Reviewerly/exHarmony huggingface dataset:
Global Boundary Refinement for Semantic Segmentation via Optimal Transport

Semantic boundary prediction is an important but challenging problem in semantic segmentation. Previous methods usually regard the boundary prediction as a pure classification problem using binary cross-entropy loss, which does not consider the spatial distance between the predicted boundary and ground truth. To address this issue, we formulate semantic boundary prediction as the optimal transport problem where the minimum transport cost reflects the spatial distance. Specifically, the predicted boundary and the true boundary are formulated as source distribution and target distribution, respectively. Then, we calculate the spatial distance between source pixels and target pixels as the transport cost matrix. Finally, we solve the transport problem and obtain the minimum transport cost for effective boundary supervision. Additionally, our method does not explicitly fuse boundary and semantic feature, which only provides supervision for the predicted boundary during the training. And the boundary branch can be discarded during the inference, thus will not bring extra computations and parameters. Experiments show that our method can significantly and consistently improve the segmentation accuracy of various models on public segmentation benchmarks: Cityscapes and CamVid.
Sistem Pendukung Keputusan Untuk Menentukan Tarif Maksimum Kamar Hotel Garuda Plaza Medan Dengan Metode Fuzzy Tsukamoto

Medan City is a Metropolitan city and also many tourist objects that are currently popular among Indonesians and foreigners. In the hotel industry, the most important thing in determining the hotel rental price is because it only estimates the price of the hotel being rented based on the type of hotel and the facilities provided. Where the problems that occur to consumers do not know the price of the hotel for rent based on the type of hotel room, so that the customer has difficulty in determining the choice of room to use. Based on this problem, Fuzzy Tsukamoto was chosen because it has monotonous reasoning in each of its rules, where each consequent to the rule in the form of IF-THEN must be represented by a fuzzy set with a monotonous membership function. Fuzzy logic is used to solve problems linguistically or variables that contain uncertainty rather than through numbers. The Tsukamoto method has 3 stages, namely; fuzzification, fuzzy inference system, and defuzzification. Fuzzification functions to change crisp values ??to fuzzy values. Fuzzy inference system is drawing conclusions based on fuzzy rules or rules. Defuzzification is the process of converting fuzzy output into crisp values ??using the weighted average concept. In this study the rules will be searched automatically by the system based on the data that has been entered. The data that has been entered will be added with "events" to distinguish holidays, significant price changes, and the like. The results of this study obtained an error using MAPE of 28.41% for the test data with the Studio room type and 27.85% for the test data with the Premiere room type.
Emotional intelligence as an instrument to control occupational stress and Burnout Syndrome

Burnout Syndrome consists of a disease, also known as professional burnout syndrome, characterized by a process based on emotional exhaustion, depersonalization and low professional achievement, defined as a chronic psychological phenomenon that occurs in people whose work involves relationships of intense attention and common to people who need assistance and care. The objective was to demonstrate the importance of emotional intelligence as a means of preventing Burnout Syndrome. Methodologically, this is a bibliographical, descriptive and exploratory review of works published between 2015 and 2022, through consultations of scientific articles in the Virtual Health Library network, carried out between July and October 2022. It is possible to verify that we live in an immediatist era, which has been directly reflected in professional practice, mainly among people who occupy leadership positions, and may contribute to the compromise of the mental health of such professionals. It is essential for workers to reflect until what moment it is worth dedicating all their time to their professional life, in this sense, the need for a skill called self-management is latent, which can contribute significantly to improving the quality of life of workers. It was concluded that the main problems arising from mental exhaustion can cause irreversible harm to an individual's health, therefore, the appearance of the syndrome must be avoided from the first signs and symptoms; the most successful professionals are not necessarily those with the highest intelligence, but those with better emotional control, which means they have greater emotional intelligence. The ability of professional self-management is interpreted as breaking the alienation of work by emphasizing that overcoming the division of intellectual and manual labor is the basis for sustaining a society divided into classes and an oppressive structure.
Türkiye in the Global Cybersecurity Arena: Strategies in Theory and Practice

The threat of malicious actors is a growing fear among all cyberspace stakeholders, particularly states, who are primarily responsible for national security, including the protection of critical infrastructure. Taking into consideration the major cyberattacks of the last three decades and their impacts on international society, a comprehensive strategy is needed to defend cyberspace against malware and to provide a safe and free cyber ecosystem. Türkiye, like many countries, has developed cyber strategies to respond to such threats in the last decade. This paper addresses a set of interrelated cybersecurity issues; central among them is the question, “Is Türkiye’s cybersecurity strategy properly devised to cope with the new security environment in the anarchic world of cyberspace?” Türkiye’s national cybersecurity strategy has undergone several changes since 2013, each representing an attempt to address the evolving cybersecurity landscape. These strategies have been successful in some areas, such as Türkiye’s legal, capacity-building, and organizational structure, but have been less successful in terms of technical measures. This article applies a macro-analysis framework that encompasses both quantitative and qualitative research to analyze Türkiye’s cybersecurity strategies in theory and practice. The findings of this analysis suggest that Türkiye is still vulnerable to a possible major cyber-attack.
Statistical Analysis and Economic Models for Enhancing Cyber-security in SAINT

SAINT analyses and identifies incentives to improve levels of collaboration between cooperative and regulatory approaches to information sharing. Analysis of the ecosystems of cyber-criminal activity, associated markets and revenues drive the development of a framework of business models appropriate for the fighting of cyber-crime. The role of regulatory approaches as a cost benefit in cyber-crime reduction is explored within a concept of greater collaboration to gain optimal attrition of cyber-criminal activities. Experimental economics aid SAINT in designing new methodologies for the development of an ongoing and searchable public database of cyber-security indicators and open source intelligence. Comparative analysis of cyber-crime victims and stakeholders within a framework of qualitative social science methodologies deliver valuable evidences and advance knowledge on privacy issues and deep web practices. Equally, comparative analysis of the failures of current cyber-security solutions underpins a model for greater effectiveness and improved cost-benefits. SAINT advances the metrics of cyber-crime through the construct of a framework of a new empirical science that challenges traditional approaches and fuses evidence-based practices with more established disciplines. Innovative models, algorithms and automated framework for metrics benefit decision-makers, regulators, law enforcement, at national and organisational levels providing improved cost-benefit analysis and estimation of tangible and intangible costs for optimal risk and investment incentives.
Automatic parking trajectory tracking control strategy on LQR and PID

This study selects two degree of freedom dynamic model as the research object, and the automatic parking process is analyzed. The longitudinal cascade controller of speed PID and position PID is used to control the longitudinal motion of the vehicle. The lateral tracking controller based on LQR optimal theory is adopted, and the feedforward control is added to form the optimal feedforward LQR controller as the lateral tracking controller. Based on the co-simulation platform of MATLAB/Simulink and CarSim, the function of the designed automatic parking trajectory tracking control strategy is verified. The car test was completed. The ultrasonic sensor, raspberry pie and Arduino were used as the main parts to build an intelligent car test platform to simulate the performance of the designed tracking control strategy in the real vehicle. Software simulation results and vehicle test results showed that the designed control strategy can ensure the path tracking performance of vehicle under normal parking speed, and has good tracking accuracy, real-time performance and vehicle driving stability.
The Necessity and Path of Cultivating Teachers’ Leadership in Basic Education Stage under the Background of ‘Double Reduction Policy’ in China

The 'Double Reduction Policy' reconstructs the pattern of basic education development and emphasizes the position of school education as the main front.As the key executive subject of the 'Double Reduction Policy', teachers have ushered in new challenges and opportunities.'Double Reduction Policy' points to improving quality and efficiency of school education, which puts forward higher requirements for the construction of school teachers and the development of teachers' comprehensive ability.Teacher leadership as a 'non-administrative' influence, its advantages and role have long been underestimated and even ignored.Teacher leadership is a key factor in promoting and practicing educational reform.Therefore, cultivating teacher leadership can effectively promote the implementation of the 'Double Reduction Policy' and deepen the reform of basic education.Based on modern system theory, this paper attempts to improve the one-sidedness of existing research results, creatively construct a system to cultivate teachers' leadership, and help basic education return to a good ecology.
Component Management Framework for Operating System Based on Microkernel Architecture

Edge computing puts forward higher requirements on operating system in terms of security, real-time and scalability. Operating systems based on microkernel architecture can effectively meet these requirements. However, the current microkernel operating system lacks a general user-mode program organization and management mechanism, which makes it difficult to develop user-mode programs such as system services, device drivers and applications. To solve this problem, this paper proposes a system service dynamic construction framework based on dynamic loading and dynamic linking technology, proposes a capability-based component security mechanism, and proposes a cross-core IPC optimization method. Based on these mechanisms, an efficient, secure and flexible component management framework can be formed. We also developed a prototype system and verified it on a real hardware platform. The experimental data show that the component framework designed in this paper is efficient and secure.
Exploring Hierarchical Prototypes for Few-Shot Segmentation

Few-shot segmentation has recently attracted much attention due to its effectiveness in segmenting unseen object classes using a few annotated images. Many existing methods employ a global prototype generated by global average pooling (GAP) to preserve general support information. However, a single prototype inevitably creates ambiguity due to its limited representation capability. In order to alleviate this problem, we explore hierarchical prototypes for few-shot segmentation in this paper. Specifically, we propose feature interaction clustering (FIC) to extract local prototypes to contain detailed support information, fully mining the relationships among support features, prototypes, and query features. In addition, to compare query features with multiple prototypes, we propose a simple but effective prototype attention module (PAM) that fuses support information from different prototypes. Our method enhances the robustness of the prototypes and improves the utilization of support information. Extensive experiments on common datasets (PASCAL- $$5^i$$ and COCO- $$20^i$$ ) demonstrate the effectiveness of our method.
PENGEMBANGAN MULTIMEDIA BERBASIS ANDROID UNTUK MENINGKATKAN PRESTASI BELAJAR PKN PESERTA DIDIK KELAS VII SMP REJANG LEBONG

The purpose of this study is to describe: (1) Learning media with Android-assisted multimedia applications are feasible to improve student achievement. (2) The effectiveness of learning media with Android-assisted multimedia applications in improving student achievement. In this study used a development design (Research and Development). The research subjects were students of class VII SMP. Data collection techniques used in the study were assessment sheets and student test results. Analysis of the data in this research with the mean (mean) and t-test. The instrument for validation of media and materials and IT experts in this research is the Likert Scale. The conclusions of this research are; (1) Learning media with an android-assisted worked example application developed is feasible to improve student achievement in learning PPKN. (2) The development of learning media using the Android-assisted worked example application can improve student achievement based on the results of a limited trial and broad-scale test for class VII students of SMP N 2 Rejang Lebong
Students’ Perception of the Presentation Activities in Online Speaking Class

The objectives of this research were to find out the benefits of presentation activities in online speaking classes. Data were collected using a questionnaire that collected a five-point Likert scale to gauge the students’ perception use of presentation activities in online speaking classes. The population of this research was eleventh-grade students of senior high school 4 Sidrap academic year 2021//2022 through random sampling technique and XI IPA 1 was selected as the sample of this research. The research finding showed that the utilization of presentation activities gave some benefits to the students as follows: easy understanding of the material; increased students’ motivation; focus deeper on learning; and Increased collaboration. It could be concluded that the utilization of presentation activities in online speaking class benefited the students in the eleventh grade of SMAN 4 Sidrap.
A Study on the Effectiveness of Blockchain Integrated Supply Chain in the Retail Market

Industry 4.0 comprises of some disruptive technologies which are creating a significant impact in every fields.Recent development in these technologies has created a huge impact in the conventional supply chain model which forces the companies to evolve new models to face the upcoming challenges.One such promising technology which will disrupt and change the entire view of supply chain transparency is Blockchain.Blockchain is basically a chain of blocks that contain information that is nearly impossible to tamper.Blockchain is defined as stack of information linked with each other in blocks and highly resistant to changes.It is a distributed ledger completely open to everyone.Once we enter data in Blockchain it becomes nearly impossible to change it which will give us the authenticity of the data.Based on the literature study, we found that integrating Blockchain in the supply chain will improve the efficiency of the supply chain exponentially.Every transaction that happens in a supply chain can be integrated and recorded in the blocks of a decentralized Blockchain (Distributed ledger) which will be shared will many computers thus increasing the transparency.As each block is connected with each other it becomes nearly impossible to change the entered record as tampering a single block may result in complete loss of data.This will improve the transparency of the supply chain in every stage and improve the supply chain security and efficiency.
Upaya Meningkatkan Kemampuan Berhitung melalui Permainan Kotak Pintar pada Anak Usia Dini

In improving children's cognitive, a game is needed so that it stimulates the ability to count. Based on this, the problem to be investigated in this study is how children's numeracy skills can be increased. The purpose of this study is to prove that children's numeracy skills can be improved through smart box games. The approach used in this research is quantitative with the type of classroom action esearch. Data collection methods used in this study were observation, interviews and documentation. The results of this study are: increasing the numeracy ability of children in group B in TK Kartika II 38 applying the smart box game obtained from the analysis of children's numeracy skills in cycle I obtained an average level of developmental achievement score 14 in the category of starting to develop, then seen in cycle I the average level of achievement in the development of numeracy was 34.33 and in the pre-cycle the score was 20.33, this showed an increase while in cycle II the overall numeracy ability of children was 42.17 in the very well developed category so that in the second cycle there was an increase of 7, It was concluded that at the end of the second cycle the research was said to be successful because the criteria for success had been reached according to the agreement between the researchers and the collaboration.
Sports Training Interaction Based on Virtual Reality Technology

At present, due to the continuous progress of computer technology and virtual reality and other technologies, human-computer interaction technology is also constantly developing, and a natural and harmonious third-generation human-computer interaction mode has emerged. In this interaction phase, users can interact naturally with computing through language, actions, and even expressions. The purpose of this paper is to study exercise training interaction based on virtual reality technology. The realization direction of system integration is determined. The system needs to record the user's basic information, training parameters and training status, so as to realize a closed-loop training-monitoring-correction-training network. Then, the role of the collision detection algorithm is introduced, and the set force feedback and its implementation are explained. Next, the horizontal comparison of the technical evaluation of the experimental group and the control group was completed through the training process. The final test shows that the average technical score of the experimental group is about 15% higher than that of the control group. The virtual reality sports training system studied in this paper can guide users to carry out scientific sports training and help users improve the effect of sports technology.
Intelligent Funds Assistant Exploiting Hierarchical Text Classification Algorithms

The need to ensure that all funding projects made available by the European Union are fully exploited by each country leads to a robust investigation of the main difficulties of applying to these projects. In this context, Portugal has verified that these funds are not being fully used and detected that one of the main setbacks in the European Union funding application system is the identification of the best call for application. Therefore, this work aims at developing a funds assistant framework to suggest the most suitable set of available calls for application based on the written description of the beneficiaries' projects, via the use of Natural Language Processing and text classification algorithms. Moreover, this problem is addressed as a hierarchical multi-class text classification, taking advantage of the hierarchical structure inside the European funds and aiming to improve the overall performance of the proposed framework. In this context, four models are chosen to compare the classification performance: Naive Bayes, Support Vector Machines, Random Forest and k-Nearest Neighbors. Simulation results highlight that the Support Vector Machine outperforms the rest of the algorithms for almost all classification scenarios. In order to enhance the results, the models are also calibrated to return the second prediction when the likelihood of the first prediction is small, achieving even higher performances for all levels of the hierarchy.
Challenges and opportunities of quantum-computational chemistry

<p indent="0mm">This article has summarized the main academic communications from the forum titled as “Theoretical Chemistry in the Age of Quantum Computational Chemistry” which has been granted by National Natural Science Foundation of China (NSFC) and held in Hefei on July 31, 2022. Firstly, the main characters of quantum-computational chemistry (QCC) have been introduced. And some current important challenges of the QCC are also pointed out. Secondly, we have mainly discussed the progress of the QCC in China and new research directions based on current noisy and future fault-tolerant quantum computing platforms. At the same time, in order to meet the major technical needs in future developments, some key scientific issues in this field are suggested in the next <sc>5–10 years,</sc> and some strategic suggestions for strengthening the support of NSFC were proposed.
Comparing random forest and AdaBoost with resampling for modeling imbalanced late payment tuition fee data

This paper compares the Random Forest and AdaBoost classifier with resampling for modeling the imbalanced late payment tuition fee data. We utilize the Random Undersampling (RUS), Random Oversampling (ROS), and Synthetic Minority Oversampling Technique (SMOTE) to have more balanced data. We used late payment tuition fee data of the IPB undergraduate program with regular admission from 2016 to 2018. The results showed that the best Random Forest classifier uses seven explanatory variables and 500 trees with Random Oversampling (ROS) method. The best AdaBoost classifier uses the optimal 80 iterations with Random Undersampling (RUS) method. The Random Forest-ROS and AdaBoost-RUS classifiers have ROC-AUC of 58.70% and 52.90%, respectively, indicating that the Random Forest-ROS classifier has better prediction than AdaBoost-RUS. The important variables for predicting the late payment tuition fee are the household’s electric capacity, the father’s income, and the number of children in the family.