Reviewerly/exHarmony huggingface dataset:
Thoreau Remembers a Story about Saddleback, a Place Worth Preserving

Thoreau’s story of climbing Saddleback (Mt. Greylock) in A Week on the Concord and Merrimack Rivers (1849) preserves his memories associated with the place and establishes the cultural significance of the mountain. The interpolation is about Thoreau’s quest to find a place of meaning and permanence amid the rapid changes in his life and the development of rural New England from industrialization. Places are integrations of space and time created by stories with personal and cultural significance. Such places must be preserved. The hike is a pilgrimage to a paradise where he has a transcendental vision of a new world. Thoreau is born again on the mountain. The Greylock story is one of the most studied places in A Week. Literary critics have discussed how Thoreau’s ascent and the sunrise at the summit are the climax of the Romantic hero’s journey through nature, space, and time. None have noticed the symbolism of Thomas Carlyle’s “untamable fly” appearing instead of God, and none have explored Greylock as a Thoreauvian place. Thoreau’s trip to Greylock and the Catskills was a search for inspiration, and he was inspired to write Walden (1854). He stayed in cabins that prefigured his house at Walden Pond, and he saw things that reminded him of Harvard, Boston, Staten Island, and other mountains he had climbed. At the summit he found water, nibbled supper with the mice, conceived of a book about nature, and watched the sunrise of his new life. The history of Greylock is the story of the environmental impact of unregulated lumbering and development and the resulting conservation efforts. The old growth forest is now protected state land, with rare birds and a ghost. The mountain is a literary place also associated with Hawthorne, Melville, and Rowling. Since 1994, Thoreau’s climb up Greylock has been commemorated by an annual hike retracing his footsteps.
Stock Market Reaction to the Trade Dispute: South Korea-Japan Trade Dispute Case

This study examines the impact of geopolitical risks on the stock market, with a focus on the 2019 South Korea-Japan trade dispute. Using the event study methodology, we estimate the abnormal returns of manufacturing companies in South Korea directly related to Japan’s export restrictions. Our findings reveal that the exclusion of South Korea from a Japanese Whitelist had a negative effect on the stock prices of manufacturing companies in South Korea. The cumulative abnormal return (CAR) of Korean manufacturing companies was -6.26%, and 86.64% of 232 sample companies experienced a negative CAR. The study also confirms that small and medium-sized enterprises (SMEs) are more vulnerable to geopolitical risks than large companies. This study provides practical implications for firm managers and policymakers as the first study that provides empirical evidence of the stock market reaction to the South Korea-Japan trade dispute.
Live Video Streaming Buffering Time Reduction using DRL Algorithm

Live video streaming is a popular and rapidly growing application that provides users with real-time access to video content over the internet. However, the performance of live video streaming is often affected by network conditions, user preferences, and video content characteristics, leading to poor video quality, long buffer times, and playback interruptions. In this project, we propose a Deep Reinforcement Learning (DRL)based adaptive video streaming system for live video streaming that incorporates a quality prediction agent, a DRL-agent, and a video delivery mechanism. The system optimizes the video streaming performance and user experience by adapting to changing network conditions and user preferences in real-time. This research study implements the proposed model in Python and evaluates the performance by using a dataset of live video streaming traces. The obtained results show that the proposed system can significantly improve the video quality, reduce the buffer time, and enhance the playback smoothness compared to traditional video streaming methods. The proposed DRL-based adaptive video streaming system represents a significant improvement over traditional video streaming methods and has the potential to enhance the user experience of live video streaming applications.
Text classification of electricity policy information based on BERT-optimized TextRNN

In order to improve the effective query of power policy information, we design and implement text classification of power policy information. Aiming at the problem of sparse news headline features and strong context dependence in power policy news text classification, a semantic enhancement-based power policy news text classification method is studied. In this paper, based on the traditional TextRNN, we use the BERT language model instead of the traditional Word2Vec model for semantic representation; at the same time, for the LSTM network used by the traditional TextRNN, the Bi-LSTM network is used to extract feature vectors, and then connect the Attention layer to get fusion. The feature vector is finally input into Softmax to get the model classification result. The final experimental results show that the TextRNN network optimized based on BERT has a better classification effect.
Design and Realization of an Online Examination System Based on Maven Framework

Online examinations have been accepted by more and more people. However, the online examination system in China is still in the development stage. With the rapid development of computer software technology, we can apply a more perfect examination system to domestic universities and primary schools. The platform system is developed in Java language, using Maven container and MySQL database to achieve overall development. The front end is built by Bootstrap framework, and the back end is built by SSM framework. This paper focuses on the design and implementation of Maven based online examination system. The system is composed of students and administrators. This paper focuses on the design and implementation of Maven based online examination system. Students can learn and test independently through the system. As administrators, teachers can manage students’ learning process through the learning platform, so as to accurately master students’ learning progress. The system can intuitively realize the visualization of test questions, make the page more concise and clearer, improve the comfort of user interaction, and increase the user experience.
Design and development of environmental pollution source monitoring management information system

According to the multi-layered system, environmental pollution monitoring and control information is developed through the use of network, database technology, GIS and other technologies, which improve the modern level of environmental monitoring and management. The system is characterized by technology, reasonable design, simple operation, and high intelligence.
Lightweight target detection method based on improved YOLOv5

Target detection is one of the hot research issues in the field of computer vision in recent years. Large scale convolution networks can effectively improve the accuracy of target detection, but they are not suitable for application scenarios with limited computing and storage capacity. In order to solve the above problems, this paper proposes a lightweight target detection network based on improved YOLOv5 - CS-YOLOv5 network. This network uses a lightweight network ShuffleNetv2 to reconstruct backbone, effectively reducing the network complexity, and adds a CA attention module to the feature extraction network to enhance the network feature expression ability, and solves the problem of neuron deactivation through H-Swish activation function. The final model size was compressed to 1.64MB, and the mAP50 on the COCO test set reached 47.6%, reducing the model size by 13.7% and improving the detection accuracy by 1.9% compared with the original YOLOv5 model.
Fast real-time semantic segmentation for autonomous driving

Semantic segmentation for autonomous driving has high-speed requirements, but the current semantic segmentation model requires a lot of computing resources and is very time-consuming, which makes it less suitable for autonomous driving. To solve this problem, we propose a fast real-time semantic segmentation network, which has a minimal amount of computing and can run in real-time, so it is very suitable for the self-driving field. The framework contains two essential modules, FPN of images and shallow Backbone. It ensures the efficiency of image multi-scale features and feature extraction, respectively. Experimental results on cityscapes show the effectiveness of our method.
Minimizing Cost of Controlled Narcotics Drugs Consumption using Predictive Modelling

The prediction of controlled narcotic drugs consumption for cost minimization is a complex task that requires the use of multiple data sources and statistical techniques.In the pharmaceutical industry, forecasting is important since it informs drug procurement, budgeting, supply chain, and logistics planning.The pharmaceutical supply chain is characterized by high complexity and uncertainty, which are viewed as fundamental impediments to increasing pharmaceutical supply chain performance.Furthermore, the volatility of the international drug market and changes in drug prices, the country's economic situation, and unexpected supplier practices make it difficult for hospitals and pharmacies to obtain drugs.This study highlights the potential of using predictive modeling to optimize controlled narcotic drugs consumption and reduce costs.The objective of this research is to identify the optimal forecasting technique to improve forecasting accuracy while minimizing cost.A case study with five samples of controlled medicines (Narcotic drugs) been presented applying simple moving average, exponential smoothing, double exponential smoothing, and simple Linear Regression Model.In error measurement metrics, Mean Absolute Deviation, Mean Absolute Percentage Error, Root Mean Square Error, and Tracking Signal.The study recommends combining minimum cost with error measurement metrics to decide which forecasting method should be used.Future research in this area could focus on developing more sophisticated predictive models that incorporate a wider range of data sources and consider the complex interplay between various factors that influence drug usage.
KESALAHAN BERBAHASA BIDANG MORFOLOGI DALAM BERITA ONLINE MAJALAHLARISE.COM RUBRIK PENDIDIKAN

This study aims to describe and correct the location of language errors in the morphology field in its affixation which is published from online news namely Larise.com Magazine. The focus of this research is on describing aspects of language errors in morphology, especially in affixation. Sampling of data sources was carried out purposively and snowballing, and the collection technique was using download, reading, and note-taking techniques. The data analysis technique used the agih method. The results of the research will be described systematically, factually, and accurately regarding language errors in morphology in the online news Magazinelarise.com. The data was collected and then analyzed whether there were errors in the use of words in the writing of the online news. After that, they were totaled and grouped based on the morphological process of their affixation. The data of this research is the form of error words that are written in the news and the data sources are collected from 10 online news in the education rubric of the June-December 2021 edition. This research is a descriptive qualitative research. In this study, the researcher focuses more on the affixation section which includes: prefixation, infixation, suffixation, confix, and simulfix. The results of this study contained 10 educational rubrics above, the results of this study contained 9 language errors with 5 types of morphological affixation including 2 prefixation errors, namely the addition of prefixes (meN-) and (ber-), 3 suffixation errors including the addition of suffixes ( -an) and Foreign (-al), 1 infixation error, namely the addition of infixation (-in-), 3 affix combination errors, namely the addition of confixes (ber-kan), (di-kan), and (meN-i), and 1 Simufik's error is the addition of simulfixation (pen-kan).
Research on offline signature verification based on image domain transfer Siamese network

Verifying individual Chinese handwritten signatures is an essential biometric technology that is widely used in banking, finance, and legal business. The forging of signatures for the purpose of cheating is a serious detriment to the interests of these industries. This paper proposes a Siamese network verification signature based on image domain transfer. The Siamese network uses a convolutional neural network as a sub-network to build the structure of Siamese network by combining the genuine signature network and the unauthenticated signature network. Each sub-network transfers ImageNet weights for training in the Chinese recognition task so that the new weighted image domain is suitable for the Chinese signature image domain. This Siamese network is trained with new weights to determine the authenticity of the signature. Currently, there is no publicly available dataset of Chinese handwritten signatures. This paper develops the Chinese signatures dataset from 295 induvial persons, 885 different persons participated, including approximately 9,000 pictures. The Siamese network achieves 92.75% accuracy on the test set, and the verification time for a single Chinese handwritten signature is 0.32 seconds. Finally, the Siamese network model achieves more than 90% accuracy on the public handwritten signature datasets CEDAR, BHSig-B and BHSig-H in three different languages, and the experiments demonstrate the good generalization of the proposed method.
Image caption generation based on object detection and knowledge enhancement

Existing image caption generation methods and results mainly focus on identifying existing image contents and their relationships in images, but cannot generate descriptions with fine-grained background knowledge, to overcome the problem that traditional image caption models cannot describe deep-level semantics in pictures. On the one hand, this paper presents an image caption generation method based on target detection and knowledge enhancement. First, in the phase of target detection, this paper presents a Fusion target classification detector (FTCD) which fuses multidimensional information to get tags of human faces, goods, and objects in the graph. Secondly, the knowledge map is introduced, and the related knowledge is queried in the knowledge map using the target tags obtained by the target classification detector. Finally, the target's tag set and related knowledge are jointly fed into the model for coding. On the decoding side of the model, an attention mechanism is introduced to guide the model to select appropriate information and generate an image description. On the other hand, there is a lack of common knowledge about manual descriptions in MSCOCO datasets. This paper presents an evaluation index SPICE-K, which can be used to evaluate image descriptions with common sense knowledge. The experimental results show that the accuracy of the proposed method is 1.3% higher than that of the standard LBPF model. The experimental analysis shows that, compared with the benchmark model, the performance improvement of this method mainly comes from the introduction of the knowledge map and the target classification detector proposed in this paper.
A robust one-shot online multi-object tracking method

Current studies suggest that one-shot multi-object tracking approaches are as competitive as methods that separate inference into two branches. However, current methods neglect the inherent differences and relationships between tasks. To address this question, we propose RoTrack, which incorporates a novel inference network and efficient association approach. Our network module learns both inter-task associations and difference to alleviate inference inequality. We introduce the coordinate associating opportunity (CAO) strategy, which employs fair voting to avoid errors resulting from manual selection of association bases. We evaluate our algorithm on public MOT Challenge datasets and demonstrate outstanding results. Experiments show that RoTrack can well deal with the above-mentioned problems in the field of multi-object tracking.
Effects of Grit and Personality Type on College Life Adjustment of Nursing Students

본 연구는 간호대학생이 학업과 임상실습을 진행하며 겪게 되는 대학생활 적응의 어려움을 그릿과 성격유형을 통해 중재 전략을 마련 하고자 실시된 서술적 조사 연구이다. 자료 수집은 C지역과 K지역 간호대학생 1학년부터 4학년까지 130명을 대상으로 2022년 6월 1일부터 7월 30일까지 진행 되었고, 수집된 자료는 SPSS 21 프로그램을 이용하여 분석 하였다. 본 연구의 결과 간호대학생의 그릿 정도는 3.23점(5점 척도), 대학생활 적응 정도는 3.05점(5점 척도) 이었다. 또한 간호대학생의 성격 유형별 빈도와 정도는 A type이 69명으로 27.12점(10-40점) 이었고, B type은 61명으로 18.79점(10-40) 이었다. 간호대학생의 그릿은 대학생활 적응에 유의한 정적 상관관계를 보였으며(r=.37, p <.001), 간호대학생의 대학생활 적응에 영향을 미치는 요인은 전공 선택 동기 중 취업률(β=-.24, p =.009), 전공만족도 중 만족(β=.21, p =.02)과 그릿(β=.29, p =.001)으로 나타났다. 이 요인들은 대학생활 적응을 23.0% 설명하고 있었다. 따라서 본 연구의 결과를 바탕으로 간호대학생의 대학생활 적응을 위한 그릿 수준을 향상 시킬 수 있는 중재 프로그램이 개발되어지기를 제언한다. This descriptive research study was conducted to prepare an intervention strategy by considering grit and personality type for the difficulties encountered during college life adaptation that nursing students experience during academic and clinical practice. Data were collected from June 1 to July 30, 2022, enrolling 130 nursing students from the 1st to 4th grades in C and K areas. The collected data were analyzed using the SPSS 21 program. Analysis using the 5-point scale revealed the degree of grit to be 3.23 points and the degree of adaptation to college life was 3.05 points. In addition, the frequency and degree of each personality type were 27.12 points (10-40 points) comprising 69 type A subjects, and 18.79 points (10-40 points) for 61 type B subjects. The grit of nursing students was significantly and positively correlated with adaptation to college life (r=.37, p <.001). The factors as motives for major selection influencing college life adaptation of nursing students were employment rate (β=-.24, p =.009), satisfaction of the major selected (β=.21, p =.02), and grit (β=.29, p =.001). These factors were found to explain 23.0% of the variance in college life adjustment. Based on the results of this study, we suggest an intervention program that improves the grit level for the adaptation of nursing students to college life should be developed.
The Influence of Capability-Supportive Culture on Alliance Performance: A Study of Small Industrial Sector Managers in Syria

This study explores the impact of Capability-Supportive Culture (CSC) on alliance performance among small industrial sector managers in Syria. Employing a sample of 189 managers, the study uses a quantitative approach, leveraging a survey methodology and regression analysis to assess the relationship. The findings indicate a significant positive relationship between a capability-supportive culture and alliance performance. These insights contribute to the understanding of cultural determinants of alliance performance and have practical implications for managerial practice in the small industrial sector.
An Evaluation of NoSQL Databases Using the Analytical Hierarchy Process (AHP)

With the rapid growth of big data, NoSQL databases (DB) offer an opportunity to harness big data sets. The emergence of NoSQL technologies stresses the importance of performance evaluation models that aid the decision of NoSQL DB selection. The purpose of this paper is to aid the decision-maker with evaluating and selecting the most appropriate cloud NoSQL DB, with a particular focus on big data contexts. To serve this purpose, evaluation criteria for the NoSQL DB were proposed based on a critical analysis of the relevant literature. Evaluation criteria consisted of six factors: data storage and distribution, data model and design, querying, system, security, and deployment model. These factors were incorporated into the AHP to facilitate the evaluation of sampled NoSQL DBs. Results indicated that the performance of these DBs varies, and suggested that DBs are not necessarily appropriate for all use-cases.
E-Schools Project-Framework for Digital Competencies of School Principals: Competencies in the Area of Digital Technologies in Learning and Teaching

The role of principals of schools facing digital transformation in and for the 21st century is to assure and promote effective use of digital technologies in all aspects of school functioning.Within the project "e-Schools: Development of the System of Digitally Mature Schools" (e-Schools project), which is in the second phase of its implementation in the Republic of Croatia, the Framework for Digital Competencies of School Principals has been developed. Within the Framework there are five areas of digital competencies expected from and needed by each school principal in their everyday job: (1) Planning, management and leadership, (2) Digital technologies in learning and teaching, (3) Development of digital competencies, (4) Digital culture and (5) Digital infrastructure. For each of these areas, a set of digital competencies and related key school principal tasks were identified based upon a review and analysis of the relevant literature and strategic documents and with the help and involvement of experts and the school principals themselves.In this paper, the digital competencies of school principals related to the area (2) Digital technologies in learning and teaching have been presented and elaborated. The role of school principals in promoting the use of digital technologies in schools and some related challenges has been emphasized.
Design of an Efficient Multidomain Augmented Data Aggregation Model to Solve Heterogeneity Issues for IoT Deployments

Heterogeneity of data is a common issue in data retrieval in Internet of Things (IoT) Networks, when dealing with data from multiple sensors & sources. To retrieve data from heterogeneous sources, there are several techniques proposed by researchers, which include, Data integration, Data federation, Semantic mapping, Management of Metadata, and efficient Visualization of Data Samples. But most of the existing models that perform these tasks either have limited scalability or showcase lower processing efficiency on the collected data samples. To overcome these issues, this text proposes design of an efficient multidomain augmented data aggregation model to solve heterogeneity issues for IoT deployments. The proposed model initially converts all input data samples into 1D vectors via convolutional flatting operations. These vectors are further converted into multidomain feature sets via a combination of Frequency, Gabor, Entropy, Wavelet and Convolutional analysis. These feature sets assist in identification of differential & spatial data patterns, which can be used for further analysis. To demonstrate efficiency of the proposed model, the collected IoT datasets were given to an Auto Regressive Integrated Moving Average (ARIMA) Model for prediction of temperature and humidity levels. These predicted levels were compared with existing models, and it was observed that the proposed model was able to improve the accuracy of prediction by 8.3%, while improving the precision by 5.9%, and recall by 2.5% under real-time deployment data samples.