Reviewerly/exHarmony huggingface dataset:
Pengembangan Lembar Kerja Siswa Matematika Berbasis PMRI dengan menggunakan Qr Code pada Siswa Kelas V Sekolah Dasar

The purpose of this project is to provide reliable, usable, and efficient PMRI-based Student Worksheets (LKS) for the fifth grade mathematics curriculum at SD N 13 Rejang Lebong. The 4D development model is used in this study together with the research and development methodology. There were 54 fifth graders at SD N 13 Rejang Lebong who served as the research participants. The analysis's findings demonstrated that the student worksheets, which had an average score of 0.86 (V&gt; 0.8), were classified as legitimate. LKS falls into the very practical category with an average score of 4.04 and a maximum score of X&gt; 3.4, making it very practical. Based on the results of the pretest and posttest completed by the students, it can be concluded that LKS is classified as Effective since N-gain(g) of 0.70 is included in the interval category g&gt; 0.7 with high categorization.&#x0D; Keywords : LKS, PMRI, Qr Code
Strategi Belajar &amp; Pembelajaran dalam Meningkatkan Keterampilan Bahasa

This study aims to determine learning and learning strategies in improving language skills so that they can provide good results and achievements for generations of nations and countries. This study uses a qualitative method. The emphasis on the components of the teaching program includes centered on the teacher (teacher), students (students), the techniques used are discussion lectures, and team teaching techniques. The results of the study obtained several important points, namely regarding the concept of learning strategies, language skills strategies. Strategies that relate directly to teachers and students so as to generate stimulus and response play an important role. Components of teaching programs centered on teachers, students and teaching materials also need to be implemented so that learning can be carried out properly. A student-centered strategy is a learning strategy that provides opportunities for students to be active so that the teacher only acts as a facilitator and motivator. In conclusion, learning strategy is a major factor in improving the learning process and language skills. A planned strategy plays an important role in the learning process&#x0D; &#x0D; Keywords: Language Skills, Learning, Learning Strategies
Philosophical foundations of the development of students' intellectual culture

The article analyzes the philosophical foundations of professional personnel training and development, the main ways of forming the intellectual meaning of students and the concept of development. The conceptual foundations of the studied phenomenon include clarification of the process of development of students' intellectual culture as a socio-pedagogical system. Socio-pedagogical system for the development of intellectual culture of students in the educational process, components of the social-pedagogical system for the development of intellectual culture of students in the process of social and humanitarian education, humanitarian direction of professional university training, social development of intellectual culture of students - the pedagogical system is analyzed. The article also reflects the clarification of the process of development of the intellectual culture of students as a socio-pedagogical and cultural system of the philosophical basis of the studied topic. At the same time, the main structural components of the philosophical system of intellectual culture of students in the educational process are characterized: need-motivation, cognitive, organizational-activity and ergonomic. The content aspect of students' intellectual culture was analyzed. Here, the main approaches of its formation, development system and functioning are considered in determining the essence of the concept of "intellectual culture".
Analysis of Tasks Parameters of Solve the Problem of Determining Delays and Risks in Agile Projects

Agile methodology is actively used for project management. This article presents the results of determining which task parameters are important in determining delays and risks in Agile projects. The article provides information on the influence of parameters on the likelihood that a task is a risk or a delay. These parameters are typical for the Atlassian Jira bug tracker.
Model of Development of Continuous-Learning

Introduction. Lifelong e-learning (LEL) reflects a global view on the possibility of obtaining educational resources, advice, and experience for the purpose of acquiring knowledge and skills, taking into account the individual needs of a particular person. LEL development issues are not tied to a specific organization and are addressed by creating means and tools to support the continuous development of each individual professionally and personally, using learning resources, technologies and strategies that meet the needs and preferences of that individual. Thus, LEL is implemented by means of e-learning, but LEL research, along with the problems of taking into account the previous experience of users and recommending them educational resources that correspond to their cognitive strategies and the context of application, covers the problems of the development of a global network environment supporting lifelong learning, the transformation of the technological basis of educational processes, sharing and protection of personal data. Purpose. The purpose of this study is to identify and explore the main components and models of lifelong e-learning and to develop an integrated structural model of LEL that will be useful for researching its development and ongoing transformations. Methods. A modified structural model for e-learning by B. Khan is proposed as a basis for integral lifelong e-learning model development. Results. The problem of lifelong learning in the digital dimension was analyzed and its main components were determined. To determine whether e-learning model is appropriate for LEL modeling, the distinct features of e-learning and LEL are determined and compared. The specifics of each component transformation in the modified e-learning model and interrelationships between them are determined. Models of technology development are analyzed and examples of e-learning technologies transformation in the context of LEL are provided. Finally, an integral structural model of LEL intended for the analysis of its development is suggested. Conclusion. As a result of the study, it was proved that LEL has its own specific features and is not reduced to a set of e-learning processes that are implemented at each level or life stage. Khan’s modified structural model, which is the basis of LEL modeling, contains redundant components required only for the model of e-learning in the organization. At the same time, the LEL model should contain a new component that represents a collective user, i.e. all LEL user groups that influence its development. The proposed integrated model of LEL provides a generalized structure with a defined direction of interaction (influence) between individual components and thus not only contributes to the understanding of LEL transformations in the past, but also creates a basis for analyzing trends and building forecasts of LEL development in the future.
[Methodology and progress in adjusting time-dependent covariates in clinical prediction models].

Adjusting time-dependent covariates into prediction models may help improve model performance and expand clinical applications. The methodology of handling time-dependent covariates is limited in traditional regression strategies (i.e., landmark model, joint model). For example, the number of predictors and practical situations which can be handled are restricted when using regression models. One new strategy is to use machine learning (i.e., neural networks). This review summarizes the methodology of handling time-dependent covariates in prediction models, such as applicable scenarios, strengths, and limitations, to offer methodological enlightenment for processing time-dependent covariates.预测模型中考虑时依性变量可改善模型的总体表现，提高其临床应用价值。界标模型、联合模型等基于传统回归策略在处理时依性变量个数和适用情境等方面存在局限，神经网络等机器学习算法有望对其灵活处理。本文针对传统模型、机器学习算法，总结各自纳入时依性变量的建模思路，梳理各方法的适用场景，概括现有方法仍存在的问题，以期为未来预测建模处理时依性变量提供方法学启示。.
Research on Autonomous Learning Terminal System of College English Translation Based on Distributed Network Intelligent Editing Technology

Based on distributed network intelligent editing technology, the mobile learning mode can effectively mobilize the enthusiasm of college students in foreign language learning, comprehensively improve college students' language skills, teach and cultivate students' translation ability, change the traditional teaching mode of teaching instead of learning, and strengthen students' autonomy Learning ability, this research starts from the autonomous learning theory of college English translation, and hopes to cultivate students' initiative and active awareness by mastering practical and effective online English translation methods, and accumulate a certain English vocabulary foundation in the process of actual knowledge learning and translation knowledge points.
Blowfish based encryption model in real cloud environment

&lt;p&gt;The introduction of the internet has made security a top anxiety. And the preceding of security permits for improved knowledge in the creation of security tools. Several concerns about safekeeping might have arisen just from the way the internet was set up. Many businesses use firewalls and encryption techniques to protect themselves online. Businesses can design an “intranet” that is protected from potential risks while being linked to the internet. For increased security, better encryption techniques are needed to retain data integrity. For better encryption, it is essential to consider into explanation a few of issues, including key size, chunk size, and encoding ratio. Documents transferred to subordinate storage strategies (such as Hard disk or SD card) must be encrypted to provide security and prevent unwanted access. If a key is kept on secondary storage with the document, it is quite simple to decrypt it. It is desirable to create encryption keys from operator passwords when encoding or decoding the folder rather than keeping the key together with the document.&lt;/p&gt;
Artificial intelligence for non-mass breast lesions detection and classification on ultrasound images: a comparative study

Abstract Background This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images. Methods A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121_448 and MobileNet_448. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed. Results A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet_448 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.999 (95%CI: 0.997-1.000),96.5%,96.9% and 96.1%, respectively. It was no statistically significant compared to DenseNet121_448. In the classification experiment, the MobileNet_448 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0.837 (95%CI: 0.990-1.000), 70.5%, 80.3% and 74.6%, respectively. Conclusions This study suggests that the AI models, particularly MobileNet_448, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.
Adaptive Quantization Mechanism for Federated Learning Models Based on DAG Blockchain

With the development of the power internet of things, the traditional centralized computing pattern has been difficult to apply to many power business scenarios, including power load forecasting, substation defect detection, and demand-side response. How to perform efficient and reliable machine learning tasks while ensuring that user data privacy is not violated has attracted the attention of the industry. Blockchain-based federated learning (FL), proposed as a new decentralized and distributed learning framework for building privacy-enhanced IoT systems, is receiving more and more attention from scholars. The framework provides some advantages, including decentralization, scalability, and data privacy, but at the same time its consensus mechanism consumes a significant amount of computational resources. Moreover, the number of model parameters has increased dramatically, leading to an increasing amount of transmitted data and a vast communication overhead. To reduce the communication overhead, we propose an FL framework in the directed acyclic graph (DAG)-based blockchain system, which achieves efficient and trusted sharing of FL networks. We design an adaptive model compression method based on k-means to compress the FL model and reduce the communication overhead of each round in FL training. Meanwhile, the original accuracy-based tips selection algorithm is optimized, and a tips selection algorithm considering multi-factor evaluation is proposed. Simulation experimental results show that the method proposed in this paper reduces the total bytes of communication of the blockchain-based federated learning system while balancing the accuracy of the FL model compared to previous work.
The Influence of Tourism Constraints on Behavior Intention : Focused on Moderating Effect of Virtual Reality Experience Value

This study examines the moderating effect of virtual tourism experience value between tourism constraints and behavioral intentions, aiming to alleviate the negative impact of tourism constraints on tourists' behavioral intentions. The research model and three hypotheses were established through theoretical investigation, and empirical analysis was carried out. This study conducted a questionnaire survey among tourists who had never visited Zhangjiajie National Forest Park in China, obtained 386 valid samples, and conducted frequency analysis, reliability analysis, exploratory factor analysis, multiple regression analysis, and moderation effect analysis. The main analysis results show that, first, travel restrictions have a significant negative impact on behavioral intentions. Secondly, the value of virtual tourism experience has a significant positive impact on action intention. Thirdly, it is the significant moderating effect of the value of virtual tourism experience, which has been confirmed： 1) The influence of internal constraints and interpersonal constraints on behavioral intentions differs depending on the value of visual appeal. 2) It has been established that the effect of internal constraints, interpersonal constraints, structural constraints on behavioral intentions varies by flow value. These analysis results can not only be used as research data on the value of virtual tourism experience, but also can be used as basic data to alleviate the constraints perceived by tourists.
Hybrid Integrated Photonic/Electronic Homodyne Detector for GHz Baud Rate Continuous Variable Quantum Key Distribution

Implementing CVQKD using integrated photonics presents many advantages, such as small form factor, and the ability to mass manufacture at a high volume with a lost cost, by making use of mature electronics manufacturing technologies. Another significant advantage of using integrated photonics is for implementing CV-QKD receivers. By making use of integrated photodiodes with integrated amplification electronics it is possible to achieve high-performance wideband (GHz) homodyne detectors [1], using standard telecoms electronics at negligible cost ($10s). Increasing the detector bandwidths allows for faster baud rates, and therefore faster key rates [2]. In this work we implement a hybrid integrated photonic/electronic circuit design to demonstrate an integrated photonic CV-QKD receiver with a bandwidth of approximately 1.4GHz and our work towards making use of the design to implement a CV-QKD system with GHz baud rates. This represents an improvement in bandwidth of 2 orders of magnitude over previous chip-based CV-QKD experiments where the bandwidth of the detector was 10MHz. [2]
Key management and access control based on combination of cipher text-policy attribute-based encryption with Proxy Re-Encryption for cloud data

&lt;p&gt;In various cloud computing models, the data need to be protected and to access these data in secure manner is important. The cryptographic key which is used to secure these data using both in the encryption as well as in decryption it is mandatory to manage these keys to secure these keys by disclosing in public networks such as any wireless and cloud environment. Utilizing Ciphertext Policy Attribute-based Encryption (CP-ABE), which provides effective data governance and key management, for cloud data encryption. The work based on the combination of Cipher Text-Policy Attribute based Encryption and Proxy Re-Encryption is elaborated in the article (CP-ABE-PRE). The encrypted data should ideally be transformed such that it may be unlocked with new keys, without an intermediate decryption step that would allow the cloud provider to read the plaintext this process is known as data re-encryption. The computational and communication burden on users connecting to the cloud from resource constrained devices can be reduced using the proposed technique. The experimental results show for Cipher Text-Policy Attribute-Based Encryption are compared to the current algorithm (CP-ABE) demonstrate good results in encryption and decryption times. Additionally, the CP-ABE offers crucial distribution and administration options for cloud data. CP-ABE with Proxy Re-Encryption does appear to be highly efficient which proves verifiability and fairness for cloud data users to which also address revocation problem as well as collusion resistant model.&lt;/p&gt;
Towards Whispering-Gallery-Resonance Photonic Learning Machine: CH - Optical Sensing and Microscopy

The world of photonics is experiencing, like many other research fields, the growing impact of machine learning techniques. Several neuromorphic photonic scheme have been proposed, and experimentally demonstrated over the last years [1] as novel tools able to boost classification and feature extraction processes.
Controlling the Recovery Time of the Superconducting Nanowire Single Photon Detector using a Tunable Resistor

Over the past two decades, the fast development of superconducting nanowire single photon detector (SNSPD) has made them the detector of choice for modern stringent quantum optics experiments. SNSPD offers remarkably high efficiencies, approching unity efficiency, a few pico-second time resolution, and gigahertz counting rates. The recovery time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex> of the SNSPD is one of the key parameters influencing its efficiency at high photon fluxes. Here we report on preliminary results of controlling the response speed of the SNSPD by introducing a tunable resistor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R_{S}$</tex> in the electric circuit.
Demonstration of a Low-Depth Universal Linear Optical Circuit

Universal linear optical (ULO) circuit is a versatile device that plays a fundamental role in quantum computation, optical machine learning, and MIMO processing for optical communications. The design of the ULO, i.e., a universal multiport interferometer, was initially proposed by Reck et al. in 1994 [1] and improved in terms of circuit depth and robustness to optical losses by Clements et al. in 2016 [2]. Recently, a further compactified circuit design was proposed Bell and Walmsley [3]. In Bell's scheme, the circuit layout consists of a network of symmetric Mach-Zehnder interferometers (MZI) with two variable phase shifters in each arm. The design significantly reduces the whole device length compared to the conventional designs, where the circuit consists of a network of asymmetric MZIs, each of which has a phase shifter in one arm and an external phase shifter outside of itself. This downsizing becomes significantly important toward the realization of large-scale ULOs in the limited chip area, particularly when each phase shifter is long or when the spacing between phase shifters cannot be made small. This time we experimentally demonstrate that Bell's scheme can express arbitrary unitary matrices as well as Clements' scheme.
Pulse Collision Computing with Spiking Micropillar Lattices

Spiking microlasers with integrated saturable absorber have been shown to behave as ultrafast photonic neurons, mimicking many of the main computational properties of biological neurons but with much faster timescales [1] (sub-nanosecond typically). Micropillar lattices can be designed by evanescently coupling the microlasers and have been shown theoretically to sustain saltatory propagation of solitonic-like excitations [2]. They hold good promise for fabrication of advanced and integrated photonic processing circuits. By studying a model for evanescently coupled excitable microlaser lattices with integrated saturable absorber, we have investigated how pulse interaction can lead to non trivial responses [3]. By carefully choosing the system parameters, we show the some-what counter-intuitive result that two counter-propagating pulses from two input ports in a three-port system can collide and exit or not in a third port, thus giving rise to the not-linearly separable XOR response function and to universal logic temporal gates (see Fig. 1). Other types of temporal gates can be obtained such as the temporal OR, AND and NAND gates. We discuss the physics underlying these responses. We also present first experimental results obtained in this system with lattices in the linear geometry, enabling to demonstrate the propagation of spikes, and discuss the extension to the theoretically demonstrated temporal gates.
Control of Information Flow in Arrays of Spin-Torque Oscillators

Arrays of spin-torque oscillators (STOs) can be used in neural networks. Adjacent STOs can be synchronised via magnetostatic interactions. In this work we show that by varying the diameter of the STOs it is possible to control the flow of information. Unidirectional information flows can be enabled by a consistent change in diameter along a row of STOs. A switch to control the direction of information flow can be constructed using rows of STOs with different diameters.
UAV Multi-object Tracking by Combining Two Deep Neural Architectures

Detecting and tracking multiple objects from unmanned aerial vehicle (UAV) videos is an high challenging task in a wide range of practical applications. Almost all traditional trackers meet some issues on UAV images due to camera movements causing view change in a 3D directions. In this work, we propose a Convolutional Neural Network specialized in multi-object tracking (MOT) for images captured from UAV. The architecture we introduced is composed by two main blocks: i) an object detection block based on YOLOv8 architecture; ii) an association block based on strongSORT architecture. We investigated different versions of YOLOv8 architectures with the strongSORT as association trackers. Experimental results on the VisDrone2019 dataset show that the proposed solution outperforms the up to date state-of-the-art tracking algorithms performance on UAV videos reaching the 42.03% in Multi-Object Tracking Accuracy (MOTA).