Reviewerly/exHarmony huggingface dataset:
Dynamic Target Tracking Method of Surveillance Video Based on Image Processing

In surveillance video, target tracking is an important part. Based on image processing technology, this paper studies a real-time and effective method to collect and recognize camera motion information. Firstly, the influence of visual dead angle and illumination on recognition is analyzed. Secondly, according to the characteristic of background light intensity, the corresponding algorithm is designed to realize the positioning and tracking control strategy of the target and surrounding environment scenery. Finally, the correctness of the method is verified by MATLAB simulation software, so as to obtain a better and scalable scheme, which is more economical and feasible after the occlusion rate is minimized.
FPGA-based Convolutional Neural Network Design and Implementation

Deep convolutional neural networks have prominent advantages in fields like image identification and natural language processing, but because of their high storage costs and massive computational volumes, they are frequently widely used in servers with GPU acceleration capability. As autonomous driving, aerospace, and other industries evolve, some scenarios have higher requirements for real-time detection than others. Since it is not feasible to search for targets by sending video streams to the server for inference and then returning the results, low-power hardware acceleration options for neural networks must be investigated. In this paper, we suggest an FPGA-based specialized accelerator for convolutional neural networks. To support the parallel execution of each convolution module, we analyze the computational properties of neural networks and design a convolutional computational structure with the deep flow and high parallelism. In addition, each layer of convolution is internally divided into multiple computational units along the channel direction to further enhance the computational parallelism. In this study, we use the Xilinx xc7z100 platform to implement an onboard test for a yolov5s-based neural network. According to the experimental findings, this design structure's acceleration ratio can reach 142 times and its power consumption is only 4.5W, which could provide a significant performance boost at a low power consumption when compared to the 800MHz ARM cortexA9.
A Comparative Study of Cross-Sentence Features for Named Entity Recognition

Recently, a growing number of Named Entity Recognition (NER) methods utilize cross-sentence features (also known as contexts) to improve the performance of NER models, instead of using single-sentence information alone. As far as we know, most NER models choose to exploit pre- and post-sentences to capture cross-sentence features. Generally, current NER studies focus only on the model architecture to capture better token representations. However, there is no in-depth exploration on how to better model cross-sentence features. In this paper, based on the span classification model, we investigate the effect of cross-sentence features under different settings. Specifically, we evaluate the impact of context stitching, context window size, context window padding, and classifier token of pre-trained language model (PLM) on model performance. Comparative experimental results show that appropriate incorporation of document-level contexts can considerably improve the NER metrics. Furthermore, we find that several factors can be used to improve the performance of NER models: (1) use domain-specific PLMs, but not classifier tokens; (2) use only preceding contexts for generic text, and random contexts for specialized text; (3) truncate overly long contexts when the context window is small, and preserve sentence integrity when the window is large; (4) set the context window size to about 200 for the basic size PLM.
Knowledge Distillation by Multiple Student Instance Interaction

Knowledge distillation is an efficient method in neural network compression, which transfers the knowledge from a high-capacity teacher network to a low-capacity student network. Previous approaches follow the ‘one teacher and one student’ paradigm, which neglects the possibility that interaction of multiple students could boost the distillation performance. In this paper, we propose a novel approach by simultaneously training multiple instances of a student model. By adding the similarity and diversity losses into the baseline knowledge distillation and adaptively adjusting the proportion of these losses according to accuracy changes of multiple student instances, we build a distillation system to make students collaborate and compete with each other, which improves system robustness and performance. Experiments show superior performance of the proposed method over existing offline and online distillation schemes on datasets with various scales.
A Rankable Boolean Searchable Encryption Scheme Supporting Dynamic Updates in a Cloud Environment

At present, three problems exist in searchable encryption in cloud storage services: firstly, most traditional searchable encryption schemes only support single-keyword search while fail to perform Boolean searches; even if a few schemes support Boolean searching, the storage efficiency is also unsatisfactory. Secondly, most existing schemes do not support dynamic keyword updates, so the update efficiency is low. Thirdly, most existing schemes cannot meet all demands of users, to perform rankable searching over search files according to the importance of keywords. To solve these problems, a rankable Boolean searchable encryption scheme supporting dynamic updates in a cloud environment (RBDC) is proposed. By using Paillier and GM encryption algorithms, secure indices supporting dynamic updating are established. Based on applicable knowledge gleaned from cryptography and set theory, the indices of keyword intersections and the intersection search trapdoors are constructed to achieve multi-keyword Boolean search. With assistance of the SCP, score indices of each file are constructed according to the TF-IDF index, which allow ranking of files. Security analysis proofs that our scheme can ensure security in the known ciphertext model and the known background model. Experimental results prove that the scheme improves the search efficiency and the index storage efficiency.
Transfer Learning on Trial: A Case Study to Apply Existing Models to Heterogeneous Datasets

Nowadays, transfer learning is getting more and more popular in both industry and academia. It enables people to benefit from current advanced AI technologies, which used to be only accessible to professional teams with the most powerful talents, software and hardware resources. It has been proved that transfer learning is the best available option to apply learned patterns for one problem to a different but related problem. But rare research has been done to evaluate the performance of employing an existing model to a less related problem. In this paper, we apply the pre-trained model in the computer vision field, VGG, to a radar dataset, Ionosphere, which is heterogeneous to the above vision data, and carry out extensive experiments. The results show that the classification accuracy is much lower than that in the early research work, and the application of transfer learning should depend on certain situations.
Hybrid Attention Deep Adaptive Residual Graph Convolution Network for Few-shot Classification

Few-shot learning is a challenging task in the field of machine learning that aims to acknowledge novel class with a few amount of labeled samples. To address this problem, researchers have proposed several methods, with metric-based methods being one of the most effective approaches. These methods learn a transferable embedding space for classification by computing the similarity between samples. In this context, Graph Neural Networks (GNNs) have been employed to describe the association among support samples and query samples. However, existing GNN-based methods face limitations in their capability to achieve deeper layers, which restricts their ability to effectively transport information from the support images to the query images. To overcome the limitation, we propose a deep adaptive residual graph convolution network with deeper layers that better explores the relationship between support and query sets. Additionally, we design a hybrid attention module to learn the metric distributions, which helps to alleviate the over-fitting problem that can occur with few samples. The proposed method has been shown to be effective through comprehensive experimentation on five benchmark datasets.
ECG Signal Extraction Method Based on Singular Value Selection and Wavelet

Aiming at the noise of the received ECG signal, a extraction method of ECG signal based on singular value selection and wavelet is proposed. The singular value decomposition on the ECG signal is performed firstly, and the ECG signal component corresponding to each singular value is obtained. Then the signal component corresponding to the maximum singular value is used to calculate cross-correlation coefficients with other components. The cumulative contribution rate of singular values is combined to determine the number of singular values for ECG signal reconstruction. The wavelet threshold de-noising method is used to de-noise the final determined signal components. Finally, the de-noising ECG signal is obtained by reconstructing the signal components. The experimental results show that the method can suppress noise and extract signal effectively, and it has a good noise reduction effect compared with wavelet threshold method.
One-Class Classification for Radar-Based Human Fall Event Detection

Existing deep learning and machine learning techniques for radar-based fall event detection require a large number of fall activity instances at the training stage to produce reliable results. However, to replicate real-life scenarios, falls should be treated as rare events. This work pioneers the application of one-class classification (OCC) technique for radar-based fall event detection. A comparative analysis of three different types of OCC techniques using abstract features extracted from six different pretrained models is shown. An improved novel feature fused OCC model is proposed that achieves an area under the curve value of 0.912 for fall event detection. All evaluations are done on a publicly available dataset of human radar signatures.
Modeling and Optimization of Data Center Energy Consumption

In this era of high-speed data development, the demand for computing that data centers(DC) need to process is rapidly increasing, and its high energy consumption problem is also becoming increasingly prominent. Currently, the total energy consumption of DC has occupied about 2.7% of the whole energy consumption of society, and optimizing the energy consumption of DC has been a research hotspot of concern in last several years. However, the current study ignores the start-stop relationship and climbing constraints of information technology (IT) equipment between different time periods, and the optimization strategy given by it may lead to frequent start-up and shutdown of IT equipment in actual operation when the arithmetic power demand changes greatly in a short period of time, resulting in excessive IT equipment loss. Based on this, the optimization model of data center energy consumption is established by considering the energy consumption characteristics of IT equipment and the heat exchange relationship of air conditioning equipment, as well as the start-stop relationship and the climbing constraint of IT equipment. For the dynamic and nonlinear characteristics of the temperature exchange process between IT equipment and the air conditioner, the DDPG algorithm is proposed for solving. Finally, the practicality and efficiency of the method are verified by a large-scale data center simulation example with 100,000 IT equipment.
DetTrans: A Lightweight Framework to <b>Det</b>ect and <b>Trans</b>late Noisy Inputs Simultaneously

Neural machine translation (NMT) systems trained on clean data usually suffer from performance degradation when translating noisy inputs. Existing works attempt to improve the robustness of NMT normally via data augmentation, where synthetic noisy data are mixed with original clean data, either for training NMT with the standard NMT loss alone, or for tuning auxiliary tasks in a multi-task learning manner. Typical auxiliary tasks include detecting and correcting noises, exploiting noisy outputs for contrastive learning etc. The aforementioned two auxiliary tasks are generally designed independently, and the modules for detecting and correcting noises are heavyweight. In this article, we propose a new framework, DetTransNet ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Det</b> ector- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trans</b> lator <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Net</b> work), aiming to detect positions of noises in the input and translate the input simultaneously. The newly introduced noise detector module is essentially a lightweight binary classifier built upon the final layer of the encoder of the original Transformer model for the translation task, which is to identify at which position of the input has potential noise. The module has a very few parameters. In order to help the model capture the relationship between clean instances and their noisy counterparts, an extra loss is further introduced to enhance the interaction between clean and noisy data. In this way, we combine noise detection and contrastive learning together. As the model is able to identify and locate noises, a heuristic method is proposed to correct detected noises, in order to achieve better translations. Experiments show that DetTransNet is robust to four types of noises (deletion, insertion, swapping, keyboard), and obtain a substantial improvement of up to 1.6 BLEU points across different datasets.
Image Dense Captioning of Irregular Regions Based on Visual Saliency

Traditional Dense Captioning intends to describe local details of image with natural language. It usually uses target detection first and then describes the contents in the detected bounding box, which will make the description content rich. But captioning based on target detection often lacks the attention to the association between objects and the environment, or between the objects. And for now, there is no dense captioning method has the ability to deal with irregular areas. To solve these problems, we propose a visual-saliency based region division method. It focuses more on areas than just on objects. Based on the division, the local description of the irregular region is carried out. For each area, we combine the image with the target area to generate features, which are put into the caption model. We used the Visual Genome dataset for training and testing. Through experiments, our model is comparable to the baseline under the traditional bounding box. And the description of irregular region generated by our method is equally good. Our model performs well in image retrieval experiments and has less information redundancy. In the application, we support to manually select the region of interest on the image for description, for assist in expanding the dataset.
Multi-Modal Cross-Attention Learning on Detecting Anomalies of Electrical Equipment

Currently, in field of electrical power system, techniques of anomalies detection are constantly innovating. Applications of neural network on processing of patrol images spare analyzers plenty of time, but subject to the relatively low resolution of the object contour under heavy weather, results do not show well on recognition of anomalies in electrical equipment, while multi-modal methods can import more information to the objects detected, thus may improve the success rate of capture. In this paper, we propose a feature-fusion model which uses cross-attention learning method to augment features of the anomalies with text of corresponding description and environment condition. After comparing experiments on self-constructed datasets of images and text, our model has achieved the state of art on multiple metrics. More importantly, it is found that adding additional features to the model can achieve better results through ablation experiments, which shows our model is scalable for a better solution.
Spatio-Temporal-based Context Fusion for Video Anomaly Detection

Video anomaly detection (VAD) detects target objects such as people and vehicles to discover abnormal events in videos. There are abundant spatio-temporal context information in different objects of videos. Most existing methods pay more attention to temporal context than spatial context in VAD. The spatial context information represents the relationship between the detection target and surrounding targets. Anomaly detection makes a lot of sense. To this end, a video anomaly detection algorithm based on target spatio-temporal context fusion is proposed. Firstly, the target in the video frame is extracted through the target detection network to reduce background interference. Then the optical flow map of two adjacent frames is calculated. Motion features are used multiple targets in the video frame to construct spatial context simultaneously, re-encoding the target appearance and motion features, and finally reconstructing the above features through the spatiotemporal dual-stream network, and using the reconstruction error to represent the abnormal score. The algorithm achieves frame-level AUCs of 98.5% on UCSDped2 and 86.3% on Avenue datasets. On UCSDped2 dataset, the spatio-temporal dual-stream network improves frames by 5.1% and 0.3%, respectively, compared to the temporal and spatial stream networks. After using spatial context encoding, the frame-level AUC is enhanced by 1%, which verifies the method’s effectiveness.
Efficient TFI-Based Depth-Tunable LPI Radar Waveform Recognition Network

Low probability of intercept (LPI) radar waveform recognition (LWR) based on deep learning can significantly enhance the recognition accuracy. However, most existing LWR networks overlook the algorithmic complexity and disparities between visual images and time-frequency images (TFIs). In practical applications, it is critical to enhance the recognition accuracy while minimizing the computational complexity to satisfy the reliability and timeliness requirements of an LWR network. To address this issue, we propose an efficient depth-tunable network (EDTN) for LWR. The EDTN reduces the computational complexity and enhances the recognition accuracy by adopting a flexible network structure, using convolution methods appropriate for TFIs, and combining several beneficial designs. Our experiments with a classical LPI radar waveform dataset demonstrate that the EDTN reduces the computational complexity by more than 95% over that of the state-of-the-art networks while maintaining the recognition accuracy.
Trajectory Planning Method Based on Optimization in Complex Environment

This paper's major objective is to rapidly and precisely plan the optimal trajectory in a complex environment. Because collisions must be avoided to ensure driving safety and compliance with kinematic constraints to enable precise tracking, this task can be characterized as an optimal control problem(OCP). The outcome of the optimal control problem is determined by the initial solution. A good first solution can reduce the time needed to solve the problem and enhance the effectiveness of the planning process. Hybrid A* is a well-known heuristic method that has a short planning time and satisfies vehicle but it is not complete. It offers a good initial solution for following optimal control problems. In order to achieve the ideal balance between optimality and solvability, we therefore suggest that, in the event that Hybrid A* planning fails, the complete two-dimensional A* planning result be employed as the initial solution of the optimal control problem. It is difficult and time-consuming to avoid contact with any obstacles when the initial solution indicates the homotopy path. The collision avoidance constraint is changed into a linear constraint by the addition of a space corridor, making it independent of the overall quantity of obstacles and solely related to obstacles that are approaching the trajectory. Soft constraints are utilized to further simplify the optimal control problem and guarantee its solvability. We conduct simulation experiments in multiple scenes to prove the feasibility of the algorithm.
Robust Salient Object Detection via Adversarial Training

Deep salient object detection has experienced noticeable progress. Unfortunately, most existing methods focus on clean samples regardless of the noise disturbance induced by human or natural factors. This results in the detection performance being extremely vulnerable to small perturbations. To this end, this paper proposes robust salient object detection via adversarial training (ATSOD). In specific, we introduce the classical DSS algorithm and inject it into an adversarial training framework favoring salient object detection. This ensures that, apart from clean samples, adversarial examples involving tiny disturbances are also explored for model training. Comparative experiments are conducted on five popular benchmarks. Experimental results show that despite the slight performance degradation for natural examples, there is a significant performance improvement for adversarial examples.
Sentiment Analysis of the COVID-19 Epidemic Based on Deep Learning

Twitter text sentiment analysis has important applications in public sentiment monitoring. The results of sentiment analysis based on traditional machine learning models and sentiment dictionaries are often unsatisfactory. How to optimize the performance of public opinion sentiment analysis has become an important challenge in this field. This paper uses the BERT model based on deep learning to complete the language understanding task and compares the performance with the traditional practice. The results show that the BERT model achieves better performance, reaching more than 90%. The model was then used to perform three classifications to analyze Twitter comments during the COVID-19 outbreak, and overall positive sentiment and neutral sentiment dominated. In addition, we also conduct related analysis on word frequency, word cloud and time comparison, in order to achieve the purpose of comprehensively understanding the social-emotional state during the epidemic.
Efficient Multi-Task and Transfer Reinforcement Learning With Parameter-Compositional Framework

In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting. We identify several challenges towards this goal and propose a transferring approach with a parameter-compositional formulation. We investigate ways to improve the training of multi-task reinforcement learning which serves as the foundation for transferring. Then we conduct a number of transferring experiments on various manipulation tasks. Experimental results demonstrate that the proposed approach can have improved performance in the multi-task training stage, and further show effective transferring in terms of both sample efficiency and performance.