Reviewerly/exHarmony huggingface dataset:
Improved YOLO-based Pedestrian Detection Algorithm

When the YOLO algorithm is directly used for pedestrian detection, it suffers from the problem that the algorithm is complex and not suitable for deployment in mobile terminal. Therefore, an improved YOLO-based pedestrian detection algorithm is proposed. The algorithm is based on YOLOv5n by introducing CBAM attention mechanism, which makes the network pay more attention to the feature information of channel and space, and is conducive to extracting the important features of the target. Secondly, the lightweight CARAFE module is selected for the up-sampling module on the neck layer, which can reduce the complexity of the algorithm and obtain a larger perceptual field to improve the learning ability of the network. Finally, the Focal-EIoU loss function is chosen to solve the problem of inaccurate bounding box regression due to variable human morphology in pedestrian detection tasks. Experimental results show that the mAP value of this algorithm on INRIA Person Dataset reaches 98.4%, which is 2.4% higher than the original algorithm. The size of the improved algorithm model is less than 4 MB, which improves the detection performance while maintaining a small scale, and is more conducive to deployment on mobile terminal.
Lighter Transformer for Online Action Detection

Online action detection aims to determine what is happening without waiting for the end of action. Most recent works have taken advantage of the excellent modeling capabilities of transformers to capture temporal contextual information. The basic transformer models the global relation of input tokens, and the computation and the number of parameters are huge. Though global relation is important for online action detection, the dominant factor should be the local dependencies among consecutive frames. In this paper, we improve the basic transformer for online action detection in three folds. Firstly, we replace the fully connected projection of an input token in standard self-attention block in transformers with a convolutional layer, which encodes local motion information among consecutive frames explicitly. As the modeling capacity is improved with both global and local correlation, less self-attention blocks are needed, which reduces computation and parameters. Secondly, we replace similarity computation between each pair of tokens with only computation similarities between the current frame and historical frames. Moreover, the distribution of such similarities is guided by a Gaussian function centering at current frame. A more convolutional layer with stride of two is added between two attention blocks to reduce the number of tokens in the next attention block to half. Therefore, the number of parameters and computation cost decrease. Thirdly, we design a decoder to retrieval the information of action categories by learning a query token for each action category. The final decision is made by combining the information from the encoder and decoder. We see that the proposed method achieves significant improvements on two benchmark datasets, THUMOS’14 and TVSeries datasets.
SAW: A Robust Watermarking based on Visual Structure Measurement and Spatial-Channel Attention

Deep image watermarking still faces the image quality degeneration of the watermarked and recovered watermark. A robust watermarking method, SAW is proposed to embed watermarks in an imperceivable way and improve the recovered watermark. It uses visual structural measurement, which provides better image quality supervision, to guide the watermark embedding process. In the watermark recovery, SAW introduces the spatial-channel attention module, which focuses on watermark extraction. Compared with UDH, the experimental results show that the method remains robust and obtains noticeable image quality improvement of both the watermarked image and the watermark.
Development of Landmark-based Facial Asymmetry Evaluation

This paper presents a novel development of a real-time evaluation system for facial asymmetry. Using this tool, it is expected possibly to assess the severity of facial asymmetry based on local and global comparisons of facial components. While the local one measures distances between the landmarks inside each facial region, the global one focuses on the overall difference of all facial regions about the sagittal plane. This reported preliminary work is focused on assessing the suitability of the existing image landmark detection methods for a real-time evaluation. Three commonly used deep learning-based methodologies have been implemented and tested in order to identify robust facial landmark detection under various challenging conditions. It is hypothesized, that the proposed system will be able to provide a more accurate measurement of facial asymmetry. The key is the proposed use of the geodesic distance calculated based on the geometry of human faces, with the help of the state-of-art depth camera.
Learning Path Planning Based on Knowledge Graph on MOOC Platform

Based on the edX platform, this paper designs the knowledge graph model by combining expert method and reference method, selects Neo4j as the knowledge storage database of knowledge graph; selects Echarts as the visualization tool of Neo4j, and finally realizes the visual search and display of knowledge points on MOOC platform, and gives learning feedback and update according to the learning situation of learners.When planning the learning path through the designed knowledge graph, Ripplenet algorithm is introduced to enrich the diversity of courses in the learning path planning between courses.Professional learning path planning is provided for learners to improve the learning efficiency of online learning.
Application of Video Surveillance Intelligent Analysis System Based on KNN Algorithm

Video surveillance intelligent analysis is to use routers or access technology to realize real-time information collection, processing and control of video storage streams and front-end equipment under the condition of limited network bandwidth resources. Therefore, this paper studies the application of video surveillance intelligent analysis system based on KNN algorithm. The research on intelligent analysis of video surveillance is to solve the current problems in network security, multimedia information and so on. This paper mainly uses the experimental method and the comparison method to describe the video surveillance intelligent analysis system based on the KNN algorithm in detail. The experimental data shows that when the bit rate is controlled at 18, the decoding rate changes with the accelerator.
Detection of Pancreatic Tumor from Computer Tomography Images Using 3D Convolutional Neural Network

Pancreatic cancer (PC) is one of the most challenging chronic conditions in recent years. Pancreas cancer is a highly malignant tumor in the digestive tract. Basically, dealing with this cancer variant was difficult in the early stages, both in screening and treatment. Manual disease detection method is error prone and time consuming. As technology advances, the most advanced technology, deep learning (DL), provides potential applications to healthcare domain. Deep learning (DL)-based healthcare equipment provides solution for the treatment of various diseases. This research work focuses on the efficient segmentation and classification of pancreatic tumor using DL model with the following stages of process: (a) The MRI images collected from reputed hospitals are used for the diagnosis process, (b) initially, the raw images are pre-processed by using boosted anisotropic diffusion filter (BADF) and contrast limited adaptive histogram equalization (CLAHE) methods, (c) further, the segmentation process isolates the nodules using fuzzy c-mean-clustering (DMFCM) method, (d) the features are then extracted and selected by using HHO-based SVM, and (e) finally, the CNN-based classification methods are assessed by using the following metrics: Dice Coefficient (DC), Matthews Correlation Coefficient (MCC), accuracy, and Jaccard Coefficient (JC).
Personalized Internet Celebrity Check-In Recommendation System Based on Hybrid Recommendation Algorithm

Personalized Internet Celebrity Check-in Recommendation System Based on Hybrid Algorithm, aims at providing users with super quality, real and effective Internet celebrity check-in information. The system can offer users with personalized online celebrity check-in recommendation services based on disparate user characteristics, integrate lots of information that including essential information of check-in places, travel strategy, user experience and evaluation, along with corresponding travel notes, strategies and other recommended contents of check-in places. Through the hybrid recommendation algorithm, the system will complete user’s personalized and precise recommendation in view of the user’s feedback information, which will greatly improve travel efficiency of user’s and the satisfaction of playing progress of the Internet celebrity check-in.
Methods of Image Enhancement in Low-light Situations

Poor visibility not only affects the consumer photography but also computer vision application. Low-light image enhancement explains the image details or perception of information in images for human viewers and understanding of imagery. Using the pixel values existing in the digital image, the details of low-light images are boosted under low illumination. The primary purpose of enhancing low-vision image is to increase visibility of the image. Dealing with noise is a significant challenge while enhancing. Previous studies have offered a variety of approaches for enhancing low-vision images. Low contrast, low brightness, severe noise, dark colors are the issues with image enhancing in low-light situations. Monochromatic atmospheric scattering model is used to form proposed absorption light scattering model. The concealed details and hidden features were reproduced with sufficient and homogeneous illumination. This strategy will help to achieve high result in terms of structure, details and local feature similarity. Here, some mathematical formulas were used to produce brightest image, which is produced from monochromatic ASM and absorption light scattering model. When we compare absorption light scattering method with other techniques, it will produce better result. This strategy highlights major details of absorption light scattering model. This method will produce better result in terms of contrast, brightness, sharpness, etc.
Prediction of Osteoporosis Disease Using Enhanced Elman Recurrent Neural Network with Bacterial Colony Optimization

Osteoporosis is a disease where a lack of calcium results in a loss of bone mass and weakening of the bones. The prediction of osteoporosis is a very significant and challenging task for diagnosis. In this present article, an efficient prediction methodology developed using Elman recurrent neural network (ERNN) with bacterial colony optimization (BCO) named BCO-ERNN. The BCO is used to train the ERNN to obtain high prediction accuracy and escape local optima. The effectiveness of the proposed BCO-ERNN is compared with other prediction algorithms and tested using three kinds of osteoporosis datasets. When compared to other predictors, the experimental findings revealed that the suggested BCO-ERNN achieved good accuracy and fast convergence.
Face Mask and Social Distance Detection Using Deep Learning Models

Coronavirus disease 2019, also known as COVID-19, has become a global threat. In dealing with this global problem of the COVID-19 pandemic, flattening the curve of COVID-19 cases will be difficult unless residents take steps to stem the spread of this virus. The most critical measures during this epidemic are wearing a mask and maintaining social distance in crowded places. This research aims to propose the efficient deep learning model among the few of the most used models in the literature to detect face mask and social distancing violations. The deep learning models MobileNet, You Only Look Once (YOLO), and ResNet-50 are implemented, and their results are compared to ascertain people in a frame and scan for social distance by calculating the Euclidean/Manhattan distance between the bounding boxes generated. Similarly, the proposed model determines if the two people are within 6 feet of the frame; if not, a warning is issued. In this research, Face Mask Lite, Real-World Masked Face, MaskedFace-Net, and Face Mask Detection datasets are considered with 90:10, 80:20, 70:30, and 60:40 ratios to train and test these deep learning models. These deep learning models are processed in Anaconda + Python 3.x (3.8 or older) environment. The proposed model can be useful in hospitals and clinics, airports, shops and workplaces, and other crowded places where social distancing and face masks are required to avert the spread of not only COVID-19 but also other communicable diseases as well.
Analysis of Precision Vectors for Ising-Based Linear Regression

Quantum computing has been much attention as one of the new computational principles. In particular, annealing machines that use the Ising model of statistical mechanics are emerging and feasible next-generation computational technology. Annealing machines can solve combinatorial optimization problems that have been considered difficult to solve in classical computing principles. Currently, Ising-based algorithms are being vigorously developed to perform various applications such as machine learning by solving them as combinatorial optimization problems. On the other hand, the amount of data, such as sensor data and simulation data, used in machine learning applications is drastically increasing. It becomes difficult to handle large amounts of data. In particular, since the number of qubits of annealing machines is limited, Ising-based algorithms independent of data size are strongly required. This paper focuses on Ising-based linear regression that utilizes a precision vector instead of each data element of target data. Although the use of a precision vector is a key point that can reduce the number of qubits against the amount of data, detail such as how many elements of a precision vector is necessary and how these elements are set to be is not clarified yet. This paper discusses the characteristics of a precision vector through performance evaluation of Ising-based linear regression with a practical data set in empirical ways. The experimental results show that a proper precision vector considering the input data set can improve the quality of the linear regression.
Optimizing Depthwise Convolutions on ARMv8 Architecture

Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for classic convolutions so that direct algorithms are often more efficient than indirect ones (matrix multiplication-, Winograd-, and FFT-based convolutions) with additional memory accesses. However, the existing direct implementations of depthwise convolutions on ARMv8 architectures feature a bad trade-off between register-level reuse of different tensors, which usually leads to sub-optimal performance. In this paper, we propose a new direct implementation of depthwise convolutions by means of implicit padding, register tiling, etc. Compared to the existing ones, our new implementations can incur much less communication overhead between registers and cache. Experimental results on two ARMv8 CPUs show that our implementation can averagely deliver 4.88 $$\times $$ performance improvement over the existing direct ones in open-source libraries.
A Blockchain-Based Certificateless Aggregate Signature Scheme for Fog-Enabled Smart Grid Environment

The smart grid-based power grid systems face many challenges like-higher computational power requirements, higher latency, security and privacy issues, and single point of failure. In the power grid systems, multiple distribution centers are responsible for supplying electricity in an area. Thus, to address the issues such as single point of failure and higher latency, this paper integrates fog computing and blockchain into the smart grid utilizing distribution centers as blockchain peers and proposes a distributed smart grid environment. Moreover, to address the security and privacy issues, the existing signature schemes face issues like-certificate management, key escrow, higher computational cost, etc. Therefore, this paper employs a certificateless approach and proposes a blockchain-based certificateless aggregate signature scheme that provides authentication, integrity, and privacy in the proposed smart grid environment. Next, a thorough security analysis has been done to prove the security strength of proposed scheme against different types of attackers and attacks. Finally, the performance analysis based on Hyperledger Fabric and cryptography libraries proves the computational and communication efficiency of the proposed scheme.
Superpixel Consistency Saliency Map Generation for Weakly Supervised Semantic Segmentation of Remote Sensing Images

The weakly supervised semantic segmentation (WSSS) method aims to assign semantic labels to each image pixel from weak (image-level) instead of strong (pixel-level) labels, which can greatly reduce human labor costs. However, there are some problems in WSSS of remote sensing images such as how to locate labels accurately, and how to get precise segmentation edges. To address these issues, we propose a novel framework directly transferring the scene classification model to perform semantic segmentation. We first train a multi-label scene classification network as the encoder to obtain the pre-trained model, then the feature learned by the model is transferred to the decoder. Different from other methods, we propose a saliency map generator instead of the Class Activation Map for more accurate location information by making pixels belonging to the same class lie close together while different classes are separated in feature space. Meanwhile, we take the superpixel patch as processing unit to provide precise boundary inhibition for the saliency map. To assign semantic labels for each patch, combined with extracted salient region, we propose a module responsible for exploiting the consistency of spatial and semantic similarity between different patches. Finally, we incorporate the above two modules to supervise the training process of the decoder without generating pseudo labels as most methods do, thus simplifying the training process. Experimental results show that our method outperforms other weakly supervised approaches on DLRSD and WHDLD datasets with at least a 3% improvement on mean intersection over union.
Explainability as a Method for Learning From Computers

Humans have rich experience applying linear models and logical thinking, but only experts understand the behaviour of non-linear systems. However, the deep neural network (DNN) implementation of non-linear systems outperforms optimal linear models. Therefore, the forward DNN (the pattern recognition system in this paper) attracts attention to the necessity of interpreting the results obtained by DNN. To preserve the high performance of DNN, we focus on a post hoc explanation; this approach means building an explainable model for the decision obtained by the black box. To avoid the interpretation of a set of millions of non-linear functions, we divide DNN into two parts: the feature extractor and the classifier. Following that, we argue for a specific interpretation of each of them. While for classifiers, we have several suitable explainable models (and we decided on the fuzzy logical function), we believe that feature interpretation is a creative scientific activity corresponding to the usual research. The paper presents a tool to help researchers and users understand extracted features not necessarily known in the specific application domain. Explanation of the new features is the way of learning from computers.
Research on Korean “Long-Before-Short” Preference from the Perspective of Dependency Distance

The word order of different language types has different syntactic arrangement. Under the framework of dependency syntax, the change of constituent position in a sentence directly affects the size of dependency distance. Based on the general rule of word order in different language types, this paper makes a quantitative analysis of the reasons from the perspective of dependency distance. It is found that: First, the general rule that SVO has a smaller mean dependency distance than SOV cannot be obtained when considering the additional constituent of sentences. Second, in the dative-accusative construction of Korean, the “long-before-short” order is more preferred. Finally, the mean dependency distance has nothing to do with the specific constituents before the root verb, but only with its ordering rules.
Certifiably Robust Neural ODE With Learning-Based Barrier Function

Neural Ordinary Differential Equations (ODEs) have gained traction in many applications. While recent studies have focused on empirically increasing the robustness of neural ODEs against natural or adversarial attacks, certified robustness is still lacking. In this work, we propose a framework for training a neural ODE using barrier functions and demonstrate improved robustness for classification problems. We further provide the first generalization guarantee of robustness against adversarial attacks using a wait-and-judge scenario approach.
Safe Distance Monitoring of Live Equipment Based Upon Instance Segmentation and Pseudo-LiDAR

Electrocution accidents caused by operation and maintenance personnel and high-voltage live equipment frequently occur in substations. Although many cameras have been installed for the surveillance of critical energized equipment, they cannot complete the monitoring of safe distance. In addition, the current research based on vision or LiDAR suffers from computationally intensive, lack of real-time or expensive equipment. In order to meet the low cost, easy maintenance and real-time requirements of substation safe distance monitoring, a monocular vision method based on 2D-3D fusion is proposed in this study. Specifically, instance segmentation, depth estimation, depth reconstruction, and back projection transformation are used to predict the 3D distance of objects in 2D images. The work is mainly implemented in two aspects: a) To obtain the high-quality masks of Power Transformers and Person, SOLOv2 is optimized on three aspects: feature extraction, feature fusion and ability to cope with object geometric transformation. b) Pseudo-LiDAR output from RGB images via depth estimation and back projection techniques. Subsequently, combined with the point cloud of the object and the actual-to-virtual conversion ratio, the actual distance between Power Transformer and Person can be calculated. Experimental results show that the maximum error of ranging is within 9%, with an average error rate of 5.34% on the Power Transformer-Person dataset. It can be seen that the proposed method has achieved good ranging effects, and can realize the automatic measurement of the safe distance of Power Transformer.