Reviewerly/exHarmony huggingface dataset:
INCREASING THE STUDENTS’ WRITING SKILL IN DESCRIPTIVE TEXT THROUGH PLEASE STRATEGY AT THE TENTH GRADE OF SMK NEGERI 1 ULU MOROO IN 2022/2023

through topics provided. Then, students can share their ideas by writing the descriptive text especially describing a personality in correctly. Passing the MCC is a sign of a student's learning progress and is related to their academic achievement, particularly in the English subject. The study aims to improve students' descriptive writing abilities, particularly when it came to using the PLEASE Strategy to describe a personality. The researcher employed Classroom Action Research (CAR) to carry out the study. The eighteen pupils in the tenth grade at SMK Negeri 1 Ulu Moroo were the research subject. In order to gather data, the researcher employed assessment, field notes, and observation as tools. The researcher used essay writing as a tool for evaluation in order to assess the students' writing proficiency. The students' average score in Cycle I was 67.22, and they were unable to meet the MCC. However, the pupils performed well in Cycle II, with an average score of 82.5. The researcher finds that the PLEASE improved the students' writing skills in descriptive text at the tenth grade of SMK Negeri 1 Ulu Moroo in 2022/2023 since the students' marks in Cycle II are higher than those in Cycle I. It can be summed that the PLEASE can be applied in writing process at SMK Negeri 1 Ulu Moroo
Hand Gesture Recognition Applied to the Interaction with Video Games

In this work, a hand gesture recognition system was created for 11 different gestures. The system employed CNN-LSTM artificial neural networks and followed the CRISP-ML(Q) process model. The aim was to incorporate software engineering practices into machine learning projects. The system uses Electromyography (EMG) and Inertial Measurement Unit (IMU) signals as input to compute a gesture label and the time of occurrence in the signal. The system is integrated with a video game that utilizes hand gestures as input. A system usability scale (SUS) survey was done by ten final users in order to measure the interaction with the video game using gestures as the main way of interaction. The complete application evaluation obtained a SUS score of 75, or a B grade.
Research on tourism cross-language intention recognition method based on XLM-R

Intention recognition task is an important subtask in intelligent question answering research. At present, the task of intention recognition is usually oriented to a single language, and there are relatively few studies on cross-language intention recognition. In this paper, the "XLM-R+TextCNN" model is proposed for cross-language intention recognition task. The text vector of multi-language representation is generated by XLM-R pre-training model, and text features are obtained by TextCNN model to realize cross-language intention recognition. The experimental data set is the self-built tourism data set. The experimental results show that the model has achieved good results in a number of comparative evaluation, which verifies the feasibility and effectiveness of the proposed method.
Target detection based on improved swin transformer and cascade RCNN

This paper proposes two improvements to address the issues of high complexity and computational burden in the Swin Transformer backbone network for feature extraction and the feature mismatch problem caused by coupled detection in the Cascade R-CNN detection network. First, a lightweight PoolFormer Block based on pooling is introduced in the third and fourth stages of the Swin-T network to reduce its complexity. Then, to improve the feature extraction capability of the lightweight Swin-T network, a coordinate attention mechanism is introduced. Second, the classification and regression tasks of objects in the Cascade R-CNN detection network are decoupled to alleviate the issue of feature mismatch in the two tasks and further improve detection performance. The experimental findings obtained from the PASCAL VOC dataset indicate that the proposed approach increased the average detection precision by 2.2% compared to the baseline model.
Statistical and clustering analysis of attributes of Bitcoin backbone nodes

Bitcoin is a decentralized digital cryptocurrency. Its network is a Peer-to-peer(P2P) network consisting of distributed nodes. Some of these nodes are always online and in this article are called Bitcoin backbone nodes. They have a significant impact on the stability and security of the Bitcoin network, so it is meaningful to analyze and discuss them. In this paper, we first continuously collect information about Bitcoin nodes from July 2021 through June 2022 (which is the longest duration of data collection to date). In total, we collect information on 127,613 Bitcoin nodes. At the same time, we conclude that the fluctuation of Bitcoin nodes is directly related to the fluctuation of onion network nodes. Further, we filtered 2694 Bitcoin backbone nodes based on our algorithm. By analyzing the backbone nodes’ attributes such as geographic distribution, client version, operator, node function, and abnormal port number, it is demonstrated that these nodes are centralized and play an important role in the Bitcoin network. Based on this, three unsupervised machine learning algorithms are selected to cluster multiple attributes of backbone nodes in a more scientific way. In this paper, the whole process from data collection to cluster analysis is completed and the best results are obtained by comparison. The experiments proved the existence of centralization of Bitcoin backbone nodes and obtained the number of nodes within each cluster. Finally, cluster nodes are de-anonymized based on the optimal results. Through our experiments, we obtain organizational information about the deployers of 103 nodes, linking the Bitcoin backbone nodes to the real world, thus accurately demonstrating the existence of Bitcoin centrality.
Large Language Models for Telecom

Large Language Models (LLMs) have shown remarkable success in natural language processing (NLP) tasks, such as language translation, text summarization, and sentiment analysis. They can also help in identifying network faults, improving network security, and facilitating spectrum sharing. LLM-based solutions can be trained on largescale datasets to capture the heterogeneity and diversity of wireless networks. These models can be deployed on resource-limited devices, such as smartphones, to provide intelligent wireless services. In this talk, we will discuss our recent progress on LLM features and the potential of LLM in enabling intelligent wireless communication systems.
The Value, Challenges, and Pathways for Restructuring Compulsory Education Ecosystem in the Context of "Double Reduction"

The "Double Reduction" policy holds significant value for restoring balance in the education ecosystem and optimizing the educational ecosystem. However, examining compulsory education ecology under the lens of ecology reveals that the entrenched "robbing Peter to pay Paul" culture in student learning factors hinders innovative assignment design. The rapid fluctuations in the teacher ecosystem and the weakening of teacher "ecological resilience" affect teaching quality. The segregation of the "home-school-community" nurturing structure leads to a "collective absence" of family and social educational functions. The stable structure of the examination culture leads to a "two-way squeeze" between "intelligence education" and "grades." Ecological damage under the "middle-class trap" results in a "clear reduction but hidden increase" in students' extracurricular training burden, exacerbating the urban-rural curriculum and teaching "polarization." For the "Double Reduction" policy to be effective, it should focus on the "ecological balance perspective" and the "ecosystem perspective." This involves exploring "smart burden reduction" methods for student assignments, improving the mechanism for "double reduction" work for teachers, establishing a collaborative "home- school- community" education internet, returning to the original mission of moral education, enhancing the governance system for selecting and nurturing talent, and achieving "misplaced" high-quality development of urban and rural education. This will promote the education ecosystem from imbalance to balance and disorder to systematization, allowing students' learning to return to the essence of education.
A Fast Method to Defend Against SSDF Attacks in the CIoV Network: Based on DAG Blockchain and Evolutionary Game

This letter investigates the problem for Spectrum Sensing Data Falsification (SSDF) attacks in the Cognitive Internet of Vehicles (CIoV) network. The high-speed movement of Vehicle Users (VUs) leads to rapid changes in Channel State Information (CSI) and location. This leads to unstable detection probabilities and unstable probabilities of reporting errors. These unstable probabilities increase the error rate of traditional methods to identify VUs as Malicious Vehicle Users (MVUs). And high-speed movement makes it difficult to detect MVUs, which may result in massive MVUs’ attacks. To address the above problems, this letter establishes the Cooperative Spectrum Sensing (CSS) and spectrum access process under a Directed Acyclic Graph (DAG) blockchain framework, models MVUs’ attack strategy selection which is determined by revenue as an evolutionary game, and proposes a smart contract that changes the mining difficulty of VUs based on the correctness of local spectrum sensing decisions to influence VUs’ revenue. Finally, the simulation results verify the theoretical analysis and prove that the proposed method is superior to the traditional method.
Aerial Light Field Spectrum Analysis for Unmanned Aerial Vehicle Optimization Sampling and Rendering in Large-Scale Scenes

Unmanned aerial vehicles (UAV) can capture multiview images of large-scale scenes, and then using aerial light field (ALF)-rendering technology, they can render high-quality, large-scale 3D scenes. However, the reconstruction of large-scale scenes poses challenges, such as rendering distortion and high memory consumption. In this article, we study the multiview image-capturing and novel view-rendering method of UAV sampling to address these issues. First, we present an ALF sampling model using the spectral analysis of light field and obtain an exact spectrum expression of the ALF. Through the spectral support of ALF, we determine the bandwidth and calculate the minimum required UAV sampling rate. Finally, we demonstrate that our sampling and rendering methods can improve the rendering quality of UAV 3D reconstruction and reduce the minimum sampling rate.
The Way to make Blind People Use E-Mail System: Voice Based E-Mail Generating System Using Artificial Intelligence

In today's world, effective communication is essential for connecting with others. Communication technologies play a vital role in improving social and personal interactions. When combined with the internet, these technologies make communication convenient. However, individuals who are physically challenged face difficulties in utilizing such technologies due to visual and physical impairments. While there have been advancements in technology, they are often inaccessible to these individuals. This paper aims to address this issue by creating an email system that is user-friendly and accessible to new users and those with physical impairments, without requiring extensive prior experience. The system operates solely through mouse actions and voice conversion, enabling even illiterate individuals to send emails using speech recognition and text-to-speech functionalities. The system relies on responsive voice interaction, ensuring a hassle-free and easy-to-use experience. It is designed to send emails quickly and provides all necessary options and functions for effective email communication. Key Words: Speech Recognition, Text-To-Speech (TTS), Interactive Voice Response (IVR), Speech-To-Text (STT), Email, System for Physically challenged.
Campaign Finance and State Capture

This chapter uses a novel database on contractual arrangements between politicians, political brokers, and businessmen in Benin to investigate the way the nature of these arrangements depends on the level of political competition. We find that firms provide financial support to local and national politicians in exchange for policy concessions, direct budget support of firms, ‘favourable’ procurement auctions (bid-rigging), and various forms of state capture. In addition, while bid-rigging features constantly in politician–firm contracts, increased electoral uncertainty is associated with less demand for policy concessions and stronger preference for direct forms of state capture, that is, the appointment of firms’ agents or cronies to key government positions. In other words, electoral uncertainty could simultaneously contribute to democratic consolidation through political turnover, and to worse forms of corruption through state capture by business elites.
Learning Mealy Machines with Local Timers

Active automata learning (AAL) algorithms infer accurate automata models of black box applications, letting developers verify the behavior of increasingly complex real-time systems (RTS). However, learning models of larger RTS often takes very long or is not feasible at all. We introduce Mealy machines with local timers, a new class of Mealy machines that permit multiple location-bound timers and that can be learned efficiently. We design an efficient learning algorithm for them and validate our method across diverse case studies ranging from automotive systems to smart home appliances, where we drastically reduce runtimes compared to the state-of-the-art approach, thus, making AAL available for a wide range of RTS.
Secret learning for lung cancer diagnosis—a study with homomorphic encryption, texture analysis and deep learning

Abstract Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is a popular method for decision making which can be used to classify cancerous and non-cancerous lungs from CT-Scan images. There are many experiments which show the uses of deep learning for performing such classifications but very few of them have preserved the privacy of users. Among existing methods, federated learning limits data sharing to a central server and differential privacy although increases anonymity the original data is still shared. Homomorphic encryption can resolve the limitations of both of these. Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data. In our experiment, we have proposed a series of textural information extraction with the implementation of homomorphic encryption of the CT-Scan images of normal, adenocarcinoma, large cell carcinoma and squamous cell carcinoma. We have further processed the encrypted data to make it classifiable and later we have classified it with deep learning. The results from the experiments have obtained a classification accuracy of 0.9347.
The Sugeno Integral Used for Federated Learning with Uncertainty for Unbalanced Data

Data is crucial in the digital economy.Many businesses collect and use their data to enhance their performance.However, limited data or low data quality can hinder model development, particularly in dynamic environments.To overcome this, companies collecting similar data may opt to exchange knowledge without sharing their data, due to privacy or legal issues.This is where federated learning comes in.In horizontal federated learning, each client (organization) iteratively improves its model, so that it can be regularly aggregated and shared with all clients participating in the federation for further improvements.In federated averaging, the aggregation mechanism is based on the weighted average and the weights depend on the amount of data available to each client.In this paper, we propose to use a more advanced aggregation mechanism, namely the Sugeno integral.The initial results are promising.
Improving the Reliability of Medical Diagnostic Models through Rule-Based Decision Deferral

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, and accurate assessment of tumor resectability is crucial for determining appropriate treatment. AI-based models have shown promise in classifying tumor resectability, but reliability concerns have impeded clinical implementation. We propose extending the AI-based VasQNet model for classifying tumor resectability on AI-generated segmentations of computed tomography scans (CTs) to improve the models’ reliability. This extension allows VasQNet to defer decisions when the AI-generated segmentations violate pre-established rules on vascular anatomy, tumor location, and tumor size. We conducted experiments using CTs of (borderline) resectable and non-resectable PDAC patients. We evaluated the performance of the baseline VasQNet and the extended VasQNet with rule-based decision deferral (RBDD) by comparing their classifications to a ground-truth provided by a radiologist, employing agreement as a metric. Our results demonstrate that the extended VasQNet achieved a significantly higher agreement (90%) with the radiologist’s classification than the baseline VasQNet (67%). Notably, 17/31 (54%) deferred decisions would have been incorrect had they not been deferred. Our study demonstrates the effectiveness of RBDD in improving the reliability of clinical diagnostic models through the exemplification of VasQNet. In conclusion, RBDD can enhance the reliability of clinical diagnostics models, facilitating integration into clinical practice. The documented code is available on GitHub (https://github.com/PHAIR-Consortium/Vessel-Involvement-Quantifier).
Federated Similarity-Based Learning with Incomplete Data

In the analysis of social, medical, and business issues, the problem of incomplete data often arises. In addition, in situations where privacy policy makes it difficult to share data with organizations conducting related activities, it is necessary to exchange knowledge instead of data, that is, to use federated learning. In this scenario there are several private data clients, whose models are improved through the aggregation of model components. Here, we propose a methodology for training local models to deal well with missing data, with an algorithm using similarity measures that take into account the uncertainty present in many types of data, such as medical data. Therefore, this paper describes a federated learning model capable of processing imprecise and missing data. Federated learning is a technique to overcome limitations resulting from data governance and privacy by training algorithms without exchanging the data itself. The performance of the proposed method is demonstrated using medical data on breast cancer cases. Results for different data loss scenarios and corresponding measures of classification quality are presented and discussed.
Moodle

This chapter consists of an investigation on the motivational factors that entail the use of the Moodle platform as an auxiliary tool within the teaching-learning process. The objective of the research was to recognize the student's perception of the use of virtual educational platforms (Moodle) as a motivational and influential factor in their learning process focused on the results it achieves. According to the results, it is possible to affirm that there is a high motivational level of the students regarding the use of the Moodle platform as well as an elevated level of motivational perception. In the same way, it was observed that there are five main components that summarize the variation between the items, motivational characteristics that were obtained and proposed as a conclusion for the effective obtaining of results when using the Moodle platform within the teaching-learning process.
Study of the Impact of Managers' Attitudes Towards Creativity in Terms of Taking up Creative Activities

Purpose: This article deals with the issues of managers' attitudes towards creativity and their impact on the development of creative organizations.The scientific aim of the article is to explain and extend the conceptualization in the field of creative activities performed by managers and the relationships that occur with the relevant characteristics determining the development of creative activities.Design/Methodology/Approach: The consideration formed the basis for the formulation of the research hypothesis: There is a relationship between the selected attitudes of managers and creative activities.To verify such a hypothesis, the results of survey study conducted among among managers working in creative organizations who acquire knowledge in educational institutions were used.Findings: The results of the research showed the existence of significant relationships between the selected characteristics that determine appropriate attitudes toward creativity and the performance of creative activities.It turns out that a manager who performs creative activities is characterized by only selected attitudes from among the many presented in this article.Practical Implications: The obtained research results are of particular importance for the development of appropriate attitudes of managers towards creativity in organizations.The indication of guidelines that may change this state of affairs and the developed research allowed us to identify of areas whose improved functioning could affect their more effective development and meet the requirements of competitiveness in the form of a greater number of new ideas generated.Originality/value: The obtained research results showed which organizational conditions are most conducive to the development of creativity.The results obtained will allow organizations to focus on these elements in order to achieve better results in the form of more innovations.