Reviewerly/exHarmony huggingface dataset:
Three classes of partitioned difference families and their optimal constant composition codes

&lt;p style='text-indent:20px;'&gt;Cyclotomy, firstly introduced by Gauss, is an important topic in Mathematics since it has a number of applications in number theory, combinatorics, coding theory and cryptography. Depending on &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ v $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; prime or composite, cyclotomy on a residue class ring &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ {\mathbb{Z}}_{v} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; can be divided into classical cyclotomy or generalized cyclotomy. Inspired by a foregoing work of Zeng et al. [&lt;xref ref-type="bibr" rid="b40"&gt;40&lt;/xref&gt;], we introduce a generalized cyclotomy of order &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ e $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; on the ring &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ {\rm GF}(q_1)\times {\rm GF}(q_2)\times \cdots \times {\rm GF}(q_k) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ q_i $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ q_j $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; (&lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ i\neq j $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;) may not be co-prime, which includes classical cyclotomy as a special case. Here, &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ q_1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ q_2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ \cdots $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, &lt;inline-formula&gt;&lt;tex-math id="M12"&gt;\begin{document}$ q_k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are powers of primes with an integer &lt;inline-formula&gt;&lt;tex-math id="M13"&gt;\begin{document}$ e|(q_i-1) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; for any &lt;inline-formula&gt;&lt;tex-math id="M14"&gt;\begin{document}$ 1\leq i\leq k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Then we obtain some basic properties of the corresponding generalized cyclotomic numbers. Furthermore, we propose three classes of partitioned difference families by means of the generalized cyclotomy above and &lt;inline-formula&gt;&lt;tex-math id="M15"&gt;\begin{document}$ d $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-form functions with difference balanced property. Afterwards, three families of optimal constant composition codes from these partitioned difference families are obtained, and their parameters are also summarized.&lt;/p&gt;
Fast Object Detection on the Road

Autonomous vehicles using Artificial Intelligence (AI) technologies requires various sensors such as radars, lidar, ultrasonic, and etc. to mimic the human visual perception in monitoring the road condition. Wide-angle camera is also often adopted for better coverage of view. Those sensors generates massive amount of data that could be processed with the cloud computing through the wireless communication. However, the cloud computing may not be a feasible solution, such as for real-time detection systems. In this work, we examine the implementation of the deep-learning (DL) real-time object detection models on the edge devices that is connected to the wide-angle camera. This visual system can achieve real-time object detection with a latency of less than 0.2 ms. The DL model also help to mitigate the distortion that is introduced by the wide-angle camera. Such a detection system will be able to warn the user of his or her surrounding road conditions.
HDR Image Compression with Convolutional Autoencoder

As one of the next-generation multimedia technology, high dynamic range (HDR) imaging technology has been widely applied. Due to its wider color range, HDR image brings greater compression and storage burden compared with traditional LDR image. To solve this problem, in this paper, a two-layer HDR image compression framework based on convolutional neural networks is proposed. The framework is composed of a base layer which provides backward compatibility with the standard JPEG, and an extension layer based on a convolutional variational autoencoder neural networks and a post-processing module. The autoencoder mainly includes a nonlinear transform encoder, a binarized quantizer and a nonlinear transform decoder. Compared with traditional codecs, the proposed CNN autoencoder is more flexible and can retain more image semantic information, which will improve the quality of decoded HDR image. Moreover, to reduce the compression artifacts and noise of reconstructed HDR image, a post-processing method based on group convolutional neural networks is designed. Experimental results show that our method outperforms JPEG XT profile A, B, C and other methods in terms of HDR-VDP-2 evaluation metric. Meanwhile, our scheme also provides backward compatibility with the standard JPEG.
Beyond Relevance: Trustworthy Answer Selection via Consensus Verification

Community Question Answering (CQA) sites such as Yahoo! Answers and Baidu Knows have emerged as rich knowledge resources for information seekers. However, answers posted to CQA sites often vary a lot in their qualities. User votes from the community may partially reflect the overall quality of the answer, but they are often missing. Hence, automatic selection of "good'' answers becomes a practical research problem that will help us manage the quality of accumulated knowledge. Without loss of generality, a good answer should deliver not only relevant but also trustworthy information that can help resolve the information needs of the posted question, but the latter has received less investigation in the past. In this paper, we propose a novel matching-verification framework for automatic answer selection. The matching component assesses the relevance of a candidate answer to a given question as conventional QA methods. The major enhancement is the verification component, which aims to leverage the wisdom of crowds, e.g., some big information repository, for trustworthiness measurement. Given a question, we take the top retrieved results from the information repository as the supporting evidences to distill the consensus representation. A major challenge is that there is no guarantee that one can always obtain reliable consensus from the wisdom of crowds for a question due to the noisy nature and the limitation of the existing search technology.Therefore, we decompose the trustworthiness measurement into two parts, i.e., a verification score which measures the consistency between a candidate answer and the consensus representation, and a confidence score which measures the reliability of the consensus itself. Empirical studies on three real-world CQA data collections, i.e. YahooQA, QuoraQA and AmazonQA, show that our approach can significantly outperform the state-of-the-art methods on the answer selection task.
A Noise-aware Asymmetric Spectral Regularization Collective Matrix Factorization Algorithm for Recommender System in Cloud Services

In cloud computing environment the problem of low recommendation accuracy caused by the influence of social noise is ignored in the existing social recommendation researches, we propose a novel approach to compute the trust values by using the asymmetry of trust network, which can mitigate the impact of social noise. At the same time the overall distribution information captured by different modeling schemes is significantly different when it exists a large amount of data in cloud services, and it affects the user's cold start, as that is not conducive to the personalized recommendation. We study the sparseness problem of the recommender systems, and report the research status of social recommendation. Accordingly we propose a noise-aware asymmetric social collective matrix factorization model called AsySocialCoMF to solve the problem above. The experimental results show that the proposed approach can improve the recommendation accuracy by 2%-15% compared with the current works, and it also performs better for the user's cold start problem. It is worth noting that the approach is also applicable to other information retrieval scenarios.
Reversible Data Hiding Algorithm with High Imperceptibility Based on Histogram Shifting

Histogram shifting based reversible data hiding (RDH) algorithm embeds the secret messages into the peak point (with the highest frequency) in the image histogram. Using RDH however results in the number of peak points in the histogram to drop by half of its original quantity, thus results in depression of the image histogram. This significantly affects the imperceptibility of the secret message. To address this issue, in this paper we propose an RDH algorithm with high imperceptibility in which the embedding point (the grayscale value corresponds to the pixels embedded secret message) in the image histogram is selected based on the length of the secret message and the specific pixels to embed the secret message are chosen by taking into account the value of their adjacent pixels. The adjustment made to the quantity of embedding point and its surrounding points in the histogram causes the number of pixels to have a smooth transition around the embedding point in the histogram. This significantly enhances imperceptibility. Experimental results confirm the effectiveness of the proposed algorithm.
Towards a model for Managing Diversity and Inclusion in Software Development Teams

[Context] Information technology companies increasingly understand the importance of diversity and inclusion in software development teams. For this reason, they seek to implement diversity management models to enhance the positive effects of this inclusion. However, there is no single model for managing diversity, which makes it possible for each company to develop a model that works according to their expectations and demands. The literature about diversity management mentions that a large number of these models focus their efforts on managing distinct skills for team composition and on recruiting and selecting professionals with different profiles as a way of responding to the demand for more diversity in software development teams. [Objective] This work developed an initial model for managing diversity and inclusion in software development teams. Our goal is to explore how the practice of diversity management has occurred in this context. [Method] To achieve this goal, we developed an action research within a multinational organization in Brazil. [Results] We proposed a model for managing diversity characterized by three stages: Inclusion, Development, and Representation.[Conclusion] The main contribution of this paper is the model to help companies to manage diversity and inclusion and the discussion about the importance of thinking on diversity and inclusion in a structured way in the context of software development teams.
Privacy Preserving Issues in the Dynamic Internet of Things (IoT)

Convergence of critical infrastructure and data, including government and enterprise, to the dynamic Internet of Things (IoT) environment and future digital ecosystems exhibit significant challenges for privacy and identity in these interconnected domains. There are an increasing variety of devices and technologies being introduced, rendering existing security tools inadequate to deal with the dynamic scale and varying actors. The IoT is increasingly data driven with user sovereignty being essential - and actors in varying scenarios including user/customer, device, manufacturer, third party processor, etc. Therefore, flexible frameworks and diverse security requirements for such sensitive environments are needed to secure identities and authenticate IoT devices and their data, protecting privacy and integrity. In this paper we present a review of the principles, techniques and algorithms that can be adapted from other distributed computing paradigms. Said review will be used in application to the development of a collaborative decision-making framework for heterogeneous entities in a distributed domain, whilst simultaneously highlighting privacy preserving issues in the IoT. In addition, we present our trust-based privacy preserving schema using Dempster-Shafer theory of evidence. While still in its infancy, this application could help maintain a level of privacy and nonrepudiation in collaborative environments such as the IoT.
The Development and Application of Multimedia Technology in College Gymnastics Teaching

Abstract In recent years, information technology has developed at a rapid pace and is almost everywhere closely linked in people’s work, life, and learning. Information technology has a certain role in improving people’s lives, enhancing learning, and promoting the quality of work. Using literature methods, interview methods, and analytical inductive methods, etc, on the basis of reading a large number of professional theoretical books on gymnastics teaching, multimedia technology, and modern teaching and reform, the application of gymnastics teaching in colleges in China has been studied theoretically. This article discusses the college gymnastics teaching compared with other cultural studies with the characteristics and studies the feasibility of multimedia technology in college physical education and the supporting role of teaching in gymnastics and gymnastics multimedia courseware design and production. It is hoped that through the research carried out in this paper, the application of multimedia technology in college gymnastics teaching in China can be given theoretical guidance and reference that can play a role in solving and answering questions in the current multimedia application process. College gymnastics workers will apply multimedia technology more effectively and skillfully in the future.
Usage of Seaweed as a Biocomposite Material in Green Construction

The need for finding sustainable and environmentally friendly materials in the modern building industry is increasing. This research aimed to determine the compressive strength of mortar cube based on a replacement ratio of 15% by seaweed (Gracilaria species). The sample was washed until the pH value is neutral, oven-dried at 105 °C for 24-h and was burnt at 600 °C for 3-h. The seaweed granules and seaweed ashes were characterized via Brunauer Emmett and Teller (BET), Field Emission-Scanning Electron Microscope (FESEM), X-ray diffraction (XRD). The sample was then incorporated into the mortar and the compressive strength was determined at 3,7,14 and 28 days respectively. After 28 days, the compressive strength test showed that a 15% burnt sample has the highest compressive strength surpassing the control mix design. Therefore, eco-friendly seaweed ash could be a substitute as a green construction material.
A New Approach of Moving Object Detection Using Background Subtraction Method

Moving object detection has introduced a new challenging horizon in the era of image processing. The main challenge to detect moving objects in all environments because of the complex background. This paper proposes a method that detects a moving object more accurately in almost all the environments. Background subtraction is used in our proposed method for detecting moving objects where the Sauvola algorithm is used on a subtracted image for binary conversion. Median filtering and boundary labeling are used to increase the accuracy of moving object detection. Finally, a morphological operation is performed in our proposed method for detecting the moving object. We tested our proposed method on Wallflower datasets and compare the results of moving object detection on the basis of recall, precision and F-measure with other existing methods. Our proposed method attained successful results for detecting the moving object.
Development of Relationship Between Steel Billet Temperature and Data on Its Heating History for Continuous Furnace of Rolling-Mill Shop

This study is to apply the Data Mining concept to process technological data, which are received from a field level of an industrial control system of a steel billet heating in a continuous furnace of a rolling-mill shop. The main scope of the study is to find a regression dependence of the steel billet temperature on a history of its heating in such a furnace. This paper describes: 1) the principles of data warehouse implementation to store values of signals obtained from a steel billets tracking system, 2) the chosen features (input variables, regressors) to solve the problem under consideration, such as temperature in all furnace zones and time of a steel billet heating in each of them, 3) the applied algorithms of data pre-processing and exploratory analysis. Then the results of research on various machine learning algorithms application to find the required dependence are presented. The best quality of approximation is achieved using a model, which is based on classification and regression trees. They are trained with the help of an extreme gradient boosting algorithm modification - DART. Obtained prediction accuracy is ~9°C.
When will gradient methods converge to max‐margin classifier under ReLU models?

We study the implicit bias of gradient descent methods in solving a binary classification problem over a linearly separable data set. The classifier is described by a non‐linear ReLU model and the objective function adopts the exponential loss function. We first characterize the landscape of the loss function and show that there can exist spurious asymptotic local minima besides asymptotic global minima. We then show that gradient descent (GD) can converge to either a global or a local max‐margin direction or may diverge from the desired max‐margin direction in a general context. For stochastic gradient descent (SGD), we show that it converges in expectation to either the global or the local max‐margin direction if SGD converges. We further explore the implicit bias of these algorithms in learning a multineuron network under certain stationary conditions and show that the learned classifier maximizes the margins of each sample pattern partition under the ReLU activation.
H-V Shadow Detection Based on Electromagnetism-Like Optimization

Shadow detection is useful in a variety of image analysis applications, as it can improve scene understanding. Most of the recent shadow detection approaches use near-infrared (NIR) cameras and deep learning to provide enhanced segmentation of the shadow areas in images. In this paper a novel shadow detection method is proposed, exploiting the perceptual color representation of the HSV color space and a physics-inspired optimization algorithm for image segmentation. The comparative advantage of this method over the state-of-the-art ones is that its performance is comparable without requiring any special equipment, such as NIR cameras, while it is simpler. Quantitative and qualitative experiments on publicly available datasets in comparison with three state-of-the-art methods, validate its effectiveness.
Hardware Resource Reliability Analysis based on Deep Learning for Virtual Machine Deployment Optimization

This paper proposes a new method based on the Apriori algorithm and deep learning technology to improve the flexibility of hardware deployment and to analyze hardware usage reliability for VM. This method can significantly enhance the usage rate of reliable hardware resource.
Isolated Handwritten Balinese Character Recognition from Palm Leaf Manuscripts with Residual Convolutional Neural Networks

The versatility of machine learning tools creates new opportunities to preserve cultural heritage and promote cultural diversity. One important task for such preservation and promotion is the processing of local languages, of which the digitisation of traditional document written in the local scripts is a fundamental building block. We are hereby concerned with the recognition of isolated handwritten Balinese characters from palm leaf manuscripts. We propose a method based on a residual convolutional neural network to recognise handwritten characters written on palm leaf manuscripts in the Balinese script. The proposed method essentially consists of the combination of identity and convolution blocks. A comparative empirical performance evaluation, using a publicly available data set, shows that the proposed method improves on existing alternatives.
Web Service Discovery Based on Knowledge Graph and Similarity Network

Service discovery aims to address the problem of service information explosion and find and locate services that meet the needs of service requesters. Because service description information is mostly composed of short text with noise and has the characteristics of semantic sparseness, it is difficult to extract the implied context information of service description. This paper proposes a service discovery framework based on Knowledge graphs and neural Similarity Network (KSN). Which uses knowledge graphs to connect entities to obtain rich external information to enhance the semantic information of service descriptions. convolutional neural network and similarity network is utilized to extract context information. Through extensive experiments on a real service data set show that KSN is superior to existing web service discovery methods in terms of multiple evaluation metrics.
Performance evaluation of English learning through computer mode using neural network and AI techniques

Learning performance evaluation is an important part of computer English teaching. Through evaluation, students can understand whether they have reached the learning goals of a lesson, and what are their shortcomings, so that they can continuously improve and improve themselves against these shortcomings. Combining actual needs, based on neural network and artificial intelligence technology, this paper constructs a computer English learning performance evaluation model based on machine learning. The computer English learning performance evaluation system in this paper is constructed on the basis of auditory characteristics and introduces a wavelet entropy feature based on the best tree wavelet packet decomposition, and it is applied to the establishment of an adaptive model. Moreover, this paper uses controlled experiments to analyze the model performance and combines mathematical statistics to visually display the model effect. From the research results, it can be seen that the performance of the model constructed in this paper basically meets the expected requirements and actual needs.
Construction of a machine learning dataset for multiple AI tasks using Korean commercial multimodal video clips

Accordingly a lot of broadcasting medias pursuing various concepts have been appeared and the major type of contents consumed on the web has been changed to multimodal contents, the attempt to actively utilize multimedia content in artificial intelligence research is also starting. This paper introduces a study that constructs a converged information dataset in an integrated form by analyzing various types of multimodal information on video clips. The constructed dataset was released with various semantic labels for artificial intelligence research about various information classification. The labels and descriptions in this dataset include various context, intention and emotion information describing with vision, speech and language in each video clips. The constructed dataset can be resolved the problem of lack of public data for multimodal interaction research with Korean. It is expected that this dataset can be applied in the constructions of various artificial intelligence services like Korean dialogue processing, visual information extractions and various multimodal data analysis tasks.