Reviewerly/exHarmony huggingface dataset:
DTHN: Dual-Transformer Head End-to-End Person Search Network

Person search mainly consists of two submissions, namely Person Detection and Person Re-identification (re-ID). Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network (CNN) (e.g., ResNet). While these structures may detect high-quality bounding boxes, they seem to degrade the performance of re-ID. To address this issue, this paper proposes a Dual-Transformer Head Network (DTHN) for end-to-end person search, which contains two independent Transformer heads, a box head for detecting the bounding box and extracting efficient bounding box feature, and a re-ID head for capturing high-quality re-ID features for the re-ID task. Specifically, after the image goes through the ResNet backbone network to extract features, the Region Proposal Network (RPN) proposes possible bounding boxes. The box head then extracts more efficient features within these bounding boxes for detection. Following this, the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds. Extensive experiments on two widely used benchmark datasets, CUHK-SYSU and PRW, achieve state-of-the-art performance levels, 94.9 mAP and 95.3 top-1 scores on the CUHK-SYSU dataset, and 51.6 mAP and 87.6 top-1 scores on the PRW dataset, which demonstrates the advantages of this paper’s approach. The efficiency comparison also shows our method is highly efficient in both time and space.
Comparative Evaluation of Data Mining Algorithms in Breast Cancer

Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and accuracy of various algorithms for breast cancer diagnosis such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, Additive Regression (AR), Back Propagation Neural Network (BP), and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the author found that Support Vector Machine and Bagging had the highest precision and accuracy, respectively. Also, assess the number of studies that provide machine learning techniques for breast cancer detection.
Multi-Modal Military Event Extraction Based on Knowledge Fusion

Event extraction stands as a significant endeavor within the realm of information extraction, aspiring to automatically extract structured event information from vast volumes of unstructured text. Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data. Although researchers have proposed various methods to accomplish this task, most existing event extraction models cannot address these challenges because they are only applicable to text scenarios. To solve the above issues, this paper proposes a multi-modal event extraction method based on knowledge fusion. Specifically, for event-type recognition, we use a meticulous pipeline approach that integrates multiple pre-trained models. This approach enables a more comprehensive capture of the multidimensional event semantic features present in military texts, thereby enhancing the interconnectedness of information between trigger words and events. For event element extraction, we propose a method for constructing a priori templates that combine event types with corresponding trigger words. This approach facilitates the acquisition of fine-grained input samples containing event trigger words, thus enabling the model to understand the semantic relationships between elements in greater depth. Furthermore, a fusion method for spatial mapping of textual event elements and image elements is proposed to reduce the category number overload and effectively achieve multi-modal knowledge fusion. The experimental results based on the CCKS 2022 dataset show that our method has achieved competitive results, with a comprehensive evaluation value F1-score of 53.4% for the model. These results validate the effectiveness of our method in extracting event elements from multi-modal data.
Random forest-based multi-hazard loss estimation using hypothetical data at seismic and tsunami monitoring networks

This article presents a novel approach to estimate multi-hazard loss in a post-event situation, resulting from cascading earthquake and tsunami events with machine learning for the first time. The proposed methodology combines the power of random forest (RF) with data that are simulated at seismic and tsunami monitoring locations. The RF model is well-suited for predicting highly nonlinear multi-hazard loss because of its nonparametric regression and ensemble learning capabilities. The study targets the cities of Iwanuma and Onagawa in Tohoku, Japan, where seismic and tsunami monitoring networks have been deployed. To encompass a diverse range of future multi-hazard loss estimation, an RF model is constructed based on 4000 simulated earthquake events with peak ground velocity and tsunami wave amplitude captured at ground-motion monitoring sites and offshore wave monitoring sensors, respectively. The incorporation of 10 ground-motion monitoring sites and five offshore wave monitoring sensors significantly enhances the model’s forecasting power, leading to a notable 60% decrease in mean squared error and 20% increase in the R2 value compared to scenarios where no monitoring sensors are utilized. By harnessing the capabilities of RF and leveraging detailed sensing data, RF achieves R2 values over 90%, which can contribute to enhanced disaster risk management.
Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Crowdsourcing has gradually become an effective e-government process to gather citizen complaints over the implementation of various public services. In practice, the collected complaints form a massive dataset, making it difficult for government officers to analyze the big data effectively. It is consequently vital to use data mining algorithms to classify the citizen complaint data for efficient follow-up actions. However, different classification algorithms produce varied classification accuracies. Thus, this study aimed to compare the accuracy of several classification algorithms on crowdsourced citizen complaint data. Taking the case of the LAKSA app in Tangerang City, Indonesia, this study included k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost for the accuracy assessment. The data were taken from crowdsourced citizen complaints submitted to the LAKSA app, including those aggregated from official social media channels, from May 2021 to April 2022. The results showed SVM with a linear kernel as the most accurate among the assessed algorithms (89.2%). In contrast, AdaBoost (base learner: Decision Trees) produced the lowest accuracy. Still, the accuracy levels of all algorithms varied in parallel to the amount of training data available for the actual classification categories. Overall, the assessments on all algorithms indicated that their accuracies were insignificantly different, with an overall variation of 4.3%. The AdaBoost-based classification, in particular, showed its large dependence on the choice of base learners. Looking at the method and results, this study contributes to e-government, data mining, and big data discourses. This research recommends that governments continuously conduct supervised training of classification algorithms over their crowdsourced citizen complaints to seek the highest accuracy possible, paving the way for smart and sustainable governance.
The Implementation of Artificial Intelligence, Chatgpt, And Critical Thinking Method for Academic Endeavors at Theological Higher Education

The primary aim of this research is to incorporate the use of Artificial Intelligence, ChatGPT, and the Critical Thinking Method to enhance academic endeavors. Employing Artificial Intelligence and ChatGPT in academic work offers advantages to educators and students. Although educators and students are still required to verify data and references independently, the use of Artificial Intelligence and ChatGPT enables the quick and precise presentation of information. Therefore, it is essential to integrate the Critical Thinking methodology with the utilization of ChatGPT and Artificial Intelligence. Verifying and validating data improves the credibility of the knowledge acquired by both professors and students. The research methodology employed in this project involved library research, where the author collected primary and secondary data. Subsequently, content analysis methodology was applied, and the data was presented descriptively. The study results indicate that the implementation of Artificial Intelligence, ChatGPT, and the Critical Thinking method has enhanced the quality of academic work produced by educators and students. Furthermore, this combination can expedite the production of scholarly articles. When engaging in academic research, educators and students should adhere to the guidelines on Artifact Intelligence and ChatGPT policies.
Exploring Trends in Innovation within Digital Economy Research: A Scientometric Analysis

Significant advancements have been made in studying innovation within the digital economy over the past 20 years. Research on innovation and the digital economy is crucial since it changes all facets of human existence, including business models and entrepreneurial trends. Research regarding innovation in the digital economy has experienced growth over time. However, only a small number of research works have investigated their references using the most widely utilized citation mapping approach, scientometric analysis. This scientometric analysis used 822 published innovation and digital economy research papers from 2000 to August 2023 from the Scopus database. Data analysis and visualization were carried out using biblioshiny (bibliometric package) in R and VOSviewer. According to the data, the study on innovation within the digital economy has grown by 22.75% yearly since 2000. This study offers valuable insight for society, academics, academic institutions, researchers, policymakers, and businesses. The findings reveal the pivotal aspects of the research, derived from the most frequently referenced subjects, publications, authors, and keywords to determine current and future trends in innovation in the digital economy.
Scheduling algorithms for data-protection based on security-classification constraints to data-dissemination

Communication networks have played a vital role in changing people's life. However, the rapid advancement in digital technologies has presented many drawbacks of the current inter-networking technology. Data leakages severely threaten information privacy and security and can jeopardize individual and public life. This research investigates the creation of a private network model that can decrease the number of data leakages. A two-router private network model is designed. This model uses two routers to manage the classification level of the transmitting network packets. In addition, various algorithmic techniques are proposed. These techniques solve a scheduling problem. This problem is to schedule packets through routers under a security classification level constraint. This constraint is the non-permission of the transmission of two packets that belongs to the same security classification level. These techniques are the dispatching rule and grouping method. The studied problem is an NP-hard. Eight algorithms are proposed to minimize the total transmission time. A comparison between the proposed algorithms and those in the literature is discussed to show the performance of the proposed scheme through experimentation. Four classes of instances are generated. For these classes, the experimental results show that the best-proposed algorithm is the best-classification groups' algorithm in 89.1% of cases and an average gap of 0.001. In addition, a benchmark of instances is used based on a real dataset. This real dataset shows that the best-proposed algorithm is the best-classification groups' algorithm in 88.6% of cases and an average gap of less than 0.001.
A deep learning and clustering‐based topic consistency modeling framework for matching health information supply and demand

Abstract Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply‐side and demand‐side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease‐related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy‐making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.
Visual Attention Patterns in Finding Source Code Defects

Existing research used visual effort metrics to determine the visual attention patterns of participants with varying skill levels while finding source code defects. While most of the findings of these studies agree on the results for fixation count metrics, there are differences in the results for fixation duration metrics. Therefore, there is a need for further investigations on the use of visual effort metrics to determine the difference in the visual effort of experts and novices between multiple programs. Thus, we aimed to identify the factors affecting the varying results on fixation duration metrics and validate the results on fixation count metrics. We used visual effort metrics to identify the visual attention patterns of high and low-performing students engaged in defect-finding tasks on multiple programs. We performed statistical tests on the total fixation count, fixation counts on the error lines, total fixation duration, and fixation duration on the error lines to determine the difference in the visual attention patterns between the groups. Among the fixation metrics used, only the total fixation duration metric revealed a significant difference between the high and low-performing students across all programs. High-performing students spent less time on simple programs with simple error types but spent more time on complex programs with logical and semantic error types. In contrast, low-performing students focused more attention on easy programs with one or more syntax errors compared to high-performing students. The results of this study could shed some light on the contrasting findings of previous studies regarding fixation duration. These findings suggest that visual attention patterns of high and low-performing students may vary on multiple programs. The amount of visual effort exerted by the group depends upon the program’s complexity, location of errors in the source code, type of errors injected, and the number of lines of code. This implies that the time spent finding the errors may be associated with the characteristics of the programs and the location and type of injected errors. Therefore, researchers must provide detailed information on these characteristics when describing differences in visual effort metrics between subjects engaged in bug-finding tasks.
Serialized Drug Traceability in the Supply Chain Using Distributed Ledger Technology

Currently, Drug Counterfeiting is the biggest challenge facing the pharmaceutical industry. They are encountering this threat due to high market demand for the drugs and their profit margin. The lack of data transparency and traceability also lured criminals into the counterfeiting of drugs which, is impacting people’s health and put their life in danger. Through the drug supply chain, a substantial portion of counterfeit drugs are injected and distributed through the healthcare supply chain network, so the supply chain plays a vital role in drug distribution and impacts patient lives. Through digitalization in the healthcare sector, Distributed Ledger Technology (DLT) provides a platform with ground-breaking results by providing a system for drug traceability with consideration of the critical requirements of transparency, privacy, and authenticity without involving any third party. In DLT, each distribution partner is registered to maintain transparency with the drug information. Real-time transfer of information about the change of ownership with date and time in the form of blocks gives visibility to all the partners in real time about the authenticity of drugs. This article will give information about the benefits of Distributed Ledger Technology to the pharmaceutical industry and the traceability of drugs from end-to-end of the pharmaceutical supply chain.
Short Text Classification Based on Explicit and Implicit Multiscale Weighted Semantic Information

Considering the poor effect of short text classification due to insufficient semantic information mining in the current short text matching methods, a new short text classification method is proposed based on explicit and implicit multiscale weighting semantic information interaction. First, the explicit and implicit representations of short text are obtained by a word vector model (word2vec), convolutional neural networks (CNNs), and long short-term memory (LSTM). Then, a multiscale convolutional neural network obtains the explicit and implicit multiscale weighting semantics information of short text. Finally, the multiscale weighting semantics is fused for more accurate short text classification. The experimental results show that this method is superior to the existing classical short text classification algorithms and two advanced short text classification models on the five short text classification datasets of MR, Subj, TREC, SST1 and SST2 with accuracies of 85.7%, 96.9%, 98.1%, 53.4% and 91.8%, respectively.
Behavioral intention &amp; use behavior of citizens in Iligan city towards the staysafe.ph application during the covid 19 pandemic

The Covid-19 pandemic has encouraged the use of digital solutions such as contact tracing applications to manage the spread of the virus infection. The use of Staysafe.ph app in particular is central to the management of public health emergencies in the country, however, it is important to understand the needs and tolerances of different users to address factors that may hinder the adoption of such strategy. Prior research in contact-tracing apps focuses solely on measuring people’s intentions to use the app. However, the effectiveness of contact-tracing apps is dependent on a significant percentage of the population installing and using the app. Hence, to investigate the intention to use and usage behavior of citizens towards the Staysafe.ph application during the pandemic, the UTAUT model is used, expanding it with three constructs: privacy concern and self-efficacy as direct determinants for behavioral intention; and fear of Covid-19 as a moderator. A survey experiment (N = 400) in Iligan City is used, in which a web-based version of the survey questionnaire was distributed through social networks. The measurement and structure model were appraised using structural equation modelling (SEM). The result shows that effort expectancy, social influence, facilitating conditions, and self-efficacy played an important role in behavioral intention and usage of the app, except for performance expectancy and privacy concerns. Moreover, respondents showing behavioral intention to use the application are more likely to use the app. The multiple regression analysis showed that fear of Covid-19 and age as moderating factors do not have a significant impact on behavioral intention and use behavior. These findings were interpreted in the light of current conceptualizations in the area of the study and their implications for the future were pointed out. You should leave 8 mm of space above the abstract and 10 mm after the abstract. The heading Abstract should be typed in bold 9-point Arial. The body of the abstract should be typed in normal 9-point Times in a single paragraph, immediately following the heading. The text should be set to 1 line spacing. The abstract should be centred across the page, indented 17 mm from the left and right page margins and justified. It should not normally exceed 200 words.
Implementation of a Hybrid Triple-Data Encryption Standard and Blowfish Algorithms for Enhancing Image Security in Cloud Environment

In recent years, technological advancements have provided the world with cloud computing which can transfer, store, and process huge data chunks in the form of video, audio, images, and text efficiently. In spite of the universal hype on the subject across the information technology world, protecting sensitive data stored in the cloud server is one of the crucial problems. The large volume and sophistication of cyberattacks conclude to the fact that private pictures need exceptional care than other forms of data on the cloud. Since the user who has stored their private pictures in the cloud has no control over the privacy protection of data, the cloud vendors have to assure a greater level of security in terms of authentication and prevention from cyberattacks. Image encryption algorithms secure visual data by transmuting pictures into an unintelligible form to preserve the confidentiality of pictures over reliable unrestricted social media. This work aims to develop a method for enhancing the security of user photographs on a cloud platform by means of cryptography algorithms. The proposed hybrid technique presents the idea of protecting images in two straightforward steps. First, we generate a chipper text (i.e., secret key) using Triple Data Encryption Standard (TDES) by giving a plaintext and a key as input. Then, the cipher text obtained from TDES is given to the Blowfish algorithm for encrypting the user images. The encrypted image is then uploaded to the database of the cloud server and can be retrieved whenever the user requests it. Both image encryption and decryption processes are analyzed and evaluated based on performance metrics such as cloud storage time, encryption time, decryption time, and encryption throughput. A comparative study with conventional image encryption methods will demonstrate the effectiveness and robustness of our proposed method.