Reviewerly/exHarmony huggingface dataset:
The Reason, Solution and Empirical Evidence of Chinese School Can’t Cultivate Outstanding Talents

This paper analyzes the phenomenon that "why our school can't cultivate outstanding talents?" Pointed out the reason is that the direction of talent cultivation in Chinese schools is unreasonable, focusing on the benefits of batch training of labor force and neglecting the benefits of personality growth of talents.If we want to take both into account under the condition of limited teaching resources, the essence of the problem is to promote teacher-student behavior and institutional change through rational redistribution of limited resources.Therefore, the author designed and implemented the teaching reform solution.On the one hand, reform the traditional class teaching system and the credit system, adopt the teaching content, education methods and teaching system that encourage students' personality growth; on the other hand, establish a six closed loop quality management system for all students, teachers, courses, majors, secondary colleges and functional departments.The teaching comparative experiments prove that the redistribution of teaching resources along the direction of improving market efficiency and talent cultivation efficiency is conducive to the growth of students and the improvement of the overall competitiveness of the school.It also proves that the Model of Promoting Institutional Change Through Optimized Allocation of Limited Resources (PICTOALR Model) can be applied to the field of education.
DTTP Model - A Deep Learning-Based Model for Detecting and Tracking Target Person

Deep learning models have proven effective in various computer vision tasks, including object detection and tracking. In this paper, we propose a method for detecting and tracking a specific person in a video stream using deep learning models, called DTTP model. Our approach utilizes a combination of object detection and re-identification techniques to accurately track the target person across multiple frames after recognizing the face of that one. We evaluate our method on publicly available datasets and demonstrate its effectiveness in tracking a specific person with high accuracy and improving the model’s speed by feature extraction of human pose and re-parameterization of deep learning models. Our DTTP model achieved 51.07 MOTA, 59.64 IDF1, and 47.31 HOTA on the test set of MOT17, which is higher accuracy and shows a favorable accuracy-speed trade-off compared to the state-of-the-art model like ByteTrack.
Visual Object Tracking Using Machine Learning

Visual object tracking has become a very active research area in recent years. Each year, a growing number of tracking algorithms are proposed. Object detection and tracking is a critical and challenging task in many critical computer vision applications, including automated video surveillance, traffic monitoring, autonomous robot navigation, and intelligent environments. Object tracking is segmenting an object of interest and tracking its velocity, orientation, and occlusion in a video scene to extract useful information. Over the last two decades, several object tracking approaches have been developed to design a robust object tracker that covers all practical obstacles in real-world operations. This paper reviews recent trends and advances in tracking and assesses the reliability of various trackers based on feature extraction techniques. In video processing, visual tracking has a wide range of applications. When a target is identified in one video frame, it is frequently advantageous to track that object in subsequent frames. Every successful frame in which the target is tracked yields more information about the target’s identity and activity. Because tracking is more straightforward than detection, tracking algorithms can require fewer computational resources than object detectors.
A Unified Document-Level Chinese Discourse Parser on Different Granularity Levels

Discourse parsing aims to comprehend the structure and semantics of a document. Some previous studies have taken multiple levels of granularity methods to parse documents while disregarding the connection between granularity levels. Additionally, almost all the Chinese discourse parsing approaches concentrated on a single granularity due to lacking annotated corpora. To address the above issues, we propose a unified document-level Chinese discourse parser based on multi-granularity levels, which leverages granularity connections between paragraphs and Elementary Discourse Units (EDUs) in a document. Specifically, we first identify EDU-level discourse trees and then introduce a structural encoding module to capture EDU-level structural and semantic information. It can significantly promote the construction of paragraph-level discourse trees. Moreover, we construct the Unified Chinese Discourse TreeBank (UCDTB), which includes 467 articles with annotations from clauses to the whole article, filling the gap in existing unified corpus resources on Chinese discourse parsing. The experiments on both Chinese UCDTB and English RST-DT show that our model outperforms the SOTA baselines.
Bidirectionally self-normalizing neural networks

The problem of vanishing and exploding gradients has been a long-standing obstacle that hinders the effective training of neural networks. Despite various tricks and techniques that have been employed to alleviate the problem in practice, there still lacks satisfactory theories or provable solutions. In this paper, we address the problem from the perspective of high-dimensional probability theory. We provide a rigorous result that shows, under mild conditions, how the vanishing/exploding gradients problem disappears with high probability if the neural networks have sufficient width. Our main idea is to constrain both forward and backward signal propagation in a nonlinear neural network through a new class of activation functions, namely Gaussian–Poincaré normalized functions, and orthogonal weight matrices. Experiments on both synthetic and real-world data validate our theory and confirm its effectiveness on very deep neural networks when applied in practice.
A Multi-Label Recommendation Algorithm Based on Graph Attention and Sentiment Correction

Traditional recommendation algorithms overlook the abundant information present in user feedback and are limited to the binary relationship between items and users. In contrast, this paper proposes an algorithm that takes advantage of item labels and user feedback to generate faster and more precise recommendation lists. The algorithm employs graph attention link prediction and sentiment analysis to calibrate parameters and enhance traditional recommendation algorithms by utilizing user information comprehensively. Empirical results on publicly available datasets indicate that the proposed algorithm outperforms both traditional and sentiment-uncorrected recommendation algorithms in terms of accuracy.
Suppression of Fading Noise in Satellite-Mediated Continuous-Variable Quantum Key Distribution via Clusterization

The satellite-mediated continuous-variable quantum key distribution (CV-QKD) protocol, which relies on off-the-shelf telecommunication components, has the potential for a global quantum communication network with all-day operation. However, the transmittance fluctuation of satellite-mediated links leads to the arriving quantum state showing non-Gaussian property, introducing extra fading noise in security analysis and limiting the secret key rate of the protocol. Here, we consider the clusterization method for data post-processing to suppress the fading noise in both downlink and uplink scenarios, where the measurement data are divided into several clusters, and we perform security analysis separately. In particular, we set the optimal upper and lower bounds of each cluster in terms of the probability distribution of transmittance (PDT), while finding an optimal cluster number for the trade-off between fading noise and the composable finite-size effect. Numerical analysis shows that the proposed method can improve the composable finite-size rate when the fading noise is large enough, even with only two clusters. Moreover, a high-speed CV-QKD system with a higher frequency of signal preparation and detection can extend the proposed method to work in the case of lower fading noise.
SSA Net: Small Scale-Aware Enhancement Network for Human Pose Estimation

In the field of human pose estimation, heatmap-based methods have emerged as the dominant approach, and numerous studies have achieved remarkable performance based on this technique. However, the inherent drawbacks of heatmaps lead to serious performance degradation in methods based on heatmaps for smaller-scale persons. While some researchers have attempted to tackle this issue by improving the performance of small-scale persons, their efforts have been hampered by the continued reliance on heatmap-based methods. To address this issue, this paper proposes the SSA Net, which aims to enhance the detection accuracy of small-scale persons as much as possible while maintaining a balanced perception of persons at other scales. SSA Net utilizes HRNetW48 as a feature extractor and leverages the TDAA module to enhance small-scale perception. Furthermore, it abandons heatmap-based methods and instead adopts coordinate vector regression to represent keypoints. Notably, SSA Net achieved an AP of 77.4% on the COCO Validation dataset, which is superior to other heatmap-based methods. Additionally, it achieved highly competitive results on the Tiny Validation and MPII datasets as well.
Controlled cyclic remote preparation

Abstract Multi-party quantum communication has gradually attracted widespread attention. To realize the perfect transmission of quantum states among multiple participants, a novel multi-party controlled cyclic remote preparation protocol for arbitrary single-qubit states with three senders is proposed. With the permission of one controller, each sender can transmit an arbitrary single-qubit state to its neighbor. In addition, we give a universal protocol for multi-party controlled cyclic remote preparation of arbitrary single-qubit states in the case of multiple senders, which can realize deterministic cyclic preparation of multiple quantum states in one direction. The scheme shows that the communication task can be successfully achieved only if all senders cooperate with the controller, and there is no need for the senders to employ information splitting and additional operations before performing measurements. Finally, we discuss the cyclic remote preparation protocol with three senders under five types of noisy environment, and the closeness between the output state and original state is measured by calculating fidelity.
Implementation of System Development Life Cycle (SDLC) on IoT-Based Lending Locker Application

Libraries are social institutions that provide information services that can be accessed publicly to meet the information needs of librarians. Based on the results of a survey conducted on 58 students who have visited the library of the Electrical and Computer Engineering Department of the Universitas Syiah Kuala, an information system was needed that provided research book information related to the author's name, year of writing, field concentration, and abstract of the research book, and there was a division of categories based on field concentration, and there was an online borrowing feature. Based on these problems, this study aims to implement an Android application system with IoT-based lending lockers using the SDLC (system development life cycle) prototyping method. This study produces an application with a locker-based online lending feature, several other features as the user desires, and one prototype lending locker. The locker-based online lending system integrated with ESP32-WROOM-32 can connect to Firebase storage and send locker key codes to Firebase so that the application can access them. Through experiments and tests conducted on the application, it is obtained that the application can access the locker key code and display it to the user. The application has also been validated using black-box and white-box testing and can be accepted by users based on the System Usability Scale (SUS) average score with a very feasible interpretation category.
Designing a QR Code Model Examination System With The Zachman Framework

The Digital Business study program is a new study program within the Faculty of Economics and Business of Universitas Pancasakti Tegal where previously there were only 3 study programs, namely the Management, Accounting, and Taxation study programs. Every semester the Faculty of Economics and Business always holds Mid Semester Test (MST) and a Final Semester Test (FST), In both implementations, there are still queues of students who have already registered and paid for exams at the Front Office to take exam cards. This queue creates a large crowd at the Front Office and often causes the Administrative division that prints the cards and the Front Office staff to be overwhelmed in printing and distributing test cards to students. This study uses the Zachman Framework architectural model approach which is a framework in providing the basic organizational structure that supports access, integration, interpretation, development, management, and changes to the architectural devices of organizational information systems in the form of a matrix with a combination of planners and owners perspectives and the addition of two more perspectives. namely the perspective of the information system architecture which provides an overview of the design and architectural testing of the system as a novelty method.
Pelatihan Pemanfaatan Teknologi Informasi pada Era Revolusi Industri 4.0 bagi Siswa SMK Bina Profesi Pekanbaru

This Community Service Activity (PKM) aims to provide information technology training in the era of the industrial revolution 4.0 as additional basic knowledge of the world of information technology for class X students of SMK BinaProfesi Pekanbaru. Rapid technological advances and the increasing need for information technology and several IT trends that are developing at the present time are important parts that need to be introduced to students so that students are able to use the information technology they use every day wisely. The use of information technology is grouped into the science that produces the technology. The grouping of knowledge is explained by the training techniques provided, namely discussing and explaining clearly each of the basics of the science group; internet of things, big data, cyber security, artificial intelligence, augmented reality and cloud computing. The results of this activity are expected to contribute to students being able to group the technologies they encounter into the knowledge groups they have acquired during the training.
The Capacity of 4-Star-Graph PIR

Introduced by Sadeh et al., the K-star-graph private information retrieval (PIR) problem, so-labeled because the storage graph is a star-graph with K leaf nodes, is comprised of K messages that are stored separately (one-each) at K dedicated servers, and a universal server that stores all K messages, for a total of K + 1 servers. While it is one of the simplest PIR settings to describe, the capacity C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</inf> of K-star-graph PIR is open for K ≥ 4. We study the critical K = 4 setting, for which prior work establishes the bounds 2/5 ≤ C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> ≤ 3/7. As our main contribution, we characterize the exact capacity of 4-star-graph PIR as C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> = 5/12, thus improving upon both the prior lower-bound as well as the prior upper-bound. The main technical challenge resides in the new converse bound, whose non-trivial structure is deduced indirectly from the achievable schemes that emerge from the study of a finer tradeoff between the download costs from the dedicated servers versus the universal server. A sharp characterization of this tradeoff is also obtained for K = 4.
Evaluating English Teaching Quality in Colleges Using Fuzzy Logic and Online Game-Based Learning

The contemporary education community has been trying to use video games or game elements in teaching in recent years.The pioneers in this field have tried to improve the quality of education by means of educational games or gamified teaching, but it has been difficult to be widely promoted for various reasons.In recent years, attempts have found that although there are certain advantages over educational video games and gamified teaching, like the former, there are still considerable difficulties in popularizing them in practice.Therefore, this paper proposes a learning structure design based on the big data video game task mode, hoping to make the learning process really arouse the learners' interest in learning, achieve good learning results in a relaxed and focused learning atmosphere, and achieve unsupervised high efficiency.Self-learning.Finally, the F1 value of simple teaching resources is increased by 8.6% on average compared with the other four algorithms, and it can reach 0.888 when the test set is 10%; when recommending complex teaching resources, the F1 value is increased by an average of 5.6% compared with the other four algorithms.It can reach 0.958 at 10% of the test set.The above data show that the CUPMF method can effectively improve the recommendation accuracy and improve the recommendation effect.
HARNESSING YOLO ALGORITHMS FOR EFFICIENT BREAST CANCER DETECTION IN MAMMOGRAPHY

OBJECTIVE The primary aim of this study is to investigate the accuracy and reliability of employing YOLO (You Only Look Once) algorithms in detecting breast lesions using contrast-enhanced mammograms (CEM) and to compare their performance. MATERIALS & METHODS A retrospective study was conducted, utilizing a total of 121 successfully collected samples comprising 218 2D images. The YOLO algorithms were implemented using Resnet50 and Darknet53 architectures. The hyperparameters included batch size, epoch size, and learning rates (0.001). The dataset was divided into 70%, 20%, and 10% for training, validation, and testing of the YOLO model, respectively. Additionally, single optimizer, including ADAM (Adaptive Moment Estimation), was evaluated to assess the detection performance. Performance indices included hit counts (or hit rate) and mean average precision (mAP). RESULTS The YOLO algorithm implemented with Darknet53 and the ADAM optimizer yielded the highest hit rate of 97% (=212/218). In contrast, the Resnet50 architecture combined with the ADAM optimizer produced the highest mAP of 82% among all tested models. CONCLUSION The findings demonstrate the feasibility and usefulness of YOLO algorithms in detecting breast lesions on mammography. The accuracy and reliability of this approach suggest potential for clinical application. However, further validation with larger samples and image sets is required in the near future to solidify these CONCLUSION: s. The primary aim of this study is to investigate the accuracy and reliability of employing YOLO (You Only Look Once) algorithms in detecting breast lesions using contrast-enhanced mammograms (CEM) and to compare their performance. A retrospective study was conducted, utilizing a total of 121 successfully collected samples comprising 218 2D images. The YOLO algorithms were implemented using Resnet50 and Darknet53 architectures. The hyperparameters included batch size, epoch size, and learning rates (0.001). The dataset was divided into 70%, 20%, and 10% for training, validation, and testing of the YOLO model, respectively. Additionally, single optimizer, including ADAM (Adaptive Moment Estimation), was evaluated to assess the detection performance. Performance indices included hit counts (or hit rate) and mean average precision (mAP). The YOLO algorithm implemented with Darknet53 and the ADAM optimizer yielded the highest hit rate of 97% (=212/218). In contrast, the Resnet50 architecture combined with the ADAM optimizer produced the highest mAP of 82% among all tested models. The findings demonstrate the feasibility and usefulness of YOLO algorithms in detecting breast lesions on mammography. The accuracy and reliability of this approach suggest potential for clinical application. However, further validation with larger samples and image sets is required in the near future to solidify these CONCLUSION: s.
A Blockchain Framework of Improved Security for Internet of Things Health Applications

The design and development of the Internet of Things (IoT) have a remarkable advancements in recent years. An “Internet of Things” (IoT) is a term used to describe an interconnection of physical objects with a network that incorporates software, sensors, and other devices to exchange information. Because patient health records (PHR) are valuable and significant, security is the most crucial component of encryption over the Internet. Sensitive data is typically secured by a third party during transmission in an IoT setting, which leads to complex and sometimes deadly problems. To address security issues and do away with third parties’ involvement in an IoT environment, blockchain technology is the modern solution. The idea of a secure blockchain for Internet of Things (IoT) health applications is presented in this paper. Electronic health records (EHRs), which are used to deliver healthcare directly to patients while reducing physical contact between patients and healthcare providers, are the health information that is collected and saved electronically for educational purposes in today’s healthcare. Decision makers can enhance the healthcare industry and gain from the current e-government cloud computing infrastructure by using the provided framework. As a result, the proposed Blockchain work enhances the dependability of IoT systems. It also has the potential to reduce costs and boost efficiency.
Combat capability graphical description method by ontology extension UML

For the issues of strong specialization, semantic duality, and unfavorable cross-domain communication of combat capabilities description, an ontology enhanced UML technique for graphical description of combat capabilities is presented. The application of ontology theory to create a conceptual model of combat capabilities is made more readable by the definition of a standard for UML-style graphical descriptions of ontologies. The specification is used to create a combat capability metamodel that eliminates semantic duality and unifies the top-level semantics of articulating combat capability ideas across all domains. In order to establish the combat capability description language, twelve viewpoints from the capability, combat and information perspectives were defined in conjunction with the needs of the combat capability domain description. The combat capability description language is built using the UML Profile technology. The example demonstrates how the model for defining combat capabilities takes into consideration the benefits of ontology formalization and precision as well as UML standards and communication ease.
Improved BP neural network based on genetic algorithm

A BP neural network algorithm based on a genetic algorithm is proposed. This algorithm performs genetic evolutionary operations using the initial weights and thresholds of the BP neural network, optimizing the topology and learning parameters of the network, thereby enhancing network stability and optimization ability. Through comparative experiments, the findings demonstrate that when compared to the conventional BP neural network, the improvement algorithm has significantly improved the stability and optimization performance of the network.