Reviewerly/exHarmony huggingface dataset:
Assessing treatment preferences among patients with advanced/metastatic renal cell carcinoma in the United States: A discrete choice experiment.

630 Background: The introduction of immunotherapies has changed the first-line treatment landscape for advanced/metastatic renal cell carcinoma (aRCC). This study examines patient preferences in this rapidly changing environment to better understand the tradeoffs patients with aRCC are willing to make when choosing treatment. Methods: Patients with self-reported aRCC in the United States completed an online, cross-sectional survey. A discrete choice experiment was used to assess preferences for attributes of aRCC treatments. Patients completed a series of choice tasks showing 2 treatment profiles that varied in 7 important attributes identified through literature and qualitative research: overall survival, progression-free survival (PFS), objective response rate (ORR), duration of response (DOR), risk of adverse events, quality of life (QOL) changes, and treatment administration. Descriptive statistics were reported, and a hierarchical Bayesian logistic model was used to calculate preference weights. Relative importance estimates (mean ± standard error) were computed for each attribute; these represent the mean percentage of the variation in preferences explained by the attribute. Results: Survey results from a total of 299 patients were analyzed (male, 50%; mean age, 56 years). All 7 attributes were statistically significant for influencing the choice of treatment. Key attributes included treatment regimen convenience and QOL improvement, which ranked similarly to increasing survival time. Among the efficacy attributes, increasing survival time was most important, followed by ORR, PFS, and DOR. Reducing the risk of serious adverse events from 82% to 65% was prioritized after the efficacy parameters. Conclusions: Patients with aRCC highly value less burdensome treatment regimens and improved QoL in addition to improvement in survival. This highlights the need for a broader context beyond efficacy and safety when discussing treatment options with patients. Funding: This study was supported by Bristol Myers Squibb.
Blockchain and Machine Learning Approaches for Credit Card Fraud Detection

A credit card is a convenient and widely recognized method of making cashless transactions both online and offline. One of the most significant benefits of using a credit card rather than a debit card is that it allows you to borrow money to pay for your transactions. As well as the majority of online fraud occurs during a card or online transaction when a user attempts to buy something or move money. Nowadays lots of technology introduced for secured money transactions, that's blockchain technology. The blockchain has the potential to evolve into a distributed ledger, offering a revolutionary new form of trustworthy third-party authentication. Because of the long history of credit card systems, it is easier to understand and security has always been triggered by a process of delegating risk to third parties. Blockchain technology has the potential to avoid these types of losses from occurring in the first place. This study examines how Blockchain technology may be applied, how it might br made safe, and how it might be used to reduce the danger of credit card data being compromised. Additionally, this article identifies and discusses a mechanism that may be created utilizing current technologies, such as multiple identification, SR4S randomized OTP (One Time Password), and biometric tools, to avoid the loss of credit cards.
Analyzing the Accuracy of AI Techniques for Breast Cancer Detection

Breast cancer, the foremost prevailing style of cancer in girls, accounts for 14 percent of cancer cases in Indian girls. In keeping with reports, a girl in India receives a carcinoma designation every four minutes. Each rural and concrete Republic of India area unit seeing a rise in carcinoma cases. Girls in their early thirties to fifties area unit at a high risk of developing carcinoma as a result of it’s the foremost prevailing style of cancer in Indian women; this risk rises till it reaches its peak by the time they’re fifty to sixty-four years previous. Most breast cancers are carcinomas, which are tumors that start in the epithelial cells that line organs and tissues throughout the body. In India, the period risk of developing carcinoma is one in twenty-eight girls. The unfolding of the malady may be stopped by making machine-based carcinoma forecasts, which can speed up the time it takes to find carcinoma. Machine learning uses a spread of applied mathematics, probabilistic, and optimization techniques to change the system to find out from previous experiences and notice difficult-to-detect patterns from strident or advanced information sets. This work provides you with a fast summary of machine learning strategies combined with cybersecurity that aid in women’s carcinoma early detection. Finding the foremost correct formula to predict carcinoma in terms of its categories is the main goal of this work. This study assessed the effectiveness and potency of every algorithm’s information classification. conjointly contrasted with alternative works that are printed in this field. The algorithms that have been used to train the model are Logistic Regression, Random Forest, Decision Tress. Among the three models, random forest has the highest accuracy while logistic regression has the highest precision. The main purpose of the research is to find the best algorithm for breast cancer detection. Our primary goal is to find the best-suited algorithm with high accuracy and good F1 score, precision, and recall.
Competencies of a Requirement Engineer -Systematic Literature Review Protocol

Requirement Engineering (RE) is a core activity of software engineering that guarantees successful project delivery within time and budget. It needs to be considered with respect to the competencies and skills of RE to effectively execute the RE processes and individual sustainability of requirement engineers. Several skill and competency frameworks are proposed and remember in various fields, however there is no competency framework for software requirement engineers. In this research work a systematic literature review protocol is presented as an initial step to design the framework that constitutes the competencies required for RE. Focusing on the competencies and skills of a requirement engineer will help to effectively perform requirement engineering tasks that will eventually lead to the success of a project.
Supporting KP Journalists through Professional Internships (DOS)

Federal Grants & ContractsVolume 47, Issue 8 p. 6-7 Grants Alerts Supporting KP Journalists through Professional Internships (DOS) First published: 14 March 2023 https://doi.org/10.1002/fgc.32930Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume47, Issue816 March 2023Pages 6-7 RelatedInformation
A Framework with Elaborate Feature Engineering for Matching Face Trajectory and Mobile Phone Trajectory

The advances in positioning techniques have generated massive trajectory data that represent the mobility of objects, e.g., pedestrians and mobile phones. It is important to integrate information from various modalities for subject tracking or trajectory prediction. Our work attempts to match a face with a corresponding mobile phone based on the heterogeneous trajectories. We propose a framework which associates face trajectories with their corresponding mobile phone trajectories using elaborate and explainable features. Our solution includes two stages: an initial selection of phone trajectories for a given face trajectory and a subsequent identification of which phone trajectory provides an exact match to the given face trajectory. In the first stage, we propose a Multi-Granularity SpatioTemporal Window Searching (MGSTWS) algorithm to select candidate mobile phones that are spatiotemporally close to a given face. In the second stage, we first build an affinity function to score face–phone trajectory point pairs selected by MGSTWS, and construct a feature set for building a face–phone trajectory matching determinator which determines whether a phone trajectory matches a given face trajectory. Our well-designed features guarantee high model simplicity and interpretability. Among the feature set, BGST intelligently leverages disassociation between a face and a mobile phone even if there exists some co-occurence for a non-matching face–phone pair. Based on the feature set, we represent the face–phone matching task as a binary classification problem and train various models, among which LightGBM achieves the best performance with 92.6% accuracy, 96.9% precision, 88.5% recall, and 92.5% F1. Our framework is acceptable in most application scenarios and may benefit some downstream tasks. The preselection-refining architecture of our framework guarantees the applicability and efficiency of the face–phone trajectory pair matching frame.
Exploiting Semantic Space to Enhance Event Detection Combined with Event Knowledge

Event detection (ED) aims to entail the identification of triggers in text and the determination of the appropriate categories. Event detection is frequently hampered by labeled data scarcity, and most current methods ignore the correlations between events. Aiming at the issues, a novel event detection framework leveraging semantic space and event knowledge (EDSSEK) is proposed. To introduce event knowledge, the pre-trained model is extended and fine-tuned based on the elaborately constructed domain corpus. The presentations of event types are encoded through the pre-trained model and mapped into the semantic space. The feature vectors of event triggers are gained using a document-level attention mechanism and then projected into the same vector space. The document embedding networks are trained by minimizing the distances between the event triggers and the relevant types. Experiments on benchmark datasets demonstrate that EDSSEK outperforms other state-of-the-art methods, and justify the effectiveness of semantic space combined with event knowledge.
A deep learning based scene text detector combining two strategies

Detecting scene text has been a challenging task due to the complex geometric layouts of texts. We can broadly classify the state-of-the-art scene text detection methods into two categories. The first category is the top-down methods, which view text as a whole and locate text by regression learning on the points of text bounding boxes or by learning the geometric properties of text, but most algorithms have difficulty in separating neighboring text. The second category is the bottom-up methods, which treat the text as composed of simple local components and obtain text instances by post-processings, but most algorithms rely on accurate segmentation results. In this paper, we propose a method that combines these two types of ideas while avoiding their drawbacks. Specifically, we use a top-down strategy to obtain text contours, and then use a contour scoring module to score the text contours to obtain more accurate results. In addition, we use a bottom-up strategy to obtain kernels and similarity vectors. Subsequently, pixel aggregation is used to combine the results of the two parts to obtain a more flexible representation of the text instances. Experiments on several benchmark datasets demonstrate the effectiveness of the proposed method.
FedDDB: Clustered Federated Learning based on Data Distribution Difference

Clustered federated learning is a federated learning method based on multi-task learning. It groups similar clients into the same clusters and shares model parameters to solve the problem that the joint model is trapped in local optima on Non-IID data. Most of the existing clustered federated learning methods are based on the difference of model parameters for clients clustering. During the client model training process, the model parameters are biased and the clustering result is affected due to insufficient samples and missing eigenvalues in the dataset. In this paper, we develop a clustered federated learning method based on data distribution difference (FedDDB) in the dataset level. The method in this paper focuses on the distribution of label probability and eigenvalues, analyzes the difference of data distribution difference between clients and measures the distance between datasets which is used for client clustering. Every cluster will be trained independently and in parallel on the cluster center model. At the beginning of each round of training, the client clustering process needs to be repeated. We conduct relevant experiments and demonstrate the effectiveness of our method.
Research on Webpage Text Extraction Integrating Webpage Structure and Role

Abstract: In the face of different web page mining goal of different users, the existing methods cannot provide accurate result, and there is a problem of "one size fits all". To solve the problem, this paper proposes a method for extracting web page text that integrates web page structure and roles. Firstly, the analysis of web page structure is carried out, secondly, based on the extraction of user-oriented and emotional roles, three parts of quantitative processing are proposed, including emotional polarity judgment, negative word detection, and feature word tendency value calculation, finally, the web page text extraction is realized by a comprehensive system framework. Through comparative experiment, the result shows that this method not only forms a set of easy-to-operate training models, but also has high practicability, and the extraction accuracy has been improved by more than 6%.
Research and application of matching network

With the rapid development of deep learning and natural language processing, more and more systems have applied deep learning models. However, a large number of data for training is a major bottleneck of deep learning at present. For the postgraduate thesis oral defense system, our model still utilizes the word retrieval method to match teachers and students who have the same research field because of the small amount of data and information. In this paper, we propose a two-stage training framework to improve the system matching correlation which fine-tunes the pre-trained model on specific downstream data and then utilizes contrastive learning and matching network to conduct self-supervised training. At the same time, the framework uses adversarial training to improve the robustness of the model. We evaluate our approach on the dataset of our system, and experiment results demonstrate the effectiveness of our approach.
Applying Embedding Methods to Process Mining

The performance of process mining algorithms on a particular event log highly depends on the number of different processes present in the logs. Prior event log clustering can help find out which processes certain events in the logs belong to. Since log clustering is not always a simple task, a preliminary transition from logs to log embeddings can be an important step in solving process mining problems.
Density Peaks Clustering Algorithm Based on Density Stratification and Subcluster Fusion

In the density peaks clustering algorithm (DPC), points in high density regions are more likely to be selected as cluster centers, resulting in poor performance on complex datasets that are of various densities. A Density Peaks Clustering Algorithm Based on Density Stratification and Subcluster Fusion (DSSF-DPC) was proposed. The algorithm firstly fits the local density distribution of the points by Gaussian mixture model, and finds the local density change turning points of the points; then, according to the turning points of the density change, the dataset is divided into different density regions, and the density peaks clustering algorithm is used separately in each density region. Finally, the subcluster fusion algorithm is used to obtain the final clustering results. The metrics used are Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI). Experimental results show that our method can recognize clusters regardless of their size, shape and dimension; is robust to noise; and is remarkably superior to DPC, SNN-DPC (Shared-nearest-neighbor-based Clustering by Fast Search and Find of Density Peaks), OPTICS, DBSCAN and K-means.
Deep Knowledge Tracing Integrating Multiple Learning Behaviors

By analyzing students' external learning behaviors, knowledge tracing quantifies students' latent knowledge state on this learning task, so as to further develop targeted learning and teaching plans and promote personalized learning. Students' learning behaviors in online learning platforms are diverse, such as exercise, exam and tutorial browsing. However, most of the existing knowledge tracing models only consider exercise and do not fully utilize other behaviors that also reflect students' learning process. In order to solve this problem, this paper proposes a deep knowledge tracing with multiple learning behaviors model (DKT-MLB), which combines multiple learning behaviors with knowledge concepts. The effectiveness of the proposed model is verified by experiments in a dataset built in real online learning platforms.
ContextCLIP: Contextual Alignment of Image-Text pairs on CLIP visual representations

State-of-the-art empirical work has shown that visual representations learned by deep neural networks are robust in nature and capable of performing classification tasks on diverse datasets. For example, CLIP demonstrated zero-shot transfer performance on multiple datasets for classification tasks in a joint embedding space of image and text pairs. However, it showed negative transfer performance on standard datasets, e.g., BirdsNAP, RESISC45, and MNIST. In this paper, we propose ContextCLIP, a contextual and contrastive learning framework for the contextual alignment of image-text pairs by learning robust visual representations on Conceptual Captions dataset. Our framework was observed to improve the image-text alignment by aligning text and image representations contextually in the joint embedding space. ContextCLIP showed good qualitative performance for text-to-image retrieval tasks and enhanced classification accuracy. We evaluated our model quantitatively with zero-shot transfer and fine-tuning experiments on CIFAR-10, CIFAR-100, Birdsnap, RESISC45, and MNIST datasets for classification task.
Accurate Prediction of Global Mean Temperature through Data Transformation Techniques

It is important to predict how the Global Mean Temperature (GMT) will evolve in the next few decades. The ability to predict historical data is a necessary first step toward the actual goal of making long-range forecasts. This paper examines the advantage of statistical and simpler Machine Learning (ML) methods instead of directly using complex ML algorithms and Deep Learning Neural Networks (DNN). Often neglected data transformation methods prior to applying different algorithms have been used as a means of improving predictive accuracy. The GMT time series is treated both as a univariate time series and also cast as a regression problem. Some steps of data transformations were found to be effective. Various simple ML methods did as well or better than the more well-known ones showing merit in trying a large bouquet of algorithms as a first step. Fifty-six algorithms were subject to Box-Cox, Yeo-Johnson, and first-order differencing and compared with the absence of them. Predictions for the annual GMT testing data were better than that published so far, with the lowest RMSE value of 0.02 $^\circ$C. RMSE for five-year mean GMT values for the test data ranged from 0.00002 to 0.00036 $^\circ$C.
The Grammar of Textual Meaning in Korean: theme

This chapter focuses on grammatical resources for composing information flow, focusing on theme. It begins with a basic introduction to textual clause structure – Theme and Rheme. It then describes the different types of Theme (Topical Theme, Interpersonal Theme and Textual Theme) and their grammatical realisation. The contribution of each type of Theme is illustrated in short texts illustrating their role in managing texture. The chapter concludes with a discussion of thematic progression and introduces guidelines for recognising types of Theme in discourse.
A Study on the Custom Classification Criteria of HS Code 9032.81 and 9032.89: Focusing on the Classification of Mass Flow Controllers

Purpose – This research aims to define the criteria of classification of HS Codes 9032.81 and 9032.89. Furthermore, this research suggests a way for taxpayers to classify goods of HS Code 9032 into 9032.81 and 9032.89 more easily by amending the Explanatory Notes for HS Code 9032. Design/Methodology/Approach – This research explores three possible theories interpreting the classification of HS Codes 9032.81 a nd 9032.89, a nd t ries to f ind t he most suitable explanation v ia a thorough analysis of HS Explanatory Notes and Decisions of Customs. Findings – The results showed that the criteria of classification of HS Codes 9032.81 and 9032.89 can be identified, but some might disagree with it due to the explanatory notes' redundant concepts and ambiguity. Thus, the research concludes the explanatory notes should be amended to extinguish these problems. Research Implications – Although there are needless concepts of Type (I) and (II) of automatic regulating instruments in the HS explanatory notes, there were few improvement proposals on the issue. With this redundant concept and ambiguity on the classification criteria, a conflict between taxpayers and authorities was born. As long as HS explanatory notes are not amended, similar problem can continuously occur. This study gives some suggestions to improve HS explanatory notes.
User Interface Using Hand Gesture Recognition Based on MediaPipe Hands Model

This paper is related to a user interface based on hand gestures. The purpose of this paper is to propose a real-time hand gesture recognition method that recognizes hand point, hand mouse, hand dial, and handwriting gestures using MediaPipe Hands model. In this paper, various static poses are detected using landmarks, which are the output values of MediaPipe Hands model, and then dynamic gestures are detected through the movement and change of these static poses. Then, using these dynamic gestures, hand point, hand mouse, hand dial, and hand writing functions are implemented. In this paper, when a usability test was performed on the proposed hand gesture recognition method, an average recognition rate of 96.4% was obtained. A computer simulation was performed focusing on the scenario of controlling a virtual smart home device using the proposed hand gesture recognition method.