Reviewerly/exHarmony huggingface dataset:
Efficient and Secure Bit-Level Chaos Security Algorithm for Orbital Angular Momentum Modulation in Free-Space Optical Communications

Currently, secure multimedia applications are becoming a very hot research topic, specifically over the Internet and wireless communication networks due to their rapid progress.Several researchers have implemented various chaotic image and video encryption algorithms to achieve data stability and communication security.This paper presents a novel bit-level video frame cryptosystem that is dependent on the piecewise linear chaotic maps (PWLCMs).It is implemented for orbital angular momentum (OAM) modulation over different turbulence channels.Firstly, the mathematical model for the bit error rate (BER) of OAM modulation is derived over the gamma-gamma turbulence channel.After that, a comparison between the theoretical results from Mathematica and the simulation results from MATLAB for different turbulence strengths, signal-to-noise ratios (SNRs), and propagation distance values is presented to assure that there is a perfect match.The proposed video cryptosystem is checked using entropy analysis, histogram testing, attack analysis, time analysis, correlation testing, differential analysis, and other quality and security evaluation metrics.The simulation results and the performance analysis confirm that the proposed cryptosystem is reliable and secure for video frame encryption, and communication with different turbulence conditions in free space.
An Intelligent Instrument Reader: Using Computer Vision and Machine Learning to Automate Meter Reading

A novel algorithm using computer vision and machine learning techniques has been developed in this research and applied to automate the reading of analog meters. This approach does not rely on any prior information about the meter being read or any human intervention during the process. High-level features of the meter, including the graduation values and angles, are extracted using a cascade of image contour filters with a series of digit classifiers. The features are refined and used to train regression models that return the reading of the analog meter automatically.
Countering Spoof: Towards Detecting Deepfake with Multidimensional Biological Signals

The deepfake technology is conveniently abused with the low technology threshold, which may bring the huge social security risks. As GAN-based synthesis technology is becoming stronger, various methods are difficult to classify the fake content effectively. However, although the fake content generated by GANs can deceive the human eyes, it ignores the biological signals hidden in the face video. In this paper, we proposed a novel video forensics method with multidimensional biological signals, which extracting the difference of the biological signal between real and fake videos from three dimensions. The experimental results show that our method achieves 98% accuracy on the main public dataset. Compared with other technologies, the proposed method only extracts fake video information and is not limited to a specific generation method, so it is not affected by synthetic methods and has good adaptability.
HowDIY: Towards Meta-Design Tools to Support Anyone to 3D Print Anywhere

The promise of anyone being able to 3D print anywhere relies on both technological advances and incremental shifts in social organizations to trigger changes in human behavior. While much research has focused on how people learn aspects of predefined printing processes, such as expressively utilizing particular design-software (e.g. CAD) and fabrication-machinery (e.g. 3D Printers), this work explores how anyone may gain an understanding of what can be 3D printed through dynamic-processes in computationally-guided exploration of online resources and 3D printing facilities. Investigations surrounding online printing services reveal accessible 3D printing processes that do not require end-users to have experience with design-software or fabrication-machinery, only requiring end-users to specify printable ideas. We present these accessible printing processes alongside associated technologies in a meta-design framework for supporting end-users' specification of 3D printing ideas. Informed by this framework and a series of formative studies, we designed the website HowDIY to introduce anyone to 3D printing by encouraging and facilitating the intelligent exploration of various online resources. HowDIY was deployed over several weeks with diverse newcomers to 3D printing, validating that intelligent user interfaces can support anyone to participate in the utilization and design of 3D printing tools and processes.
Attent: Active Attributed Network Alignment

Network alignment finds node correspondences across multiple networks, where the alignment accuracy is of crucial importance because of its profound impact on downstream applications. The vast majority of existing works focus on how to best utilize the topology and attribute information of the input networks as well as the anchor links when available. Nonetheless, it has not been well studied on how to boost the alignment performance through actively obtaining high-quality and informative anchor links, with a few exceptions. The sparse literature on active network alignment introduces the human in the loop to label some seed node correspondence (i.e., anchor links), which are informative from the perspective of querying the most uncertain node given few potential matchings. However, the direct influence of the intrinsic network attribute information on the alignment results has largely remained unknown. In this paper, we tackle this challenge and propose an active network alignment method (Attent) to identify the best nodes to query. The key idea of the proposed method is to leverage effective and efficient influence functions defined over the alignment solution to evaluate the goodness of the candidate nodes for query. Our proposed query strategy bears three distinct advantages, including (1) effectiveness, being able to accurately quantify the influence of the candidate nodes on the alignment results; (2) efficiency, scaling linearly with 15 − 17 × speed-up over the straight-forward implementation without any quality loss; (3) generality, consistently improving alignment performance of a variety of network alignment algorithms.
Multi-filter dynamic graph convolutional networks for skeleton-based action recognition

Action recognition plays an important role in video understanding, and the dynamic changes of human bones provide important information for it. In order to solve the problem that the source point can only obtain the characteristics of adjacent nodes and the static structure of the skeleton diagram can not meet the adaptive requirements, this paper proposes an Inception structure and dynamic skeleton diagram based on the convolutional neural network, namely, the multi-filter dynamic graph convolutional neural network. The method can not only enable the source point to obtain the characteristics of distant nodes, but also can use the dynamic skeleton diagram structure to generate different body associations for different actions, which improves the adaptability and accuracy of the model and achieves better results.
Hotel review analysis for the prediction of business using deep learning approach

Sentiment analysis is a widely used topic in Natural Language Processing that allows identifying the opinions or sentiments from a given text. Social media is the scope for the customers to share their opinion over the products or services as part of customer reviews. Dissect this review has become an important factor for business analysis since online business is exponentially growing in today's techno-friendly competitive market. A large number of algorithms have been found in recent articles. Among those deep learning is an important approach. In the proposed methodology, long short-term memory (LSTM) and Gated recurrent units (GRUs) have been used to train the hotel review data where the accuracy rate of identifying customer opinion is 86%, and 84% respectively. The dataset is also tested by using Naïve Bayes, Decision Tree, Random Forest, and SVM. For Naïve Bayes obtains an accuracy of 75%, for Decision Tree obtains an accuracy of 71%, for Random Forest the accuracy is 82% and for SVM our accuracy result is 71%. Deep learning is used to obtain better business performance and also get the review from customers and also to predict the sentiment about customer review. Our algorithm works properly and gives better accuracy.
Medical Triage Chatbot Diagnosis Improvement via Multi-relational Hyperbolic Graph Neural Network

Medical triage chatbot is widely used in pre-diagnosis by asking symptom and medical history-related questions. Information collected from patients through an online chatbot system is often incomplete and imprecise, and thus it's essentially hard to achieve precise triaging. In this paper, we propose Multi-relational Hyperbolic Diagnosis Predictor (MHDP) --- a novel multi-relational hyperbolic graph neural network-based approach, to build a disease predictive model. More specifically, in MHDP, we generate a heterogeneous graph consisting of symptoms, patients, and diagnoses nodes, and then derive node representations by aggregating neighborhood information recursively in the hyperbolic space. Experiments conducted on two real-world datasets demonstrate that the proposed MHDP approach surpasses state-of-the-art baselines.
DeepLabV3-Refiner-Based Semantic Segmentation Model for Dense 3D Point Clouds

Three-dimensional virtual environments can be configured as test environments of autonomous things, and remote sensing by 3D point clouds collected by light detection and range (LiDAR) can be used to detect virtual human objects by segmenting collected 3D point clouds in a virtual environment. The use of a traditional encoder-decoder model, such as DeepLabV3, improves the quality of the low-density 3D point clouds of human objects, where the quality is determined by the measurement gap of the LiDAR lasers. However, whenever a human object with a surrounding environment in a 3D point cloud is used by the traditional encoder-decoder model, it is difficult to increase the density fitting of the human object. This paper proposes a DeepLabV3-Refiner model, which is a model that refines the fit of human objects using human objects whose density has been increased through DeepLabV3. An RGB image that has a segmented human object is defined as a dense segmented image. DeepLabV3 is used to make predictions of dense segmented images and 3D point clouds for human objects in 3D point clouds. In the Refiner model, the results of DeepLabV3 are refined to fit human objects, and a dense segmented image fit to human objects is predicted. The dense 3D point cloud is calculated using the dense segmented image provided by the DeepLabV3-Refiner model. The 3D point clouds that were analyzed by the DeepLabV3-Refiner model had a 4-fold increase in density, which was verified experimentally. The proposed method had a 0.6% increase in density accuracy compared to that of DeepLabV3, and a 2.8-fold increase in the density corresponding to the human object. The proposed method was able to provide a 3D point cloud that increased the density to fit the human object. The proposed method can be used to provide an accurate 3D virtual environment by using the improved 3D point clouds.
Software Defect Prediction with Naïve Bayes Classifier

The defect is an error state, which is not according to the requirement of the given software. The error in the software system can affect the efficiency and reliability of the software therefore, early prediction of software defects can save the companies from a bigger loss. For this purpose, multiple algorithms have been proposed in the past decade. Still, the accuracy achieved is not suitable for the market demand which means that a lot of improvement needs to be done. In this paper, we have proposed an efficient and reliable framework for the prediction of the software defects. The framework is divided into three main steps which involves data preprocessing steps that includes: removal of noise and normalization. After that, feature extraction is done using correlation-based analysis and relevant features are selected, and finally applies machine learning models including naïve Bayes and linear regression. Results show that the proposed method can reach an accuracy of 98.7% using Naïve Bayes algorithm. Consequently, the significance of the proposed technique is that it will lower the cost of maintenance and reduce the code complexity by predicting the defects earlier in the software systems that will help the developers to remove those defects and ultimately improves the software quality before the deployment phase.
An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 °Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate.
Destination-Directed Trajectory Modeling, Filtering, and Prediction Using Conditionally Markov Sequences

In some problems, there is information about the destination of a moving object. An example is a flight from an origin to a destination. Such problems have three main components: an origin, a destination, and motion in between. We call such trajectories ending up at the destination destination-directed trajectories (DDTs). Described by an evolution law and an initial probability density, a Markov sequence is not flexible enough to model DDT well. The future (including destination) of a Markov sequence is completely determined probabilistically by its initial density and evolution law. One class of conditionally Markov (CM) sequences, called the CM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> sequence (it includes the Markov sequence as a special case), has the following main components: a joint density of two endpoints and a Markov-like evolution law. This article proposes modeling DDT as CM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> sequences. We study the CM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L </sub> sequence, its dynamic model, and its realizations, all for DDT modeling. We demonstrate that CM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> sequences naturally model DDT, enjoy several desirable properties for DDT modeling, and can be easily and systematically generalized if necessary. In addition, we study DDT filtering and trajectory prediction based on a CM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> model and compare them with those based on a Markov model. Several simulation examples are presented to illustrate DDT modeling and inference.
Governing risk society: the socio-technological experiences of China and South Korea in the COVID-19 pandemic

This is a critical comparative analysis of the systematic ways in which technology and big data practices are employed in the fight against the COVID-19 pandemic in China and South Korea. This study argues that the success of the strategies depends not on isolated applications of technological tools but the responsive socio-technological webs of a wide range of actors and resources mobilized and coordinated through well-functioning technological, public health, and administrative infrastructures in the two countries. Moreover, the characteristic responses in the two countries, ranging from the coordination and mobilization by the state, health care and digital infrastructures as public good provision, to risk communication with the public, shed light on how the dynamics of risk society plays out along the developmental paths of the two East Asian societies. The future-oriented infrastructures for governance in risk society, the study concludes, are constituted in the imbrication of institutional, historical, and cultural factors, of which technologies are only but one part.
Fast covariance parameter estimation of spatial Gaussian process models using neural networks

Gaussian processes (GPs) are a popular model for spatially referenced data and allow descriptive statements, predictions at new locations, and simulation of new fields. Often, a few parameters are sufficient to parameterize the covariance function, and maximum likelihood (ML) methods can be used to estimate these parameters from data. ML methods, however, are computationally demanding. For example, in the case of local likelihood estimation, even fitting covariance models on modest size windows can overwhelm typical computational resources for data analysis. This limitation motivates the idea of using neural network (NN) methods to approximate ML estimates. We train NNs to take moderate size spatial fields or variograms as input and return the range and noise‐to‐signal covariance parameters. Once trained, the NNs provide estimates with a similar accuracy compared to ML estimation and at a speedup by a factor of 100 or more. Although we focus on a specific covariance estimation problem motivated by a climate science application, this work can be easily extended to other, more complex, spatial problems and provides a proof‐of‐concept for this use of machine learning in computational statistics.
Clustering-Based Online News Topic Detection and Tracking Through Hierarchical Bayesian Nonparametric Models

In this paper, we propose a clustering-based online news topic detection and tracking (TDT) approach based on hierarchical Bayesian nonparametric framework that allows topics to be shared across different news stories in a corpus. Our approach is formulated using the hierarchical Pitman-Yor process mixture model with the inverted Beta-Liouville (IBL) distribution as its component density, which has shown superior performance in modeling text data than the widely used Gaussian distribution. Moreover, we theoretically develop a convergence-guaranteed online learning algorithm that can effectively learn the proposed TDT model from a stream of news stories based on varational Bayes. The merits of our TDT approach are illustrated by comparing it with other well-defined clustering-based TDT approaches on different news data sets.
Large-scale Comb-K Recommendation

Promotion recommendation, as a new recommendation paradigm in recent years, plays an important role in stimulating the purchase desire of users and maximizing the total revenue. Different from previous recommendations (e.g., item/group recommendation), promotion recommendation aims to select a set of K items based on all user preferences in selection phase and maximize the total revenue in delivery phase. Although these two phases are closely related with each other, existing methods usually focus on item selection in selection phase, largely ignoring the delivery phase and leading to sub-optimal performance. To solve the promotion recommendation problem, we propose the comb-K recommendation model, a constrained combinatorial optimization model which seamlessly integrates the selection phase and delivery phase with delicately designed constraints. When selecting K items, the comb-K recommendation is able to simultaneously search the optimal combination of item selection and delivery with the full consideration of all user preferences. Specifically, we propose a novel heterogeneous graph convolutional network to estimate user preference and propose the user-level comb-K recommendation model through solving a binary combination optimization problem. In order to handle combination explosion for large-scale users, we furtherly cluster massive users into limited groups and present a group-level comb-K recommendation model in which a novel heterogeneous graph pooling network is proposed to perform user clustering and estimate group preference. In addition, considering the ”long tail” phenomenon in e-commerce, we design a restricted neighbor heuristic search to accelerate the solving process. Extensive experiments on four datasets demonstrate the superiority of comb-K model for large-scale promotion recommendation. On billion-scale data, when clustering 2.5 × 107 users into 103 groups, our model is able to preserve 98.7% personalized preferences in group-level and significantly improves the Total Click and Hit Ratio by 9.35% and 7.14%, respectively.
RETRACTED: Application of Clustering Algorithm in Ideological and Political Education in Colleges and Universities

Abstract In recent years, with the rapid development of information technology, China has entered the information age. Information technology has changed our life, but also affected the form of ideological and political education(IAPE) management in colleges and universities(CAU). Data mining technology based on clustering algorithm has been widely used in university education management, but the application research of this technology in the field of IAPE is still insufficient. Therefore, this paper puts forward the application research of clustering algorithm in IAPE in CAU. After research, this paper believes that the existing IAPE in CAU is still based on the traditional management mode, lack of data sensitivity and insufficient utilization of teaching information data. In view of this situation, according to the education and management needs of IAPE, combined with the characteristics of clustering algorithm, the traditional clustering algorithm is optimized and improved. Compared with the traditional algorithm, the improved algorithm simplifies the calculation steps, improves the accuracy of calculation, and is more suitable for the application in the field of IAPE in CAU. In order to further verify the actual effect of this algorithm, the improved clustering algorithm is verified by experiments. In this experiment, the proportion of the three types of fraction is 25%, 64.4%, 10.6%, which is much higher than the previous sample data. Analysis shows that the algorithm in this paper has played a positive role in improving the management effect of IAPE in CAU and promoting the optimization and reform of IAPE in China.