Reviewerly/exHarmony huggingface dataset:
Spatial Graph Convolutional and Temporal Involution Network for Skeleton-based Action Recognition

research-article Spatial Graph Convolutional and Temporal Involution Network for Skeleton-based Action Recognition Share on Authors: Huifan Wan Chengdu University of Information Technology, China Chengdu University of Information Technology, ChinaView Profile , Guanghui Pan Chengdu University of Information Technology, China Chengdu University of Information Technology, ChinaView Profile , Yu Chen Chengdu University of Information Technology, China Chengdu University of Information Technology, ChinaView Profile , Danni Ding Chengdu University of Information Technology, China Chengdu University of Information Technology, ChinaView Profile , Maoyang Zou Chengdu University of Information Technology, China Chengdu University of Information Technology, ChinaView Profile Authors Info & Claims ACM TURC 2021: ACM Turing Award Celebration Conference - China ( ACM TURC 2021)July 2021 Pages 204–209https://doi.org/10.1145/3472634.3474073Online:02 October 2021Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Privacy Preserving Machine Learning for Malicious URL Detection.

Phishing remains the most prominent attack causing loss of billions of dollars for organizations and users every year. Attackers use phishing to obtain sensitive information from users, install malware and obtain control over their systems. Currently, web browsers counter this attack using blacklisting method, however it fails to detect newly generated malicious websites, hence ineffective. In the recent times, machine learning based URL classification techniques where trained models are deployed on server side, emerged as an effective solution to detect new malicious URLs and provide it as a service to the user. While malicious URL detection continues to be a problem, another potential concern is the user’s query privacy (when offered as malicious URL detection as a service, where server can learn about the URL) . Hence to address the query privacy, we propose privacy enabled malicious URL detection.
Video Abnormal Event Detection and Location Based on Spatial Attention

research-article Video Abnormal Event Detection and Location Based on Spatial Attention Share on Authors: Zhenzhou Guo Gansu University of Political Science and Law, China Gansu University of Political Science and Law, ChinaView Profile , Guangli Wu Gansu University of Political Science and Law and Northwest Minzu University, China Gansu University of Political Science and Law and Northwest Minzu University, ChinaView Profile , Leiting Li Gansu University of Political Science and Law, China Gansu University of Political Science and Law, ChinaView Profile Authors Info & Claims ICGSP 2021: 2021 the 5th International Conference on Graphics and Signal ProcessingJune 2021 Pages 76–81https://doi.org/10.1145/3474906.3474917Online:06 October 2021Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Ordered Cipher Technology (OCT) for the Secured Network Transmission

Cipher refer to certain explicit set of instructions and are used to secure a network. These security are done through various Secure Sockets Layers, Transport Layer Security, and so on. It also provisions about communicating secure data during the use of various protocols (like, HTTPS, FTPS, SMTP, etc). In the ordered cipher technique (OCT), we have applied the permutation technique to the bit streams in such a way that the input binary string breakdowns into acceptable-sized small sub-blocks (8 – 8192 bits), build a secret key algorithm to perform bit shuffle recursively for a finite number of times on each chunk, generate encrypted binary stream. The bit shuffle is symmetric in nature, i.e. bit shuffle is performed on each input chunk for n number of times the original stream is regenerated. Therefore, the session keys are generated through the combination of these sub blocks of bits. To get the encrypted chunk, perform the bit shuffle operation to each input chunk for (n-m) times. Likewise, the decoding is performed using the same process i.e. after performing the bit shuffle operation to each encrypted chunk for ‘m’ times. The proposed model is compared with the conventional and industrially accepted techniques.
Original Code for Encrypting and Decrypting Caesar Ciphers

The original code set forth in this paper allows users to both decrypt received messages and send encrypted messages to others using Caesar Ciphers. Please note that this operation is performed using Java. Examples of the decryption and encryption processes (with their accompanying original code) are demonstrated in this paper.
Multi-Stage Enhanced Rain Image Restoration Network

The restoration task of rain images is beneficial to the further development of computer vision tasks. However, recent rain removal methods have difficulty coping with dense rain streaks. In this paper, we propose a Multi-stage Enhanced Rain Image Restoration Network (MERNet) for the task of rain removal. Our main idea is to design a three-stage network architecture, which makes full use of the enhanced information of the previous stage and complements the information of the next stage through transmission step by step. We give consideration to information enhancement at the same time of information transmission, fully improve the information utilization rate and the overall network effect.
KNN (K-Nearby Neighbor) for identifying agricultural land

Abstract Agricultural land in Malang Regency is a vital element because most of the population are farmers and working in the agricultural sector. The agricultural land area of Malang Regency is around 15.74% (49,593 hectares) which is paddy fields and 31.31% (98,641 hectares) are fields. Not all types of crops can flourish on each of these farms because every land and plant has different characteristics. Therefore, to help determine the type of food crops that are suitable for a land, a method system for identifying land for food crop cultivation using the KNN (K-Nearest Neighbor) method was developed. With the existence of a land identification decision system for food crop cultivation, this method approach makes it easier to determine which plants are in accordance with the conditions of the land, in order to increase agricultural productivity in the southern part of Malang Regency.
Adaptive Spatio-Temporal Convolutional Network for Video Deblurring

Video deblurring is a challenging task due to the spatially variant blur caused by camera shake, object motions, and depth variations, etc. However, for the blurred area in the current video frame, the corresponding pixels of its neighboring video frames are often clear. Based on this observation, we propose an Adaptive Spatio-Temporal Convolutional Network (ASTCN) to compensate for blurry pixels in the current frame by using clear pixels in adjacent frames. In order to use the spatial information of adjacent frames in the current frame, the video frames must be aligned first. Existing methods usually estimate optical flow in the blurry video to align consecutive frames. However, they tend to generate artifacts when the estimated optical flow is not accurate. In order to overcome the limitations of optical flow estimation, we use deformable convolution in ASTCN to complete multi-scale adjacent frame alignment at the feature level. Secondly, we propose an adaptive spatio-temporal feature fusion module based on dynamic filters, which uses the features of the clear regions of adjacent frames to perform adaptive feature transformation on the intermediate frame to remove the blur. Extensive experimental results show that the proposed algorithm has shown superior performance on the benchmark datasets as well as real-world videos.
Unpaired MR Image Homogenisation by Disentangled Representations and Its Uncertainty

Inter-scanner and inter-protocol differences in MRI datasets are known to induce significant quantification variability. Hence data homogenisation is crucial for a reliable combination of data or observations from different sources. Existing homogenisation methods rely on pairs of images to learn a mapping from a source domain to a reference domain. In real-world, we only have access to unpaired data from the source and reference domains. In this paper, we successfully address this scenario by proposing an unsupervised image-to-image translation framework which models the complex mapping by disentangling the image space into a common content space and a scanner-specific one. We perform image quality enhancement among two MR scanners, enriching the structural information and reducing noise level. We evaluate our method on both healthy controls and multiple sclerosis (MS) cohorts and have seen both visual and quantitative improvement over state-of-the-art GAN-based methods while retaining regions of diagnostic importance such as lesions. In addition, for the first time, we quantify the uncertainty in the unsupervised homogenisation pipeline to enhance the interpretability. Codes are available: https://github.com/hongweilibran/Multi-modal-medical-image-synthesis.
Application of Detection and Correlation Filter in Long-Term Vehicle Tracking

An elevated tracking algorithm is proposed to deal with the tracking bounding box loss, which is due to fast discriminative scale space tracker algorithm (fDSST) when the vehicle is in complete occlusion and reappears after complete disappearance. Based on fDSST, the maximum response value, the average peak correlation energy, and the peak sidelobe ratio are used as the parameters to check whether the previous tracking is correct. Next, when in complete disappearance, the vehicle is redetected by SSD algorithm and then automatically matched by SURF algorithm, that is how the disappeared vehicle can continue to be framed and tracked. Through the combination of tracking and detection, the problem of complete occlusion of the tracking vehicle can be solved, and the tracking of the vehicle that appears again after disappearing can be continued. Finally, through the actual captured image sequences, the accuracy of the elevated algorithm is verified and compared with previous ones. Based on experimental analysis, it has been shown that when in complete occlusion, long-term disappearance, and scale change, better results can be received in the long-term tracking of vehicles through deep learning detection and filtering.
Learning to Play Hard Exploration Games Using Graph-Guided Self-Navigation

This work considers the problem of deep reinforcement learning (RL) with long time dependencies and sparse rewards, as are found in many hard exploration games. A graph-based representation is proposed to allow an agent to perform self-navigation for environmental exploration. The graph representation not only effectively models the environment structure, but also efficiently traces the agent state changes and the corresponding actions. By encouraging the agent to earn a new influence-based curiosity reward for new game observations, the whole exploration task is divided into sub-tasks, which are effectively solved using a unified deep RL model. Experimental evaluations on hard exploration Atari Games demonstrate the effectiveness of the proposed method. The source code and learned models will be released to facilitate further studies on this problem.
FOVEA: Foveated Image Magnification for Autonomous Navigation

Efficient processing of high-resolution video streams is safety-critical for many robotics applications such as autonomous driving. Image downsampling is a commonly adopted technique to ensure the latency constraint is met. However, this naive approach greatly restricts an object detector's capability to identify small objects. In this paper, we propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. The magnified regions are those that are believed to have a high probability of containing an object, whose signal can come from a dataset-wide prior or frame-level prior computed from recent object predictions. The magnification is implemented by a KDE-based mapping to transform the bounding boxes into warping parameters, which are then fed into an image sampler with anti-cropping regularization. The detector is then fed with the warped image and we apply a differentiable backward mapping to get bounding box outputs in the original space. Our regional magnification allows algorithms to make better use of high-resolution input without incurring the cost of high-resolution processing. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. Additionally, building on top of the previous state-of-the-art in streaming detection, our method sets a new record for streaming AP on Argoverse-HD (from 17.8 to 23.0 on a GTX 1080 Ti GPU), suggesting that it has achieved a superior accuracy-latency tradeoff.
Educational meaning of on-location practice for the students of architectural specialties

The subject of this research is the vocational training of the students of architectural specialties and the the need for on-location practices in the Russian and foreign historical cities. The relevance of this topic is defined by the fact that the importance of such practices is questioned in the context of development of new educational curricula. Analysis is conducted on the historical aspect, the role of practice in vocational training, as well as peculiarities of perception of reality by the future architects and designers and cognitive activity in the open-air environment. The author outlines and substantiates the goals and tasks aimed at the development of knowledge and skills acquired the students during on-location practice. The research leans on the experience of conducting practices by the Institute of Architecture and Design of the Pacific National University in Russia and abroad. The authors discuss the positions of the planners, heads and participants of the on-location practices. The conclusion is made that such practices and not only the immersion into the historical environment, but its grasp by means of visual arts, which is an integral part of vocational education. The experience acquired by the student in the course of on-location practice is irreplaceable and necessary for professional growth of future architects and designers in accordance with modern requirements.
Multi-level Features Selection Network Based on Multi-attention for Salient Object Detection

Both the attention mechanism and Salient Object Detection aim to locate the most obvious regions in an image. However, common algorithms focus on micro attention but neglect the similarity in the macro perspective. Besides, they also ignore the differences among multi-scale information. To tackle these problems, we propose MFS-Net that progressive select features to predict salient regions. First, we design the Pyramid Attention module that integrates channel and spatial attention to extract semantic information for multi-scale high-level features and design the Self-Interaction Attention module to extract detailed information for multi-scale low-level features. Besides, to refine the saliency edge, we propose the Semantic-Detail Attention module which exploits high-level features to guide low-level features in a macro-attention manner. Finally, we selectively integrate global context information by the Interaction-Fusion Attention module, aiming to learn the relationship among different salient regions and alleviate the dilution effect of features. Experimental results on six benchmark datasets demonstrate that the proposed method performs well compared with 20 state-of-the-art methods.
Reactive Obstacle Avoidance Method for a UAV

Obstacle avoidance is very important for UAV flying in unknown environment. In this paper, the UAV’s obstacle avoidance method in unknown environment is proposed from two different view combined with the forward looking perception information of UAV. One is to propose a local real-time planning method from the perspective of optimization. The other is to combined with reinforcement learning to propose a method of avoiding action selection. In this paper, simulation experiments and comparative experiments are carried out to prove the effectiveness of the method.
Blockchain as a Technology Contributor: A Survey

Blockchain is a technology that is initially used for cryptocurrencies like bitcoin. The biggest advantage of this technology is that it is immutable data stored in blocks that cannot be changed, rather a new block is added in the chain and a new record can be inserted into it. The previous record shall remain the same and the record of every transaction can be tracked. Many studies and surveys have been done on blockchain mentioning the implications and contributions of blockchain. This paper presents a detailed survey of blockchain mentioning its features, advantages and protocols. In this paper, the authors have presented the working, features and usage of blockchain technology. In the future, based on the characteristics of blockchain technology, the authors will implement blockchain with the cloud of things.
Weak Illumination Image Enhancement Using Lightening Network

Weak illumination image enhancement is a tall order task which has gotten a lot of scrutiny. Weak light photography is notorious for having poor visible quality. To resolve this task, this research work considers weak light image enhancement to be a residual learning problem in which the residual between weak and regular light images must be estimated. This research work presents a novel Deep Lighting Network(DLN) model that takes advantage of recent advances in Convolutional Neural Networks(CNNs). Several Lightening Back Projection (LBP) blocks makeup the planned DLN. To learn the surplus for regular light estimations, the LBPs undertake recurrent brightening and darkening procedures. The proposed model has been implemented on different datasets. The suggested DLN methodology beats previous approaches on both objective and subjective metrics according to numerical results.
Hybrid integrated modeling of thermal field temperature in Cz silicon single crystal growth process based on VMD-CNN-LSTM-ELM

Czochralski (Cz) silicon single crystal growth is a dynamic time-varying process with complex physical changes, multi-phase coupling of multiple fields, model uncertainty and large hysteresis and nonlinearity, while mechanistic models based on a large number of assumptions are difficult to be applied practically. In order to establish a stable and reliable model of crystal growth process, this paper proposes a variational modal decomposition- -convolutional neural network-long and short-term memory network-extreme learning machine (VMD-CNN-LSTM-ELM) nonlinear hybrid integrated modeling method based on the idea of data-driven modeling to construct a model between heater power and thermal field temperature. The model consists of feature extraction, deep learning network, and optimization algorithm. First, VMD is used to decompose the original data into multiple Intrinsic Mode Function (IMF) from low to high frequencies, preserving the variation characteristics of the signal at different frequencies. Second, for each eigenmode function sequence, CNN feature extraction and LSTM prediction are performed in turn. Then, ELM is used to nonlinearly integrate the prediction results of multiple sequences to obtain the final prediction output. Finally, the simulation experimental results based on industrial data show that the proposed VMD-LSTM-ELM based thermal field temperature has excellent prediction performance and generalization ability.
Constructing Context-Aware Sentiment Lexicon for Amharic Language

Sentiment lexicon is a crucial tool for determining sentiment polarity toward sentiment words. Manual, Dictionary, and Corpus based are categorized as lexicon-based methods to generate sentiment lexicon. Different researchers have conducted research to generate sentiment lexicon for Amharic language on different domains. However, there is still a gap in this domain to classify and handle context-specific opinions. In this research, Context-Aware Sentiment lexicon is generated for the Amharic language which is the second most spoken Semitic language next to Arabic, a low resourced and morphologically rich language of Ethiopia. Word2vec approach is applied to generate sentiment lexicon and gives the ability to handle contextual sensitivity terms from the hotel domain. The common evaluation metrics such as recall and precision were used to measure our proposed method's performance. Amharic sentiment lexicons are developed and used in testing the performance of the collected Amharic review text. Finally, this study shows a result of 91.3% recall and a precision of 51.8. Promising results are obtained and comparative experiments have shown that the proposed approach can be utilized to generate lexicon for the Amharic language.
Shadow Detection and Removal Based on Multi-task Generative Adversarial Networks

The existence of shadows is difficult to avoid in images. Also, it will affect object recognition and image understanding. But on the other hand, shadow can provide information about the light source and object shape. Therefore, accurate shadow detection and removal can contribute to many computer vision tasks. However, even the same object, its shadow will vary greatly under different lighting conditions. Thus it is quite challenging to detect and remove shadows from images. Recent research always treated these two tasks independently, but they are closely related to each other actually. Therefore, we propose a multi-task adversarial generative networks (mtGAN) that can detect and remove shadows simultaneously. In order to enhance shadow detection and shadow removal mutually, a cross-stitch unit is proposed to learn the optimal ways to fuse and constrain features between multi-tasks. Also, the combination weight of multi-task loss functions are learned according to the uncertainty distribution of each task, which is not set empirically as usual. Based on these multi-task learning strategies, the proposed mtGAN can jointly achieve shadow detection and removal tasks better than existing methods. In experiments, the effectiveness of the proposed mtGAN is shown.