Reviewerly/exHarmony huggingface dataset:
Implementasi Simple Additive Weighting Dalam Sistem Rekomendasi Beasiswa di SMK Tarbiyatul Ulum Karawang

Scholarships are a program that students get to continue their studies. Tarbiyatul Ulum Vocational School is a formal secondary level vocational institution which has a large number of students. Some of these students are entitled to get scholarships available at Tarbiyatul Ulum Vocational School. So far, the Administrative Staff to select students who are entitled to receive scholarships is done manually so that it takes a long time and the possibility of human error occurs so it is not efficient to use in selecting students who will get scholarships with a large number of students. Therefore, a scholarship recommendation system is needed that can provide consideration to Administrative Staff in grouping students based on scholarships that are suitable for them to participate in, so that with this recommendation system Administrative Staff can clearly group students without the occurrence of human error because it has been calculated automatically by the system. The system uses the Simple Additive Weighting method as a decision-making method with various criteria with weighted values according to the existing criteria and the Waterfall method as a method for making the system starting from the planning stage to the maintenance stage so that it is sequential in the development of this recommendation system. The results of this system can directly recommend selected students for the relevant scholarship based on the highest recommendation value which is calculated using the Simple Additive Weighting method.
A Lightweight Chinese Summarization Generation Method Based on Knowledge Distillation

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models.
A Desensitization Detection Algorithm Based on YOLOv7-tiny

With the continuous development of smart cars, many high-precision cameras have been applied to cars, which means that more videos will be processed through the vehicle computer network, which also leads to the problem of information leakage. To address these issues, a video desensitization processing model is proposed, which desensitizes the license plate and facial information in high-definition videos, maximizing privacy protection without losing video clarity and vehicle recognition accuracy. This model is based on YOLOv7-tiny for improvement and upgrading. GSConv is used to replace the original convolution to reduce model parameters and improve running speed. The original neck structure is improved, and a GSN framework structure is proposed to replace YOLOv7-tiny's original ELAN structure, further improving model accuracy.
Single-Camera Gaze Estimation Based on ShuffleNetV2

The estimation of the user's gaze is useful in many aspects and future development trend is single-camera gaze estimation, which is conducive to the application of gaze estimation technology on mobile devices. At present, the accuracy and rapidity of head pose estimation are necessary for single-camera gaze estimation. Therefore, a ShuffleNetV2 based head pose estimation is proposed in this paper and verified in data sets of 300W-LP and BIWI. The result shows that the proposed algorithm is superior to the current advanced algorithms. Then the head pose and the simplified eye model are combined to ensure the accuracy of gaze estimation and the robustness of the user's head movement. The result shows that the average accuracy of gaze estimation can reach 5°under the extreme state of head motion.
A Bagged Tree Identification Method Combining Higher Order Cumulant and Permutation Entropy for Communication Modulated Signal

The channel environment of wireless communication is more and more complex, and the modulation modes of communication signals become more diverse, which brings new challenges and problems to the modulation signal recognition technology. In this paper, a bagged tree recognition method combining high order cumulant and permutation entropy is proposed. It's found that compared with other recognition methods, the proposed method has some advantages in prediction speed (~40000obs / sec) and accuracy (92.8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). After adding the time-domain and frequency-domain features, the recognition accuracy can be further improved, but it will increase the training time and reduce the classification speed.
YOLO-CCA: An Encoder-Decoder Framework Vehicle Detector Based on Channel Attention

Vehicle detection (VD), aiming at detecting the position of vehicles, can largely promote the development of connected and autonomous vehicles and intelligent transportation systems. Most VD algorithms cannot meet the demand of real-time road monitoring, due to some unsolved problems: (i) low accuracy for small vehicle targets that is demanding for real-time vehicle monitoring; (ii) high computational cost that is hard to deploy in edge devices. To solve these problems, this paper introduces a novel VD algorithm called YOLO-CCA inspired by channel attention technique. Efficient Channel Attention (ECA) and Encoder-Decoder modules are adopted into YOLO-CCA algorithm for VD. With the introduction of the Complete-IoU (CIoU) loss function, the convergence rate is also accelerated. YOLO-CCA algorithm is capable of capturing cross-channel information and maximizing it, which leads to high accuracy for vehicle detection, especially for small vehicle targets detection. Extensive experiments based on the UA-DETRAC public dataset demonstrate that YOLO-CCA outperforms the other four baseline algorithms, and improve 5.6% to the best baseline algorithm in small vehicle targets detection.
Research and Improvement of Path Planning Algorithms for Different Types of Destinations

Considering the needs of various groups of people such as tourists and crew on the cruise ship, this paper establishes a vectorized map data model which is suitable for navigation for the large and complex space of the cruise ship. For different types of destinations, this paper researches and improves the applicable path planning algorithms. For a single designated destination, the Dijkstra Algorithm is used for path planning, and the path relaxation rules are improved for the case of no destination and the path is fixed in length, and the path with the minimum distance error is obtained. For multiple designated destinations, this paper improves the Ant Colony Algorithm with considering the fact that the starting point is in the middle of the road section and the road section can be passed repeatedly. The probability rules for the ant colony to transfer between nodes are improved, and finally a better solution of the multi-destination path planning is obtained.
Natural Language Guided Attention Mixer for Object Tracking

The current research on object tracking using visual and language information shows promise. A significant amount of research has been done to achieve this goal by constructing models that uniformly characterize multimodal information. Different from previous work, this paper introduces a language-guided attention mixer. The mixer adaptively guides visual tracking attention using natural language and continuously corrects visual bias through linguistic information. This approach not only improves target feature recognition during the tracking process but also reduces training costs and introduces smaller parameters, resulting in significant improvements. The tracker proposed in this paper achieves state-of-the-art performance on four benchmarks with language annotation, all while running in real-time.
Deep Q-network Based UAV Autonomous Obstacle Avoidance with Prior Reward Shaping

Autonomous decision-making methods in complex environments have become a crucial technology for unmanned aerial vehicles (UAVs) to expand their application scenarios. In this paper, a UAV obstacle avoidance method based on Deep Q-Network (DQN) is present to achieve end-to-end UAV sensor-perception-to-action mapping. In this study, we form the real-time perceived observation of the UAV based on the target location, UAV orientation information, and the distance to the obstacle sensed by the ray sensor, and generate the corresponding decision by evaluating the current state. To improve the convergence of the approach, we consider the external factors that affect the UAV policy and design a set of reward functions based on prior knowledge which suitable for autonomous UAV obstacle avoidance. With reward function design, the UAV can be guided to take more accurate actions, enabling efficient training and safe navigation. To validate the performance of the algorithm, a typical UAV obstacle avoidance scenario in the Unity3D environment is built and carried out algorithm evaluation. Experimental results show that the reward function can support the UAV to achieve convergence of the policy with limited training steps, which is a significant advantage to the comparison methods.
Research on Key Technologies and Methods of Trajectory Monitoring Based on Attitude Recognition in College Teaching

The trajectory monitoring based on attitude recognition is the object of this paper. The integration of Internet of Things technology and computer technology is the research feature of this paper. Attitude recognition has been widely used, so a large number of scholars have conducted in-depth research. The research on trajectory monitoring of attitude recognition in colleges and universities has important promotion value and application prospect for teaching or the future application of other gesture related disciplines. The three key processes of attitude recognition algorithm are based on which the research on real-time monitoring of motion trajectory is carried out. Through the design and implementation of software and hardware systems of real-time monitoring system of motion trajectory, the hardware design based on MPU6050 is realized, Through the design of the software, the real-time monitoring of the movement track is realized, and the acceleration and displacement functions of the test system are debugged. The test shows that the system can complete the recognition and monitoring of posture, and this method can be used for teaching effect feedback in sports teaching in colleges and universities.
A Data Privacy Protection Scheme Integrating Federal Learning and Secret Sharing

The explosive growth of power data in the process of new power system business operation has high utilization value, how to maximize the value of data mining at the same time, to ensure its privacy in the collection, transmission, aggregation and analysis is a critical issue that needs to be addressed. This paper proposes a data privacy protection scheme (FL-SS) based on federal learning and secret sharing. Firstly, based on the federal learning framework, the information of gateway local training model parameters is distributed and reconfigured to ensure the security of the model updating process while preventing the betrayal of internal gateways; meanwhile, a lightweight communication key negotiation mechanism is designed to protect the transmission process of model parameters and data. The comparison experiments show that FL-SS has better performance in terms of accuracy and energy consumption compared with other similar schemes.
Adaptive Phase Switching Snake Optimizer Based on Orthogonal Learning and Joint Opposite Selection

An adaptive phase switching snake optimizer based on orthogonal learning and joint opposite selection (OJA-SO) is proposed to address the problems of poor inter activity in the optimization phase of the snake optimizer(SO), serious randomness of the initial population, and the tendency to fall into local optimal solutions. Firstly, an orthogonal matrix is used to initialize the snake population to make the distribution of individuals more uniform; secondly, an adaptive equation is designed to explore the development phase switching to replace the original food quantity and temperature threshold to make the algorithm perform adaptive phase switching; finally, a joint opposite selection strategy is used to replace the original new individual hatching method of the algorithm to improve the convergence accuracy of the algorithm while accelerating the convergence efficiency of the algorithm. Twenty benchmark test functions were selected for experiments on the OJA-SO algorithm to test the algorithm performance, and the Wilcoxon rank sum test was used to prove the algorithm significance. The results show that the OJA-SO algorithm improvement has been improved in terms of merit-seeking ability, robustness, and practicality.
MII-DFNet: A Medical Image Inpainting Framework Based on Deep Fusion Network

Since medical images are easily captured with missing areas due to uncertainties such as instrument or human factors, which can seriously affect doctors' diagnosis and patients' treatment, medical image inpainting techniques have become very important. How to make the restored images not only rich in structural and textural information, but also smooth transitions in the boundary regions to blend into the adjacent regions of the missing parts has always been a research focus in the field of computer vision. In order to address the above problems, this paper proposes a medical image inpainting framework (MII-DFNet) based on deep fusion network, through which the medical images covered by mask are restored to complement the original information. And two common medical images, CT and MRI, are used as our test datasets to verify the performance of the model. The experiments show that our method is better in restoration and generates higher quality images.
Authentication Scheme Based on Alliance Chain Cross-System

At present, many solutions solve the cross-domain identity authentication scheme of cryptosystem, but cannot realize the entity heterogeneous cross-domain authentication between PKI and CLC users. In this regard, this paper proposes a cross-domain authentication identity scheme for heterogeneous members in the consortium chain, which uses the characteristics of blockchain, such as decentralization, trust, and traceability and tamper resistance. The model uses the blockchain certificate server and verification server to realize cross-domain authentication between users of heterogeneous systems, and the authentication algorithm meets the efficient identity authentication protocol. Compared with other schemes in terms of security and authentication efficiency, this method reduces the computational authentication overhead, simplifies the heterogeneous cross-domain authentication model, and realizes the secure and efficient cross-domain authentication of heterogeneous members.
Optimized Design of Landscape Lighting Control System Based on FPGA

In view of the insufficient adaptability between the lighting fixture wiring and the control system, as well as the problem of asynchronous video playback among multiple players due to random frame loss, this paper presents an optimized design of the landscape lighting control system based on Field-Programmable Gate Array (FPGA). In-depth research is conducted on the arrangement of pixel data and extraction of video frame numbers in the system. Firstly, a landscape lighting control system is built with the aim of achieving landscape lighting display. Subsequently, pixel data addressing and package modules are introduced to enhance the system's adaptability to lighting fixture layout and wiring. Furthermore, a video frame number extraction module is designed to accurately extract specific frame data and ensure its transmission within the system, thereby avoiding frame loss. Experimental verification demonstrates that the system can adapt to different lighting fixture wiring requirements and ensure synchronization of video playback across multiple players. This optimization solution improves the system's universality and flexibility, providing strong support for the development of landscape lighting control systems.
Multi-Objective Optimization Task Scheduling Method Based on Dynamic Programming for Multi-Cloud Environment

Cloud computing is one of the most successfull technologies for providing services on request via the Internet, bringing extreme convenience to customers and businesses with its advantages of hyperscale, modernisation, reliability, universality, high scaleability and payment on request. However, with the growth of services for managing content distribution and interactive computing, such as social networking and scientific processes online, the capacity of cloud data centers is limited and cannot meet the business needs during peak hours. To handle this huge data volume, multiple cloud systems that has been introduced to pool multiple clouds together to provide a single set of services in a collaborative manner. Aiming at the resource allocation problem in cloudy environments, this paper proposes a method of multi-objective task optimization scheduling based on dynamic programming, constructs a scheduling model of time and cost, and designs an efficient multiobjective optimisation approach is used to address the module. The method first describes the resource allocation process in a multi-cloud environment, then establishes a progress model for multi-user tasks in a cloud environment, then formalises the multi-user task optimisation problem and designs a solution algorithm based on a dynamic programming approach, and finally conducts simulation experiments to evaluate the effectiveness of the method. The experimental results show that the method can achieve time and cost optimisation in the scheduling of multi-user tasks.
UAV Maritime Search and Rescue Based on Improved A* and RCPP Algorithms for Path Planning

The purpose of this paper is to realize the path planning of the search and rescue process of the UAV at sea. The first part of this paper wonder to find local optimal path planning from the UAV launch base station to the target area. Meanwhile the next part is to find full-coverage optimal path planning of the UAV in the target area, and to ensure the minimum energy consumption in the process of achieving the goal. For local path planning, this paper adopts the A* algorithm as the main algorithm and integrates the DWA algorithm to improve it, for the purpose to reduce the number of UAV turns and turn angles while achieving dynamic obstacle avoidance during the flight. For full-range path planning, this paper adopts the RCPP algorithm to achieve full-area coverage of the UAV on various irregular sea areas, and the energy efficiency of the UAV flight can be improved by determining the best starting and ending points. Through the implementation of the two-part algorithm, it can meet the speed and accuracy requirements of maritime UAV search and rescue, ensure the shortest search and rescue time, and achieve the most efficient energy utilization.
Multi-Key Clustering Method for Cloud Environments' Privacy-Preserving

Data clustering is a crucial tool for data analysis, and it is reasonably efficient to outsource computational work to cloud servers in real-world applications where clients providing sample data frequently have CPU and storage capacity restrictions. Sadly, there is frequently a risk of customer data leakage when computing is outsourced. We provide a novel multi-secret K-Means clustering outsourcing strategy to protect privacy in cloud computing. The data owner separately encrypts sensitive data with the use of a distributed double-trap public key cryptosystem (DT-PKC) before uploading it to the cloud server. Then, using their respective secret keys, the cloud server and the service requester jointly compute K-Means clustering on the encrypted data. This system uses safe multiplication, secure squaring, and secure comparison protocols to support the underlying ciphertext operations. A secure Euclidean distance protocol for mutual cooperation is developed to safely calculate distances to update the set of prime numbers. The method effectively safeguards data privacy from all phases of the data source, calculation process, and output outcomes based on security analysis and time complexity analysis. The amount of computation and communication overhead is greatly reduced because the data owner only needs to communicate with the cloud once. Performance analysis demonstrates that our solution is both more beneficial than competing ones and functionally and safely similar to those already in use.
Towards a Robost Framework for Deep Person Reidentification

This paper aims to further improve the two key metrics of efficiency and accuracy of the existing pedestrian reidentification (ReID) method, based on which a more effective ReID model is proposed. Adding cutout data augmentation method to the original network backbone, embedding BN layers between network layers, and applying GEM (Generalized Mean Pooling) pooling method, experimental data show that the model has significantly improved both experimental results on Market1501 dataset, with 11.9% improvement in mAP and Rank-1 metrics respectively and 6.7%, respectively. The improvement in prediction efficiency and accuracy is achieved.
Multi-Scale Residual Network for Speaker Recognition

In recent years, deep learning models have been widely used in the field of speaker recognition and have surpassed traditional speaker recognition methods in terms of performance. However, the data distribution of speech is diverse, and current deep learning models applied in the field of speaker recognition commonly use a single convolutional kernel for feature extraction, which is unable to extract scale-dependent features and has insufficient adaptability to noise conditions and poor robustness. To address these issues, we propose a speaker recognition model called SE-MSResnet34 that is based on a multi-scale convolutional residual network. A double bypass network is constructed in the conventional residual block to increase the perceptual field of the model and extract scale-dependent speech features, while the Squeeze-and-Excitation (SE) module in the channel attention model is combined to make the model more focused on important features, so that more discriminative vocal features can be extracted. Experiments on the Voxceleb1 dataset showed a 23% performance improvement of the proposed method compared to the Thin Resnet34 baseline network model.