Reviewerly/exHarmony huggingface dataset:
Thai Handwritten Character Recognition Using Deep Convolutional Neural Network

A handwritten character recognition is a key for many applications that rely on digital documents, such as healthcare, insurance, and banking sectors. Deep learning is an innovative way of carrying out the handwritten character recognition that is typically performed by humans. This research discusses supervised deep learning using teachable machine, web-based tool, as a tool to train, evaluate and test a convolutional neural network (CNN) model for 44 Thai handwritten consonants. The best model is selected to apply in web and mobile application for practicing writing Thai consonants. A pre-trained model, Mobilenet, is used in the training. The Burapha-TH Thai handwriting dataset is used to train, evaluate and test the model. The experimental results show that the number of epochs and the number of images in dataset affects model accuracy. The dataset that has the maximum number of images, achieves the highest training accuracy, 99.53%, at 60 epochs and 0.001 learning rate and achieves the highest testing accuracy, 83.43%, at 70 epochs and 0.0001 learning rate.
End-to-end Structure-Aware Model for Meeting Minutes Generation

Meeting minutes record any key information discussed at the meeting, which can help people quickly digest the content of the meeting. Traditional methods of summarizing meetings pay little attention to the structural information of the meeting. Sentences at different positions in a paragraph have different importance. The structural features generated by the text segmentation task can be used to perform meeting summaries to improve the accuracy of the results. Based on this idea, we propose an end-to-end model for joint learning of meeting summarization and text segmentation. We design a structure-aware mechanism for acquiring structural features and use joint learning to jointly optimise the summarization and segmentation tasks. Furthermore, due to the inadequacy of the Chinese meeting dataset, we construct a Chinese meeting corpus UNGA containing segmentation labels as well as summary annotations. Experimental results show that our model outperforms the baseline model in terms of both automatic metrics and manual evaluation.
A Filtering Algorithm for Trend Maintenance of Mud-Water Meter Data

Supersonic mud-water meter is designed according to the principle of supersonic echo ranging. The surface elevation of ditch bed is subtracted from the surface elevation of debris flow body, which is the mud depth when debris flow flows. However, because the supersonic echo will be affected by the surrounding environment and the disturbance of the ditch bed, the abnormal measurement value will appear. Aiming at the problem that the prediction accuracy is affected by the jump of mud-water meter monitoring data, a trend preserving filtering algorithm based on the sliding average filtering method is proposed. By processing the mud-water meter monitoring data, the invalid data is identified and corrected, so as to reduce the monitoring errors and improve the monitoring accuracy.
Spatial-Temporal Multimodal End-to-End Autonomous Driving

Autonomous driving requires precise perception of the surrounding environment, and considering the complementarity of sensor data, we propose an end-to-end model of spatial-temporal multimodal fusion using an attention mechanism. Our model uses a fusion of camera and light detection and ranging (LiDAR), which works as follows: (i) The spatial network performs spatial feature learning using images from the range view (RV) representation of LiDAR and red, blue, and green (RGB) images as input, followed by a parallel ResNet18 network for feature extraction and fusion through an attention mechanism; (ii) the temporal network performs the learning of the temporal dimension of spatial features, and uses the current spatial features from the spatial network and the historical spatial features to do attention learning, which enhances the features relevant to the autonomous driving task; (iii) finally, the model uses spatial-temporal features to select a different prediction branch by navigation instructions to perform regression of waypoints. Our model was trained and tested in the CARLA simulator, and experiments showed that it enabled to complete autonomous driving tasks in complex environments, achieving a success rate of 85%, especially with many dynamic objects.
Adder Neural Networks for Speaker Verification

In recent years, the speaker verification (SV) technology based on deep neural networks is the most advanced and mainstream technology in the industry. However, since deep neural networks require a lot of storage resources and energy costs, this kind of SV technology cannot be deployed directly on devices with limited resources and low power consumption. In order to address this issue, we propose to use Adder Neural Network (AdderNet), which can replace the traditional multiplication in the network architecture with cheap addition. Additionally, we introduce an additive self-attention pooling layer, to apply the adder operation in the Self-attention pool (SAP) layer. The proposed speaker verification network can significantly reduce energy consumption and model computation costs with the performance maintained. Experimental results on the VoxCeleb1 dataset show our model consumes half of the energy of the original model, while the optimal EER is only reduced by 0.22%. Further, experiments on the CN-celeb dataset show that our method can achieve the CNN approach to approximate the results, demonstrating its generality.
Scoring Mechanism for Clustering Training in Wireless Federated Learning

Federated learning (FL) is a distributed learning framework, which can be widely applied into wireless networks to improve network intelligence without disclosing users' private data. However, due to the scarce wireless resources, it is inefficient for massive user devices to participate in FL training simultaneously. To address this challenge, in this work, we introduce the client clustering scheme and decompose the training process into two stages. Firstly, we divide user devices into different clusters based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Low Energy Adaptive Clustering Hierarchy (LEACH) algorithms. Then a scoring mechanism considering user devices' locations, velocities, link retention time, and training latency is proposed to determine the cluster head among the devices in each cluster. Finally, simulation results validate the performance of the proposed client clustering method in terms of model accuracy and training latency.
A Quality-improved Method based on Attention Mechanism and Contrastive Learning for Image Style Transfer

With the significant advances in deep learning, arbitrary style transfer (AST) methods are easy to achieve. However, artifacts and distortions occur constantly. The image quality improvement for AST has become a hot topic to research. In this paper, we propose a quality-improved method based on AesUST. First, a visual enhancement module guided by an attention mechanism is creatively presented to implement shallow and deep features integration on a per-point basis. Then, a contrastive coherence preserving loss is built to have a patch-wise calculation between content images and results, making the outputs softer and smoother. After the ablation study and the comparison with previous methods, the results show that our model visually produces more pleasant images and has a prosperous performance on quality evaluations.
UAV-YOLOX: An Accurate Object Detection Method for UAV Images

Object detection for Unmanned Aerial Vehicle (UAV) images has become a hotspot in the field of computer vision. Accurate object detection is significant for abundant UAV utilities. However, tiny objects and complex backgrounds in UVA images challenge the performance of object detection. To address the challenges, in this paper, we propose an accurate object detection method for UAV images, named UAV-YOLOX, by proposing some improvements to modify the state-of-the-art YOLOX detector. First, an Aug-Rotation data enhancement method is used to enhance the rotation invariance of the convolutional neural network, by alleviating inconsistent angles of similar objects in the dataset caused by different shooting angles of UAV. Then, a weighted cyclic feature pyramid is designed to improve the performance of object detection, by avoiding the loss of high-level semantic features when fusing features in a traditional feature pyramid structure. Finally, in the part of prediction head, this paper proposes an attention head structure, which regards the regression and classification of the bounding box as two tasks. The prediction head based on the attention mechanism can learn different features for different tasks. Experimental results on the public datasets and collected datasets show that the proposed method achieves much higher accuracy.
A Privacy Protection Scheme for Blockchain Aggregation Platform Based on Attribute-based and Order-preserving Encryption

Using traditional databases in the aggregation platform will result in too much power on the aggregation platform and unfair to the small platform it manages. With the development and wide application of blockchain technology, the openness, transparency and tamper proof of decentralized blockchain solves the problems caused by using centralized database on aggregation platform to some extent. However, it brings about the privacy protection and access control problems of the blockchain. Online car-hailing aggregation platform belongs to location-based service, location privacy is very important to users. In order to solve this problem, this paper proposes a privacy protection scheme based on attribute-based encryption and order-preserving encryption gridding location coordinates. By protecting the hailing user's departure and destination, the aggregation platform and its sub-platform cannot get the user's specific location information except the owner who serves the user, thus protecting the user's location privacy. Security analysis and performance analysis demonstrate that this scheme does realize location privacy protection and provides more secure services for users.
A Two-Level Multi-task Fair Allocation Mechanism in Cloud Computing

Aiming at the fair scheduling of multi-task and multiple resources in a heterogeneous cloud computing environment, in order to address this issue, each task of a single user is viewed as an independent sub-user, and the corresponding user is viewed as the upper-level user, thus constructing a two-level resource scheduling problem. That is, the bottom level is the fair allocation problem of multi-resource for sub-users, and the upper level is the scheduling problem of multi-task for upper-level users. In this paper, we propose a Two-level Multi-task fair allocation mechanism (TLMT). At the bottom level, the m-constrained bottleneck queue resource optimization model is used to ensure the rationality of resource allocation among sub-users. For upper-level users, an efficiency-aware allocation strategy based on multi-task share fairness (MSFEAA) is used. MSFEAA provides a quantitative measurement method to evaluate the fairness of multi-task allocation among upper-level users. The experimental results show that MSFEAA algorithm performs better in terms of multi-task share and provides full utilization of resources compared with DRF and WRDRF.
Intelligent Hermite Neural Control for Reaction Wheel Inverted Pendulums

In order to solve the problem that the reaction wheel inverted pendulum (RWIP) is highly nonlinear, unstable and underactuated nature, this study proposes an intelligent Hermite neural control (IHNC) system which has two main components: one is a speed controller and the other is a neural controller. The speed controller is designed to overcome the effect of reaction wheel rotation speed on the inverted pendulum. The neural controller, which employs a Hermite broad-learning neural network (HBNN), is designed to drive the pendulum at an upward unstable balance point and keep it controlled there. The HBNN extends the network structure in terms of width and internal feedback loop to achieve fast learning and dynamic mapping capabilities. Finally, the experimental results demonstrate the effectiveness of the proposed IHNC system.
A Joint Unsupervised Super-resolution and SAR Object Detection Network

Synthetic aperture radar (SAR) is an active microwave coherent imaging technology, which can provide images showing scattering properties of targets for target recognition regardless of adverse light and weather conditions. However, the low-resolution and speckle noise problems restrict the accuracy of object detection. Hence, super-resolution (SR) algorithms, providing additional details of targets and suppressing noise, are designed to guide the detection network. Nevertheless, how to effectively combine the two tasks turn out to be a challenge, i.e., the double parameters produced by the extra task consume more computing capacity and memory and thus restrict the interaction speed. Besides, the improper guidance of super-resolution also limits the detection accuracy. In this paper, a joint unsupervised super-resolution and SAR object detection network is proposed. First, a mixed loss derived from two SAR SR metrics replaces the original enormous SR dataset, thereby reducing the computational requirement for an unsupervised SR network. Second, an epoch-aware training strategy is applied in the whole training process to balance the two tasks and prevent overfitting for one of the two tasks. Experiment results on SSDD and MSAR demonstrate the enhancement of the proposed method in terms of average precision with less computing memory.
Research on Secret Sharing for Cyberspace Mimic Defense

Cyberspace mimic defense (CMD) is an emerging defense technology that combines dynamic heterogeneous redundancy (DHR) to enhance the security of information systems. At present, the applications of CMD focus more on data tamper resistance and do not more pay attention to the privacy disclosure of executors. This paper proposes to introduce the secret sharing mechanism into the CMD and use a secret sharing scheme with dimension switching to achieve the security of the private data of executors without adding extra key management. And experiments proved that the combination of the secret sharing scheme and mimic defense can increase data security at a tolerable cost.
EfficientNet-Based Deep Learning Approach for Breast Cancer Detection With Mammography Images

Breast cancer is a major health concern affecting women worldwide. Early detection and accurate diagnosis of breast cancer are crucial for improving patient outcomes. In recent years, deep learning techniques have been increasingly applied to medical imaging, including mammography, for the detection and diagnosis of breast cancer. In this study, we proposed a deep learning-based approach using the EfficientNet architecture for the detection and classification of breast cancer. We evaluated the performance of our proposed approach using mammography images from the CBIS-DDSM dataset and achieved accuracy of 0.75 and AUC of 0.83. Our results demonstrate the effectiveness of using deep learning techniques in medical imaging for breast cancer detection and diagnosis.
Research on Security Audit Technology of Smart Grid Database Based on Neural Networks

Smart grid databases store a vast amount of sensitive data and require secure auditing to ensure the protection of the data. Traditional methods of auditing smart grid databases are time-consuming and often rely on manual processing, leading to inefficiencies in the audit process. In this paper, we propose the use of artificial intelligence to address these limitations. Specifically, we introduce a deep convolution neural network for the extraction of smart grid data information and the conduct of data security audits. This method offers several advantages over traditional audit methods. Firstly, it eliminates the need for manual analysis of the data, reducing the time and effort required to perform the audit. Secondly, by feeding the data directly into the model for processing, the proposed method is able to process the data much more quickly and efficiently than traditional methods. Experimental results show that the proposed method is highly effective in conducting smart grid database security audits. The proposed method offers a promising solution for the efficient and effective auditing of smart grid databases, and has the potential to play a significant role in ensuring the security and protection of the critical infrastructure.
Automatic Modulation Classification Based on wavelet Image and Convolutional Neural Network

Automatic modulation recognition is an important fundamental technology in the field of wireless communication, and effective modulation identification of target signals is a necessary prerequisite for applications such as software radio and spectrum management. The complexity of modulation patterns and the increasing congestion of spectrum occupation bring great challenges to traditional classification methods. The authors propose an end-to-end signal modulation classification method based on wavelet image and convolutional neural network, which characterizes the intra-pulse signal based on continuous wavelet transform(CWT), and design a convolutional neural network to extract and classify the characteristics of signal wavelet time-frequency images. At the same time, a small sample dataset of 8 signal modulation types is generated, and the effectiveness of the proposed method is verified by combining data enhancement in 10 signal-to-noise ratio environments, and the classification accuracy is about 87% when the signal-to-noise ratio is 8dB.
Uncertainty-aware Comparison Scores for Face Recognition

Estimating and understanding uncertainty in face recognition systems is receiving increasing attention as face recognition systems spread worldwide and process privacy and security-related data. In this work, we investigate how such uncertainties can be further utilized to increase the accuracy and therefore the trust of automatic face recognition systems. We propose to use the uncertainties of extracted face features to compute a new uncertainty-aware comparison score (UACS). This score takes into account the estimated uncertainty during the calculation of the comparison score, leading to a reduction in verification errors. To achieve this, we model the comparison score and its uncertainty as a probability distribution and measure its distance to a distribution of an ideal genuine comparison. In extended experiments with three face recognition models and on six benchmarks, we investigated the impact of our approach and demonstrated its benefits in enhancing the verification performance and the genuine-imposter comparison scores separability.
Signal Deinterleaving Based on U-Net Networks

Signal deinterleaving is a key part of electronic warfare radar signal processing. As signal environments become more complex, classical sorting methods are being put to the test. This paper draws on the idea of using fully convolutional network to achieve image segmentation, proposes a new signal deinterleaving method based on U-Net network, and introduces the algorithm principle and network training method in detail. Simulation results show that if the PDW sequence of a single target can be fully intercepted, signal sorting based on U-Net segmentation network can be well realized in a complex signal environment as long as the target signal parameters are separable in any dimension.
A Deep Learning Based Receiver for Wireless Communications Systems With Unknown Channel Models

In this paper, we consider the signal detection problem for practical communication systems where the transmission suffers from non-idealities effects such as I/Q imbalance, non-Gaussian noise, and amplifier nonlinearity. In this context, traditional model-based receiver algorithms are not applicable because the exact model for the end- to-end channel is unavailable. To deal with this challenge, a deep learning based receiver is developed, which learns the detection criterion from data and realizes signal detection without knowing the underlying channel model. To improve the generalizability of the proposed detector, domain knowledge in channel estimation and equalization is used to guide the design of the receiver structure. Moreover, the idea from transfer learning and adversarial learning is adopted to assist the training of the network. The performance of the proposed detector is evaluated in terms of bit error rate (BER), and the superiority of the proposed design is shown compared to the state-of-the-art technologies.
Video superresolution in real forensic cases

Visual data, such as images and videos, are frequently used as evidence in court trials. If the data quality is insufficient to convince the court, a carefully tailored data processing algorithm supported with expert’s opinion is necessary. We present two real cases from our forensic expertise practice, in which we demonstrate a successful application of video superresolution that helped to convict offenders. The most important feature of image processing algorithms to be legally accepted by the court, is to rule out artifacts with realistic details, which are known to appear for example in deep learning methods.
Research on Multi-XCTDs Measurement Information Receiving and Data Mining System

To receive synchronous observations data, that transmitted by the XCTD automatic delivery acquisition device, and to realize intensive and gridded ocean observation, A multi-XCTDs measurement information receiving and data mining system is designed. This paper describes the composition of the system, expounds the implementation strategy of synchronous receiving algorithm, studies the observation information data mining, and discusses the principles of human-computer interface design. After the system was developed, it was tested at sea, the results show that the system realizes receiving multi-xctds data synchronously, and displaying the measurement information at the same time. It has the characteristics of high efficiency and practicability of data reception. The system breaks the simple model of just receiving and storing data, and begins a crucial step toward data mining. It plays a good foundation for the later application of data products.
A Case Study of using Cryptography for the Improvement of Data Security in E-commerce Industry in the Philippines

Data security is a major concern in the e-commerce industry in the Philippines, leading to trust issues among customers regarding the safety of their personal information. Cryptography is a crucial aspect of securing data in e-commerce by providing privacy to data transmission in the presence of potential threats. The study aimed to discuss and understand the improvement of Data Security in the E-commerce industry in the Philippines. A survey was conducted with 21 e-commerce platform users ranging from 18 to 30 years old to evaluate whether cryptography has improved data security in e-commerce from the consumers' perspective. The result showed that most respondents are satisfied with the current data security methods but strongly agreed to improve the E-commerce websites' security further. Thus, the recommendation of Cryptography as a data security method. The findings of this case study highlight that the best cryptography algorithm is the RSA algorithm due to its superior security, effectiveness, and widespread use. Next-generation encryption was found to be the best cryptographic tool, offering multi-layered protection by acting on threat detection across the entire network.