Reviewerly/exHarmony huggingface dataset:
LSTM-assisted evolutionary self-expressive subspace clustering

Massive volumes of high-dimensional data that evolve over time are continuously collected by contemporary information processing systems, which bring up the problem of organizing these data into clusters, i.e. achieving the purpose of dimensional reduction, and meanwhile learning their temporal evolution patterns. In this paper, a framework for evolutionary subspace clustering, referred to as LSTM–ESCM, is introduced, which aims at clustering a set of evolving high-dimensional data points that lie in a union of low-dimensional evolving subspaces. In order to obtain the parsimonious data representation at each time step, we propose to exploit the so-called self-expressive trait of the data at each time point. At the same time, LSTM networks are implemented to extract the inherited temporal patterns behind data in the overall time frame. An efficient algorithm has been proposed. Numerous experiments are carried out on real-world datasets to demonstrate the effectiveness of our proposed approach. The results show that the suggested algorithm dramatically outperforms other known similar approaches in terms of both run time and accuracy.
Variance Reduction for Matrix Computations with Applications to Gaussian Processes

In addition to recent developments in computing speed and memory, methodological advances have contributed to significant gains in the performance of stochastic simulation. In this paper we focus on variance reduction for matrix computations via matrix factorization. We provide insights into existing variance reduction methods for estimating the entries of large matrices. Popular methods do not exploit the reduction in variance that is possible when the matrix is factorized. We show how computing the square root factorization of the matrix can achieve in some important cases arbitrarily better stochastic performance. In addition, we detail a factorized estimator for the trace of a product of matrices and numerically demonstrate that the estimator can be up to 1,000 times more efficient on certain problems of estimating the log-likelihood of a Gaussian process. Additionally, we provide a new estimator of the log-determinant of a positive semi-definite matrix where the log-determinant is treated as a normalizing constant of a probability density.
Design and Implementation of Regional Food Distribution Platform Based on Big Data

In recent years, the rapid development of big data has made people's daily life very convenient, and at the same time, tasting all kinds of food has become an important activity for people to travel. Due to the vast territory of China, there are many types of cuisines with great differences, it is very important for travelers to understand the special regional cuisines in the area. This project aggregates regional food data based on big data, and provides tourists with efficient, stable and professional data retrieval and analysis services through a visual data interface, and provides intuitive, accurate, and real-time data support for the decision-making of finding characteristic regional food. This thesis first conducted a relevant understanding of big data and the overall situation of regional cuisine, analyzed the distribution of food in the sub-provincial city of Xi'an, explored the research methods and implementation methods of related projects at home and abroad, based on this, summarized the research of this project the goal. At the same time, the focus of this project is to analyze the price, score and popularity of regional food data analysis in my country, and to summarize, proofread and organize the data obtained before into standard and standardized data. Through the classification of basic information, the visual analysis and display of data is realized, and the key data urgently needed by decision makers are extracted from it. To analyze the needs of decision makers, establish corresponding strategies and measures to improve the quality of data services. The establishment of this system provides a useful supplement and improvement to the existing industry data analysis system. The system is mainly divided into six modules, which are data collection, data review, data summary, and visual data display modules. Among them, data collection includes crawling relevant data from the Internet and retrieving key data. The data audit function includes classifying the crawled data and reviewing its effectiveness. Data aggregation includes summarizing the filtered data in an excel table and passing Excel generates a visual chart that you want to know, and at the same time generates a word cloud by associating certain two related items in the data in the excel table. The visual data display module can more clearly show the results of data analysis to users, so that users can make better decisions. The visualization data uses AmChart Flash charts. The implementation of this system adopts Django Python Web framework, the development language chooses Python, the development tool adopted is PyCharm, and the database tool adopts MySQL.
Design of multi-robot system based on target detection

Abstract Traditional target-following robots are not only easy to lose targets, but also unable to solve all kinds of unexpected situations in time. Based on the above problems, this paper designs a multi-robot following system based on target recognition. The system can complete the task of target following through image recognition and processing and PID algorithm control. While completing target following, it can also complete the cooperative control of multi-robots by constructing local networks among robot systems, which makes the system have better processing ability for various situations. After experimental tests, the robot system has higher following robustness and practical value.
The effects of depth perception viewing on hand–eye coordination in virtual reality environments

Abstract Virtual reality (VR) has been applied to many fields such as visualization, architecture, education, medical science, and sports simulation. Recent VR advancements allow direct manipulation of three‐dimensional (3D) objects in interactive displays, but problems of inefficiency in movement due to inaccuracy in depth perception have been reported. As human interaction in virtual environments (VE) is being realized, factors that may affect depth perception and movement need to be addressed. This study investigated the effects of viewing angle, stereo viewing technology, and visual cues on hand movement performance in a 3D movement task. Ten right‐handed participants performed continuous Fitts' pointing tasks using a computer mouse. Participants were asked to move a cursor to a target ball as quickly as possible. Fitts' law was employed to test effects to capture differences while the hand was moving in the 3D space under three factors, angle of declination, visual depth, and space cue and with varied index of difficulty (ID). Movement time (MT), error rate, and throughput (TP) were recorded as dependent variables. This study found significant effects of angle of declination, visual depth, space cue, and ID. This study investigated the effectiveness of hand movements in 3D environments for a further immersive VE using a two‐dimensional (2D) mouse. Potential applications of this research include the design of interactive 3D display interfaces in which fast and accurate selection is required and the manipulation of 3D objects in 3D environments may be expected.
Robust image watermarking against JPEG recompression for websites

A robust image watermarking method based on neural network is proposed for websites, such as the social networks (Facebook, Twitter), which adopts different JPEG recompression strategies for uploaded images. The basic network structure consists of three parts: the encoder inputs a cover image and watermark message, outputs the watermarked image and realizes watermark embedding; the decoder inputs a watermarked image, outputs extracted watermark message; the discriminator and the encoder constitute a generating antagonistic network, which promotes the encoder to generate watermarked images which are inseparable from the cover image. A noise layer is designed in the network structure to simulate the attacks that watermarking may be subjected to, and the whole network is trained end-to-end to make the watermarking model robust to the attacks. The experimental results show that the noise layer is designed to simulate JPEG recompression process reasonably, and the model is robust to different JPEG recompression processes on Facebook and Twitter, with the average bit error rate less than 1%. In fact, embedding watermark adds noise to the cover image; considering the influence of the counter sample on the deep neural network, this paper verifies the interference of embedding watermark to the target classification network. By adding the target classification module into the original network structure, the influence of watermark embedding on the target classification network is considered at the same time. The experimental results show that the target classification module is helpful to reduce the interference of watermark embedding on various target classification networks.
Discussion on the Influence of 5G and Blockchain Technology on Digital Virtual Assets

In recent years, digital virtual assets such as virtual currency, digital copyright, equipment and game currency in electronic games have been inseparable from people’s lives. However, for the protection of digital virtual assets, relevant research at home and abroad is in the preliminary stage of exploration. Digital copyright confirmation, secure transactions and traceability of digital virtual assets are also facing severe challenges. 5G network has the characteristics of high speed, ubiquitous network, low power consumption, and low latency. It aims to realize the interconnection of everything. Blockchain technology has the characteristics of decentralization, non-tampering, de-trust, and openness, and aims to realize mutual trust of everything. Blockchain can assist 5G network to ensure data security, privacy, and trust. 5G network can also make up for the deficiencies of blockchain technology. Cross fusion of 5G network and blockchain technology can optimize business models and improve network information security. This paper discusses the influence of 5G network and blockchain technology on digital virtual assets in depth.
Image Segmentation and Nonuniformity Correction Methods

The delineation of target object on medical image is known as image segmentation. Successful printing of a three-dimensional (3D) object replies on accurate segmentation of the object on the image. Manual segmentation has limitations such as being labor-intensive and susceptible to human errors. Therefore, computational algorithms of automated segmentation have been widely used in 3D printing. In this chapter, we covered the basic concepts of image segmentation and common algorithms of automated segmentation. We also covered the necessary preprocessing (such as nonuniformity correction) and postprocessing steps, as well as commonly used segmentation software.
Research on Proofreading Method of Semantic Collocation Error in Chinese

With the rapid development of network technology and the popularization of electronic documents, Chinese text automatic proofreading technology has attracted increasing attention. Automatic proofreading of semantic errors in Chinese text is a key and difficult point in the field of Chinese information processing. Aiming at this problem, we propose a semantic error proofreading method that contains dependency parsing and statistical theory, and construct a two-layer semantic knowledge base to assist error detection and error correction. The two-layer semantic knowledge base includes (1) knowledge base of word collocations containing structured information of sentences extracted from a large-scale corpus; (2) knowledge base of sememe collocations obtained by sememe mapping through HowNet. On this basis, cubic association ratio and degree of polymerization are introduced to evaluate the proofreading results to reduce false positives and improve the accuracy of error correction opinions. The experiment result shows that our method will be of great use for the construction of semantic proofreading knowledge base and semantic error automatic proofreading methods.
A Shift in The Politeness Actions of Grade 5 Elementary School Students With a Javanese Cultural Background

The shift in the act of politeness directive set in Javanese culture in elementary school children began to shift and shift in a negative direction. This study aims: (1) to describe the form of shift in the act of politeness directive class 5 students in interacting with teachers with Javanese cultural backgrounds in elementary school. (2) To find out the factors that influence the shift in directive politeness acts towards teachers with Javanese cultural background. The approach used in this research is descriptive qualitative. Data collection using the Case study method: direct observation, listening and field records (Whaatsaap recording). Results of the study: 1) language politeness acts in grade 5 students of SDN Polokarto 01 in communicating and interacting with speech partners are still very low, a shift in politeness in language with a Javanese cultural background needs to be fostered or efforts to restrain the level of shift so as not to deviate too much from the upload .2.2) factors that make the shifting politeness act further include a) family, and parents. b) demographic factors in the surrounding environment. 3) strategic efforts to withstand the shift by reversing the existing cultural norms in society by prioritizing 1) Angon Rasa, 2) Adu Rasa. 3) Board 4) Humility 5) Respect 6) Salira tepa attitude.
Efficient sign language recognition system and dataset creation method based on deep learning and image processing

New deep-learning architectures are created every year, achieving state-of-the-art results in image recognition and leading to the belief that, in a few years, complex tasks such as sign language translation will be considerably easier, serving as a communication tool for the hearing-impaired community. On the other hand, these algorithms still need a lot of data to be trained and the dataset creation process is expensive, time-consuming, and slow. Thereby, this work aims to investigate techniques of digital image processing and machine learning that can be used to create a sign language dataset effectively. We argue about data acquisition, such as the frames per second rate to capture or subsample the videos, the background type, preprocessing, and data augmentation, using convolutional neural networks and object detection to create an image classifier and comparing the results based on statistical tests. Different datasets were created to test the hypotheses, containing 14 words used daily and recorded by different smartphones in the RGB color system. We achieved an accuracy of 96.38% on the test set and 81.36% on the validation set containing more challenging conditions, showing that 30 FPS is the best frame rate subsample to train the classifier, geometric transformations work better than intensity transformations, and artificial background creation is not effective to model generalization. These trade-offs should be considered in future work as a cost-benefit guideline between computational cost and accuracy gain when creating a dataset and training a sign recognition model.
Comparison of Direct and Vicarious VR Learning Experience: A Perspective from Accessibility and Equity

A common challenge for adopting virtual reality (VR) in education is that limited VR devices are often shared among a large group of students. Consequently, there are two types of VR learners: Performers who acquire virtual learning experience through direct engagement in VR and observers who acquire such experience vicariously through observation. To explore the influence of learner type on VR learning, this study conducted a quasi-experiment with 53 elementary school students to examine the difference in VR learning experiences between the performers and the observers. The study results supported the observed VR learning experience as an adequate alternative to direct VR engagement as the observers demonstrated overall comparable learning patterns in reflection, emotion, engagement, and social interaction during the post-VR debriefing, except for the behaviors of recall and interpretation. The research findings can shed light on the issues of accessibility and equity in VR-based instruction and inform the design and implementation of large-scale VR educational programs.
Why Dilated Convolutional Neural Networks: A Proof of Their Optimality

One of the most effective image processing techniques is the use of convolutional neural networks that use convolutional layers. In each such layer, the value of the layer’s output signal at each point is a combination of the layer’s input signals corresponding to several neighboring points. To improve the accuracy, researchers have developed a version of this technique, in which only data from some of the neighboring points is processed. It turns out that the most efficient case—called dilated convolution—is when we select the neighboring points whose differences in both coordinates are divisible by some constant ℓ. In this paper, we explain this empirical efficiency by proving that for all reasonable optimality criteria, dilated convolution is indeed better than possible alternatives.
Open Challenges in Genetic Improvement for Emergent Software Systems

Genetic improvement for emergent software systems faces unique challenges due to its deployment in highly dynamic environments. In this paper, we discuss four of those challenges along with our initial plans for new research.
Direct Alignment Maximization for Clustering Ensemble

Given multiple clustering algorithms with different candidate hyper-parameter configurations, we will generate multiple weak partition results for the same data set. By integrating a group of such base clustering results, clustering ensemble methods try to find the final consensus clustering result where the stability and even the performance of base clustering results could be further improved. Although the exact one-to-one mapping across clusters from different base clustering may not exist, we still have chance to directly find such weak correspondence between the final consensus clusters and the input base clusters. In this paper, we propose a novel method which maps all the clusters from base clustering to new permuted clusters and finds the consensus clustering by maximizing the alignment from permuted bases. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets.
Designing Biotech Ethics Cards: Promoting Critical Making During an Online Workshop with Youth

There are ethical concerns surrounding how youth interact with biowearable technology and the potential effects it has on their psychological and physiological health. We need to give youth the tools to critically reflect and explore ethical issues surrounding biowearables in order for them to make informed decisions about how they interact with them. To address this, we developed the Biotech Ethics cards as part of a critical making workshop. They are a set of design cards designed to scaffold critical reflection during a critical making workshop where youth prototype a biowearable from a kit. We focus this short paper on the requirements, initial design and revisions we made after studying card use in our workshop. We identified key design elements that are important in the cards and that may generalize to the design of other card sets meant to be integrated into a critical making process.
Teaching PDC in the Time of COVID: Hands-on Materials for Remote Learning

In response to shifts in the hardware foundations of computing, parallel and distributed computing (PDC) is now a key piece of the core CS curriculum. For CS educators, the COVID-19 pandemic and the resulting switch to remote-learning add new challenges to the tasks of helping learners understand abstract PDC concepts and equipping them with hands-on practical skills. This paper presents several novel teaching materials for teaching PDC remotely, including: (i) using a Runestone Interactive "virtual" handout to learn how to run OpenMP multithreaded programs on a Raspberry Pi, and (ii) using Google Colab and Jupyter notebooks to run mpi4py instances on remote systems and thus learn about MPI distributed multiprocessing. The authors piloted these strategies during a multi-day faculty development workshop on teaching PDC. Assessment data indicates that the materials greatly aided professional development and preparedness to teach PDC.
Joint Coding Scheme Based on Reed-Solomon Codes

The joint source-channel coding (JSCC) scheme is applicable to the resource-constrained, real-time, time-varying and low-cost communications. A high-efficient JSCC scheme based on Reed-Solomon (RS) codes is proposed, which can reduce the amount of data transmitted and ensure the reliability of communication system. In addition, a low complexity joint source-channel decoder is proposed, which can implement signal reconstruction and channel decoding through one circuit with high efficiency. The results illustrate that our decoder only needs about 41.1k gates and operates at 500 MHz to achieve the throughput of 3.05 Gb/s, which can be applied to the scenarios of Internet of Things (IoT) and Artificial Intelligence (AI).
U<sup>2</sup>-Net for Image Forgery Detection and Localization

Until now, image forgery has caused great harm in many aspects, such as certificate falsification, fake news, Internet rumors. As a result, the algorithm for image forensics is of great importance. Existing image forensics algorithms are mature in image forgery detection. Nevertheless, there is still room for improvement when it comes to image forgery localization. This paper modified and migrated U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net to image forensics, and conducted several experiments to demonstrate the effectiveness of U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net in contrast to ManTra-Net. The experimental results demonstrate that U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net is not only capable of image forgery detection but also image forgery localization and that in some cases U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net is even more powerful than ManTra-Net in image forgery localization.
Forward thinking pedagogical education as a tool for managing the future

The article examines the problem of advancing education, including pedagogical, from an interdisciplinary perspective. Its relevance during the humanitarian and technological revolution, its essence, goals and objectives are substantiated. The ways of formation of higher education as forward thinking one are proposed. The importance of forward thinking pedagogical education and social demand for the teacher of the future are substantiated. The experience of implementation of forward thinking pedagogical education at the Belarusian State University named after Maxim Tank is presented.