to another, provide/improve elevators and escalators that can accommodate
Cascading Global and Local Deep Features for Smartphone-based Human Activity Classification

The advancement in technology with multiple sensors embedded in smartphones results in the widespread of smartphones in the applications of human activity analysis and recognition. This promotes a variety of ambient assistive living applications, such as fitness tracking, fall detection, home automation system, healthcare monitoring etc. In this paper, a human activity recognition based on the amalgamation of statistical global features and local deep features is presented. The proposed model adopts temporal convolutional architecture to extract the long-range temporal patterns from the inertial activity signals captured by smartphones. To further enrich the information, statistical features are computed so that the global features of the time series data are encoded. Next, both global and local deep features are combined for classification. The proposed model is evaluated by using WISDM and UCI HAR datasets for user-dependent and independent protocols, respectively, to ensure its feasibility as user-dependent and independent HAR solutions. The obtained empirical results exhibit that the proposed model is outperforming the other existing deep learning models on both user-dependent and independent testing protocols.
Light Field Reconstruction Via Deep Adaptive Fusion of Hybrid Lenses

This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses, including a high-resolution camera surrounded by multiple low-resolution cameras. The performance of existing methods is still limited, as they produce either blurry results on plain textured areas or distortions around depth discontinuous boundaries. To tackle this challenge, we propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input from two complementary and parallel perspectives. Specifically, one module regresses a spatially consistent intermediate estimation by learning a deep multidimensional and cross-domain feature representation, while the other module warps another intermediate estimation, which maintains the high-frequency textures, by propagating the information of the high-resolution view. We finally leverage the advantages of the two intermediate estimations adaptively via the learned confidence maps, leading to the final high-resolution LF image with satisfactory results on both plain textured areas and depth discontinuous boundaries. Besides, to promote the effectiveness of our method trained with simulated hybrid data on real hybrid data captured by a hybrid LF imaging system, we carefully design the network architecture and the training strategy. Extensive experiments on both real and simulated hybrid data demonstrate the significant superiority of our approach over state-of-the-art ones. To the best of our knowledge, this is the first end-to-end deep learning method for LF reconstruction from a real hybrid input. We believe our framework could potentially decrease the cost of high-resolution LF data acquisition and benefit LF data storage and transmission. The code will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jingjin25/LFhybridSR-Fusion</uri> .
RESULTS OF ONLINE SURVEY OF TEACHERS OF HIGHER EDUCATION INSTITUTIONS OF KAZAKHSTAN «ONLINE COMPETENCE OF TEACHERS»

The results of an anonymous survey of teachers of Kazakhstan universities made it possible to determine their priorities and the degree of mastery of the competencies of an online teacher. The data obtained will be used to adjust the advanced training courses. 593 teachers from 20 member universities of the Educational and Methodological Department in the areas of food, light industry, restaurant and hotel business at the Almaty Technological University in Almaty took part in the online survey. In total, teachers were offered 5 groups of online competencies. To be assessed on a 5-point scale, the importance of each competency and the level of one's skills.
Innovative Design of Wastewater Treatment Plant Based on TRIZ Theory

With the rapid development of society, the demand for water in agriculture and cities has increased, leading to a shortage of freshwater resources available in people's lives.Therefore, everyone must realize the importance of protecting water resources and saving water.Human life cannot live without water, but the available freshwater resources are very limited.In daily life, people install washing pools in kitchens, bathrooms, and other places, wash their hands in the sink, wash pots and pans, or wash fruits and vegetables.The treated water is discharged directly from the cleaning tank into the groundwater pipeline.If water can be recycled, it can not only save household water costs, but also save water resources.Therefore, it is necessary to carry out innovative design for wastewater treatment devices based on TRIZ theory in this paper to solve this problem.