Reviewerly/exHarmony huggingface dataset:
Dynamic Planning for Obstacle Avoidance of Crawler Based on Gaussian Model

In order to solve the problems of obstacle avoidance planning and trajectory optimization of unmanned platform in hilly orchards, this paper proposes a dynamic planning algorithm based on Gaussian probability model to predict dynamic obstacle behavior. The technical route and method of theoretical derivation-algorithm design-simulation analysis-real vehicle testing are both adopted. Firstly, the global reference path of orchard is optimized based on the quadratic programming. Secondly, the intentions of surrounding pedestrians and other dynamic obstacles are estimated based on the Gaussian probability model. Subsequently, a dynamic grid map is created based on the Frenet coordinate system. Then, the obstacles avoidance paths are dynamically planned to obtain discrete optimal paths. Finally, the quintic polynomial curve is utilized to connect the sampling points to generate the path with continuous curvature. The analysis by ROS/Rviz simulation indicates that the planning time decreases by 57.97%, the number of sampled nodes decreases by 79.57%, and the curvature is smoother and continuous compared with the RRT algorithm. A field test is conducted on a hilly orchard road. With the Gaussian model, the algorithm can effectively predict the behavioral state of dynamic obstacles. The platform can autonomously plan a continuous, smooth and safe driving route in a shorter period of time, effectively avoiding obstacles and reducing the impact of steering-in-place on operating efficiency. Through the research, a dynamic obstacle behavior prediction and obstacle avoidance algorithm is proposed for the crawler chassis in hilly orchards based on the Gaussian probability model and dynamic programming, and the obstacle avoidance curve obtained by the algorithm satisfies the kinematic constraints of the crawler chassis, and has good safety, better real-time and enforceability. Additionally, a comprehensive set of software and hardware solutions for spontaneous static obstacle avoidance planning in hilly orchards is presented.
A Comprehensive Survey on Face Quality Detection in a Video Frame

The correctness of the generated face data, which is impacted by a number of variables, significantly affects how well face analysis and recognition systems perform. By automatically analysing the face data quality in terms of its biometric value, it might be able to identify low-quality data and take the necessary action. With a focus on visible wavelength face image input, this study summarises the body of research on the evaluation of face picture quality. The use of DL-based methods is unquestionably expanding, and there are major conceptual differences between them and current approaches, such as the inclusion of quality assessment in face recognition models. In addition to image selection, which is the topic of this article, face picture quality assessment can be used in a wide range of application scenarios. The requirement for comparative algorithm assessments and the difficulty of creating Deep Learning (DL) techniques that are intelligible in addition to providing accurate utility estimates are just a few of the issues and topics that remain unanswered. For each frame, the suggested method is compared to traditional facial feature extraction, and for a collection of video frames, it is compared to well-known clustering algorithms.
Peran Taman Bacaan Masyarakat berbasis inklusi sosial dalam mendukung tercapainya tujuan pembangunan berkelanjutan di Kabupaten Bandung

Introduction Libraries with a variety of resources and information services can support the Sustainable Development Goals (SDGs) in society. This study describes the role of social inclusion-based TBM in Bandung Regency, West Java to support SDGs.&#x0D; Data collection methods. This study was qualitative with a descriptive approach. Data analysis was started with data reduction, data display and conclusion.&#x0D; Results and Discussion. TBM contributes to build a community-based literacy ecosystem based on the community characteristics and information needs in their environment. Literacy activities at TBM may improve individual or group skills and give impacts to the community's quality of life through collborations with multiple parties. TBM is a place for consultation regarding life problems in the community by providing access to information and knowledge to support the community's productivity and welfare.&#x0D; Conclusion and suggestions. The presence of community-based TBM supports poverty alleviation efforts. Literacy programs at TBM can help improve people's life skills by giving them more empowerment, training, and mentoring. These activities have an impact on improving individual abilities and skills. TBM becomes a bridge for public libraries as information service providers and a place to find information to overcome life's problems.
Model Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors

Deep learning-based models generalize better to unknown data samples after being guided “where to look” by incorporating human perception into training strategies. We made an observation that the entropy of the model’s salience trained in that way is lower when compared to salience entropy computed for models training without human perceptual intelligence. The research problem addressed by this paper is whether lowering the entropy of model’s class activation map helps in further increasing the performance, on top of the performance increase we observe for human saliency-based model’s training. In this paper we propose and evaluate four new entropy-based loss functions controlling the model’s focus, covering the full range of the level of such control, from none to its “aggresive” minimization. We show, using a problem of synthetic face detection, that improving the model’s focus, through lowering entropy by the proposed loss components, leads to models that perform better in an open-set scenario (in which the test samples are synthesized by unknown generative models): the obtained average Area Under the ROC curve (AUROC) ranges from 0.72 to 0.78, compared to AUROC = 0.64 observed for a state-of-the-art human-salience-only-based control of the model’s focus. We also show that optimal performance is obtained when the model’s loss function blends three aspects: regular classification performance, low-entropy of the model’s focus, and closeness of the model’s focus to human saliency. The major conclusion from this work is that maximization of the model’s focus is an important regularizer allowing the models to generalize better in an open set scenario. Future work directions include methods of blending classification-, human salience-, and model’s salience entropy-based loss components to achieve optimal performance in other domains than the synthetic face detection.
Dual Convolutional Neural Network Classifier with Pyramid Attention Network for Image-Based Kinship Verification

A family is the smallest entity that formed the world with specific characteristics. The characteristics of a family are that the member can/may share some similar DNA and leads to similar physical appearances, including similar facial features. This paper proposed a dual convolutional neural network (CNN) with a pyramid attention network for image-based kinship verification problems. The dual CNN classifier is formed by paralleling the FaceNet CNN architecture followed by family-aware features extraction network and three final fully-connected layers. A channel-wise pyramid attention network is added after the last convolutional layers of FaceNet CNN architecture. The family-aware features extraction network is used to learn family-aware features using the SphereFace loss function. The final features used to classify the kin/non-kin pair are joint aggregation features between the pyramid attention features and family-aware features. At the end of the fully connected layer, a softmax loss layer is attached to learn kinship verification via binary classification problems. To analyze the performance of our proposed classifier, we performed experiments heavily on the Family in The Wild (FIW) kinship verification dataset. The FIW kinship verification dataset is the largest dataset for kinship verification currently available. Experiments of the FIW dataset show that our proposed classifier can achieve the highest average accuracy of 68.05% on a single classifier scenario and 68.73% on an ensemble classifier scenario which is comparable with other state-of-the-art methods.
An effective stacked autoencoder based depth separable convolutional neural network model for face mask detection

The COVID-19 pandemic has been infecting the entire world over the past years. To prevent the spread of COVID-19, people have acclimatised to the new normal, which includes working from home, communicating online, and maintaining personal cleanliness. There are numerous tools required to prepare to compact transmissions in the future. One of these elements for protecting individuals from fatal virus transmission is the mask. Studies have indicated that wearing a mask may help to reduce the risk of viral transmission of all kinds. It causes many public places to take efforts to ensure that its guests wear adequate face masks and keep a safe distance from one another. Screening systems need to be installed at the doors of businesses, schools, government buildings, private offices, and/or other important areas. A variety of face detection models have been designed using various algorithms and techniques. Most of the articles in the previously published research have not worked on dimensionality reduction in conjunction with depth-wise separable neural networks. The necessity of determining the identities of people who do not cover their faces when they are in public is the driving factor for the development of this methodology. This research work proposes a deep learning technique to determine if a person is wearing mask or not and identifies whether it is properly worn or not. Stacked Auto Encoder (SAE) technique is implemented by stacking the following components: Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). PCA is used to reduce the irrelevant features in the images and resulted high true positive rate in the detection of mask. We achieved an accuracy score of 94.16% and an F1 score of 96.009% by the application of the method described in this research.
Running a Sustainable Social Media Business: The Use of Deep Learning Methods in Online-Comment Short Texts

With the prevalence of the Internet in society, social media has considerably altered the ways in which consumers conduct their daily lives and has gradually become an important channel for online communication and sharing activities. At the same time, whoever can rapidly and accurately disseminate online data among different companies affects their sales and competitiveness; therefore, it is urgent to obtain consumer public opinions online via an online platform. However, problems, such as sparse features and semantic losses in short-text online reviews, exist in the industry; therefore, this article uses several deep learning techniques and related neural network models to analyze Weibo online-review short texts to perform a sentiment analysis. The results show that, compared with the vector representation generated by Word2Vec’s CBOW model, BERT’s word vectors can obtain better sentiment analysis results. Compared with CNN, BiLSTM, and BiGRU models, the improved BiGRU-Att model can effectively improve the accuracy of the sentiment analysis. Therefore, deep learning neural network systems can improve the quality of the sentiment analysis of short-text online reviews, overcome the problems of the presence of too many unfamiliar words and low feature density in short texts, and provide an efficient and convenient computational method for improving the ability to perform sentiment analysis of short-text online reviews. Enterprises can use online data to analyze and immediately grasp the intentions of existing or potential consumers towards the company or product through deep learning methods and develop new services or sales plans that are more closely related to consumers to increase competitiveness. When consumers experience the use of new services or products again, they may provide feedback online. In this situation, companies can use deep learning sentiment analysis models to perform additional analyses, forming a dynamic cycle to ensure the sustainable operation of their enterprises.
Unsupervised Monocular Depth and Camera Pose Estimation with Multiple Masks and Geometric Consistency Constraints

This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers in challenging scenes such as those with dynamic objects and occluded regions. As a result, multiple mask technologies and geometric consistency constraints are adopted in this research to mitigate their negative impacts. Firstly, multiple mask technologies are used to identify numerous outliers in the scene, which are excluded from the loss computation. In addition, the identified outliers are employed as a supervised signal to train a mask estimation network. The estimated mask is then utilized to preprocess the input to the pose estimation network, mitigating the potential adverse effects of challenging scenes on pose estimation. Furthermore, we propose geometric consistency constraints to reduce the sensitivity of illumination changes, which act as additional supervised signals to train the network. Experimental results on the KITTI dataset demonstrate that our proposed strategies can effectively enhance the model's performance, outperforming other unsupervised methods.
AV PV-RCNN: Improving 3D Object Detection with Adaptive Deformation and VectorPool Aggregation

Three-dimensional object detection has attracted more and more attention from industry and academia due to its wide application in various fields such as autonomous driving and robotics. Currently, the refinement methods used by advanced two-stage detectors cannot fully adapt to different object scales, different point cloud densities, partial deformation and clutter, and excessive resource consumption. We propose a point cloud-based 3D object detection method that can adapt to different object scales and aggregate local features with less resources. The method first passes through an adaptive deformation module based on a 2D deformable convolutional network, which can adaptively collect instance-specific features from where the information content exists. Secondly, through a VectorPool aggregation module, this module can better aggregate local point features with less resource consumption. Finally, through a context fusion module, the key points can filter out relevant context information for the refinement stage. Our proposed detection method not only achieves better accuracy on the KITTI dataset, but also consumes less resources than the original detectors and has faster inference speed.
Cybernetics and Directed Evolution

Results. The place and role of cybernetics methods for solving the global problem of directed evolution are considered. The author investigates the eventual phenomenon of the interaction of two coryphaei of Ukrainian science V.I. Vernadsky and V.M. Glushkov in the formation of these scientific fields and the synergistic effect of this interaction. A special attention is paid to the applied aspects of using the scientific theories when implementing complex topical processes at the state and global levels. The list of the applied fields of cybernetics with their implementation in the projects of the Institute of Cybernetics under the leadership of V.M. Glushkov is endless, and his scientific ideas are successfully brought to life today. Keywords: cognitive intelligent technology, transdisciplinary scientific research (TSR), systemology of TSR, scientific picture of the world, directed evolution, noospheric theory, ontological engineering, convergence of technologies, consolidated intelligence, collective consciousness, research design, eventual analysis.
METHODOLOGY OF PEDAGOGICAL SUPPORT TO STUDENTS' SELF-DETERMINATION

Pedagogical support to students' self-determination is one of the urgent tasks of the modern educational process, however, in the literature there is no unified methodological approach to research and technology that will ensure the performance of this professional function. The purpose of this study is to select and validate the methodology applicable for the formation of this parameter and suitable for the implementation of pedagogical support. To achieve this goal the following methods were used: analysis of the existing methodological approaches, dialectical synthesis based on the analysis of the literature, and the method of design and implementation for a pedagogical model. The author offers a concept of pedagogical support for students' self-determination based on a system-synergetic approach and provides the rationale for the use of system-synergetic approach and additional tools for teacher training in students’ self-determination. The methodological approach described can be applied to the development of methodical recommendations aimed to train teachers how to provide pedagogical support to facilitate students’ self-determination.
A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment

Image aesthetic assessment (IAA) with neural attention has made significant progress due to its effectiveness in object recognition. Current studies have shown that the features learned by convolutional neural networks (CNN) at different learning stages indicate meaningful information. The shallow feature contains the low-level information of images, and the deep feature perceives the image semantics and themes. Inspired by this, we propose a visual enhancement network with feature fusion (FF-VEN). It consists of two sub-modules, the visual enhancement module (VE module) and the shallow and deep feature fusion module (SDFF module). The former uses an adaptive filter in the spatial domain to simulate human eyes according to the region of interest (ROI) extracted by neural feedback. The latter not only extracts the shallow feature and the deep feature via transverse connection, but also uses a feature fusion unit (FFU) to fuse the pooled features together with the aim of information contribution maximization. Experiments on standard AVA dataset and Photo.net dataset show the effectiveness of FF-VEN.
Adaptation of a multilingual speech representation model for a new, underresourced language via multilingual fine-tuning and continued pretraining

<h2>Abstract</h2> Neural models learned through self-supervised pretraining on large scale multilingual text or speech data have shown promising results for underresourced languages, particularly when there is access to a relatively large amount of data from related language(s). While the technology has the potential to aid in language documentation tasks, such as speech transcription, it is expected that most users will restrict themselves to fine-tuning publicly available models due to the high cost of pretraining such a model from scratch. We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language, focusing on actual fieldwork data from Ainu, a critically endangered language used in East Asia. Specifically, we (i) address the question of whether the benefits of cross-lingual transfer extend to fine-tuning as well; (ii) study the effect of additional pretraining using the small amount of data available for the target language. Our results show that continued pretraining on target language data leads to a substantial reduction in error rates. Furthermore, we find that if a model pretrained on a related speech variety or an unrelated language with similar phonological traits is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance when there is very little labeled data in the target language.
Action Recognition Network Based on Local Spatiotemporal Features and Global Temporal Excitation

Temporal modeling is a key problem in action recognition, and it remains difficult to accurately model temporal information of videos. In this paper, we present a local spatiotemporal extraction module (LSTE) and a channel time excitation module (CTE), which are specially designed to accurately model temporal information in video sequences. The LSTE module first obtains difference features by computing the pixel-wise differences between adjacent frames within each video segment and then obtains local motion features by stressing the effect of the feature channels sensitive to difference information. The local motion features are merged with the spatial features to represent local spatiotemporal features of each segment. The CTE module adaptively excites time-sensitive channels by modeling the interdependencies of channels in terms of time to enhance the global temporal information. Further, the above two modules are embedded into the existing 2DCNN baseline methods to build an action recognition network based on local spatiotemporal features and global temporal excitation (LSCT). We conduct experiments on the temporal-dependent Something-Something V1 and V2 datasets. We compare the recognition results with those obtained by the current methods, which proves the effectiveness of our methods.
Blockchain Architecture for IoT: Comparative Survey

Blockchain technology is increasingly being used in the internet of things (BCoT: Blockchain for IoT) to ensure the security and reliability of data exchanged between connected devices. There are several generic blockchain architectures for IoT that differ in their network and consensus characteristics. Blockchain architectures for IoT can be compared based on various criteria such as the type of storage used, consensus, and security. They should be selected based on the specific needs of each use case, and the resource constraints of connected devices and security and resilience requirements. In this article, we present a review of blockchain architectures for the internet of things that have been studied and applied in some well-known contemporary applications.
Performance Evaluation of Cryptographic Security Algorithms on Cloud

Cloud computing is a cost-effective approach to provide on demand computing and data storage solutions. Data storage is one of the key services provided by the cloud. Cloud offers improved efficiency, flexibility, and scalability, but all these advantages can be overturned if security is not taken into consideration. It is the cloud vender’s responsibility to keep the data safe with highly secure cloud services, which helps them earn the trust of the customers. Today, cloud security is critical since most organizations are already using cloud computing in alternate forms. There is always a concern that the highly sensitive business information and intellectual property may be exposed or misused due to increasingly sophisticated cyber threats. This research paper provides a distributed architecture for cloud data security which is independent of the underlying platforms. A cloud security architecture provides written and visual model to define how to configure and secure operations within the cloud. This paper compares the performance of RC4 against AES-256. The performance of the proposed encryption algorithms is evaluated on a widely used database MySQL. This paper provides a better solution to ensure the security of cloud databases by using two encryption algorithms.