Reviewerly/exHarmony huggingface dataset:
Shared Nearest Neighbour in Text Mining for Classification Material in Online Learning Using Mobile Application

There are many resources for media learning in online learning that all of the teachers made many media which it made a problem if there have the same subject and material. This problem made online learning having a big database and many materials made useless because the material has the same purpose. The big problem in overload database is that online learning can't be accessed by everyone. This research to fix this problem developed an algorithm in Artificial Intelligence for the classification of material in online learning with the same subject and purpose so that teachers can use already media. This algorithm is text mining and Shared Nearest Neighbour (SSN) that is embedded in the mobile application to display the classification and the location of searching media in database online learning. The testing in this research applied in 142 media with 130 data training and 12 data testing is the result of testing is 94,7% of the accuracy of the algorithm and The average of validation is 73,33%.
A blind medical image denoising method with noise generation network

In the process of medical images acquisition, the unknown mixed noise will affect image quality. However, the existing denoising methods usually focus on the known noise distribution.In order to remove the unknown real noise in low-dose CT images (LDCT), a two-step deep learning framework is proposed in this study, which is called Noisy Generation-Removal Network (NGRNet).Firstly, the output results of L0 Gradient Minimization are used as the labels of a dental CT image dataset to form a pseudo-image pair with the real dental CT images, which are used to train the noise generation network to estimate real noise distribution. Then, for the lung CT images of the LIDC/IDRI database, we migrate the real noise to the noise-free lung CT images, to construct a new almost-real noisy images dataset. Since dental images and lung images are all CT images, this migration can be achieved. The denoising network is trained to realize the denoising of real LDCT for dental images by using this dataset but can extend for any low-dose CT images.To prove the effectiveness of our NGRNet, we conduct experiments on lung CT images with synthetic noise and tooth CT images with real noise. For synthetic noise image datasets, experimental results show that NGRNet is superior to existing denoising methods in terms of visual effect and exceeds 0.13dB in the peak signal-to-noise ratio (PSNR). For real noisy image datasets, the proposed method can achieve the best visual denoising effect.The proposed method can retain more details and achieve impressive denoising performance.
RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.
Improved A-star Algorithm based on Topological Maps for Indoor Mobile Robot Path Planning

Aiming at the problem of low planning efficiency of traditional A-star algorithm in large indoor scenes, this paper proposes to improve the A-star algorithm based on the use of topological maps to improve the efficiency of A-star path planning in large scenes. First, use 2D lidar to construct a grid map to obtain high-precision environmental information. Secondly, the depth camera is used to capture indoor objects and construct a topological map. Finally, the connectivity of nodes in the topological map is used to improve the path planning problem of the A-star algorithm in large scenarios. Experiments show that the improved algorithm can quickly plan a path in large scenarios. Compared with the original A-star algorithm, this algorithm shortens the time cost and reduces the amount of calculation. Improve the planning efficiency of the algorithm.
Efficient Architectures for Full Hardware Scrypt-Based Block Hashing System

The password-based key derivation function Scrypt has been employed for many services and applications due to its protection ability. It has also been employed as a proof-of-work algorithm in blockchain implementations. Although this cryptographic hash function provides very high security, the processing speed and power consumption to generate a hashed block for the blockchain network are low-performance. In this paper, a high-speed and low-power hardware architecture of the Scrypt function is proposed to generate blocks for the Scrypt-based blockchain network. This architecture minimizes the number of main computational blocks to reduce the power consumption of the system. In addition, the proposed sharing resources and pipelined architectures make the calculation speed increase significantly while the hardware cost is reduced by half compared to the parallel non-pipelined architecture. The full hardware system is designed and implemented on Xilinx Virtex-7 and Aveo U280 FPGA platforms. The hash rate of the proposed system reaches 229.1 kHash/s. Its hash rate, hardware and energy efficiencies are much higher than those of the other works implemented on FPGA and GPU hardware platforms. The proposed hardware architecture is also successfully implemented in an ASIC design using ROHM 180 nm CMOS technology.
The expanding network of mineral chemistry throughout earth history reveals global shifts in crustal chemistry during the Proterozoic

Abstract Earth surface redox conditions are intimately linked to the co-evolution of the geosphere and biosphere. Minerals provide a record of Earth’s evolving surface and interior chemistry in geologic time due to many different processes (e.g. tectonic, volcanic, sedimentary, oxidative, etc.). Here, we show how the bipartite network of minerals and their shared constituent elements expanded and evolved over geologic time. To further investigate network expansion over time, we derive and apply a novel metric (weighted mineral element electronegativity coefficient of variation; wMEE CV ) to quantify intra-mineral electronegativity variation with respect to redox. We find that element electronegativity and hard soft acid base (HSAB) properties are central factors in mineral redox chemistry under a wide range of conditions. Global shifts in mineral element electronegativity and HSAB associations represented by wMEE CV changes at 1.8 and 0.6 billion years ago align with decreased continental elevation followed by the transition from the intermediate ocean and glaciation eras to post-glaciation, increased atmospheric oxygen in the Phanerozoic, and enhanced continental weathering. Consequently, network analysis of mineral element electronegativity and HSAB properties reveal that orogenic activity, evolving redox state of the mantle, planetary oxygenation, and climatic transitions directly impacted the evolving chemical complexity of Earth’s crust.
Category guided attention network for brain tumor segmentation in MRI

Objective. Magnetic resonance imaging (MRI) has been widely used for the analysis and diagnosis of brain diseases. Accurate and automatic brain tumor segmentation is of paramount importance for radiation treatment. However, low tissue contrast in tumor regions makes it a challenging task.Approach. We propose a novel segmentation network named Category Guided Attention U-Net (CGA U-Net). In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost. Moreover, we propose an intra-class update approach to reconstruct feature maps by aggregating pixels of the same category.Main results. Experimental results on the BraTS 2019 datasets show that the proposed method outperformers the state-of-the-art algorithms in both segmentation performance and computational complexity.Significance. The CGA U-Net can effectively capture the global semantic information in the MRI image by using the SAM module, while significantly reducing the computational cost. Code is available athttps://github.com/delugewalker/CGA-U-Net.
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation

Sign language recognition and translation first uses a recognition module to generate glosses from sign language videos and then employs a translation module to translate glosses into spoken sentences.Most existing works focus on the recognition step, while paying less attention to sign language translation.In this work, we propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network.In this way, the pre-trained model's language ability can be well explored and utilized to further boost the translation performance.Moreover, by exploring the representation space of sign language glosses and target spoken language, we propose a multi-level data augmentation scheme to adjust the data distribution of the training set.We conduct extensive experiments on two challenging benchmark datasets, PHOENIX-2014-T and ASLG-PC12, on which our method outperforms former best solutions by 1.65 and 1.42 in terms of BLEU-4.Our code is published at https://github.com/yongcaoplus/TIN-SLT.
Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization

In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.
TVShowGuess: Character Comprehension in Stories as Speaker Guessing

We propose a new task for assessing machines' ability to understand fictional characters in narrative stories.The task, TVSHOWGUESS, builds on the scripts of TV series and takes the form of guessing the anonymous main characters based on the backgrounds of the scenes and dialogues.Our human study supports that this form of task covers comprehension of multiple types of character persona, including understanding characters' personalities, facts and memories of personal experience, which are well aligned with the psychological and literary theories about the theory of mind (ToM) of human beings on understanding fictional characters during reading.We further propose new model architectures to support the contextualized encoding of long scene texts.Experiments show that our proposed approaches significantly outperform baselines, yet still largely lag behind the (nearly perfect) human performance.Our work serves as a first step toward the goal of narrative character comprehension.
ROS-Ethereum: A Convenient Tool to Bridge ROS and Blockchain (Ethereum)

Robot Operating System (ROS) has received widespread utilization with the development of robotics, self-driving, etc., recently. Meanwhile, the other technology blockchain is frequently applied to various fields with its trustworthy characteristics and immutability in data storage. However, ROS has no ability to interact with the blockchain, which hinders research in related fields. Therefore, we wonder if we can develop a convenient tool to bridge ROS and blockchain. Inspired by this, we propose ROS-Ethereum. It bridges ROS and Ethereum, a widely used blockchain platform. ROS-Ethereum is based on the User Datagram Protocol (UDP) communication mechanism and the SM algorithm family along with Ethereum technology. Simply put, ROS-Ethereum allows users to invoke the contract when interacting with the blockchain, which makes this process easier and safer. We conduct experiments in real robots to verify the effectiveness of ROS-Ethereum and evaluate it from the following metrics: (1) the encryption efficiency and stability of the algorithm and (2) ROS-Ethereum transaction response time and packet loss rate.
Adaptive Multi-Modal Fusion Framework for Activity Monitoring of People With Mobility Disability

The development of activity recognition based on multi-modal data makes it possible to reduce human intervention in the process of monitoring. This paper proposes an efficient and cost-effective multi-modal sensing framework for activity monitoring, it can automatically identify human activities based on multi-modal data, and provide help to patients with moderate disabilities. The multi-modal sensing framework for activity monitoring relies on parallel processing of videos and inertial data. A new supervised adaptive multi-modal fusion method (AMFM) is used to process multi-modal human activity data. Spatio-temporal graph convolution network with adaptive loss function (ALSTGCN) is proposed to extract skeleton sequence features, and long short-term memory fully convolutional network (LSTM-FCN) module with adaptive loss function is adapted to extract inertial data features. An adaptive learning method is proposed at the decision level to learn the contribution of the two modalities to the classification results. The effectiveness of the algorithm is demonstrated on two public multi-modal datasets (UTD-MHAD and C-MHAD) and a new multi-modal dataset H-MHAD collected from our laboratory. The results show that the performance of the AMFM approach on three datasets is better than the performance of the video or the inertial-based single-modality model. The class-balanced cross-entropy loss function further improves the model performance based on the H-MHAD dataset. The accuracy of action recognition is 91.18%, and the recall rate of falling activity is 100%. The results illustrate that using multiple heterogeneous sensors to realize automatic process monitoring is a feasible alternative to the manual response.
Course Recommendation based on Sequences: An Evolutionary Search of Emerging Sequential Patterns

Abstract To provide a good study plan is key to avoid students’ failure. Academic advising based on student’s preferences, complexity of the semester, or even background knowledge is usually considered to reduce the dropout rate. This article aims to provide a good course index to recommend courses to students based on the sequence of courses already taken by each student. Hence, unlike existing long-term course planning methods, it is based on graduate students to model the course and not on external factors that might introduce some bias in the process. The proposal includes a novel sequential pattern mining algorithm, called (ES) $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> P (Evolutionary Search of Emerging Sequential Patterns), that properly identifies paths followed by good students and not followed by not so good students, as a long-term course planning approach. A major feature of the proposed (ES) $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> P algorithm is its ability to extract the best k solutions, that is, those with a best recommendation index score instead of returning the whole set of solutions above a predefined threshold. A real study case is performed including more than 13,000 students belonging to 13 faculties to demonstrate the usefulness of the proposal not only to recommend study plans but also to give advices at different stages of the students’ learning process.
An Exploratory Study on Aesthetic Ideology and Beauty Education in the Development Process in China

The purpose of this study is to utilize and practice the meaning and value of human beauty by exploring the development process of Chinese aesthetic ideology and beauty. Looking at the development process of aesthetic thought and beauty in ancient China, it went through the development process of Various schools and debates before the Qin dynasty, Confucianism after the Han dynasty, Wei-Jin
ExSum: From Local Explanations to Model Understanding

Interpretability methods are developed to understand the working mechanisms of blackbox models, which is crucial to their responsible deployment.Fulfilling this goal requires both that the explanations generated by these methods are correct and that people can easily and reliably understand them.While the former has been addressed in prior work, the latter is often overlooked, resulting in informal model understanding derived from a handful of local explanations.In this paper, we introduce explanation summary (EXSUM), a mathematical framework for quantifying model understanding, and propose metrics for its quality assessment.On two domains, EXSUM highlights various limitations in the current practice, helps develop accurate model understanding, and reveals easily overlooked properties of the model.We also connect understandability to other properties of explanations such as human alignment, robustness, and counterfactual similarity and plausibility. Input Local Explanation Model UnderstandingThe model thinks that adjectives are important.Model
Tensorial Multiview Representation for Saliency Detection via Nonconvex Approach

In the study of salient object detection, multiview features play an important role in identifying various underlying salient objects. As to current common patch-based methods, all different features are handled directly by stacking them into a high-dimensional vector to represent related image patches. These approaches ignore the correlations inhering in the original spatial structure, which may lead to the loss of certain underlying characterization such as view interaction. In this article, different from currently available approaches, a tensorial feature representation framework is developed for the salient object detection in order to better explore the complementary information of multiview features. Under the tensor framework, a tensor low-rank constraint is applied to the background to capture its intrinsic structure, a tensor group sparsity regularization is posed on the salient part, and a tensorial sliced Laplacian regularization is then introduced to enlarge the gap between the subspaces of the background and salient object. Moreover, a nonconvex tensor Log-determinant function, instead of the tensor nuclear norm, is adopted to approximate the tensor rank for effectively suppressing the confusing information resulted from underlying complex backgrounds. Further, we have deduced the closed-form solution of this nonconvex minimization problem and established a feasible algorithm whose convergence is mathematically proven. Experiments on five well-known public datasets are provided and the simulations demonstrate that our method outperforms the latest unsupervised handcrafted features-based methods in the literature. Furthermore, our model is flexible with various deep features and is competitive with the state-of-the-art approaches.
Entanglement-assisted concatenated quantum codes

Entanglement-assisted concatenated quantum codes (EACQCs), constructed by concatenating two quantum codes, are proposed. These EACQCs show significant advantages over standard concatenated quantum codes (CQCs). First, we prove that, unlike standard CQCs, EACQCs can beat the nondegenerate Hamming bound for entanglement-assisted quantum error-correction codes (EAQECCs). Second, we construct families of EACQCs with parameters better than the best-known standard quantum error-correction codes (QECCs) and EAQECCs. Moreover, these EACQCs require very few Einstein–Podolsky–Rosen (EPR) pairs to begin with. Finally, it is shown that EACQCs make entanglement-assisted quantum communication possible, even if the ebits are noisy. Furthermore, EACQCs can outperform CQCs in entanglement fidelity over depolarizing channels if the ebits are less noisy than the qubits. We show that the error-probability threshold of EACQCs is larger than that of CQCs when the error rate of ebits is sufficiently lower than that of qubits. Specifically, we derive a high threshold of 47% when the error probability of the preshared entanglement is 1% to that of qubits.
The Curse of Dimensionality

The curse of dimensionality is a phenomenon that appears in Machine Learning models when algorithms must learn from an ample feature volume with abundant values within each one. Reaching samples with each combination of values when training would be very complicated. Thus, it can happen (as it will be appreciated later in this Chapter) that classifier or regress or accuracy first improves including more dimensions but then could even decrease. This Chapter deals mainly with this problem and to that end several feature selection and feature selection ranking (FSR) methods are considered. These methods are basically algorithms which include wrappers and filters, and they can provide a ranking of all the analyzed features.