Reviewerly/exHarmony huggingface dataset:
Rating the Acquisition of Pre-writing Skills in Children: An Analysis Based on Computer Vision and Data Mining Techniques in the Ecuadorian Context

Pre-writing skills are a set of essential skills to learn to write. Commonly, in South America’s public schools, a teacher has a class with approximately 30 or more students. As a result, the teacher has the challenging task to detect if a child has difficulties in pre-writing essential activities. In light of the above, in this paper, we present an analysis to determine the feasibility of using computer vision and data mining techniques to determine if a child fails to meet, meets few, or meets a pre-writing skill. We conducted the process with the open corpus “UPS-Writing-Skills,” containing the HU moments and the shape signature descriptors extracted from a collection of 358 images drawn by children.
Sign language recognition based on spatiotemporal convolutional neural network and attention mechanism

Sign language recognition is an important technology for facilitating communication between deaf and hearing people in the field of computer vision. In view of the fact that the general 3D neural network model cannot achieve good recognition effect for video-level sign language and there are too many parameters, In this paper, a method based on the fusion of R(2+1)D network and attention mechanism is used to study the problem of isolated word sign language video recognition. First, the R (2+1)D network makes the network more expressive and easier to optimize by splitting the 3D convolutional structure into 2D plus 1D convolutions. On the other hand, the attention mechanism is integrated, which further enhances the feature extraction ability of the key actions in the sign language video of the network, and improves the recognition accuracy. The method adopted in this paper is trained and validated on the Chinese Sign Language Recognition dataset (CSL). The experimental results show that the method in this paper achieves better classification results in the sign language data samples extracted by CSL. Compared with the current mainstream methods, this method can further improve the accuracy of sign language recognition.
Crystal: Enhancing Blockchain Mining Transparency With Quorum Certificate

Researchers have discovered a series of theoretical attacks against Bitcoin's Nakamoto consensus; the most damaging ones are selfish mining, double-spending, and consistency delay attacks. These attacks have one common cause: block withholding. This paper proposes Crystal, which leverages quorum certificates to resist block withholding misbehavior. Crystal continuously elects committees from miners and requires each block to have a quorum certificate, i.e., a set of signatures issued by members of its committee. Consequently, an attacker has to publish its blocks to obtain quorum certificates, rendering block withholding impossible. To build Crystal, we design a novel two-round committee election in a Sybil-resistant, unpredictable and non-interactive way, and a reward mechanism to incentivize miners to follow the protocol. Our analysis and evaluations show that Crystal can significantly mitigate selfish mining and double-spending attacks. For example, in Bitcoin, an attacker with 30% of the total computation power will succeed in double-spending attacks with a probability of 15.6% to break the 6-confirmation rule; however, in Crystal, the success probability for the same attacker falls to 0.62%. We provide formal end-to-end safety proofs for Crystal, ensuring no unknown attacks will be introduced. To the best of our knowledge, Crystal is the first protocol that prevents selfish mining and double-spending attacks while providing safety proof.
Primitive Contrastive Learning for Handwritten Mathematical Expression Recognition

Contrastive learning has gained significant attention recently as it can learn a representation from a large amount of unlabeled training data to improve downstream tasks. While the existing approaches mainly focus on standard tasks of image classification and object detection, they are not easily applied to structured prediction problems. In this paper, we propose an unsupervised pre-trained model, called PrimCLR, for handwritten mathematical expression recognition. For a formula recognition model of encoder-decoder architecture, a pre-trained representation is obtained by PrimCLR, where the contrastive loss is computed from pairs of patches so as to better discriminate primitives. The pre-trained representation is transferred to downstream formula recognition with supervised fine-tuning. Experiments show that pre-training by PrimCLR can significantly improve the formula recognition performance, and PrimCLR shows superiority to conventional contrastive learning methods. Our model achieves state-of-the-art performance on standard datasets CROHME 2016 and CROHME 2019.
SILK-BPR: Identify Implicit Friends with Self-guided Walking for Social Recommendation

With the increasing scale of data to be processed by the recommendation system and the rapid growth of the number of users and commodities, the problems of data sparsity and cold start become more and more severe. Many studies have found that introducing implicit social relationships into recommendation can alleviate the above problems. Among these studies, the method of identifying implicit friends for each user in heterogeneous information networks based on meta-paths achieves good performance. However, meta paths need to be preset artificially and require strong professional domain knowledge, which makes it impossible to capture semantic information accurately and effectively. In this paper, we propose a novel social recommendation model termed as SILK-BPR which is an extension of the Self Guided Walk(SILK) model to by bypass the meta-paths and identify implicit friends for recommendation. Specially, SILK-BPR first establishes a guidance matrix to record the transition probability from user nodes to interest nodes. And then conducts a self-guided walking on the matrix to mine the implicit friends which are classified into different types according to the appear times in different sequences. Finally, these implicit friends are integrated into an enhanced social Bayesian Personalized Ranking model for Top-N recommendation. Experimental results on real-world datasets demonstrate that SILK-BPR significantly outperforms state-of-the-art methods.
Making in/with Nature: Lessons from an Eco-village for Sustainable Making

In this pictorial, we present findings from a field study in an eco-village. Eco-villages are communities where residents share environmental values and have developed long-term making practices around housing, food production and other methods of living self-reliant. We investigate sustainable making practices to study how and why makers design, build, and retrofit their settings to make their communities more sustainable. This qualitative study brings our learnings from the eco-village to the field of Human-Computer Interaction and discusses three aspects of sustainable making: non-humans as stakeholder, non-specialist solutions, and human ingenuity and sustainable identity. Perspectives of sustainable making encourage researchers to look beyond human-centered design to enable making as the catalyst that prospers sustainable and resilient future.
Feature Selection and Feature Weighting Using Tunicate Swarm Genetic Optimization Algorithm With Deep Residual Networks

Feature selection (FS) method is applied for extracting only the relevant information from the dataset. FS seemed to be an optimization concept because appropriate feature selection is the significant role of any classification problem. Similarly, feature weighting is employed to enhance the classification performance along with FS process. In this paper, feature selection and feature weighting has been performed by integrated an optimization algorithm called tunicate swarm genetic algorithm (TSGA) with deep residual network (DRN). TSGA is the combination of tunicate swarm algorithm (TSA) and genetic algorithm (GA) incorporated to increase the performance of the classifier. This wrapper method-based feature selection and feature weighting techniques are performed to reduce the computation time as well as complexity. The effectiveness of the proposed method is estimated and compared with different methods such as TSA, CS-GA, and PSO-GA. The performance of DRN classifier is also validated and compared to existing classifiers like KNN, C4.5, and RF.
PolyTracker: Progressive Contour Regression for Multiple Object Tracking and Segmentation

State-of-the-art multiple object tracking and segmentation methods predict a pixel-wise segmentation mask for each detected instance object. Such methods are sensitive to the inaccurate detection and suffer from heavy computational overhead. Besides, when associating pixel-wise masks, additional optical flow networks are required to assist in mask propagation. To relieve these three issues, we present PolyTracker, which adopts object contour, in the form of a vertex sequence along with the object silhouette, as an alternative representation of the pixel-wise segmentation mask. In the PolyTracker, we design an effective contour deformation module based on an iterative and progressive mechanism, which is robust to the inaccurate detection and has low model complexity. Furthermore, benefiting from the powerful contour deformation module, we design a novel data association method, which achieves effective contour propagation by fully mining contextual cues from contours. Since data association relies heavily on pedestrian appearance representation, we design a Reliable Pedestrian Information Aggregation (RPIA) module to fully exploit the discriminative re-identification feature. Extensive experiments demonstrate that our PolyTracker sets the promising records on the MOTS20 benchmark.
Improved Faster R-CNN Algorithm for Transmission Line Small Target Detection

Bird’s nests, as well as suspended foreign objects such as plastic and rags, are serious potential safety hazards on transmission lines. Because the Bird’s nests and suspended foreign objects in the unmanned aerial vehicle (UAV) images often belong to small targets with less pixels, more noise and easy to be disturbed, the detection of these objects puts forward higher requirements for the detection algorithm. In this paper, an deep learning-based algorithm are designed for the detection of these two kinds of small targets. By adding the attention mechanism module to the backbone network in this algorithm, the importance of each part of the feature map extracted from UAV image are refined in two different dimensions, and sufficient context learning are carried out to improve the detection of small targets. Further more, a post-processing algorithm based on Soft-NMS are designed to prevent small targets from being filtered and further improve the detection of small targets. Compared with the benchmark algorithm Faster R-CNN, the proposed algorithm achieves $$4.7\%$$ improvement in average precision (AP).
Cross-Domain Learning in Deep HAR Models via Natural Language Processing on Action Labels

Nowadays, deep learning approaches lead the state-of-the-art scores in human activity recognition (HAR). However, the supervised nature of these approaches still relies heavily on the size and the quality of the available training datasets. The complexity of activities of existing HAR video datasets ranges from simple coarse actions, such as sitting, to complex activities, consisting of multiple actions with subtle variations in appearance and execution. For the latter, the available datasets rarely contain adequate data samples. In this paper, we propose an approach to exploit the action-related information in action label sentences to combine HAR datasets that share a sufficient amount of actions with high linguistic similarity in their labels. We evaluate the effect of inter- and intra-dataset label linguistic similarity rate in the process of a cross-dataset knowledge distillation. In addition, we propose a deep neural network design that enables joint learning and leverages, for each dataset, the additional training data from the other dataset, for actions with high linguistic similarity. Finally, in a series of quantitative and qualitative experiments, we show that our approach improves the performance for both datasets, compared to a single dataset learning scheme.
Practical limits and opportunities with speckle metrology

In this presentation, the role of speckles as a carrier of information in phase-based optical metrology is re-visited. Starting with the fundamental mechanisms for speckle decorrelation it is shown that information about the state of an object is transferred through the modified mutual coherence function and can be detected either through the phase, speckle movement, speckle decorrelation or as a combination. The presentation is focusing on practical scale laws that set the limit for what is possible to achieve with present day technology and is demonstrated with a few examples incorporating measurements of microstructural changes, strain, shape, lenses and other refractive index objects.
Penyelesaian Sistem Persamaan Non-Linier Dengan Metode Perbaikan Regula Falsi Menggunakan Bahasa Pemrograman Javascript

Numerical method is a technique for solving mathematical problems with arithmetic operations. This method can solve problems where analytical calculations cannot be used. There are many types of numerical methods, one of which is the modified regula falsi method. This method is used to overcome the stagnation point case that occurs in the falsified regulation method. Solving non-linear equations with numerical methods can get results that are close to true (exact), because the calculations are done iteratively. In the numerical method there is an approximation solution or an approximation solution. The overlay solution is not the same as the true solution, so there is a difference between the two. This difference is called an error. Solutions with numerical methods always return a value in the form of a number.
Image-to-Image Steganography Using Encoder-Decoder Network

In this paper, a convolution neural network is utilized for image-to-image steganography using encoder decoder architecture. A new loss function is designed to improve the invisibility of payload image. The encoder-decoder architecture is used for the image-to-image steganography. The developed architecture is evaluated on the well-known image dataset and compared with the recently developed models. The proposed model was able to withstand the stegoanalyzer attack and better visual quality of stego image. It is able to achieve great imperceptibility.
Robust CI fusion Kalman estimators for systems with multiplicative noises, colored measurements noises and uncertain noise variances

The robust covariance intersection (CI) fusion estimation problem for multisensor system with multiplicative noises, colored measurements noises and uncertain noise variances is addressed in this paper. By using augmented state approach, combining with a fictitious noise technique, the original system is converted into a multi-model system only with uncertain noise variances. The robust local Kalman filter and smoother are designed based on the robust local Kalman predictor. Then, based on the minimax robust estimation principle, robust CI fusion Kalman estimators (filter, predictor and smoother) are presented for worst-case system with the conservative upper bounds of uncertain noise variances. The robustness is proved by the Lyapunov equation approach. A simulation example applied to Internet-based three-tank system shows the correctness and effectiveness of the proposed results.
Development of a teacher's professional career in the conditions of modern social space

The article examines the features of a teacher's professional career development in the present-day world. The purpose of the study is to determine the main directions of changes in the system of professional career development of a teacher in modern conditions. The development of methods of social interaction and digitalization of the modern world are presented by the author as factors influencing the specifics of the transformation of the existing systems of professional activity. The scientific novelty lies in the idea of changing the practice of developing a professional position in terms of using digital technologies at the level of transformation. As a result, the main directions of the implementation of the teacher's career development program in the modern conditions have been identified.
General High-Pass Convolution: A Novel Convolutional Layer for Image Manipulation Detection

Image Manipulation Detection is different from most computer vision tasks, such as Image Classification. The latter pays more attention to image content, while the former focuses more on manipulation trace. However, plain convolutional layer tends to learn features that represent image content rather than features that represent manipulation trace, which degrades the performance of traditional CNNs on Image Manipulation Detection. Inspired by constrained convolutional layer proposed by Bayar et al., we propose General High-Pass Convolution, a new form of convolutional layer which is capable of motivating CNNs to learn manipulation trace features. General High-Pass Convolution is designed to simulate a set of learnable high-pass filters to suppress the image content, thus motivating the CNNs to learn manipulation trace features which are mainly present in the high-frequency components of the image. We conduct comprehensive experiments to evaluate the effectiveness of General High-Pass Convolution. The experimental results show that General High-Pass Convolution achieves better performance than Bayar et al.’s constrained convolutional layer, and can be combined with CNN backbone networks to improve their performance on Image Manipulation Detection, such as VGG and ResNet.
Construction of Modern Industrial Colleges from the Perspective of Symbiosis

Modern industrial college is a new organizational form of industry-education integration. The process of industry-education integration is essentially a cross-border symbiotic process of education and industry promoted by the government. At present, in the process of deepening the industry-education integration, local universities are faced with the structural contradiction between supply and demand of industry and education, lack of impetus for university-industry cooperation and lack of external environment support. Symbiosis theory, as a new theoretical paradigm to study social symbiosis, reveals the internal mechanism of the coordinated development between organizations from the dimensions of symbiosis power, symbiosis mode and symbiosis environment. Based on the symbiosis theory and its analysis framework, the industrial organization form of the college is analyzed, the symbiosis system architecture of the industrial college is discussed, and the construction path of modern industrial college is given from four dimensions: optimizing the symbiotic interface symbiotic efficiency, enhancing the compatibility degree of symbiotic unit quality parameters, rationally distributing symbiotic energy, and improving the incentive degree of symbiotic environment, which has important guiding significance for solving the problems of the separation of industry and education and the integration of school and enterprise in local universities.
Siamese Network with Multi-scale Feature Fusion and Dual Attention Mechanism for Template Matching

Factors like object's appearance changes, occlusions, and background changes have a negative impact on template matching tasks. To suppress this effect and improve template matching accuracy, we propose a Siamese network with multi-scale feature fusion and dual attention module in this paper. A multi-scale feature fusion module is introduced to extract shallow structural and deep semantic features, and learnable weight coefficients are used to adaptively fuse multi-scale features to improve representation capabilities of the object. In order to improve feature recognition and eliminate redundancy caused by feature fusion, a dual attention mechanism module is introduced, which applies weights to features from separate channels and spatial positions. Finally, the features are normalized, and the cross-correlation is calculated to produce a similarity score map and locate the best matching region. When compared to the two state-of-the-art CNN-based template matching algorithms, QATM and Deep-DIM, the experimental results show that the algorithm in this paper improves template matching accuracy by 4.72 percent and 2.30 percent, respectively, and has the least time-consuming.
Problems and Perspectives of ICT in Higher Education Institutions of Kazakhstan

Contemporary teaching, learning and assessment processes involve more information and communication technologies. The main goal of using information technologies in the educational process is to improve the quality of education by individualization and strengthening intellectual capabilities of students in the information society. This research considered the information communication technology use in higher education institutions of Kazakhstan, exploring its benefits for students and suggesting possible solutions for the problems arising with ICT implementation. For the purpose of this paper a small fragment of students' survey out of the ongoing research project was used. 558 students from various Kazakh universities were surveyed. One of the questions concerned IT development and digitalization level of the universities, because it enhances students' inclination for innovation and entrepreneurship. According to survey results forty three percent of students think that their universities have been sufficiently digitalized. Other forty percent remained neutral, and seventeen percent complained about the ICT equipment, which means that universities should address this issue to remain competitive. The research contributes to previous studies by focusing on students' attitudes toward ICT implementation. It describes both advantages and disadvantages of the ICT in education, and tries to summarize the current conditions of Kazakhstani higher education. For the successful policy implementation, the suggestions should come from the bottom. Therefore, the students' hands-on knowledge and experience with ICT implementation should be taken into consideration and could be helpful for further integration into educational process.