Reviewerly/exHarmony huggingface dataset:
Intelligent Calibration Method of Urban Publicity Translation Based on Machine Learning

Considering the problems of low accuracy and recall and the large KSMR value of traditional methods in urban publicity translation calibration, an intelligent calibration method of urban publicity translation based on machine learning is proposed. By extracting the words in the candidate vocabulary set of urban publicity translation, calculate the semantic correlation between the features of urban publicity translation text, establish the relationship between semantic information entropy and word weight value, determine the semantic weight of each feature, and calculate the amount of information provided by each urban publicity translation text feature in translation, Select a large amount of information as the recognition feature of urban publicity translation text, smooth the urban publicity text by using the change of the frequency of urban publicity translation equipment based on machine learning, and construct the urban publicity translation model. Before inputting the machine learning algorithm, the translation corpus needs to be preprocessed, and the matching probability of urban publicity text is calculated. Combined with the design of intelligent calibration steps of urban publicity translation, the intelligent calibration of urban publicity translation is realized. The experimental results show that the proposed method can not only improve the accuracy and recall of urban publicity translation calibration but also reduce the difficulty of translation in terms of the KSMR value.
Deep Neural Networks for Spoken Language Identification in Short Utterances

This work presents the elements of language identification (LID) in small segments created using short duration utterances. For low-resourced languages availability of data itself is a challenge. The paper tries to apply DNN for low resourced language. This paper presents a feed-forward deep neural network (FF-DNN) for language identification using acoustic features of short-time utterances. Two network topologies for DNN have been checked for their performance in LID task. The obtained findings of the experiments are compared to a well-established technique based on i-vector system. This i-vector system uses MFCC-SDC to represent speech feature that represent the acoustic characteristics and the back end is implemented using support vector machine (SVM) that serves as a classifier. These mechanisms were put in place to help with identification of Hindi and Punjabi, two widely spoken Indian languages. The speech utterances are divided into short segments of 5 s, 10 s, 20 s and 35-s duration. The system’s efficiency is measured in EER (%) and for short time segments, a relative improvement of 3% is achieved by the DNN system, whereas the average error rate overall the utterances was decreased by 2% using DNN.
Dynamic Attendance Checking System Based on a BP Neural Network and Biological Characteristics

To strengthen the effect of attendance identification, a dynamic attendance system based on a BP neural network and biometrics is designed. In the hardware design of a dynamic attendance system, the integrated chip architecture is optimized to improve the programming ability and information receiving sensitivity. In the software design of a dynamic attendance system, a biometric recognition algorithm based on a BP neural network is designed to extract the features of human face data and gait data, establish a face gait fusion model and construct a database of dynamic attendance systems. The accuracy of attendance recognition is tested in different scenes, and the experimental results show that the accuracy of the system is between 93% and 98%, which is the best.
Research on literature cognitive strategy model by big data and statistical analysis

In recent years, with the deepening of English teaching reform and the cultivation of English thinking ability, the application of English cross-cultural communication ability has gradually become the most important task and goal in English teaching. The purpose of this paper is to analyze the current situation of English literature by using big data combined with the current social background, and then make a targeted study on the cognitive strategies of English literature. This paper mainly through consulting the relevant literature at home and abroad and using the observation and comparison method to carry out the experiment, so as to achieve our experimental goal – to study the cognitive strategies of English literature based on the background of big data, so as to improve the cognitive strategies of English literature in Colleges and universities. The experimental results show that big data can effectively carry out the research on the thinking teaching mode of English literature reading and follow the research progress at home and abroad at any time.
Multi-Modality Translation from SAR Image to Optical Image for Remote Sensing Applications

Synthetic Aperture Radar (SAR) is an indispensable remote sensing technology nowadays. However, compared to conventional optical imageries, SAR images have significant drawbacks in human interpretation, especially for those without expert knowledge. In this paper, we explore a new method to enhance the human interpretation for raw SAR imageries by turning SAR data into readable optical imageries through a multi-modality translation procedure based on a deep neural network that consists of a novel two-step architecture. To illustrate the concept, remote sensing data from Sentinel-1 and Sentinel-2 is used for training and validation. The results show significant improvement in ease of interpretation from the original SAR images with clear views of different land covers such as fields, water, highways, and etc. The statistical performance of SSIM and PSNR has achieved 0.42 and 19.10 dB respectively, indicating a promising translation quality.
Scalable Nonlinear Mappings for Classifying Large Sparse Data

Classifying very large sparse data is important in many realworld applications, such as to predict users’ gender and profitability based on product ratings. Traditionally, such datasets are often classified with linear models (logistic regression and linear SVM) for scalability, but the predictive accuracy may suffer. Existing applications demonstrate that large sparse datasets often have a low-rank structure. By computing a polynomial approximation to the low-rank data space, a previous work [9] has developed a kernelized classifier, KARMA, to improve the performance on sparse data. However, such method does not scale well to large datasets. In this paper, we develop scalable feature mappings to efficiently approximate the kernels used in KARMA. In experiments, our method inherits the good predictive performance of KARMA on small to medium sized data while showing a significantly better scalability on large datasets.
Image Steganography using RSA Algorithm for Secure Communication

Steganography is a method of transmitting a secret message from a sender to a recipient in such a way that a potential intruder is unaware of its presence. The basic goal of image steganography is to make the message invisible to the viewers' sight, removing any suspicion of a hidden message. Whereas cryptography's goal is to convert readable data into unreadable data, this is done to make decryption more difficult rather than invisible. We present information on security enhanced image steganography in this paper. To improve security, the message is encrypted into cipher text and vice versa using the cryptographic technique RSA in its asymmetric version. The sender will encrypt the message with a key, embed the cipher text in an image, and send this image object along with the key to the receiver via any transmission medium, such as the internet, so that the receiver can extract the cipher text from the image and decrypt using the key that the sender has sent. The result was satisfactory and fulfilled the purpose but was limited to grayscale images only.
Quantum companies merge to form Quantinuum

Honeywell Quantum Solutions and Cambridge Quantum Computing have merged to form a new business called Quantinuum.
Adaptively Attribute-enhanced Graph Embedding via Deep Clustering Constraints

With the rapid development of social media technology, the enormous graph is difficult to be applied in real applications such as classification, clustering, and link prediction, due to lack of efficient and available node representation. Graph embedding is widely concerned by transforming high-dimensional sparse graph data into low-dimensional, compact representations. While previous graph embedding methods mainly focus on preserving the microscopic structure, the significant characteristic of graph, namely, inherent cluster structure of nodes in the original graph is largely ignored. Besides, embedding algorithms on pure graphs have been intensively studied. However, nodes are often accompanied with abundant node attributes, which are highly correlated with node connections and potential to improve the node embedding. How to fully incorporate attributes in node representation remains to be further exploited. In this paper, we propose a deep graph embedding method with clustering optimization called AGEDC(Adaptively Attribute-enhanced Graph Embedding via Deep Clustering Constraints). It first enriches the original topological structure by an adaptive attribute-enhancement mechanism, then it incorporates deep clustering into latent embedding learning for inherent cluster-structure constraints. Further, the learnt low-dimensional representation of node is self-corrected through a convolution-like operation by local and global structure in the augmented graph. Compared with the previous graph embedding methods, experimental results in different application scenarios on real-world datasets demonstrate the effectiveness of our proposed algorithm.
The class-oriented programming paradigm

An object-oriented programming language is one in which the components of a program are objects, and the objects are capable of executing methods (i.e., short little programs made specifically for particular objects). For many programmers, the attraction of the object-oriented paradigm is that programmable objects can be created that have immediate access to libraries of previously prepared methods. Having pre-built methods at their disposal saves programmers time and facilitates the construction of short, readable, and powerful programs. When every object is assigned to a particular class, and the methods available to an object are chosen from a library of methods created specifically for the object’s class, then we have elevated our object-oriented programming language into a class-oriented programming language. When we create classes that exactly mirror the classes and the class relationships found in a well-designed classification, we can begin to write programs that analyze and draw inferences from the classification. When this occurs, the programming environment becomes a computer implementation of the classification. In this chapter, we shall explain, for non-programmers, how a traditional classification can be ported to a modern object-oriented programming environment.
A Study on Concord Adverbs for Korean Language Education

This study defines ‘concord adverbs’ as adverbs that show concordant relations with specific grammar forms in a sentence. Concord adverbs are helpful for learners to express their ideas or feeling more accurately as well as find the speakers’ intention precisely and the way it combines in specific grammar forms. Therefore, this study suggests providing chunk expressions of combining concord adverbs and grammar forms in Korean language textbooks because of its ease of study. In Korean language education it is considered proper to study not only a single form of grammar but also complex grammar that combines two or more language forms. The chunk expression that combines concord adverbs and grammar forms is one style of complex grammar and is already included in Korean textbooks. This study attempts to select concord adverbs that need to be taught additionally in terms of Korean language education. To do this, Korean textbooks and a number of previous studies were analyzed.
Research on the evaluation system for the implementation effect of the Chinese water efficiency mandatory national standard for water closets (GB 25502-2017) based on big data platform and FAHP method

To enhance the methodology to evaluate the implementation effects of "Minimum allowable values of water efficiency and water efficiency grades for water closets" (GB 25502-2017), we analyzed various influence factors and established a system using AHP method and conducted empirical research based on the Fuzzy comprehensive evaluation method. An AHP evaluation model for the implementation effect of the water efficiency standard was established for the first time, which consisted of 31 indicators. Most evaluation results of these indicators could be found though big data platform. The empirical research results mostly from big data investigation show that the implementation effect of the standard is nearly good, especially the technical requirements are consistent with other standards, regulations and market development. They are advanced with international standards. This paper establishes a method system for the evaluation of the implementation effect of water efficiency standards, conducts an empirical analysis of the implementation effect of GB 25502-2017, and explores the application of big data analysis in the evaluation of the implementation effect of the standards..
Optimization of Fuzzy Support Vector Machine (FSVM) Model in Multiple Metric Spaces

Fuzzy membership function was introduced into the Support Vector Machine (SVM) resulting in modifications. Selecting the correct membership function is an important step in the Fuzzy Support Vector Machine (FSVM) method. One of the general criteria for selecting fuzzy membership is determined by the distance between a point and its fixed center category. This study aims to develop the SVM method into Fuzzy SVM (FSVM) with several distance functions that are applied to the Early Stage Diabetes data which collects 520 data. The distance functions used include Euclid, Canberra distance, Minkowski distance, Chebyshev distance, Minkowski Chebyshev distance, and Bray-Curtis distance where this distance function is used to determine the best distance that can be seen from the results of accuracy, specificity, g-means which is best for viewing diabetes risk. The results of this comparison show that the FSVM method with several distance functions is more than the SVM method. Where the FSVM method at the Canberra distance with a penalty value of C = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> is the best distance to see the risk of diabetes, based on the results of specificity = 100%, g-means = 86.91%, and accuracy = 85.26% is superior to the SVM method at the penalty value C = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sup> with specificity = 69.36%, g-means = 77.31%, and accuracy = 79.49%. Although the FSVM method produces an evaluation value at sensitivity = 75.53%, it is lower than the SVM method with a sensitivity value = 86.17%.
Critical State Detection for Adversarial Attacks in Deep Reinforcement Learning

Deep learning plays a vital role in day-to-day applications. Recent studies show that deep learning models are not resilient against adversarial attacks, which is also applicable to Deep Reinforcement Learning (DRL) agents. Considering sensitive use cases of the DRL agents, there is a pressing need to make them robust to adversarial attacks. However, to design an efficient defense, it is imperative that we fully understand the vulnerability of such agents. In this work, we propose statistical and model-based approaches to identify critical states in an episode. Our work shows that by attacking less than 1% of the total number of states, the agent performance can be reduced by more than 40%. Furthermore, we model a long-term impact classifier to identify critical states. This method reduces the average compute time by 80.3% when compared to previous approaches.
The Train Delay Model Developed by the Genetic Programming Algorithm

The paper discusses the problem of probability distribution category identification of train delay data by a genetic programming algorithm. This train delay frequency function and the probability distribution simply derived from it are significant to train traffic modelling and management. The genetic programming algorithm was used as an uninformed tool to prevent the influence of a priori information, which should be biased. The real traffic data were aggregated into predefined bins and then the frequencies of the individual delays were computed. The genetic programming algorithm was used in the next step as a symbolic regression tool to discover their frequency function in the form of an algebraic expression. The results concluded that although data has no known distribution, their distributions are similar to exponential ones.
Close-Contact Detection Using a Single Camera for Sports Considering Occlusion

The Coronavirus disease 2019 (COVID-19) is still prevalent in the world. Exercise is important to maintain our health while dealing with infectious diseases. Social distancing is more important during exercise because we may not be able to wear masks to avoid breathing problems, heatstroke, etc. To maintain social distancing during exercise, we develop a close-contact detection system using a single camera especially for sports in schools and gyms. We rely on a single camera because of the deployment cost. The system recognizes people from a video and estimates the interpersonal distance for close-contact detection. The challenge is the occlusion of people, which leads to false negatives in close-contact detection. To solve the problem, we leverage the observation that most false negatives in human detection are caused by occlusion owing to other people. This is because there are few obstacles in sports facilities. Based on the above observation, we assume that a person still exists near the last detected position even when s/he disappears in the proximity of other people. For evaluation, we recorded 834 videos that were 112 min long in total including various scenarios with 2724 close-contacts. The results show that the F1-score of close-contact detection and tracking are 83.6% and 67.3%, respectively. We also confirmed that the start and end time errors are within 1 s for more than 80% of the close-contacts.
Multi-Agent Deep Reinforcement Learning for Walker Systems

We applied the state-of-art performance Deep Reinforcement Learning (DRL) algorithm, Proximal Policy optimization (PPO), to the minimal robot-legs locomotion for the challenging multi-agent, continuous and high-dimensional state-space environments. The main contribution of this work is identifying the potential factors/hyperparameters and their effects on the performance of the multi-agent settings by varying the number of agents. Based on the comprehensive experiments with 2-10 multi-walkers environments, we found that 1) A minibatch size and a sampling reuse ratio (experience replay buffer size containing multiple minibatches) are critical hyperparameters to improve performance of the PPO; 2) Optimal neural network size depends on the number of walkers in the multi-agent environments; and 3) Parameter sharing among multi-agent is a better training strategy than fully independent learning in terms of comparable performance and improved efficiency with reduced parameters consuming less memory.
TeknoAssistant : a domain specific tech mining approach for technical problem-solving support

Abstract This paper presents TeknoAssistant, a domain-specific tech mining method for building a problem–solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naïve Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either “problem”, “solution” or “none” categories, and a problem–solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process.
A Self-Calibration Method for 2D Vision-based Reverse Engineering

Fast copying of the profile of sample object is a requirement of vision-based reverse engineering system. However, there is a problem that the size of sample object is limited to a certain range. In this study, this obstacle is solved by a novel self-calibration method. Based on inverse perspective transformation approach, using a flat marked plane, the 2D profile of a sample object can be obtained with a freely moving camera, such as the camera of smart phone. This allows quickly collecting of the 2D shape of an object with any size. Experimental results show that the proposed method achieves high accuracy. This helps to improve the 2D vision-based reverse engineering system more efficient and flexible.
Weighted Best Linear Prediction and Its Randomized Acceleration for Poisson Image Denoising

Photon-limited Poisson image denoising is a pressing problem and faces great challenges in some fields such as emission tomography, low-exposure x-ray imaging, fluorescence microscopy, and infrared astronomy. Currently, the post- processing best linear prediction method (BLP) based on co- variance estimation of non-local similar image patches has been proposed and achieved good results in Poisson image denoising. However, the calculation of similarity is inaccurate in the photon limited case, which leads to the inaccuracy of similarity patches- based covariance estimation as well. To remedy this, we propose a new BLP method based on weighted covariance estimation (WBLP). This method searches for similar patches in a large window for each reference patch, which brings a large amount of computation. To solve this problem, we introduce a randomized acceleration technique to speed up our method.