Reviewerly/exHarmony huggingface dataset:
Image Quality Improvement of Surveillance Camera Images by Learning-based Denoising Method Utilizing Noise2Noise

In recent years, the number of surveillance cameras installed has increased. Surveillance cameras need to be able to capture images even under poor shooting conditions such as low exposure. However, noise may be generated in the captured images under such environments. Although there have been many studies on image denoising, most of them target only synthetic noise such as Gaussian noise or real image noise such as the SIDD dataset and have not demonstrated sufficient performance for captured images. In this paper, we investigate the construction of an effective CNN model for real image noise using Noise2Noise. In addition, Noise2Noise has the problem of significantly degraded performance compared to normal learning when data is small. Therefore, we propose a learning method that can build models with good performance even when data is small, by pre-training with an open dataset such as SIDD and then re-training with Noise2Noise.
3D Binary Lesion Mask Parsing

Liver lesion segmentation is a key module for an automated liver disease diagnosis system. Numerous methods have been developed recently to produce accurate 3D binary lesion masks for CT scans. From the clinical perspective, it is thus important to be able to correctly parse these masks into separate lesion instances in order to enable downstream applications such as lesion tracking and characterization. For the lack of a better alternative, 3D connected component analysis is often used for this task, though it does not always work, especially in the presence of confluent lesions. In this paper, we propose a new method for parsing 3D binary lesion masks and an approach to evaluating its performance. We show that our method outperforms 3D connected component analysis on a large collection of annotated portal-venous phase studies.
Enhancement of MRI-based Signal-to-Noise Ratio with Noise Scrambling

The noise scrambling technology improves the Spurious-free Dynamic Range (SFDR) of the sampled signal in the electronic field. In this paper, noise scrambling is introduced into the SNR improvement of the magnetic resonance imaging (MRI) receiving signal for the first time. Firstly, the SinC signal is used to simulate the MRI receiving signal for simulation. When the jitter noise amplitude reaches about 48 LSB, the signal-to-noise ratio (SNR) reaches 107.59 dB, which is about 3.3 dB higher than when there is no scrambling. The real MRI k-space data is used as the analog input signal of the simulation model. After the analog signal is processed by the simulation model, a new group of k-space data will output. Compared with the SNR of the reconstructed image from the real k-space data, the SNR of the reconstructed image from the new k-space data processed by simulation model increases by about 10%. Through the simulation experiments in this paper, the noise scrambling technology can indeed improve the SNR of the MRI receiving signal.
Automated Animal Tracking for Behavioral Experiments

Animal behavior experiment is essential in neuroscience fields. In order to analyze the trajectory of moving animals using machines instead of manual work in daily experiments, we developed a Matlab algorithm for automatic animal tracking in videos of behavioral experiments. First, the background is extracted using binarization and morphological operations, then the animal is detected and tracked by using the Kalman filter, and finally the motion trajectory and some motion parameters are obtained. We used the algorithm to delineate the motion trajectories of Parkinsonian rats before and during & after cortical stimulation, for evaluation of the efficacy of the cortical stimulation for Parkinson's disease treatment. The results suggest that the developed algorithm is very helpful in analyzing behavioral experiments, as it reduces the heavy manual workload.
A Comparison of Different U-Net Models for Segmentation of Overlapping Organoids

The interest in automatically analyzing biomedical images increased in the past years, as an accurate localization and segmentation of organoids can help with the early detection of malignancies and predict diseases, such as cancer. The morphometric appearances of these images and the high level of overlapping in the organoids make the segmentation task challenging. This paper studies a simple U-Net and also proposes a double U-Net model with a shared encoder and two decoders, one for binary segmentation of the mask and one for the multi-class segmentation of overlaps. A significant addition to the U-Net is the residual-atrous skip connections, which reduce the semantic gap between the encoder and the decoder. The issue of high imbalance between the classes is addressed using a combination between Focal Loss and Focal Tversky Loss, which significantly improved the performance of the model. Ten networks were trained on more than 20,000 crop images with overlapping and non-overlapping organoids and obtained promising results. When tested on 88 new images, the final models achieved an F1 score of 0.83 for the mask channel and 0.43 for the overlapping channel. The Jaccard Index was 0.72 for the mask and 0.34 for the overlap. To the authors’ knowledge, there exists no work in the literature that explores the detection and segmentation of overlapping objects in biomedical images, especially segmenting overlapping organoids, which also shows the novelty of this work.
Facial Features Extraction and Clustering with Machine Learning

Computer vision is always one of the most popular topics in machine learning, and face recognition is one of the essential parts of computer vision studies. Today, most studies focused on locating and distinguishing faces. In contrast, this paper focuses more on interpreting components of faces or facial features. Instead of using intentional high variance images to train robust models that fit multiple real-world situations, the study uses standardized photos from Chicago Face Database (CFD). Images are preprocessed to fit the transfer learning model using a slightly adjusted version of The Visual Geometry Group, 16 layers version model (VGG 16) for features extraction. Then data is again processed using Principal Component Analysis (PCA) for computational efficiency and clustered using the K-means algorithm with k election based on common knowledge. The results for flat facial features such as eyes and mouths show an effective clustering. In contrast, confusing results exist in clustering features such as noses and face shapes, which may require higher dimensional data for satisfying clustering. The resulting dataset consists of the name of each face image and its facial features labels, which can be used for further study relationships between facial features and races and genders. It can also be used for face generation programs based on selected facial features.
Pedestrian Abnormal U-turn Behavior Recognition Model Based on Baidu AI

With the economic development and social progress, in order to meet people's increasing travel or entertainment needs, there are more and more crowd gathering places, such as traffic stations, venues, shopping malls and stadiums. Crowd gathering places contain many cross channels, where the anisotropy of crowd behavior is obvious, which is an important part of crowd safety monitoring. In this paper, the abnormal behavior model of the crowd in the intersection of public places is established, the abnormal U-turn behavior of pedestrians in this scene is analyzed, and the human centroid model is proposed by calling the relevant Baidu AI human key point recognition module interface. Pedestrian U-turn behavior in video is recognized by continuous frame images, and a pedestrian U-turn behavior recognition model based on Baidu AI is constructed. This study can provide targeted protection for high-risk areas in large crowd gathering places, and inform security personnel in time after identifying the abnormal behavior of pedestrians, which is helpful for man-machine cooperation to ensure the safety of pedestrians and reduce the accident rate.
Hybrid Genetic Algorithms for Large Scale Optimization Problem

In the fields, such as engineering system and multiple tasks cooperation, many real-world problems can be modeled by colored traveling salesmen problem (CTSP). Since CTSP has been proved belong to the NP-hard, the intelligent algorithms, such as genetic algorithm (GA), have been used for solving small or median scale cases where the number of cities is less than 1000. This paper uses three improved hybrid genetic algorithms, including GA with greedy algorithm (GAG), hill-climbing GA (HCGA), and simulated annealing GA (SAGA), to solve large scale CTSP, where three algorithms greedy algorithm, hill-climbing and simulated annealing are used to improve GA. The experiments show that hybrid genetic algorithms demonstrate an improvement over GA in term of solution quality.
Research on Recognition Method of Floating Objects on Water Surface Based on improved ResNet

With the continuous population growth and rapid economic development, problems such as scarcity and pollution of freshwater resources are increasingly emerging. Floating objects on the water surface are one of the causes of water pollution, so the research on the identification algorithm of floating objects is of great significance. To further improve the accuracy of the identification of floating objects on the water surface, this paper proposes a DS-ResNet model based on the improved ResNet. Compared to previous work, DS-ResNet employs several innovations to improve training and testing speed while also increasing recognition accuracy. We established an actual data sample set in the field of rivers and lakes, and a modified ResNet model was constructed by Tensorflow for training. The experimental results show that the training cycle time of DS-ResNet is reduced by about 7% compared with the traditional ResNet, and the recognition accuracy of common targets reaches 98%.
Research on YOLOV5 Electric Workers Wear Detection Model Based on Channel Pruning and Attention Fusion

At present, target detection based on deep learning has become a trend. The large model in target detection has high detection accuracy, but with the huge network depth and width, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, Firstly, we build a feature extraction network based on yolov5, and the CBAM (Convolutional Block Attention Module) attention structure are used to improve the detection accuracy. Finally, we force iterative channel-level pruning to the detection network to guide model Sparse training of BatchNormalization (BN) layers. The results show that the proposed method can improve the accuracy of target detection, While keeping a high average detection accuracy, the calculation amount of the YOLOv5m model is reduced by 39%, the inference speed on GPU 40% increase, which meets the requirements of real-time detection.
An Intelligent Cockpit System HMI Engine Based on COMO

ICS (Intelligent Cockpit System) is a Human-Machine Interface (HMI) technology that integrates In-Vehicle Infotainment (IVI), Head Up Display (HUD), and Navigation (NAVI). The HMI technology stack in ICS involves engineering human-machine interaction creativity, component-based graphics systems and vehicle-level hardware system, etc. The HMI engine we developed is a middleware that implements human-machine interaction computing in in-vehicle electronic equipment, and the engine is also a software stack for graphics computing, scheduling management of computing devices in the cockpit and software runtime functions, it operates the display hardware through OpenGL. This paper introduces a COMO-based ICS HMI technology with functional safety SOA architecture. With the support of COMO RPC, devices are abstracted as services and integrated together. Under the premise of ensuring functional safety, it has a variety of ICS-oriented 2D, 3D controls with running state and design state, suitable for creative people and automotive engineers to work together.
Simplifying Access Policy Descriptions with Level Relationships between Attributes

Blockchain combined with distributed file system for storing data usually encounters the challenges of sensitive data protection and secure access control, and ABE can achieve access control to confidential data. However, an excessive number of attributes in an access policy can lead to a very complex access policy description. To solve the problem of complex access policy descriptions, this paper proposes an access control scheme that utilizes the level relationship between attributes to simplify the access policy descriptions and combines blockchain to protect the security of encrypted data. This scheme encrypts data through CP-ABE. When formulating the access policy, the level relationship between the attributes involved in the access policy is analyzed and the attribute level structure is constructed to simplify the access policy descriptions. A smart contract is constructed to store the data hash and attribute ciphertext in the blockchain to avoid malicious tampering of data. Build a cross-regional first-time ID card claiming consortium blockchain through FISCO BCOS and build IPFS cluster. Through experiments, the feasibility and effectiveness of the scheme are verified.
Neural Network Models Performance Analysis of Large-Scale Text Recognition∗

The continuous development of computer technology leads to booming image data and throws a tricky question to scholars about how to process these data intelligently. Luckily, it is a dream come true to the recognition of images with the help of progressive deep-learning technology. Nowadays, image recognition based on neural networks is widely used, and recognizing a large scale of text information is one of the critical applications. Therefore, this paper will first review the development history of image recognition technology and introduce the concept of the convolutional neural network model. After that, it will analyze the performance of multiple algorithms in recognizing a large amount of text information based on Reginal Convolutional Neural Network, Spatial Pyramid Pooling, Fast Region Convolutional Neural Network, and Faster Convolutional Neural Network. Last but not least, it also points out the prospect of the future development direction of the current image processing technology and its defections. Analysis shows that the biggest drawback of deep learning technology is its dependence on training data. More specifically, when the training data is incomplete, it will be hard for the network model to maintain its recognition accuracy, especially in large-scale text recognition. To further improve the image recognition technology, we should put the effort into constructing a deep neural network model, optimize the training data, reduce the model training parameters, and improve the model accuracy.
A generation method of dual adversarial camouflage

The object detection model based on DNNs can efficiently detect camouflaged objects that are difficult to find by human eyes, which puts forward new requirements for camouflaged performance. Researchers attack the object detection model by generating the adversarial camouflage, but the appearance of existing adversarial camouflage is abrupt and easily observed by human eyes. This paper proposes a dual adversarial camouflage generation method. The generation process is divided into two stages: in the first stage, seven kinds of corresponding camouflage textures are generated according to the scene features; In the second stage, the camouflage generated in the first stage is used as input to generate adversarial examples through GAN training. In the generation process, the texture difference between the adversarial examples and the input is constrained by perceptual loss. The attack performance and subjective and objective experiments verify that the dual adversarial camouflage can effectively attack both the object detection model and the human visual system.
A Privacy Set Intersection Algorithm Based on Batch Blind Signatures on Lattice∗

As an essential branch of secure multi-party computation, privacy set intersection, which is widely used in federated learning, federated query, and other fields, plays a critical role in privacy computation. This paper proposes a privacy set intersection protocol based on the batch blind signature on lattices. It can directly perform a blind signature on a set to obtain the signatures of elements. In the interaction, one party is the signing party, signing its own private set in general and blindly signing the other party's data. Both parties get the signature of the signer and finally obtain the set intersection by signature verification. Privacy can be protected by the blind signature and hash function. The scheme is resistant to quantum attacks, and only two signatures are required.
New Cryptocurrencies Framework

People are getting familiar with cryptocurrencies because of the rapid development of cryptography, and bitcoin, a traditional decentralized digital currency, becomes famous. Thus, it is necessary to establish a digital currency allocation framework. Two existing methods both share the same goal of reaching blockchain consensus; however, the processes are different: The proof of Work system is completely related to tasks, but the Proof of Stake system is related to tokens. Hence, service providers are more than glad to apply the Proof of Work theory after distinguishing the difference between these two systems; this system which does not have high limitations is more fair and balanced. To enhance the traditional Proof of Work system, Artificial Intelligence can properly help and make the new framework works more efficiently. AI model can pre-assign a trustworthy score via the IP address, and then it can take the responsibility to generate the puzzle for the qualification. After the model verifies the output, the trustworthy score can increase or decrease based on the performance. Finally, it can establish a loop from the trustworthy score to puzzle difficulty, and then back to the trustworthy score. Therefore, an AI assistant can accurately monitor the entire transaction process and ensure validation to be environmentally friendly.
Deep Learning-based Optimization of HDFS for Massive Geographic Image Storage

In today's era, a large number of geographical images cannot be stored properly due to their small memory and large number of characteristics. As HDFS has its limitations in storing small files, and in order to cope with the storage and reading needs of a large number of geographical images, a method is proposed to classify small files by means of a deep learning classifier, merge the classified images to establish an index, upload the metadata generated by the merger to a Redis cache database, upload the merged files to HDFS, and at the same time subsequently implement the reading of the files through the index file The merged files are then uploaded to HDFS, and the files are subsequently read from the index file.
Data augmentation for speaker verification

Data augmentation is a hot issue in neural network training. In this paper, we investigate data augmentation for speaker recognition, and we propose two data augmentation methods to enhance the performance of neural network system. One of which is spectral augmentation. Spectral augmentation is a newly proposed data augmentation method which applied to speech recognition and got state-of-the-art performance, by masking blocks of frequency channels(F-mask), and/or by masking blocks of time steps(T-mask). We also investigate the method of speed perturbation, which adjusts the time-scale of a given audio signal without altering its pitch content. Experimental results show that both two methods can boost the performance. By combining TF-mask with speed perturbation, we can obtain more than 5.2% and 8.7% relative improvements over the baseline systems in the Vox-H and WX tasks.
Deep reinforcement learning and imitation learning based on VizDoom

Reinforcement learning is a field of machine learning that focuses on intelligent agents, primarily the concept of what actions an intelligent agent takes in the environment to maximize cumulative reward. In environments where rewards are scarce, a manual approach is necessary. However, manually designing the reward function to meet the desired behavior can be very complicated. A very useful solution is Imitation Learning (IL). This paper proposes two reinforcement learning algorithms for the basic scene of the VizDoom video game, and uses IL to improve the performance of one of the models.
Research on An Encryption Method Combining RSA and Hill Cipher

As an important algorithm in cryptography, RSA (generated by Rivest, Shamir and Adelman) cipher system bases on Integer Factor Problem and has outstanding performance and security. Hence, it is widely used in data transportation, network communication and plenty of fields+. over roughly 40 years. On the other hand, based on knowledge of linear algebra, Hill Cipher is a type of classical and efficient encryption system firstly generated by Lester S. Hill in 1929. However, in practical applications, RSA has high computational complexity to encrypt data and spent a lot of time and computing resources, hence RSA is suitable to encrypt a small amount of messages. Because of the essence of Hill Cipher is linear transformation, the processes of encryption and decryption is simple and quick for modern electronic equipment. However, the system of Hill Cipher is apt to be broken for modern equipment as well. Now, there is a method called RSA-Signature-Hill, combing two algorithms and designing a safe encrypting scheme with high running speed. The algorithm combines the advantages of RSA and Hill Cipher and it is a typical scheme of improving efficiency of the entire encrypting system.
Design and implementation of AIoT-based non-contact interaction system

The non-contact interaction proposed in this paper is a new interaction method based on artificial intelligence Internet of Things (AIoT), which can effectively reduce the risk of epidemic transmission and reduce the number of trips back and forth during work in factory production lines, thus improving production efficiency. This paper presents a contactless interaction system based on computer vision and Internet of Things technology, with a visual programming platform and a front-end display platform for WeChat applets, effectively reducing the user's threshold. The system transmits the images acquired by ESP32-CAM to the cloud server, which extracts the gesture features of the images and identifies the corresponding gestures, then returns the commands corresponding to the gestures to ESP32, responsible for the master control. The ESP32 master accesses the database to obtain the corresponding actions according to the acquired commands and then executes the actions to realize a round of interaction.
A Small Sticker is Enough: Spoofing Face Recognition Systems via Small Stickers

Face recognition systems are widely used in various security-crucial applications such as financial payments, device unlocking, and personnel access. With the rapid development of deep learning, face recognition systems nowadays are usually based on deep neural networks (DNNs). However, recent studies have shown that DNN-based face recognition algorithms are vulnerable to adversarial example attacks and thus may suffer from real-world threats. In this paper, we propose Adv-Sticker, a physical adversarial attack against face recognition systems leveraging a small printed sticker. By optimizing both the attack region and the adversarial sticker, we manage to reduce the size of the sticker to 3*3 cm and make it robust across various environmental conditions. Evaluation on four commonly-used face recognition algorithms (Facenet, Mobile-Facenet, Ir152, and Irse50) shows that Adv-Sticker can physically spoof face recognition systems with an overall attack success rate of 96.9% for the dodging attack, and 70.1% for the impersonation attack.