Reviewerly/exHarmony huggingface dataset:
Research on the Improvement Strategy of Medical Image Boundary Cell Detection

Early-stage cervical cancer cells have incomplete mutations, and the characteristic features are not obvious. Many women themselves have precancerous lesions, and the cancerous cells may not be detected and treated in time. Targeted detection of cervical cancer pathology using object detection can help doctors improve diagnostic efficiency and provide references for inexperienced doctors. However, current object detection networks have low detection accuracy and poor processing performance for cells at the image boundary due to the lack of contextual information for these cells. This paper uses the YOLO network as a detector and optimizes it for boundary issues in cells within the TCT dataset. By using mirror filling strategy to enrich the contextual information of cells at the image borders, the optimized predicted bounding boxes after image pre-processing improved by 14.48% on average. To better fit the target boxes, the EIoU loss is chosen to improve the computation method of the traditional CIoU loss, resulting in reduced localization loss during training. The results showed an average detection precision improvement of 1.35% after optimizing the localization loss, with an increase of 0.85% observed in cell nucleus dataset that involves a dual nuclear region; while for non-medical datasets such as station security check datasets, there is an average detection precision improvement of 0.71%.
Multi-stream Temporally Enhanced Network for Video Salient Object Detection

Video salient object detection (VSOD) aims at locating the most attractive objects in a video by exploring the temporal and spatial features. Despite the progress made in existing VSOD models, there is still a tendency to prioritize temporal features over spatial features. This bias has been shown to result in the imprecise localization of salient objects in the spatial domain and the accumulation of interframe noise in the temporal domain. We aim to investigate saliency cues collaboration in the spatial domain, along with a straightforward, yet efficient approach for temporal feature extraction. This paper proposes a multi-stream temporal enhanced network (MSTENet) for VSOD. The MSTENet employs a multi-stream structure that leverages both foreground and background supervision to extract collaborative saliency cues in the spatial domain. Meanwhile, the temporally enhanced module focuses on extracting motion information by enhancing distinctive regions in the current frame relative to adjacent frames. The proposed framework seamlessly integrates these two components to achieve more accurate and efficient VSOD performance. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on five benchmark datasets.
A Multielement Recognition Algorithm Based on Deep Learning

With the development of deep learning, the classification and recognition of wasps has become one of the hot issues in research. The paper based on the text and images in the provided data set. Firstly, input the image data of the wasp into the deep residual network to obtain its confidence coefficient, with an accuracy of 75%; Then, the obtained text reports are generated into training sets, test sets, and validation sets, and the Skip gram method is used to supervise and train the text recognition model. Thirdly, using support vector machines for classification has an accuracy of 82.73%. Finally, the recognition results of the image and text are weighted to obtain the final classification result. Examples have shown that this method has good recognition performance.
Intelligent Evaluation Model for Role Transformation Ability of Vocational Teachers Based on Neural Networks

To optimize the effectiveness of training programs and enhance the teacher’s role and ransformation ability in higher vocational colleges, we developed an assessment model based on the backpropagation (BP) neural network. This model can be used for the systematic evaluation of the impact of training programs. Through extensive case analyses, we validated the efficacy of the evaluation model. The results demonstrated that the model effectively evaluated the effect of the teacher’s role and its transformation ability in training in higher vocational colleges. The evaluation model was highly feasible and practical and significantly improved the efficiency of evaluation compared to traditional algorithms. The model increased the efficiency by approximately 30% when compared to conventional methods.
Programming and Implementation of Themed Web Crawler for Equipment Procurement Prices in Vocational Colleges

The diversity and complexity of information on the Internet interfere with the collection of equipment procurement prices. Vocational colleges require obtaining personalized information on equipment requirements from the Internet and collecting and integrating information. Therefore, database system methods and themed web crawler methods are used using a Python-based themed web crawler. Through information acquisition and automatic keyword extraction in vocational college systems, independent analysis and query of enterprise key data are carried out on the Internet. For this, the design foundation, architecture, design and development stages, and completion process of the system are elaborated. Through practice, it has been proven that the developed themed web crawler method allowed the collection of the price data information of vocational colleges quickly, assisting in information mining work.
Interpretable Medical Recommendations Based on SHAPs

In the past few years, chronic diseases of the elderly have gradually become the killer of the elderly, seriously affecting their physical health of the elderly. Doctors can extract relevant information from the data to make patient treatment recommendations. This study proposed a smart recommendation system for doctors by using deep learning traditional methods. Proposed method firstly use six traditional deep learning methods such as naive Bayes, logistic regression, decision tree etc for recommendation purpose. Next step to get the better understanding of the recommendation we apply an interpretable recommendation system based on SHapley Additive exPlanations. (SHAP). Last time is provide recommendations based on better and weak recommenations by graphical way. The proposed methods were applied to two chronic diseases of old age, including heart disease and diabetes. After the data is preprocessed, naive Bayes, decision tree, and other machine learning operations are carried out on the data. SHAP was used to calculate the importance of each feature for randomly selected patients. Finally, the contribution coefficient of the feature to the result is presented, which impacts the output. By analyzing the influence of different features of users on recommendation results, the proposed system explains why such results are recommended to users to improve users' trust in the recommendation results, which is of great significance for medical recommendation.
Design and Development of a User-Centric Wearable Device Application for Elderly Care

In this study, Safe Sentery, a healthcare application was designed for the well-being of the elderly. As the global population ages, the prevalence of health risks and medical conditions requiring regular attention increases significantly. In recent years, wearable health applications and real-time health monitoring systems tailored for elderly care have been adopted. Safe Sentery is a comprehensive care platform that seamlessly integrates into the recipients' home life. Utilizing wearable technology, the app can be used for efficient tracking and locating of care recipients with the prompt issuance of emergency notifications during critical situations. Additionally, it helps manage medical records and health history, allowing caregivers to access crucial information about their recipients' well-being including personalized medical details, medication reminders, doctor appointment scheduling, and instant health advice. Safe Sentery ensures continuous care for elderly individuals and comfort at home. The app can be used for family caregivers and enable daily care. It also effectively alleviates the burden on family caregivers, leading to an improved quality of life for both the elderly and caregivers.
A (t, n)-Threshold Visual Secret Sharing Scheme Based on Polarization

With the development of technology, people pay more attention to the development of encryption methods. Visual secret sharing (VSS), also called visual cryptography (VC), involves encrypting a secret image into several images (called shares) that can be stacked together to reveal the secret image. The decryption method in the traditional VSS is equivalent to using logical OR operation. A new VSS idea based on the polarization of light waves was proposed in 1997. Two types of polarizers, vertical and horizontal, are used to generate the light waves. It is customary to regard the vertical polarizer as 1, and the horizontal polarizer as 0, and regard the result of the stacking of two polarizers as a logical XOR operation on two corresponding values. However, this interpretation produces errors when stacking more than two polarizers. Therefore, a new interpretation way is required for VSS based on the polarization of light, called P-VSS. Therefore, we proposed a novel (t, n)-threshold P-VSS scheme based on polarization. The experimental results and theoretical analysis showed that the proposed scheme was more efficient compared with previous studies and outperformed traditional VC schemes.
Recipe Verification Based on Age-Adjusted Multiple Servings by JavaScript and R Language

Access to nutritious and healthy diets is crucial for children's health, growth, and development. The school meal programs play an integral role in ensuring that all children, regardless of their socio-economic background can receive high-quality and nutritious food. The primary goal of these programs is to reduce the occurrence of undernutrition, overweight, and obesity as well as to promote healthy diets. Although an extensive number of verified recipes are made available to help schools prepare healthy meals, many schools still prefer to use their recipes to make school meals. Unfortunately, most school-own recipes have not been verified, and therefore is difficult to guarantee the nutritional quality and outcome of recipes. In this paper, we presented a systematic approach to verify and correct the recipes using a combination of statistics and text analysis. The proposed method incorporated the age and number of servings to facilitate and improve the process of verification and correction of recipes. A total number of 7,505 school lunch recipes were used in the study. In particular, the dataset of 1,009 verified standardized recipes was trained using the proposed model to evaluate and revise the set of 6,496 unverified test data. The experimental results demonstrated the effectiveness and benefits of the proposed method for verifying the recipes. The results further illustrated that the presented approach suggested the revision of the invalid recipes.
Upaya Meningkatkan Kemampuan Menyimak Melalui Metode Bercerita pada Anak

Preliminary observations show that listening skills in group B of Citra Blessing Kindergarten have not yet developed optimally. The problem that arises is the way the teacher presents the story is less attractive and seems monotonous, so that students are less interested in listening to the contents of the reading. Based on these conditions, the research was carried out with the aim; 1) to find out the efforts to improve listening skills through the storytelling method for children; 2) to find out the increase in listening skills through the storytelling method for children. This study used classroom action research (PTK) which was carried out in two cycles. Data collection techniques using observation, interviews, and documentation with data analysis techniques using the percentage formula. The results showed that in the first cycle, there were 8 children who developed according to expectations (BSH) with a percentage of 44.44%, then it increased in cycle II to 94.44% with very good development criteria (BSB) there were 11 students or 61. 11% and developing according to expectations (BSH) there are 6 students at 33.33% percentage gain. This shows that presenting the right story can improve listening skills in children.
Bitcoin Versus Gold Prices: Correlation or Mis-specification

Bitcoin is a newly created currency, which is also considered as “Digital Gold.” Whereas, Gold is a precious yellow metal. Bitcoins are more independent of the government than gold. Both gold and bitcoins are scanty resources, and thus, prices of both these assets appreciate or deteriorate depending on the demand and supply. An attempt was made to examine the association between prices of these two currencies. For this purpose, three different models were run: independent sample t-test, correlation analysis, and regression analysis. The outcomes of the independent sample t-test revealed that there exists a significant difference between the gold and bitcoin prices. However, the findings of correlation analysis show that movements in gold prices are statistically and positively linked to bitcoin prices. These findings indicate that gold prices move in the same direction as bitcoin prices. Further, the results of regression analysis also depicted that movement of bitcoin prices depends on movement of gold prices. Since its genesis, bitcoin prices have experienced around 37,418 appreciations, which make it an extraordinary currency. However, this study argued that bitcoin is establishing itself as an investment asset for the short term only because fluctuation in prices is very abnormal and unreliable in the long run. Moreover, robustness checks further show that gold prices are increasing at a steady rate, but increments are regular and trustworthy. Finally, the study found that bitcoin provides much higher returns to investors than gold. These results are crucial for the risk-taker investors who are looking for higher returns because bitcoins are getting growing public exposure day by day and attracting investments throughout the world.
EFFECTIVENESS OF USING ROLE PLAY STATEGY TO TEACH SPEAKING (A PRE-ECPERIMENTAL STUDY AT CLASS VIII A OF SMPN 1 JOMBANG)

This study intends to ascertain whether students' speaking abilities have improved both before and after being instructed utilizing a role-playing technique. A pre-experimental design is employed. This study was carried out at SMPN 1 Jombang. Students in class VIII-A at SMPN 1 Jombang during the academic year 2022â€“2023 served as the study's samples. Through SPSS V.25, the researcher ran a t-test to assess the data. Pre-test and post-test scores were obtained by the researcher after data collection. The average pre-test score was 43.91, while the average post-test score was 52.08. The researcher then used the t test to assess the data, with a Sig. (2-tailed) result lower than 0.05. Ha is seen as acceptable, it can be said. Several conclusions were drawn from the data analysis, including that there were considerable variations between students and that the students' speaking abilities before and after employing a role-playing technique for instruction were still less obvious. Before and after employing role-playing techniques to teach speaking abilities. Thus, it can be said that the role-playing technique works well for enhancing speaking abilities in SMPN 1 Jombang pupils in class VIII-A.
CRITICAL DISCOURSE ANALYSIS OF RHETORICAL DEVICES IN JOE BIDEN INAUGURATION SPEECH 2021

Abstract: In this study, researchers used a descriptive qualitative method to analyze data on rhetorical devices and how this was applied in the inauguration speech for the 2021 United States president. This study used theory from Jones and Peccei (2004) and the CDA approach by Van Dijk at the microstructural level. This study aims to find out how the speaker conveys the implied meaning by using rhetorical devices and also to find out how this is constructed in this speech. After data collection, the data is identified and analyzed using rhetorical devices based on the theory of Jones and Peccei 2004 coupled with critical discourse analysis which is limited to the microstructural elements of Van Dijk's theory. The researcher found that the speaker used all kinds of rhetorical devices, except for euphemisms. Those rhetorical devices are metaphor, parallelism, the rule of three, and pronouns. There are 16 devices found in the data. Based on the findings, the most dominant is parallelism, this shows that speakers like to use similar structures in explaining and conveying the contents of speech without changing the context. In addition, it can be concluded that the use of rhetorical devices and critical discourse analysis approaches in delivering speeches can contribute to making speeches more interesting and memorable.
USABILITY ANALYSIS OF CAMSCANNER APPLICATIONS USING THE SYSTEM USABILITY SCALE (SUS) METHOD

In the current digital era, technology has transformed the way we work and interact with the world around us. One significant development is the ability to convert physical documents into a digital format using scanning applications. One popular application for this task is CamScanner. This research aims to measure the CamScanner application's usability level using the System Usability Scale (SUS) analysis. The research method is a quantitative descriptive approach, aiming to provide a structured, accurate, and up-to-date understanding of the researched object. Data collection was conducted using a questionnaire created with Google Forms and distributed to 30 respondents. The findings of this research indicate that the CamScanner application exhibits usability with an acceptability level of "Acceptable," a grade of "B," and an adjective rating of "Good." This conclusion is based on calculating the average questionnaire score, which yielded a value of 74.2.
IMPROVING STUDENT'S WRITING SKILL THROUGH PROBLEM BASED LEARNING BY USING PICTURE ON DESCRIPTIVE TEXT

This research aimed to improve the studentsâ€™ skill in writing. It used Classroom Action Research (CAR). The method used by the researcher was problem-based learning by using picture. The Research subject were studentsâ€™ of class VII A in SMPN 1 Sukorame consisting of 32 students. The Classroom Action Research was conducted 4 days on the date 12th, 15th, 16th and 17th August 2022. The researcher collected the data using test and observation sheet. After collecting the data, the researcher analyzed the data using the normality test and paired sample T-test. The results showed that there was a significant difference between the results of the pre-test and post-test. This can be seen from the average increase in pre-test and post-test scores, namely 56.28 for the pretest and 77.47 for the post-test. The use of problem-based learning by using picture in teaching writing can also improve the classroom situation in writing. In this case it can be seen the participation of students in the introductory part of the descriptive text, students listen and observe carefully and students do the exercises given with enthusiasm. Based on the paired samples test, it is known that sig. (2-tailed) 0.000, while alpha (Î±) 0.05 (0.000 &lt; 0.05) means that Ho is rejected and Ha is accepted. In other words, the application of problem-based learning by using picture on descriptive text of students' writing skills increased.
Modelling and Explaining IR System Performance Towards Predictive Evaluation

Information Retrieval (IR) systems play a fundamental role in many modern commodities, including search engines (SEs), digital libraries, recommender systems, and social networks. The IR task is particularly challenging because of the volatility of IR systems performance: users' information needs change daily, and so do the documents to be retrieved and the concept of what is relevant to a given information need. Therefore, the empirical offline evaluation of an IR system is a costly and slow post-hoc procedure, that happens after the system deployment. Given the challenges linked to empirical IR evaluation, predicting a system's performance before its deployment would add significant value to the development of an IR system. In this manuscript, we place the cornerstone for the prediction of IR performance, by considering two closely related areas: the modeling of IR systems performance and the Query Performance Prediction (QPP). The former area allows us to identify those features that impact the most on the performance and that can be used as predictors, while the latter provides us with a starting point to instantiate the predictive task in IR. Concerning the modeling of IR performance, we first investigate one of the most popular statistical tools, ANOVA. In particular, we compare traditional ANOVA with a recent approach, bootstrap ANOVA, and observe the different conclusions that can be achieved using these two different statistical tools [Faggioli and Ferro, 2021]. Secondly, using ANOVA, we study the concept of topic difficulty and observe that the topic difficulty is not an intrinsic property of the information need but stems from the formulation used to represent the topic [Culpepper et al., 2022]. Finally, we show how to use Generalized Linear Models (GLMs) as an alternative to the traditional linear modeling of IR performance [Faggioli et al., 2022]. We show how GLMs provide more powerful inference with comparable stability. Our analyses on the QPP domain start with developing a predictor used to select among a set of reformulations for the same information need, the best-performing one for the systematic review task [Di Nunzio and Faggioli, 2021]. Secondly, we investigate how to classify queries as either semantic or lexical to predict whether neural models will perform better than lexical ones. Finally, given the challenges shown in the evaluation of the previous approaches, we devise a new evaluation procedure, dubbed sMARE [Faggioli et al., 2021]. sMARE allows moving from a single point estimation of the performance to a distributional one, allowing us to achieve improved comparisons between QPP models and more precise analyses. Awarded by: University of Padova, Padova, Italy on 20 March 2023. Supervised by: Nicola Ferro. Available at: https://www.research.unipd.it/handle/11577/3472979.
Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education

This report documents the program and the outcomes of Dagstuhl Seminar 23031 "Frontiers of Information Access Experimentation for Research and Education", which brought together 38 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing) and specifically focused on developing more responsible experimental practices leading to more valid results, both for research as well as for scientific education. The seminar featured a series of long and short talks delivered by participants, who helped in setting a common ground and in letting emerge topics of interest to be explored as the main output of the seminar. This led to the definition of five groups which investigated challenges, opportunities, and next steps in the following areas: reality check, i.e. conducting real-world studies, human-machine-collaborative relevance judgment frameworks, overcoming methodological challenges in information retrieval and recommender systems through awareness and education, results-blind reviewing, and guidance for authors. Date: 15--20 January 2023. Website: https://www.dagstuhl.de/23031.
DEEP LEARNING-BASED APPROACHFOR JAPANESE KEYPHRASE GENERATION

Retrieving keywords or keyphrases from a text document is one of the essential features in many applications. For example, the dataset search systems may use those keyphrases to suggest more relevant data to users. However, obtaining the keyphrases