Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF THE FEASIBILITY OF BLOCKCHAIN TECHNOLOGY IN THE NATIONAL DEFENSE INDUSTRY

The aim of this study is to analyze and evaluate the feasibility of blockchain technology in the management of technology investments in the national defense industry. Blockchain technology (BT) has the potential to provide an important tool and infrastructure in providing technical cooperation and partnerships between national defense technology companies and institutions. It is predicted that BT shall become widespread in the field of defense industry and its cost shall decrease over time. Data sending and assessment of trust in the collaborative ambiance are seen as important problem areas in the use of blockchain technology by defense industry companies. In order to use blockchain technologies effectively in Turkey’s move of national defense industry, legal foundations, and platforms should be established and a state strategy should be formed in this regard.
Brightness Based Password Safeguard System with Face Recognition in ATM

the importance of security in the authentication process as well as the increase in threat level posed by such malware has attracted many researchers to the field. Many attacks are successful in accessing social network accounts since the current password-based authentication paradigms are not efficient and robust enough as well as vulnerable to automated attacks. The traditional two-factor authentication mechanisms are not applicable to online social networks because physical token or biometric data cannot be easily used to log into users’ profiles. The simplest alternative is complementing the single factor (password-based) authentication process with additional identification elements, such as one-time PIN codes, generated by the user’s own device (e.g. the smart phone) or received via SMS. Proposed a brightness based authentication mechanism (i.e., Bright Pass) capable of enhancing the security of identity confirmation PIN codes without asking the user to memorize an additional secret value or to solve a complex cognitive task. This method introduces a new input value that is changed at every usage combining a something you know element (i.e., the PIN) with an interface element that cannot be captured by spyware, i.e., a bright or dark circle displayed on the phone screen to tell the user when to digit the correct PIN digit and when to digit a fake one. It prevents the malware from correctly inserting the PIN code, thereby disallowing the possibility to perform critical operations without the user’s agreement. Proposed work also focuses on implementing secure face recognition approach for user authentication. This approach will enhance the performance of ATM system
Anomaly detection of satellite telemetry data based on extended dominant sets clustering

Abstract To mine out anomalies in satellite telemetry data under unsupervised conditions, a cluster-based method is proposed in this paper. Firstly, an extended dominant sets clustering algorithm is proposed to cluster the telemetry data with arbitrary shapes. Secondly, objects that do not belong to any cluster or belong to small clusters are traditionally identified as anomalies. Thirdly, the anomalies in large clusters are detected according to the relative similarity. Finally, the information on anomaly windows in the telemetry sequence is obtained according to the local anomaly rate, which provides more characteristics of the anomalies. Experimental results show that: 1) The proposed extended dominant sets clustering algorithm can deal with the dataset containing multiple and arbitrarily shaped clusters; 2) The introduction of relative similarity increases the AUC values of anomaly detection by 3%~10%; 3) The proposed anomaly detection method can effectively detect the anomalies in magnetometer telemetry data of Tianping-2B satellite. Therefore, the proposed method can achieve anomaly detection of satellite telemetry data under unsupervised conditions, and provide support for improving satellite safety and reliability.
Diligent (Digital Literacy Agent) Nurturing English Future Teachers’ Competence in the Digital Era

Digital literacy has been crucial aspect of the people life currently including education.However, previous studies have shown that English preservice teachers' level of digital literacy is still alarming.Therefore, efforts are needed to improve the digital literacy skills among English pre-service teachers since they will be dealing with students from the alpha generation.The main focus of this research is on developing the DILIGENT (Digital Literacy Agent) learning model following the SIOP (Sheltered Instruction Observation Protocol) Model in the TELL course to improve students' digital literacy at the English Education Study Program, Universitas Suryakancana.Eight criteria in the SIOP model are employed to develop the DILIGENT model.Merging the digital literacy framework from JISC with the SIOP model, this research is expected to create future English teachers who are not only skillful in teaching but also in spreading good teaching practice to their colleagues.
Program completeness verification mechanism based on static analysis

Program static analysis can be utilized to automatically investigate the execution procedures of programs. However, the proof of program completeness verification is less concerned by utilizing current static analysis tools, which only focus on the execution results verification. Therefore, we initially propose a novel completeness verification mechanism to provide a method to illustrating the targeted program is completed with required functions. We transfer the programming language into abstract symbols and establish the control flow graph of program, which can apply in all programming languages. Our proposed schemes significantly demonstrate completeness of program and more efficient than existing static analysis methods. From our experimental and evaluative results, proposed static analysis mechanism can effectively proof the completement of target program compared with existing methods and the computation cost is reasonable for the analysis period, which is decided by the size of programs.
Design of computer network secure storage system based on big data clustering algorithm

With the trend of networking of storage systems, storage systems have gradually become an important target for malicious attackers, and the result of attacks is often serious data accidents or encryption accidents. Therefore, the security of the storage system has become an issue that has yet to be resolved. In order to ensure the security of the data in the storage medium, a cryptography algorithm is used to encrypt and decrypt the data. A key distribution mechanism based on the K-means algorithm is designed to provide a reliable security guarantee for key transfer between various entities in the system. By comparing the time-consuming and storage efficiency ratio of the K-means algorithm and the rough set algorithm, the experimental results show that when using K-means, the rate of the storage device is slightly higher than that of the algorithm, indicating that the performance of K-means is slightly higher than that of the rough set algorithm when using stream encryption.
Research on construction and application of stack overflow Q and A website ontology

Stack Overflow is an IT technology question and answer website in the computer field. In order to realize the sharing and utilization of knowledge in the question-and-answer website, this paper first uses the Scrapy crawler framework technology to obtain the structural data in the Stack Overflow question and answer website and stores them in the relational model. Then it uses the ontology modeling tool Protégé to build an ontology, and then use the D2RQ tool to achieve the knowledge extraction of the relational database and convert the relational schema into a triplet ontology model. In this paper, we find expert examples from the ontology model by rule inference Experiments show that expert examples extracted from Stack Overflow ontology model can improve the accuracy of API call sequence mining.
PyCaNet: Pose Estimation of Primary and Middle School Students Using Pyramid Convolutions and Coordinate Attention

Abstract 2D pose estimation task is the basics of action recognition, human-computer interaction and other applications, but there is few research on pose estimation of primary and middle school students. In this paper, we present APMSS: a new dataset based on primary and middle school scenarios. The dataset fills the gap of the current human pose estimation dataset with few campus scenes, and the human keypoints and object bounding boxes in the dataset are manually annotated. In addition, we propose PyCaNet: a new top-down human pose estimation neural network model that uses pyramid convolutions to give the network a larger receptive field for better results. Coordinate attention is also added to the network, which is a kind of channel attention and can significantly improve the network performance. Extensive experiments shows that PyCaNet outperforms the previous methods by at least 1.0 AP on APMSS val and at least 0.7 PCKh on MPII test.
A Data-Driven Approach to Interdisciplinary Collaborative Discovery and Recommendation

Abstract The convergence of different fields is crucial for scientific advancement, and interdisciplinary collaborations among scholars are necessary for fostering this development. However, identifying and recommending potential interdisciplinary collaborators systematically is a challenging task. This paper proposes an interdisciplinary collaboration discovery and recommendation model for scholars in different fields. We utilized clustering algorithms, including K-Means, DBSCAN, and Affinity, to generate a scholarly interdisciplinary collaboration discovery graph. To recommend potential collaborators, we proposed an algorithm that considers both the suitability between scholars and the individual comprehensive influence of scholars. The effectiveness of the model was validated using data from 126 scholars at the Beijing University of Posts and Telecommunications.
Unbalanced Class-incremental Learning for Text Classification Based on Experience Replay

Abstract While deep learning has achieved remarkable results for text classification, incremental learning for text classification is still a challenge. The main problem is that models suffer from catastrophic forgetting, which is they always forget knowledge learned before when labelled data comes sequentially and is trained in sequence. In this study, we propose methods of preventing catastrophic forgetting to handle unbalanced increased data. As an improvement over experience replay, our approaches improve the accuracy about 23.3% with 23% of all training data on Yahoo and 9.5% with 12% of all training data and on DBPedia.
Optimization of short-term wind power prediction of Multi-kernel Extreme Learning Machine based on Sparrow Search Algorithm

Abstract Aiming at the problem that the single kernel function of kernel extreme learning machine (KELM) cannot adapt to the variable actual wind power. This paper proposes a modified prediction model which can increase the accuracy of prediction. The prediction model uses multiple kernel functions instead of a single kernel function and optimizes the kernel parameters by using a sparrow search algorithm (SSA). Finally, through the simulation and comparison experiments, the proposed prediction model has better prediction accuracy than the conventional prediction model.
An anchor-free detector designed for dense small water object detection

Abstract The detection of dense small objects on the water surface is one of the hot topics in object detection. In this paper, a one-to-many label assignment strategy based on the OTA algorithm, which is applied to anchor-free detector, is proposed to improve the detection accuracy of dense small objects on water surface. To be specific, one-to-many label assignment means that a ground truth (GT) corresponding to multiple prediction boxes is conducted globally. The cost function used by OTA algorithm is improved to make the distribution of positive and negative samples more reasonable. Meanwhile, an efficient training strategy is designed to accelerate network convergence by adding L1 loss in the last stage of training. The results show that the proposed strategies achieve 87.9% average precision (AP). Compared to the original algorithm, we achieve 3.3% relative improvement for the detection precision of dense small objects.
CRAT-XML: Contrastive Representation Adversarial Training for Extremely Multi-Label Text Classification

Abstract Extreme multi-label text classification is a very important and challenging problem in this age of widespread internet access, with a wide range of application scenarios, such as web tagging, legal document annotation, commodity classification, etc. Most of the existing state-of-the-art methods are based on deep learning, however, there are still two problems: 1) the vector representation generated by most pre-trained models suffers from anisotropy and uneven distribution, which has a significant impact on the XMC task. 2) existing models are large in size and use many models for integration. Some of them even took hundreds of hours to train. This seriously affects the efficiency of the experiments. Therefore, in this paper, we propose CRAT-XML, which uses contrast adversarial learning to optimize text representation and enhance the acquisition of dependency relations between text and labels, thus reducing the need for integration at the representation level and achieving relatively high accuracy under low-resource and low-time conditions. Experimental results demonstrate that our model achieves SOTA results on a single model, while achieving a large reduction in training time and model size.
Remote Sensing Ship Detection and Classification Based on Improved BBAVectors Algorithm

Abstract In recent years, most of remote sensing object detection algorithms only pursue higher detection accuracy, which use two-stage anchor-based networks. However, the two-stage anchor-based network usually has many parameters, complex calculation, and low detection speed, which is not suitable for object detection tasks with real-time requirements. BBAVectors is a network based on the one-stage anchor-free type CenterNet architecture. It has a fast detection speed, but the detection accuracy is far lower than the current high-precision remote sensing object detectors. In order to improve the detection accuracy of BBAVectors so that it can be better used in real-time remote sensing ship detection and classification, we propose the BBAV-ACPP. Based on the BBAVectors architecture, we add the Coordinate Attention Module and modify the center point representation. The proposed BBAV-ACPP architecture is trained and tested on the public remote sensing ship dataset HRSC 2016. Experiments show that the proposed BBAV-ACPP architecture can improve the detection accuracy while maintaining a fast detection speed. In some remote sensing ship detection tasks with real-time requirements, BBAV-ACPP can detect ship targets quickly and accurately.
Rosa: Language Model for Landscape Text Assisted Writing

Abstract In landscape design, the text of landscape design plays an important role. However, in practice, designers are often faced with an industry situation where they need to complete a large number of design tasks in a short period. If language models are used to assist in the writing of design texts, it will be of great help not only to designers but also to the landscape design industry. A pre-trained language model trained on collected landscape design texts and used for landscape text-assisted authoring. It is better than the existing models in terms of perplexity, readability and manual evaluation. It has great potential to be used to improve the landscape design industry.
Target-driven multi-input mapless robot navigation with deep reinforcement learning

Abstract Collision-free navigation without pre-built maps is a promising and challenging problem. To make full use of the information in the environment, we propose a multi-input mapless navigation framework that combines both vision-based and ranging-based approaches. It takes the position and orientation of the robot relative to the target as the reference, incorporating RGB-D images and distance measurements as state inputs to the deep reinforcement learning model. In the framework, Dual-VAE is designed to encode RGB images and depth images simultaneously, in order to effectively mine the intrinsic information of RGB-D images. Moreover, we engineer a special reward function that enables the robot to navigate with a smooth trajectory and leave redundant space with obstacles for other tasks. Target-driven multi-input mapless navigation framework has been experimentally proven to achieve satisfactory performance in simulation environments. Our source code and simulation environments are released at https://github.com/YosephYu/MultiInput-Mapless-Navigation.
A street view semantic segmentation algorithm based on DeeplabV3+ architecture

In order to solve the problem of semantic segmentation difficulties of city street scene pictures due to uneven color and strong light changes, this paper proposes a semantic segmentation algorithm of city street scene pictures based on DeepLabV3+ architecture with dark channel a priori theory. The algorithm firstly inverts the city streetscape image and then performs color balancing through the dark channel a priori module, then extracts contextual information hierarchically using a multi-channel parallel network, fuses low-level features with high-level features hierarchically to obtain the optimized feature map, secondly completes the fusion of multi-scale features through the spatial pyramid structure, and finally fuses the obtained feature map with the decoder twice to generate the final prediction results. The experimental results show that the algorithm in this paper outperforms the original DeeplabV3+ model in terms of subjective perception and objective indicators in urban scenes, and provides a new solution and technical idea for semantic segmentation of urban scenes.
A recommendation model with multi-scale semantic fusion on heterogeneous information network

Recommendation system aims to provide effective and personalized recommendation for users and solve the problem of information overload. However, the existing recommendation system lack of effective utilization of heterogeneous data and have the problem of information loss in the process of semantic information fusion. In this paper, we propose a Multi-scale Semantic Fusion Recommendation model (MSFRec) based on heterogeneous information networks to solve the problems above. First, we use heterogeneous graph and metapath to describe the complex semantic structure in the recommendation tasks. Then we divide the neighborhood guided by metapath into multiple layers and use the multi-layer interaction to capture multi-scale semantic information. Finally, we use a two-stage relational attention to guide the multi-scale semantic fusion. Extensive experiments show that MSFRec achieves competitive results in recommendation tasks.
NABP: social network alignment algorithm based on pre-trained language models

The existing social network matching algorithms have problems in processing text attribute information, as they cannot handle polysemy issues of word meanings well and cannot effectively extract deep semantic information from the text. To address the aforementioned issues, this paper introduces pretrained models to extract information from attribute text and proposes a social network alignment algorithm called NABP (Network Alignment based on Pre-trained Language Models). NABP first preprocesses the attribute text data to extract the text feature vectors; then, it uses the DeepWalk algorithm to perform graph representation learning on the two networks to extract their structural features. Next, the attribute feature vectors, and structural feature vectors are concatenated to form the final feature vector. Then, a projection function is learned using supervised information to further improve the algorithm's performance. To verify the effectiveness of NABP algorithm, this study conducted experiments on two real social network datasets. The results show that the NABP algorithm performs better than existing algorithms in both the Accuracy and Precision@k metrics, demonstrating the effectiveness of the algorithm and pre-trained language models.
Indoor scene recognition method based on multi-scale feature attention mechanism

To address the problem of low accuracy of traditional scene recognition methods in indoor environment, an indoor scene recognition method combining multi-scale features and attention mechanism is proposed. The method uses Efficientnet-B3 as the backbone network, introduces channel and spatial attention modules to improve the refinement capability of the network for features, and designs a multi-scale feature fusion structure to enhance the adaptability of the network to scales, based on which the model parameters are optimized by adding a spatial pyramid, thus further improving the model calculation accuracy. The experimental analysis shows that the average accuracy of this model reaches 94.4% in nine types of indoor scenes, all of which are better than the calculation results of AlexNet, VGGNet16, GoogLeNet, ResNet34, EfficientNet and other models, providing a new way of thinking for indoor scene recognition.
Electric bicycle detection in elevator car based on YOLOv5

Aiming at the problems that electric bicycles are prone to fire when charged at home, such as poor effectiveness of electric bicycle blocking systems in elevator cars, and low target detection accuracy, this paper proposes an improved YOLOv5 algorithm for detecting electric bicycles in elevator cars. Firstly, an FcaNet network model is embedded in the backbone network. The FcaNet network is implemented based on discrete cosine transform, mining multiple finite frequency domain components, making full use of input channel information, and improving the network's feature extraction ability; Secondly, in the process of feature fusion, relying on the mapping of CBAM attention mechanism to channel and spatial information, the CBAM attention mechanism is fused with the C3 model to form a C3CBAM model, which optimizes the fusion process of feature information, thereby enabling the model to achieve better detection performance; Finally, replacing the activation function in the SPP structure and using ReLU to replace SiLU improves the detection accuracy and speed of the algorithm. The experimental results show that the accuracy P, the recall rate R mAP Compared with the improved YOLOv5 algorithm, the accuracy of the algorithm was improved by 3.2 percentage points, and the recall rate was improved by 2.4 percentage points, mAP Increased by 4.1 percentage points, effectively improving the detection accuracy of the electric bicycle inside the elevator car.
Adaptive SOM neural network algorithm

In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network.