Reviewerly/exHarmony huggingface dataset:
A Self-supervised Pretraining Method Based on Mixup for Ordinal Classification

Ordinal classification is an important research topic that assigns instance with ordinal category. In practical applications, it greatly relies on supervised deep models, which require massive amount of labeled samples. In this paper, for small or moderate dataset, we use self-supervised learning(SSL) as a pretraining process to guide supervised learning. In self-supervised pertraining, rotating image embedded with Mixup strategy enables model to learn richer feature representation. Pretrained model is learned by rotation difference between two combined images, instead of single image rotation. After pretraining, SSL helps supervised learning with the style of finetuning. Finally, we evaluate the effectiveness of pretraining paradigm and Mixup rotation strategy on two datasets (Adience and Carstyle) in the experiment, and achieve promising gains on classification performance.
Speech Spoofing Detection Based on One-Class Residual Attention Network

Detection of spoofed speech to ensure the authenticity and integrity of speech is of great significance to voice authentication systems, judicial evidence collection, and social stability. Recently, one-class learning has shown promising performance in speech spoofing detection. This paper proposes a one-class residual attention network to address overfitting caused by feature distribution mismatch and increased depth in residual networks. Specifically, a one-class classification approach is used in a multi-scale residual neural network (Res2Net). The attention mechanism is applied to Res2Net, and different weights are given to the speech features of different time frames, so that the attention can be effectively paid to the spoofed speech. A one-class loss function is used for training the model, which effectively improves the detection accuracy. The proposed one-class residual attention network is evaluated on the ASV spoof 2019 dataset, and an EER of 1.72% and a min t-DCF of 0.045 are obtained, which outperform the baseline methods.
Multi-object Tracking based on improved YOLO

To improve tracking by detection algorithm missed detection and IDswitch problems caused by the occlusion of the target and the change of light and darkness. We propose a multi-target tracking algorithm based on improved YOLOv5 and DeepSORT. The SE and SimAM attention are adapted to YOLO v5 to improve the detector's precision, and the FocalEIOU loss function replaces the original CIOU to further accelerate the regression rate of the bounding box and improve the localization accuracy of the detector for the target. SAConv is added to the YOLOv5 backbone network and combined with the C3 layer, C3-SAC is proposed to address the unsuccessful identification problem caused by target occlusion, The feature extraction model of DeepSORT is adjusted and DIOU is used for matching. The improved algorithm is evaluated for detection and tracking on datasets VOC07+12, MOT16 and MOT20 respectively, and the mAP of VOC grows by 8.57%, MOTA of MOT16 and MOT20 grow by 3.88% and 6.45% respectively, and FPS meets with real-time tracking requirements. The result proves the effectiveness of the improved algorithm.
Review on Key Technologies of Preprocessing in Underwater Optical Image

The first step in the development and application of marine resources is to process the captured ocean optical images. Compared with the general image, the underwater image processing is more complex, which increases the difficulty of the development of marine resources. In order to assist the development of marine resources and the identification and analysis of marine organisms, this paper summarizes and analyzes the key technologies of underwater optical image preprocessing. The underwater image preprocessing is divided into underwater image acquisition, image clarity and image segmentation. Introduce the hardware equipment of underwater image acquisition; introduces the underwater image sharpening algorithm from three aspects: traditional color constancy theory, comprehensive technology and depth learning; introduce an image segmentation algorithm based on deep learning semantic segmentation. And enumerate and analyze various datasets used for underwater light vision technology. Through the comparison and analysis of various image preprocessing algorithms, the current situation of underwater image preprocessing technology is objectively evaluated. Discusses the problems existing in underwater image preprocessing technology and gives suggestions. Assisted in the development and application of marine resources.
Next POI Recommendation Based on Spatial and Temporal Disentanglement Representation

The next Point-of-interest (POI) recommendation task is to predict the next POI that users may be interested in. POI check-in sequence implicitly reflects the user’s location transition patterns, and the sequence modeling relies on the engineering of multiple independent features. Even though traditional POI recommendation models can fulfill the predicting task via entangled features, those black box models fail to mine the intrinsic check-in intention. The disentanglement representation learning method enables models to disentangle targeting intentions and provide interpretability for recommended results. However, existing disentanglement representation studies focus on disentangling user preference but neglect the entangled location characteristic. Besides, unrecorded check-ins result in inconsecutive transition sequences, which may influence their disentanglement qualities. Therefore, we proposed CrossDR, a Cross-sequence Location Disentanglement Representation method for the next POI recommendation, to explore how spatial and temporal factors influence check-in behaviors by a global view of location transitions. We apply disentanglement learning along with the time-masked data augmentation method and frequency-domain learning technique to further alleviate the short trajectory cold start problem caused by consecutive sequence generation. Experiments on two real-world datasets show our model has competitive capability compared to strong baselines.
Generating Fake Cyber Threat Intelligence Using the GPT-Neo Model

At present, some network security defense systems expand their knowledge base by extracting network threat Intelligence (CTI) to understand the common attack techniques and processes of malicious attack groups. However, there are potential risks to this approach. Hackers can spread fake CTI through the Open-Source Intelligence (OSINT) platform to fool defense systems into learning false information. In this article, we focus on how to generate fake CTI text using the GPT-Neo model and show that the generated text is highly confident. By fine-tuning a common language model like GPT-Neo, we can generate text similar to real CTI. At the end of the paper, we propose two disinformation detection methods that can help us to eliminate unreliable content.
Research of Surface Floating Object Detection Method Based on Improved YOLOv4

This paper proposes an improved YOLOv4 water surface floating object detection method to address the problem that floating objects on the water surface are difficult to detect due to their small size and existing detection algorithms are computationally intensive and difficult to run in real time on embedded devices. The specific idea is to use the MobileNetV3 network to replace the backbone network in YOLOv4; at the same time, negative feedback is introduced, and the proportion of the loss of small-sized targets in the overall loss during the training process is used as feedback, so that selective data enhancement can be performed to improve the detection accuracy of small-sized targets. The experimental results show that compared with the traditional YOLOv4 algorithm, the model parameter number of the proposed algorithm is reduced by 82.4% and the detection speed is improved by 52%.
An Abnormal Behavior Recognition System of Equipment Delivery Based on Image Recognition Technology

The high quality of power grid equipment is the basis for the safety of the power grid, and ensuring the quality of equipment is the core task of the material department. Before putting the equipment into operation, quality inspection is essential for the State Grid Corporation to ensure equipment quality. This paper proposes an abnormal behavior recognition system based on image recognition technology to guarantee the correctness and impartiality of the samples sent for testing. The system is composed of a human body object detection module, a camera occlusion detection module, and a camera monitoring angle adjustment alarm module. In the human body object detection module, a human body part detection network is added based on Faster R-CNN framework to obtain more representative features, which is used to improve the accuracy of the human body in the case of occlusion and to reduce the false negative rate. Testing results from the monitoring video show that the system can effectively identify abnormal behaviors during the equipment samples delivery process.
Ship Draft Detection Method Based on the Canny Operator and Hough Transform

The accuracy of ship draft detection must meet higher standards since it is a crucial link in the shipping industry. This paper proposes the Canny operator and Hough transform ship draft detection method to optimize the accuracy and efficiency of image processing technology in ship draft detection with the application of image processing technology in the field of draft detection. The ship's draught line is obtained after the edge of the ship's draught picture is extracted and processed using the linear detection technology of the Hough transform. The experimental results show that this method can swiftly determine the waterline and optimize the image processing technology, which is more conducive to the operation and safety of the ship, and has certain practical application significance.
Path Planning of Manipulator Based on Improved Ant Colony Algorithm in 3D space

An improved ant colony algorithm based path planning method for robotic manipulators in three-dimensional space is proposed to solve the path planning problem of the manipulator in complex workspaces. Firstly, by setting nonuniform initial pheromone concentration, the disorder of the algorithm's search path in the early stage is reduced, and the location information of the target point is incorporated into the heuristic function to increase the influencing factors of the heuristic function, so that the ant colony search has a certain directivity. Then, the dynamic pheromone volatilization coefficient is established. With the continuous iteration, the pheromone volatilization coefficient decreases continuously. Accelerate ant colony algorithm convergence speed. The path planning simulation experiments of the improved ant colony algorithm and the ant colony algorithm are carried out in the same environment. The results show that the improved ant colony algorithm can get a better spatial path and plan faster, and the practicability and effectiveness of the algorithm on the manipulator are proved.
Modal analysis and optimization design of Unmanned Aerial Vehicle motor cabinet of dual cooling system

UAVs are simple to operate and have high application value in many fields. Therefore, it is crucial to conduct modal analysis and optimize the design of important components of UAVs. In this paper, we take the motor cabinet of UAV with dual heat dissipation system as the object of study, firstly, we construct its 3D geometric model in SolidWorks, and then analyze the deformation of the motor cabinet under the maximum tension condition with the reliable ANSYS Workbench. The modal analysis of the motor cabinet is also carried out to extract the first six orders of modal frequencies and vibration clouds of the model. Finally, based on the results of the static and dynamic analysis of the motor cabinet, optimized the dimensions of the parts affecting the static stiffness and mass of the motor cabinet. The optimized motor cabinet is rebuilt and the static and dynamic analysis results are updated. After the optimization of the motor cabinet of the UAV with dual heat sink system, not only its maximum equivalent force, maximum deformation and total mass are reduced by 65.3%, 32.2%, and 40.2%, respectively, but also the modal frequencies of all orders are increased. The optimized design results in good static and dynamic characteristics of the motor cabinet.
Accelerated Design of CNN Convolutional Calculator Based on Verilog

As convolutional neural networks (CNN) are widely used in daily life, both the network algorithm itself and the hardware facilities that carry the network are under higher requirements. Therefore, this paper first analyses the structure of convolutional neural network, and obtains the relationship between input and output of each convolutional layer and the calculation process. By analysing the computational process of convolutional neural networks, the potential parallelism in neural networks is explored. Then, the accelerated design is carried out for the convolutional computing channel, the main body of convolutional neural network. Finally, according to Verilog simulation, it is concluded that the improved adder structure realized by improved adder has excellent overall performance, reducing the power consumption (without considering IO consumption), LUT consumption and delay to 58.8%, 84.5% and 52.1% respectively, compared with the full adder structure implemented through ripple carry adder.
Improved Particle Filtering Based Motion Target Detection Method

In the field of target tracking, particle filtering has been widely used due to its good performance, while the defects of traditional particle filtering methods in particle weight updating and initial value selection have seriously affected the tracking effect. To solve such problems, an improved particle filtering method is proposed in this paper. Firstly, the prior probability of generating initial particles is selected based on a large number of target statistics, which makes it possible to obtain better results at the early stage of tracking and improve the convergence speed and tracking accuracy; secondly, in the process of particle weight updating, a threshold partitioning combined with wavelet transform weight smoothing method is used, which effectively improves the particle degradation problem, the experimental results show that this method reduces the prediction error rate to 3.5%, which greatly improves the performance of the algorithm.
Detection of Breast Cancer using Curvelet Transform and Adaptive Particle Swarm Optimization Technique

The breast cancer is the most prevalent malignancy. Better chance of curing breast cancer is early detection, which can also lower mortality rates. The best technique for early breast disease detection is the mammography. In the suggested approach, curvelet transform is utilized to extract features, and adaptive particle swarm optimization helps to choose the eminent features. Adaptive Particle Swarm optimization has been devised to speed up and simplify the process of feature selection and Support Vector Machine (SVM) aids in breast cancer classification. We present an Adaptive Particle Swarm optimization (APSO) that outperforms Particle Swarm optimization (PSO) regarding search efficiency. The suggested model is examined using a collection of 332 images from the Mammographic Image Analysis Society (MIAS) database. The executed findings are compared with the old transforms, and the results demonstrate that the suggested model has higher detection accuracy rates than the earlier approaches.
SLatAR - A Sign Language Translating Augmented Reality Application

Recent studies about Augmented Reality (AR) based interaction focus on speech and gesture, but it limits over 1.5 billion Deaf and Hard-of-Hearing (DHH) users from interaction in the context of remote collaboration. They primarily use sign language and facial expression as their main communication method, which is usually omitted in AR communication. Earlier works conducted several attempts to complement the limitation of DHH users’ interaction. By using speech recognition technology, researchers visualized the speech as a text in the AR space. Another approach augmented a translated sign language animation following images or videos. But these are only limited to text translation and are spoken-user-centred. There is a lack of studies that translate from speech to sign language in remote AR collaboration. To overcome this, SLatAR focuses on converting the speech spoken by the normal cognitive user to sign language using AR. This helps in building the communication gap between them. The speech generated by the user with normal cognitive ability is recognized and is converted into the Sign language gloss after undergoing various processes. A total of 51 distinct gloss words have been animated in this system which are mapped to its animation file and mimicked by the 3D avatar.
Deep Learning-Based Technique for Sign Language Detection

Sign languages are a form of communication used by the deaf and hard-of-hearing community. Malay Sign Language (MSL) is the official sign language practiced in Malaysia, enabling communication through hand signs and facial expressions. Each sign and its combination hold a distinct meaning, making it challenging for individuals to casually learn MLS. Therefore, this study presents an object detection model that utilizes the Single Shot Detector (SSD) and Mobilenet to detect MLS in real time. The model focuses solely on detecting static signs that do not involve complex combinations. The datasets used for training consist of 2000 sign images collected from Kaggle website, as well as images captured using a personal camera. The datasets were divided into training, validation, and testing phases in an 80:10:10 ratio, respectively. In conclusion, this study successfully developed a real-time and accurate system for recognizing MSL using the SSD-Mobilenet model. This contribution has significant implications for the field of sign language recognition and can greatly improve communication access for individuals who are deaf or hard-of-hearing.
Cyber Secure Remote Sensing Image Encryption Scheme using Blockchain and Dual Chaotic Map

We address the issue of Satellite image security with blockchain enabled secure high dimensional chaos maps. Millions of Satellite image are acquired daily basis for weather forecasting and also for many other prominent application areas. Securing the remote sensing image is still a challenging issue. This paper depicts two phase image encryption technique to Barr cyberattack. At the first phase the remote sensing image is divided into a series of sub image block. Per each sub image block Using Logistic Map random number generated to circularly shifted block for shuffling of intensities locations using as initial seed value (key1) and control parameter (key2). At second phase the circularly shuffled image undergone XOR operation based chaotic confusion using Rickers map with its initial seed value (key3) and control parameter (key4). Hash value of scrambled cipher image is calculated employing SHA-256 algorithm and stored in a block chain. The scrambled cipher image transmitted to receiver end with prior communication of symmetric keys. All the statistical analysis such as differential attack, key space analysis to resist Brute force attack, Correlation analysis, key sensitivity test and time analysis has been taken into consideration to test the cyber security aspect of the proposed algorithm with existing techniques to mitigate current state-of-art.
YOLO-SC: An Improved Lightweight Mask Detection Algorithm

In order to solve the problems of the YOLOv5 model, such as the large number of parameters, large amount of calculation and difficulty in deployment, a YOLO-SC model with higher accuracy and fewer parameters and calculation amounts was proposed. Firstly, this paper designs and proposes ShuffleNet-SC lightweight network module to strike a balance between lightweight and accuracy. Secondly, CA attention is used to consider the relationship between channels and location information to improve the accuracy of the model more effectively. Finally, the penalty failure problem of CIoU loss function is solved by EIoU loss function. The accuracy of the proposed YOLO-SC model is improved by 3.1% under the premise that the reference number and calculation amount of the original model are 41.19% and 47.5% respectively, and it is easier to be deployed in the embedded equipment for mask detection
Towards Long-term Fairness in Interactive Recommendation: A Maximum Entropy Reinforcement Learning Approach

This paper considers the problem of maintaining the long-term fairness of item exposure in interactive recommendation systems under the dynamic setting that user preference and item popularity evolve over time. The challenge is that the evolving dynamics of user preference and item popularity in the feedback loop amplify the long-term “unfairness” of item exposure. To address this challenge, we first formulate a constrained Markov Decision Process (MDP) to capture evolving dynamics of user preference. The proposed constrained MDP imposes long-term fairness requirements via maximum entropy techniques. Moreover, to illuminate the “unfairness” amplifying effect caused by the evolving dynamic of item popularity in the feedback loop, we design a debiased reward function to eliminate popularity bias in the training data. To this end, the proposed framework can maintain acceptable recommendation accuracy while exposing items as randomly as possible, ensuring long-term benefits for users. Experiments on three datasets demonstrate the effectiveness and superiority of our proposed framework in terms of recommendation performance and fairness.
Mongolian-Chinese Machine Translation Based on Text Context Information

Mongolian-Chinese neural machine translation has the problem that it cannot make full use of context information for document-level translation. In order to solve this problem, a Mongolian-Chinese neural machine translation model using the context information of the passage is proposed.The model makes better use of context information for document-level translation by introducing local encoders and global encoders in the encoder and caching mechanisms in the decoder. Through experiments, the document-level Mongolian-Chinese machine translation model integrated with context information is compared with the sentence-level Mongolian-Chinese machine translation model based on Transformer. The experimental results verify the advantages of the document-level Mongolian-Chinese machine translation model integrated with context information in translation performance.