Reviewerly/exHarmony huggingface dataset:
An artificial intelligence based speech model for linguistics teaching

The linguistic artificial intelligence teaching model can be assisted by the intelligent speech recognition model. The traditional speech recognition algorithm has certain problems, so it cannot effectively eliminate speech noise. Based on the advantages of the linguistics teaching model, this article combines the linguistics teaching model and the artificial intelligence model to build an artificial intelligence assisted teaching model that can be used for classroom teaching. Moreover, this study improves the traditional algorithm and constructs an artificial intelligence linguistics teaching model based on the improved algorithm. The filtering part of noise includes preliminary filtering of speech signals based on the short-term energy detection method, and further detection and recognition of preliminary filtering speech signals based on the artificial intelligence model detection method. After these two steps of filtering and recognition, the voice file is sent to the client for processing and control. In addition, this study set up a control experiment to analyze the performance of the model. The research results show that the algorithm in this paper has a certain effect.
ari: The Automated R Instructor

We present the ari package for automatically generating technology-focused educational videos.The goal of the package is to create reproducible videos, with the ability to change and update video content seamlessly.We present several examples of generating videos including using R Markdown slide decks, PowerPoint slides, or simple images as source material.We also discuss how ari can help instructors reach new audiences through programmatically translating materials into other languages.
Improving the Visual Perception and Spatial Awareness of Downhill Winter Athletes with Augmented Reality

This research study addresses the design and development of an augmented reality headset display for downhill winter athletes, which may improve visual perception and spatial awareness, and reduce injury. We have used a variety of methods to collect the participant data, including surveys, experience-simulation-testing, user-response-analysis, and statistical analysis. The results revealed that various levels of downhill winter athletes may benefit differently from access to athletic data during physical activity, and indicated that some expert level athletes can train to strengthen their spatial-awareness abilities. The results also generated visual design recommendations, including icon colours, locations within the field-of-view, and alert methods, which could be utilized to optimize the usability of a headset display.
Quantum search with an ensemble of single-query search processes

Recently we introduced an additive composition formulation of an iterative quantum-search algorithm of Grover type. In this paper, an ensemble-processing scheme suggested by the additive composition is presented. The ensemble-processing consists of single-query search processes and yields the quantum search with certainty. Furthermore, we propose a possible quantum circuit for the ensemble processing.
Epigenetic geochemical dynamics and driving mechanisms of chemical elemental distribution patterns in soil in Southwest China

The Earth’s surface is a complex system involving mutual interactions of its many components, including mountains, rivers, forests, farmlands, lakes and grasses. The interaction and mutual feedback of chemical elements in Earth's surface layer can drive changes in chemical elemental distribution patterns. In this study, we evaluated the mechanisms and interactions driving the distribution patterns of macroelements, probiotics, halogens and heavy metals in soils in Southwest China, based on a systematic geochemical land-quality survey at a scale of 1:250000. The results showed that the parent material determines the natural state of chemical elements in land resources. Epigenetic geochemical dynamics reshapes the distribution patterns of chemical elements in top soil; biogeochemical processes drive the evolutionary trends of land quality; and human activities, such as mining, disrupt the natural evolution of chemical elemental distribution patterns. The establishment of an epigenetic geochemical dynamics theory allows the construction of a framework for understanding the Earth's surface layer and promoting technological innovations for the comprehensive geochemical investigation of land resources.
Model of Classification of University Services Using the Polarity of Opinions Taken from Social Networks

The objective of this research is to use the Big Data generated through the opinions of the social networks of Higher Education Institutions, which are free from bias at the time of expressing themselves about the IT services offered by these institutions, providing a data bank that serves as raw material to perform a data and text mining that is used to guide the ITIL process in its phase of Service Strategies. The mining process uses the methodology known as CRISP - DM that obtains values that determine the quality of the service studied, giving an input for decision making that will maintain, improve or eliminate the IT service analyzed. Finally, it is concluded that using social networks to know the opinions of the IT services offered by the institutions and at the same time apply a mining process to guide the adaptation of ITIL best practices is a reliable process because users feel free to express their opinion about the service received.
POSTER: Oblivious Access System on Decentralized Database over Parallel Smart Contract Model

Data stored on centralized cloud servers may have some risks. Moreover, it may leak the data access pattern when accessing data on cloud servers. Oblivious RAM (ORAM) is a candidate solution to hide the data access pattern, but it inherently induces some overheads of accessing data. In this paper, we address the issues above, and propose the oblivious access system on decentralized database over parallel smart contract model. The proposed system can slightly reduce the overhead of ORAM and overcome the problem from centralized servers. The main techniques are to use parallel smart contract model to improve the performance of smart contract execution and garbled circuit to reduce the cost of communication.
Multi-human Parsing with Pose and Boundary Guidance

In this work, we present a novel end-to-end semantic segmentation framework for multi-human parsing, which integrates both the high- and low-level features. Our framework includes three modules: segmentation module, pose estimation module, and boundary detection module. The pose estimation model will guide high-level feature extraction to identify the part location; meanwhile, the boundary detection module will concentrate on the low-level feature extraction so as to distinguish the boundary. Both modules are united in the backbone segmentation module to generate the desired accurate high-resolution prediction for multi-human parsing. Experiment results on the PASCAL-Person-Part dataset demonstrate that our method achieves superior results over state-of-the-art methods. Code has been made available at https://github.com/scmales/MHP-with-Pose-and-Boundary-Guidance.
LSTM Classification under Changes in Sequences Order

Recurrent Neural Networks (RNNs) have been widely used for sequences analysis and classification. Generally, the sequences are a set of samples following a specific order, like a time-based process or a structured dataset. This type of neural network is very efficient for exploring sequences patterns and other relevant features highlighting temporal behavior and dependencies. This is accomplished because the information loops within the different stages of the network, and in this process, it remembers and tracks features at different segments of the data. In this work, we are interested in exploring how an RNN based on Long-Short Term Memory (LSTM) units behaves in a classification problem when the dataset of sequences are organized in different order and lengths. That is, the same information is presented to the network, but the order of the samples within the sequences and the length of the sequences are different in each experiment. In order to evaluate this effect, we used five datasets of grayscale images of 28 \(\times \) 28 pixels (MNIST, MNIST-C, notMNIST, FashionMNIST, and Sign Language MNIST). For every experiment, we segmented the images in different sizes and orders and built a set of sequences consisting of vectors of pixels organized following three different rules, and on each case, we set the sequences to a specifically fixed length. The results bring to light that good accuracies can be achieved for different sequences configurations. We considered the 28 \(\times \) 28 configuration as the baseline for reference. We found that this baseline generally leads to high accuracies, but for some datasets it is not the best one. We believe that this study may be useful for video tagging and for general image description.
R-PFN: Towards Precise Object Detection by Recurrent Pyramidal Feature Fusion

Object detection has been widely studied in the last few decades. However, handling objects with different scales is still marked as a challenging requirement. To solve this problem, we explore how to better utilize the features of multiple scales generated by a convolutional neural network. Specifically, we fuse a set of pyramidal features in a circular manner and propose a cascaded module, whose consideration is to enhance a single-scaled feature with information from another scale-different feature. Then, we make it recurrent to further facilitate the fusion of information among multi-scaled features. The proposed module can be integrated into any pyramid architecture. In this paper, we combine it with FPN-based Faster R-CNN, result in a framework named Recurrent Pyramidal Fusion Network (R-PFN). Experiments prove the effectiveness of R-PFN. We achieve new state-of-the-art performances, i.e., 82.0%, 43.3% on the PASCAL VOC 2007 benchmark and MS COCO benchmark in terms of mean AP, respectively.
Speckle Based Anisotropic Diffusion Filter for Ultrasound Images

Imaging of Ultrasound (US) presents significant challenges in visual medical inspection and creation of automated speckle-based analytical approaches that adversely influence tissue boundary detection and the efficacy of automatic segmentation techniques. A number of filtering strategies are usually used as a pre-processing phase before automatic review or visual inspection methods to minimize the impact of speckle. Many state of the art filters seek to decrease the speckle effect without recognizing its significance to tissue structure classification. This loss of expertise is further magnified due to the iterative process of some speckle filters, e.g. diffusion filters, which tend to produce over filtering during the diffusion period due to a progressive shortage of critical details for diagnostic reason. In this one we suggest a filter of an anisotropic diffusion that contains probabilistic-driven memory of probabilistic-driven scheme which can solve problem of over filtering by pursuing philosophy of a selective tissue. In general, we can design formula for the function of memory as a diffusion differential equation for the tensor of diffusion whose behavior depends on statistics of the tissue, by speeding up the cycle of diffusion in unnecessary regions and by utilizing the effect of memory in places where valuable knowledge must have to be stored in reliable manner. Tests of two photos which are real ultrasound and synthetic photos confirm the usage of the mechanism of probabilistic memory to maintain scientifically appropriate frameworks that the state-of-the-art filters are removing.
Research and Improvement of the SHIFT-AND Pattern Matching Algorithm

Time performance improvement of the pattern matching mainly lies in reducing the times of character comparisons in each round of matching and increasing the distance that the text string matching window skips to the right when mismatching happens. Based on the above ideas, an improved algorithm based on the combination of word frequency and SHIFT-AND and BMHS2 is proposed. The improved algorithm first uses word frequency matching in each round of matching to reduce the times of character comparisons in matching window. Then, in order to improve the moving distance of the text string matching window when mismatching happens, the BMHS2 and SHIFT-AND algorithms are introduced. Move the text string matching window to the right by the larger skip distance between the SHIFT-AND and BMHS2 algorithms when characters mismatch. The experimental results show that under the same conditions, compared with the SHIFT-AND and BMHS2 algorithms, the improved pattern matching algorithm I_SHIFT-AND_BMHS2 has a certain improvement in matching time performance.
Sparse Optical Flow Computation Using Wave Equation-Based Energy

Identification of motion in videos is a fundamental task for several computer vision problems. One of the main tools for motion identification is optical flow, which estimates the projection of the 3D velocity of the objects onto the plane of the camera. In this work, we propose a differential optical flow method based on the wave equation. The optical flow is computed by minimizing a functional energy composed by two terms: a data term based on brightness constancy and a regularization term based on energy of the wave. Flow is determined by solving a system of linear equations. The decoupling of the pixels in the solution allows solving the system by a direct or iterative approach and makes the method suitable for parallelization. We present the convergence conditions for our method since it does not converge for all the image points. For comparison purposes, we create a global video descriptor based on histograms of optical flow for the problem of action recognition. Despite its sparsity, results show that our method improves the average motion estimation, compared with classical methods. We also evaluate optical flow error measures in image sequences of a classical dataset for method comparison.
Research on the Satisfaction of Kid's English Education Application Based on Importance-Performance Analysis (IPA)

In this study, the English education app for kids is defined as a third-party application developed for kid's online English learning. In order to enhance its competitiveness, this study analyzed mainly four parts of it: Education pattern, Subject content, Teaching methods and Teaching scenarios. The empirical results were obtained in a sample of 141 parents in China. The final result of Importance - Performance Analysis in this study provides implications for developers to improve the English education app that encourages the kids to learn English more effectively.
A Image Adaptive Steganography Algorithm Combining Chaotic Encryption and Minimum Distortion Function

Digital image has the characteristics of high redundancy and convenient processing, which is the ideal carrier of information hiding application. Therefore, digital image steganography has become a hot research direction in the field of information security. In order to ensure the concealment, reliability and security of image steganographic information, an image adaptive steganographic algorithm combining chaotic encryption and minimum distortion function is designed. The algorithm generates random keys, encrypt the secret information using Logistic and ChebyShev mapping, and then embeds the key and the encrypted secret information into the carrier image using the HILL steganographic algorithm. The algorithm stores the embedded key and the decryption key separately, so the attacker cannot get the correct secret information even if he gets the embedded key, which improves the security of the key. Experiments show that the pixel change rate of the algorithm is 6.76%, the peak signal-to-noise ratio is 59.51 db, and it has a very good anti-steganographic analysis ability.
Research for Bidirectional Path Planning Based on An Improved A* Algorithm

The paths obtained by conventional A* algorithm are usually not the optimal solutions, which have many turning nodes and spend more time. To solve these problems, an improved A* algorithm is presented. First, the proposed algorithm introduced Chebyshev distance into the formula and parent heuristic function; secondly, using a method of bidirectional search paths with dynamic rectangle. The simulation results show that time is reduced by 85.9% and 58.4% comparing with traditional A* algorithm and the other improved A* algorithm. It has been demonstrated that the proposed improved A* algorithm can efficiently solve the shortest path planning problem especially on large grid maps.
MD-ST: Monocular Depth Estimation Based on Spatio-Temporal Correlation Features

Monocular depth estimation methods based on deep learning have shown very promising results recently, most of which exploit deep convolutional neural networks (CNNs) with scene geometric constraints. However, the depth maps estimated by most existing methods still have problems such as unclear object contours and unsmooth depth gradients. In this paper, we propose a novel encoder-decoder network, named Monocular Depth estimation with Spatio-Temporal features (MD-ST), based on recurrent convolutional neural networks for monocular video depth estimation with spatio-temporal correlation features. Specifically, we put forward a novel encoder with convolutional long short-term memory (Conv-LSTM) structure for monocular depth estimation, which not only captures the spatial features of the scene but also focuses on collecting the temporal features from video sequences. In decoder, we learn four scales depth maps for multi-scale estimation to fine-tune the outputs. Additionally, in order to enhance and maintain the spatio-temporal consistency, we constraint our network with a flow consistency loss to penalize the errors between the estimated and ground-truth maps by learning residual flow vectors. Experiments conducted on the KITTI dataset demonstrate that the proposed MD-ST can effectively estimate scene depth maps, especially in dynamic scenes, which is superior to existing monocular depth estimation methods.
Twitter Sentiment Analysis using Novelty Detection

This paper focuses on devising an approach, where a huge amount of data is collected from social media websites such as Twitter and further they are categorized based on their sentiment as positive, negative and neutral. This paper focuses on creating a model that would only consider tweets relevant to the topic in consideration by eliminating unwanted and irrelevant tweets from the dataset and then performing sentiment analysis on them for predicting the overall sentiment of the topic. The concept of novelty detection is used for the same. Novelty detection helps in removing the irrelevant tweets and considers only relevant tweets from a huge dataset and sentiment analysis is performed on these tweets. Novelty detection detects all the outliers present in the dataset using the k-means clustering algorithm. After detecting all the outliers, the model will eliminate those outliers and perform sentiment analysis only on novel data. Naive Bayes Algorithm is used for the classification of the novel data. The project aims to compare the accuracy of the process of sentiment analysis on Twitter data of correctly classifying the data as positive, neutral and negative before and after applying Novelty Detection on it thereby analyzing whether considering only novel data from the huge dataset will have a positive effect on the precision and accuracy of the model. Accuracy, Recall, F-score measures are used to calculate the performance of this analysis.
Rain Streaks and Snowflakes Removal for Video Sequences via Motion Compensation and Matrix Completion

Image and video deraining tasks aim to reconstruct original scenes, from which human vision and computer vision systems can better identify objects and more details present in images and video sequences. This paper proposes a three-step method to detect and remove rain streaks, even snowflakes from great majority video sequences, using motion compensation and low-rank matrix completion method. Firstly, we adopt the optical flow estimation between consecutive frames to detect the motion of rain streaks. We then employ the online dictionary learning for sparse representation technique, and SVM classifier to eliminate parts that are not rain streaks. Finally, we reconstruct the video sequence by using low-rank matrix completion techniques. In particular, by introducing the image dehazing network(GCANet) to our proposed method, the heavy rain caused dense rain accumulation and blurry phenomenon can be worked out well. The experimental results demonstrate the proposed algorithm and perform qualitatively and quantitatively better in several image quality metrics, boosting the best published PSNR metric by 4.47%, 6.05% on two static video sequences and 12.13% on a more challenging dynamic video sequence. In addition, to demonstrate the generality of the proposed method, we further apply it to two challenge tasks, which also achieves state-of-the-art performance.
High Throughput Pipelined S-Boxes for Encryption and Watermarking Applications

In the Advanced Encryption Standard Algorithm, S-box is a fundamental step. In this paper, three new S-box architectures are introduced and implemented with pipelining. They are Combined, Inverse, and Forward S-boxes. Each S-box represents a lower delay and higher throughput. This goal is achieved by pipelining and optimizing each block inside the three proposed architectures. Our designs outpace others in terms of throughput and delay. The PSNR rate is better than other designs. The proposed S-boxes are implemented in encryption and watermarking applications. The behavior of pipelined S-boxes under few attacks is also observed.
Prototype-Based Discriminative Feature Representation for Class-incremental Cross-modal Retrieval

Cross-modal retrieval aims to retrieve the related items from various modalities with respect to a query from any type. The key challenge of cross-modal retrieval is to learn more discriminative representations between different category, as well as expand to an unseen class retrieval in the open world retrieval task. To tackle the above problem, in this paper, we propose a prototype learning-based discriminative feature learning (PLDFL) to learn more discriminative representations in a common space. First, we utilize a prototype learning algorithm to cluster these samples labeled with the same semantic class, by jointly taking into consideration the intra-class compactness and inter-class sparsity without discriminative treatments. Second, we use the weight-sharing strategy to model the correlations of cross-modal samples to narrow down the modality gap. Finally, we apply the prototype to achieve class-incremental learning to prove the robustness of our proposed approach. According to our experimental results, significant retrieval performance in terms of mAP can be achieved on average compared to several state-of-the-art approaches.
Data-Driven Science of Global Seismicity

It is well known that the seismic activity is basically constraint by the plate tectonics framework and that the elastic interaction of the plate in the global earth is transmitted within 104 s. Thus, the global seismic activities of every plates and plate boundaries may be strongly correlated with each other. Those correlation patterns may be a kind of invariances in the dynamics of the global seismicity.