Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network and Support Vector Machine in Classification of Flower Images

Flowers are among the raw materials in many industries including the pharmaceuticals and cosmetics. Manual classification of flowers requires expert judgment of a botanist and can be time consuming and inconsistent. The ability to classify flowers using computers and technology is the right solution to solve this problem. There are two algorithms that are popular in image classification, namely Convolutional Neural Network (CNN) and Support Vector Machine (SVM). CNN is one of deep neural network classification algorithms while SVM is one of machine learning algorithm. This research was an effort to determine the best performer of the two methods in flower image classification. Our observation suggests that CNN outperform SVM in flower image classification. CNN gives an accuracy of 91.6%, precision of 91.6%, recall of 91.6% and F1 Score of 91.6%.
The Public Sentiment Analysis of Double Reduction Policy on Weibo Platform

Weibo platform is an indispensable transmission channel in education policy release and dissemination. The events and sentiments contained in education policies microblogs include the public sentiment and support the general management and guidance scientifically and efficiently. This study constructs a dataset based on the “Double Reduction Policy” relevant microblogs and comments. The policy events are extracted by Latent Dirichlet Allocation (LDA) model and Language Technology Platform (LTP). Based on the emotion dictionary, an attention-based BiLSTM model is constructed to classify the public sentiments. The experimental results reveal four themes: “industry impact,” “institutional supervision,” “public feedback,” and “policy implementation.” The distribution conforms to the development trend of online public sentiments.
Time Series Forecasting of Software Vulnerabilities Using Statistical and Deep Learning Models

Software security is a critical aspect of modern software products. The vulnerabilities that reside in their source code could become a major weakness for enterprises that build or utilize these products, as their exploitation could lead to devastating financial consequences. Therefore, the development of mechanisms capable of identifying and discovering software vulnerabilities has recently attracted the interest of the research community. Besides the studies that examine software attributes in order to predict the existence of vulnerabilities in software components, there are also studies that attempt to predict the future number of vulnerabilities based on the already reported vulnerabilities of a project. In this paper, the evolution of vulnerabilities in a horizon of up to 24 months ahead is predicted using a univariate time series forecasting approach. Both statistical and deep learning models are developed and compared based on security data coming from five popular software projects. In contrast to related literature, the results indicate that the capacity of Deep Learning and statistical models in forecasting the evolution of software vulnerabilities, as well as the selection of the best-performing model, depends on the respective software project. In some cases, statistical models provided better accuracy, whereas in other cases, Deep Learning models demonstrated better predictive power. However, the difference in their performance was not found to be statistically significant. In general, the two model categories produced similar forecasts for the number of vulnerabilities expected in the future, without significant diversities.
Transmission Line Object Detection Method Based on Contextual Information Enhancement and Joint Heterogeneous Representation

Transmission line inspection plays an important role in maintaining power security. In the object detection of the transmission line, the large-scale gap of the fittings is still a main and negative factor in affecting the detection accuracy. In this study, an optimized method is proposed based on the contextual information enhancement (CIE) and joint heterogeneous representation (JHR). In the high-resolution feature extraction layer of the Swin transformer, the convolution is added in the part of the self-attention calculation, which can enhance the contextual information features and improve the feature extraction ability for small objects. Moreover, in the detection head, the joint heterogeneous representations of different detection methods are combined to enhance the features of classification and localization tasks, which can improve the detection accuracy of small objects. The experimental results show that this optimized method has a good detection performance on the small-sized and obscured objects in the transmission line. The total mAP (mean average precision) of the detected objects by this optimized method is increased by 5.8%, and in particular, the AP of the normal pin is increased by 18.6%. The improvement of the accuracy of the transmission line object detection method lays a foundation for further real-time inspection.
A Combination of Sentiment Analysis Systems for the Study of Online Travel Reviews: Many Heads are Better than One

This study presents an analysis of the Rest-Mex forum task 2021, which is the first international evaluation event using tourism-related (Online Travels Reviews - OTRs) data from Mexico. In that forum, 14 specialized sentiment analysis systems were presented. The main contribution of this research is a method to successfully combine those 14 systems specialized on sentiment analysis systems for OTRs. The outputs of those 14 systems were used to evaluate the proposed combination schemes. The systems were trained and tested with 7,413 OTRs from the city of Guanajuato, Mexico, a well-known cultural destination. All of them were collected from TripAdvisor. We propose three schemes to combine the systems to predict the polarity of OTRs efficiently. The combination based on deep learning improves significantly each of the results obtained in the sentiment analysis systems at the individual level. Also, the results were improved for 4 out of the 5 polarity classes in the collection. To the best of our knowledge, this is the first paper that reports results from the combination of different specialized systems in sentiment analysis for OTRs.
Design of Multi-Parameter Compound Modulated RF Stealth Anti-Sorting Signals Based on Hyperchaotic Interleaving Feedback

Radio frequency (RF) stealth anti-sorting technology is a research hotspot in the radar field. In this study, the signal design principles of anti-cluster and anti-SDIF sorting were investigated for processes of clustering pre-sorting and sequence-difference-histogram main sorting. Then, in accordance with the signal design principle, a 2D interleaving feedback hyperchaotic system based on the cosine-exponential was designed. A method to modulate the pulse repetition interval (PRI) of the signal parameters and carrier frequency with wide intervals through the hyperchaotic system was developed. Finally, we verified the correctness of the signal design principle, the performance of the hyperchaotic system, and the anti-sorting performance of the designed signal using simulations. The results showed that the signal design principle could guide the signal design. The hyperchaotic system outperformed the classical 1D and 2D chaotic systems and the classical 3D Lorenz systems in terms of randomness and complexity. Anti-cluster sorting and anti-SDIF sorting could be realized by anti-sorting signals modulated by a hyperchaotic system, with the anti-SDIF sorting performance being better than that of the PRI random jitter signal.
Character spotting and autonomous tagging: offline handwriting recognition for Bangla, Korean and other alphabetic scripts

This paper demonstrates a framework for offline handwriting recognition using character spotting and autonomous tagging which works for any alphabetic script. Character spotting builds on the idea of object detection to find character elements in unsegmented word images. An autonomous tagging approach is introduced which automates the production of a character image training set by estimating character locations in a word based on typical character size. Although scripts can vary vividly from each other, our proposed approach provides a simple and powerful workflow for unconstrained offline recognition that should work for any alphabetic script with few adjustments. Here we demonstrate this approach with handwritten Bangla, obtaining a character recognition accuracy (CRA) of 94.8% and 91.12% with precision and autonomous tagging, respectively. Furthermore, we explained how character spotting and autonomous tagging can be implemented for other alphabetic scripts. We demonstrated that with handwritten Hangul/Korean obtaining a Jamo recognition accuracy (JRA) of 93.16% using a tiny fraction of the PE92 training set. The combination of character spotting and autonomous tagging takes away one of the biggest frustrations—data annotation by hand, and thus, we believe this has the potential to revolutionize the growth of offline recognition development.
Pose2Sim: An open-source Python package for multiview markerless kinematics

Pose2Sim provides a workflow for 3D markerless kinematics, as an alternative to the more usual marker-based motion capture methods.Pose2Sim stands for "OpenPose to OpenSim", as it uses OpenPose inputs (2D coordinates obtained from multiple videos) and leads to an OpenSim result (full-body 3D joint angles).
High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation

Learning-based methods have effectively promoted the community of image compression. Meanwhile, variational autoencoder(VAE) based variable-rate approaches have recently gained much attention to avoid the usage of a set of different networks for various compression rates. Despite the remarkable performance that has been achieved, these approaches would be readily corrupted once multiple compression/decompression operations are executed, resulting in the fact that image quality would be tremendously dropped and strong artifacts would appear. Thus, we try to tackle the issue of high-fidelity fine variable-rate image compression and propose the Invertible Activation Transformation(IAT) module. We implement the IAT in a mathematical invertible manner on a single rate Invertible Neural Network(INN) based model and the quality level(QLevel) would be fed into the IAT to generate scaling and bias tensors. IAT and QLevel together give the image compression model the ability of fine variable-rate control while better maintaining the image fidelity. Extensive experiments demonstrate that the single rate image compression model equipped with our IAT module has the ability to achieve variable-rate control without any compromise. And our IAT-embedded model obtains comparable rate-distortion performance with recent learning-based image compression methods. Furthermore, our method outperforms the state-of-the-art variable-rate image compression method by a large margin, especially after multiple re-encodings.
Emulation of greenhouse‐gas sensitivities using variational autoencoders

Abstract Flux inversion is the process by which sources and sinks of a gas are identified from observations of gas mole fraction. The inversion often involves running a Lagrangian particle dispersion model (LPDM) to generate simulations of the gas movement over a domain of interest. The LPDM must be run backward in time for every gas measurement, and this can be computationally prohibitive. To address this problem, here we develop a novel spatio‐temporal emulator for LPDM sensitivities that is built using a convolutional variational autoencoder (CVAE, a two‐piece neural network capable of condensing and reconstructing images). With the encoder segment of the CVAE, we obtain approximate (variational) posterior distributions over latent variables in a low‐dimensional space. We then use a spatio‐temporal Gaussian process emulator on the low‐dimensional space to emulate new variables at prediction locations and time points. Emulated variables are then passed through the decoder segment of the CVAE to yield emulated sensitivities. We show that our CVAE‐based emulator outperforms the more traditional emulator built using empirical orthogonal functions and that it can be used with different LPDMs. We conclude that our emulation‐based approach can be used to reliably reduce the computing time needed to generate LPDM outputs for use in high‐resolution flux inversions.
AnyPlace: Automatic Gaze Alignment of the Teleported Avatar for MR Collaborative Environments

Tele-conference systems are widely used as a form of communication media between remote sites. In order to overcome the limitations of video-based tele-conference systems with the continued technological innovations in mixed reality (MR), the use of a three-dimensional teleported avatar, in which a remote participant is teleported into a local environment, would be an effective future tele-conference system that would allow natural movement and interaction in the same location. However, technical difficulties must be resolved to enable control of the teleported avatar adapted to the environmental differences of the remote location and the user’s situation. This paper presents a novel method to adjust automatic gaze alignment of the teleported avatar with matching in the local site for MR collaborative environments. We ran comparative validation experiments to measure spatial accuracy of the gaze and evaluate the user’s communication efficiency using our method. In a quantitative experiment, the degree of gaze matching error in various environments was found to form a mirror-symmetrical U-shape, and the necessity of gaze matching gain was also recognized. Additionally, our experimental study showed that participants felt a greater co-presence during communication than in an idle situation without conversation.
Machine Learning Techniques and Systems for Mask-Face Detection—Survey and a New OOD-Mask Approach

Mask-face detection has been a significant task since the outbreak of the COVID-19 pandemic in early 2020. While various reviews on mask-face detection techniques up to 2021 are available, little has been reviewed on the distinction between two-class (i.e., wearing mask and without mask) and three-class masking, which includes an additional incorrect-mask-wearing class. Moreover, no formal review has been conducted on the techniques of implementing mask detection models in hardware systems or mobile devices. The objectives of this paper are three-fold. First, we aimed to provide an up-to-date review of recent mask-face detection research in both two-class cases and three-class cases, next, to fill the gap left by existing reviews by providing a formal review of mask-face detection hardware systems; and to propose a new framework named Out-of-distribution Mask (OOD-Mask) to perform the three-class detection task using only two-class training data. This was achieved by treating the incorrect-mask-wearing scenario as an anomaly, leading to reasonable performance in the absence of training data of the third class.
Secure Reversible Data Hiding in Images Based on Linear Prediction and Bit-Plane Slicing

Reversible Data Hiding (RDH) should be secured as per requirements to protect content in open environments such as the cloud and internet. Integrity and undetectability of steganographic images are amongst the main concerns in any RDH scheme. As steganographic encryption using linear prediction over bit-planes is challenging, so the security and embedding capacity of the existing RDH techniques could not be adequate. Therefore, a new steganographic technique is proposed which provides better security, higher embedding capacity and visual quality to the RDH scheme. In this technique, the cover image is divided into n-bit planes (nBPs) and linear prediction is applied to it. Next, the histogram of the residual nBPs image is taken, and secret data bits are encrypted using the RC4 cryptographic algorithm. To embed the encrypted secret data bits, the histogram shifting process is applied. This is achieved by using peak and zero pairs of residual nBPs images. This scheme provides security to the cover image and hidden data. The proposed RDH scheme is capable of extracting the embedded secret data accurately and recovering the original cover or residual nBPs image.
A Novel Method for Unexpected Obstacle Detection in the Traffic Environment Based on Computer Vision

Obstacle detection is the basis for the Advanced Driving Assistance System (ADAS) to take obstacle avoidance measures. However, it is a very essential and challenging task to detect unexpected obstacles on the road. To this end, an unexpected obstacle detection method based on computer vision is proposed. We first present two independent methods for the detection of unexpected obstacles: a semantic segmentation method that can highlight the contextual information of unexpected obstacles on the road and an open-set recognition algorithm that can distinguish known and unknown classes according to the uncertainty degree. Then, the detection results of the two methods are input into the Bayesian framework in the form of probabilities for the final decision. Since there is a big difference between semantic and uncertainty information, the fusion results reflect the respective advantages of the two methods. The proposed method is tested on the Lost and Found dataset and evaluated by comparing it with the various obstacle detection methods and fusion strategies. The results show that our method improves the detection rate while maintaining a relatively low false-positive rate. Especially when detecting unexpected long-distance obstacles, the fusion method outperforms the independent methods and keeps a high detection rate.
On the Uncertain Single-View Depths in Colonoscopies

Estimating depth information from endoscopic images is a prerequisite for a wide set of AI-assisted technologies, such as accurate localization and measurement of tumors, or identification of non-inspected areas. As the domain specificity of colonoscopies –deformable low-texture environments with fluids, poor lighting conditions and abrupt sensor motions– pose challenges to multi-view 3D reconstructions, single-view depth learning stands out as a promising line of research. Depth learning can be extended in a Bayesian setting, which enables continual learning, improves decision making and can be used to compute confidence intervals or quantify uncertainty for in-body measurements. In this paper, we explore for the first time Bayesian deep networks for single-view depth estimation in colonoscopies. Our specific contribution is two-fold: 1) an exhaustive analysis of scalable Bayesian networks for depth learning in different datasets, highlighting challenges and conclusions regarding synthetic-to-real domain changes and supervised vs. self-supervised methods; and 2) a novel teacher-student approach to deep depth learning that takes into account the teacher uncertainty.
Crime Scene Shoeprint Image Retrieval: A Review

Shoeprints performs a vital role in forensic investigations. It has been an advanced research issue in forensic science. The main purpose of shoeprint image retrieval is to acquire a ranking list of shoeprint images in a database, according to their feature similarities to the query image. In this way, a shoeprint can not only be used as an exhibit for bringing criminal charges but also to provide a clue to a case. The goal of this work is to present an overview of the existing works conducted in shoeprint image retrieval. We detail the different phases of the shoeprint retrieval task and present a summary of the state-of-the-art methods. We analyzed the difficulties and problems in this field and discussed future work directions. This review may help neophytes become involved in research easily and quickly.
An Evaluation Model of Agricultural Entrepreneurial Ecological Environment Quality Oriented to Public Health Topic Using the Optimized Neural Network Algorithm

As the primary industry, agriculture is a prerequisite for human survival and a basic industry of the national economy. As a result, the growth of agriculture is vital for the nation’s socioeconomic development. The construction of ecological civilization and the reform and innovation of farmers’ digital entrepreneurial behavior in the region are intimately correlated with the agricultural eco-environmental quality. Therefore, it is crucial for us to improve control and maintenance of the agricultural eco-environment. A requirement for directing the sustainable growth of the agricultural economy is evaluating and identifying the quality of the regional eco-environment and the issues it faces. In view of this, the study employs Jiangsu Province as a research example, builds a comprehensive index evaluation system, and uses the modified BP neural network to evaluate the agricultural eco-environmental quality in the target area between 2010 and 2020. First, the evaluation system is constructed from four dimensions: ecological resource status, environmental pollution degree, eco-environmental protection efforts, and agricultural development level, and 14 influencing factors are then screened out as specific secondary evaluation indicators. Second, a GA-BP model will be applied to comprehensively assess the quality of the agricultural eco-environment in the province over the past 10 years. The comprehensive score of the province’s agricultural eco-environmental quality was calculated, and the quality level was classified by this. Meanwhile, the indicators’ scores were paired with an analysis of each contributing factor’s degree of change. The findings indicate that from 2010 to 2020, Jiangsu Province’s agricultural eco-environment demonstrated a favorable trend, a reasonably high-quality level, and a tendency of continuous improvement from bad to good. The results of each indicator measuring the quality of the agricultural environment fluctuated, but overall they showed an upward trend. Finally, in order to offer a reference for agricultural eco-environment control and protection and a good ecological environment for farmers’ digital entrepreneurship in the near future, countermeasures and suggestions are proposed for the restoration of the agricultural eco-environment in Jiangsu Province referring to the results of the research and the problems already present in the area.
Augmented Reality for Solar System Learning

AbstractAlong with the rapid development of smartphones and the absence of Solar System learning media in the case study of SMPN 3 RAMPI, for this reason, research, and development of Augmented Reality (AR) for Solar System Learning were made to make Android-based smartphones a media for learning the Solar System using Augmented Reality Technology and introducing Augmented Reality technology to students. The research method is the Research and Development (R&amp;D) method with the ADDIE (analysis, Design, Development, Implementation, evaluation) development model. The results of this study are: (1) This learning application is made using Blender software, Unity, and the Vuforia database, which helps facilitate the development process; (2) This learning application can already be applied in research locations and used as learning media that are more effective, efficient, and interactive so that they can help increase students' interest in learning; (3) The results of the application feasibility test questionnaire with usability-based questions distributed to selected respondents get a score of 90%, which is converted according to the feasibility percentage table, then the application is declared very feasibly.