Reviewerly/exHarmony huggingface dataset:
Remote sensing image segmentation based on improved Mask-RCNN

Satellite remote sensing images have the problems of large image scale, dense arrangement of segmentation targets and different directions. And the specifications and clarity are far from the natural images, resulting in difficulty in feature extraction. Therefore, the detection accuracy of Mask-RCNN is poor when applied to remote sensing image instance segmentation. In this regard, an improved Mask-RCNN algorithm is proposed. First, a deformable convolution kernel is introduced into the back bone network to adaptively change the theoretical receptive field. On this basis, the FPN module is modified, and feature layered fusion is introduced to further improve the feature extraction capability of the model. At the same time, the Soft-NMS algorithm is used to screen the target candidate frame. Validated using the iSAID dataset. The experimental results based on the data set show that the improved algorithm has higher detection accuracy than the original algorithm, and the missed detection rate is reduced.
Research on consumption governance path based on big data

In the era of big data, digital economy arises at the historic moment, and strengthening consumption governance is its important proposition. Massive data provides rich resources and reliable support for consumption governance. Driven by big data, consumers' consumption behavior is more rational, and their consumption choices are more vulnerable. At the same time, consumers' dependence on brands gradually decreases, but personalization gradually increases. The change of consumption trend also brings great opportunities for consumption governance. Information disclosure makes consumption governance more intelligent. Improving services to make consumption governance more precise. Improving quality makes consumption governance more rational. Customer first makes consumption governance more humanized. Therefore, this paper explores the consumption governance path by using the data mining (DM) method through sampling survey. The results show that we need to continuously improve the efficiency of consumption governance by strengthening the technological innovation, improving the safety factor, strengthening the regulatory regulations, and improving the coordination of policies.
Improvements on style transfer from photographs to Chinese ink wash paintings based on generative adversarial networks

Although voids play an important role in Chinese paintings, when generating Chinese ink wash paintings from real life photos of horses, background detection and void-leaving remain challenges in producing more credible paintings. To address the problem, a model with a two-stage framework is proposed in this paper. The framework divides the generation process into background lightening and style transformation. In the first stage, by training a pix2pix model with paired original photos and background-lightened photos, the pix2pix model is enabled to detect and lighten the background correctly in the style-transfer process, hinting where voids should be left. In the second stage, a Cycle-GAN model trained with unmatched photos and Chinese paintings achieves the style-transfer from pre-processed photos to Chinese ink wash paintings. In preparation for training the stage-Ⅰ model, the photos from a given dataset is processed in batches to get corresponding background-lightened photos. During the training of the stage-Ⅱ model, original photos instead of processed photos is used as comparatively data augmentation, making the model more robust and independent. Compared to the baseline model, the proposed model reaches a higher accuracy in both void-leaving and detail-enhancing in the experiments, resulting in more credible and delicate generated Chinese paintings.
Immersive information visualization interaction design in an embodied cognitive perspective

The immersive visualization method expands the interaction space of traditional visualization, allows users to perceive information in multiple channels, and enriches the dimensionality of information. Embodied cognition provides theoretical support for the interaction process of immersive information visualization by emphasizing the role of the body and the environment. Based on the five elements of interaction behavior, the paper builds an interaction analysis framework of "Embodied-Behavioral" interaction framework (EB) from the perspective of embodied cognition, which includes three aspects: perception, interaction, and understanding; Introduces the current research in immersive visualization and analyzes the interaction process from different perspectives. Several aspects need to be considered when designing immersive visualization interactions, such as maintaining temporal and spatial consistency, promoting fault tolerance, and reasonably applying environmental information.
The performance analysis of generative adversarial networks

Generative Adversarial Networks have been widely used in recent years. Some extensions, such as Deep Convolutional Generative Adversarial Networks have also appeared. Currently, there is no research on the results generated by different pairings of existing generators and discriminators. Therefore, the research topic of this paper is to find the pairing combination of generator and discriminator to generate the best images. The research methods of this paper are as follows: Based on the Celeb-A Faces face dataset, the generated results were compared and analyzed by changing the generator and discriminator and considering the influence of the number of training epochs on the results to find the optimal combination. This paper has chosen two convolutional neural network generators, and discriminators, each of them has 3 and 5 layers of convolutional layers and one multilayer perceptron generator and discriminator. It is found that the generator and discriminator with higher convolutional layers generate clearer images. Multilayer perceptron generators and discriminators produce poor quality results. The larger the number of training epochs is, the better the generation effect is within a specific range. Therefore, selecting generators and discriminators with high convolutional layers is suggested for training.
Indian Tourism Websites with Special Reference to the Selected States: A Webometrics

Webometrics studies the quantitative aspects of the construction and use of information resources, structures, technologies on the web, and informetric approaches. Webometrics is concerned with measuring characteristics of the web: websites, web pages, and parts of web pages, words in web pages, hyperlinks, and web search engine results. This study investigates and explores the websites of 18 selected Indian Tourism websites based on their external backlinks, Edu backlines, Gov backlinks, Edu domains, Gov domains, Global Rank, PR quality, and desktop and mobile website loading time of state tourism in India. This study collected data from Check page rank and other SEO-analyzer search engines. The researcher suggested that Indian Tourism websites need to attract more external backlinks and the global level by introducing creative online sources and services and updates of news and information. Identify and analyze global Rank and PR quality websites of Indian Tourism.
A segmented mapping algorithm for SAR image and the corresponding quality evaluation method

Nowadays the space borne synthetic aperture radar (SAR) is an important tool to acquire the information of the ground features. As is well known, SAR image is a typical High Dynamic Range Image (HDR). HDR is generally beyond the dynamic display range of the commonly-used display equipment, and visual interpretation is the most common and direct means for researchers to obtain ground object information from SAR images. Therefore, in order to obtain high quality SAR image, it is necessary to map high dynamic range (HDR) image into low dynamic range (LDR) display output. In this article, we will analyze of SAR image brightness characteristics and propose a segmented mapping algorithm suitable for SAR image. In addition common image quality evaluation index are described.
Learning How to Program

research-article Share on Learning How to Program: An artistic perspective Author: Andrew Smith Rochester Independent College, UK Rochester Independent College, UKView Profile Authors Info & Claims XRDS: Crossroads, The ACM Magazine for StudentsVolume 29Issue 2Winter 2022 pp 53–55https://doi.org/10.1145/3571303Published:12 January 2023Publication History 0citation18DownloadsMetricsTotal Citations0Total Downloads18Last 12 Months18Last 6 weeks18 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
Examine Bitcoin price predictability with machine learning algorithms

As the most-traded digital asset, Bitcoin receives a tremendous increase in investment interests. The primary purpose of this paper is to examine and compare two commonly applied machine learning algorithms on their capability and feasibility in Bitcoin price prediction. The regression models of Extreme Gradient Boosting and Long Short-Term Memory are selected as the investigated objects in this paper. The experiments are evaluated by considering the extra impacts of sample dimensions and time-series frequency, along with the trade-offs between prediction accuracy (measured by residual error) and computational efficiency (measured by computing time). Long Short-Term Memory, as a more convoluted deep learning method, achieves better accuracy when a daily dataset with limited input features is used. However, its predictability has considerably decreased and thus become less efficient than XGBoost, when more peripheral information and higher frequency data points (trading price every 15 minutes) are available. Besides algorithmic complexity, Long Short-Term Memory also takes much longer computing time than Extreme Gradient Boosting, making it a less applicable model to use when dealing with large sample sizes.
A customized integrated smart carrier reminder platform

Today, with the proliferation of electronic system terminals and the ever-changing needs of people, a variety of reminder services have emerged, but existing reminder services are still mostly limited to smartphones, and smart wearable devices (such as smart watches with functions such as heart rate monitoring and alarms), and the relevant industries and markets are still expanding. This paper introduces a customized integrated smart carrier reminder platform, which, given the limitations of current carrier reminder functions, shifts the focus of innovation from the commonplace smartphones and wearable devices to not yet widely noticed carrier reminder services, and proposes an integrated, multifunctional smart reminder system; The idea is to receive information from the user through a variety of input methods (contact input, contactless input, programmed input, etc.), collect and process the data through a central processing system (internal and external sensors) and finally output the feedback (e.g. light, sound, smell, touch) from the output system. The advantage of this study over existing carrier alert services lies in its platform-based operation mode and high level of user interactivity. A use case is demonstrated to verify the usefulness of the proposed system.
ConBG: a contrastive learning method for Chinese unsupervised sentence representation based on Bert and GCN

Sentence representation is a typical problem in NLP, which is to use a fine vector to encode the sentence, so that the sentence can contain copious semantic. A high-quality sentence representation benefits a wide range of NLP tasks. Although BERT-based pretrained model performs well on many downstream tasks, it’s sentence representation acts poorly on semantic textual similarity (STS) task. In this article, we propose ConBG, a Contrastive Learning Method for Chinese Sentence Representation Based on Bert and GCN, which is to encode the sentence by a model combined with Bert and Graph Convolutional Network which is to incorporate syntactic information. Then we use data augmentation strategies to create samples, and adopt contrastive learning technique to train the model in a unsupervised way. Experiments on Chinese STS datasets demonstrate that ConBG exceeds previous work of over 1% on average.
Target detection based on improved Yolov4-Tiny algorithm

At present, the development of artificial intelligence is very rapid, and the intelligent assisted driving system based on deep learning is widely used in the society. For example, in unmanned driving, it can accurately identify pedestrians, vehicles and traffic signs. Convolutional neural network in deep learning has excellent achievements in the field of computer vision and has outstanding feature extraction ability. Therefore, object detection algorithm based on deep learning is a research hotspot in the field of computer vision at present. We propose a vehicle-pedestrian target detection method based on Yolov4-tiny. Firstly, the ResBlock-D module in the ResNet-D network is used to replace one CSPBlock module in Yolov4- tiny, thus reducing the computational complexity. Then, the coordinate attention mechanism is added to help the model better locate and identify targets. Experimental results show that The improved Yolov4-tiny algorithm has higher curacy than the original algorithm, and the Map is improved by 7.8 %, which has a certain reference value for the study of intelligent assisted driving technology.
ShiftMask: video behavior recognition data augmentation

Some recently proposed data augmentation methods have been used to solve the overfitting problem of neural networks and are gradually becoming a research focus in deep learning. These data augmentation methods have been widely used in tasks such as image recognition, target detection, and image segmentation. However, for the overfitting problem of neural networks on video data, the existing data augmentation methods have the limitation of feature dimensionality; they can only affect the spatial features of the training samples. Moreover, the current methods lack the filtering effect on the temporal information of training samples; the training samples after data augmentation still have more rea information, and the video behavior recognition model still suffers from the overfitting problem. Therefore, this paper proposes the ShiftMask data enhancement algorithm. The method in this paper uses a new masking approach to correlatively mask the temporal and spatial dimensions of video data to help the model identify the subject of the action and reduce the subject and scene learning bias of the model during the training process. Moreover, this paper utilizes a grid-like mask of varying sizes to preserve the data's fine-grained features. Finally, experiments on the HDMB51 and TobaccoFactory datasets improved the recognition accuracy of the I3D model by 4.5% and 2.9%, which were better than other mainstream data enhancement methods.
Visualization-based clustering algorithm experimental teaching design

Clustering is an important part of artificial intelligence and is widely used in data mining, pattern recognition, natural language processing, computer vision and other aspects. Clustering algorithms are complex and difficult to master. In order to make learning easier and more interesting, a visual clustering experiment is designed. The first is the introduction of clustering algorithms and clustering indicators; then basic experiments are designed to show the working principle and clustering characteristics of each algorithm through the visualization of the clustering process and the visualization of the clustering results, and introduce the self-generated artificial data to the method to verify the characteristics of the algorithm; the last is the application experiment, through the face clustering and image segmentation experiments to improve the students' interest in learning.
A Mixed Reality application to support the design of custom prostheses

The definition of an innovative category of implantable devices, characterized not only by a fully customization but also by improved osteoconductive and functionalized bioactive surfaces, needs to be supported by a systematic approach. In the present work, a new human-machine interface based on the Mixed Reality (MR) is proposed and focused on the implantation of a fully customized prosthesis. By means of an informal and exploratory focus group (i.e., without using a structured questionnaire), the limitations belonging to the current procedure were highlighted and a first list of user needs was subsequently defined. The MR interface was then considered to be the most suitable solution to match the gathered requirements. However, the doctors proposed also to develop a desktop interface for a finer and easier manipulation of 3D models. The proposed MR application offers several advantages from the possibility to display 3D anatomical structures and 3D models of custom prostheses in an immersive environment to the optimized communication and data exchange among the players (medical staff, doctors and engineers). A mock-up of the MR applications is presented in this work to show the results of the design stage, before the deployment of the application.
Prediction of landing speed of aircraft based on AE-ANFIS

Aiming at the problem that the advanced arresting device is difficult to obtain the landing speed of the aircraft in real time, this paper predicts the speed of the aircraft through the self-encoding fuzzy inference system (AE-ANFIS). Firstly, the working principle of the advanced arresting system is expounded, and the sensors directly related to the aircraft speed measurement are analyzed. And filter auxiliary variables through feature extraction and maximum information coefficient (MIC); then predict acceleration through adaptive fuzzy neural network (ANFIS); finally, for the problem of over-fitting caused by the large number of ANFIS rules, an auto-encoder (AE) method is proposed. Data dimensionality reduction is performed by extracting abstract features, which effectively improves the prediction accuracy of ANFIS. The experimental results show that the method proposed in this paper can fit the aircraft speed well, and the accuracy is better than traditional ANFIS and BP, LSTM, GoogleNet, AlexNet and other algorithms.
Unsupervised Detection and Clustering of Malicious TLS Flows

Malware abuses TLS to encrypt its malicious traffic, preventing examination by content signatures and deep packet inspection. Network detection of malicious TLS flows is important, but it is a challenging problem. Prior works have proposed supervised machine learning detectors using TLS features. However, by trying to represent all malicious traffic, supervised binary detectors produce models that are too loose, thus introducing errors. Furthermore, they do not distinguish flows generated by different malware. On the other hand, supervised multiclass detectors produce tighter models and can classify flows by the malware family but require family labels, which are not available for many samples. To address these limitations, this work proposes a novel unsupervised approach to detect and cluster malicious TLS flows. Our approach takes input network traces from sandboxes. It clusters similar TLS flows using 90 features that capture properties of the TLS client, TLS server, certificate, and encrypted payload and uses the clusters to build an unsupervised detector that can assign a malicious flow to the cluster it belongs to, or determine if it is benign. We evaluate our approach using 972K traces from a commercial sandbox and 35M TLS flows from a research network. Our clustering shows very high precision and recall with an F1 score of 0.993. We compare our unsupervised detector with two state-of-the-art approaches, showing that it outperforms both. The false detection rate of our detector is 0.032% measured over four months of traffic.
Spooky RLNC at a Distance: Exploiting Quantum Entanglement to Convey Coding Coefficients

In future 6G networks, quantum technologies are expected to be used mainly for security, e.g. quantum key distribution. However, additional advantages can also result from the realization of quantum-assisted classical communication. In this work, we show that for point-to-point transmission, quantum entanglement distribution can be used to improve the latency of a communication channel when Random Linear Network Coding (RLNC) is applied, a coding scheme to increase the performance of unreliable communication networks. To do this, we compare the overhead of the traditional RLNC scheme, where all coding coefficients are transmitted in the coding overhead, with the overhead required for the case where the coefficients a re pre-distributed to the nodes by entangled EPR-pairs. Hereby, we assume a quantum network capable of distributing entanglement while idle and storing qubits, which is already targeted for other applications like superdense-coding. In addition, we verify the quality of quantum randomness for RLNC codes.
On Pursuit of Privacy Preservation for Dependable Offloading in VECON: An Optimization Perspective

The Internet of Vehicles (IoVs) networks are gaining significant advantages as a result of the emergence of Vehicular Edge Computing and Networks (VECONs). For VECONs, efficient and secure task offloading is essential for users to obtain dependable services. Nevertheless, few efforts are directed towards security issues such as privacy exposure risk during offloading, which seriously affect modeling and optimization of offloading dependability. This paper presents a new measure of offloading dependability entitled cost from Energy consumption, Delay and Privacy exposure risk (cEDP). A dependable offloading framework is proposed, in which tasks are reasonably divided and the Fully Homomorphic Encryption (FHE) algorithm is implemented to mitigate privacy exposure risks. A self-learning based decentralized computation and privacy preservation offloading (SDCPO) algorithm is presented to solve the optimal offloading problem dependably and decentrally. Numerical results show that over the centralized offloading schemes, SDCPO has better convergence performance with lower cEDP, which verifies the dependability of the offloading scheme and algorithm.
Markov State Transition Modeling in Complex High-Dimensional State Space Based on Fuzzy Integral

The extremely high business volume of the financial industry brings unaffordable operating pressure to the back-end data system of financial companies. Recently, data-driven deep learning algorithms have achieved breakthroughs in analyzing and predicting system anomalies. However, in the case of high-dimensional data, deep learning faces the problems of long training time, lack of explainability and transferability. In this paper, we propose a model based on fuzzy integral for observing and modeling the state of the system. Firstly, the fuzzy integral algorithm has lower complexity, which is more suitable for the time-sensitive financial industry. Then, based on the fuzzy integral, the vector composed of the fuzzy measures of all the features is used to represent the state of the system. It is proved that the system constructed by this modeling method has the Markov property. Moreover, compared with deep learning, fuzzy integral-based methods are not only more computationally efficient but also explainable and transferable. Experimentally, we use the actual data of securities companies and have better results in the systematic anomaly analysis.