Reviewerly/exHarmony huggingface dataset:
Robust mixture regression via an asymmetric exponential power distribution

Finite mixture of linear regression (FMLR) models are an efficient tool to fit the unobserved heterogeneous relationships. The parameter estimation of FMLR models is usually based on the normality assumption, but it is very sensitive to outliers. Meanwhile, the traditional robust methods often need to assume a specific error distribution, and are not adaptive to dataset. In this paper, a robust estimation procedure for FMLR models is proposed by assuming that the error terms follow an asymmetric exponential power distribution, including normal distribution, skew-normal distribution, generalized error distribution, Laplace distribution, asymmetric Laplace distribution, and uniform distribution as special cases. The proposed method can select the suitable loss function from a broad class in a data driven fashion. Under some conditions, the asymptotic properties of proposed method are established. In addition, an efficient EM algorithm is introduced to implement the proposed methodology. The finite sample performance of the proposed approach is illustrated via some numerical simulations. Finally, we apply the proposed methodology to analyze a tone perception data.
Cyclotomy, cyclotomic cosets and arithmetic properties of some families in 𝔽l[x] 〈xptq−1〉

Arithmetic properties of some families in [Formula: see text] and hence in [Formula: see text] are obtained by using the cyclotomic classes of order 2 with respect to [Formula: see text], where [Formula: see text] is primitive root modulo [Formula: see text], [Formula: see text] and [Formula: see text]. The form of these cyclotomic classes enables us to generalize the results obtained in [S. Jain and S. Batra, Cyclotomy and arithmetic properties of some families in [Formula: see text], Asian-Eur. J. Math. 13(4) (2020) 2050077].
Information Hiding Using Domain-Specific Hierarchical Data Structure Symmetry-aware Sparse Voxel Directed Acyclic Graphs

Steganography is considered to be the science and the art of hidden communication through an open communication channel or hiding information using innocent and harmless-looking media. It has been evolving for centuries, but as in many areas of human activity, its new possibilities and effectiveness have been brought about by the development of information and communication technologies. These have not only become a means of simplifying and speeding up the implementation of operations enabling the hiding of information in stegomedia and dynamizing the transmission of such modified media, but they have also brought with them specific types of stegomedia and information hiding algorithms. They are so closely related to information and communication technologies that without their existence the meaning of those algorithms and stegomedia will be lost. They use specific data structures, file formats, communication protocols, etc. and are even adapted to the design of the hardware and related firmware. The following paper is devoted to this area, dealing with hiding information in the domain-specific hierarchical data structure Symmetry-aware Sparse Voxel Directed Acyclic Graphs. It is designed for lossless-compressed representation of voxelized three-dimensional scenes in the field of computer graphics. The paper deals with a brief description of this data structure and proposes a new steganographic algorithm. The algorithm allows information to be hidden through specific features of this data structure using its three-dimensional voxel grids.
Sentiment Analysis in Customer Experience in Philippine Courier Delivery Services using VADER Algorithm Thru Chatbot Interviews

The use of sentiment analysis of the customers' insight on the product delivery services would be an excellent opportunity to evaluate the customers' emotions in the courier delivery services. The result can link with the review to substitute for the product's performance or customer satisfaction. The primary steps in the study are data collection, data pre-processing, and sentiment analysis. The emotional data were analyzed using the VADER algorithm. A chatbot was used as an intermediary tool to collect emotional datasets. The compound score was calculated by adding the valence scores of each word in the lexicon, then adjusting them according to the guidelines and normalizing them. The negative, neutral, and positive scores represent the customer's sentiment. The test results achieved 93.33% sentiment accuracy based on the computed sentiment polarities and compound scores. The output evaluates that the system is effective based on the outcome and can be considered an innovative approach in analyzing the customers' perception of the product and services available in the market.
Data Protection and Export for Transaction Ledgers in Permissioned Blockchain Platforms

Blockchain provides an innovative architecture to store data in a decentralized nature and allows the ability to verify every transaction at any time in the future. According to Google Scholar™ web search engine, since 2018 there are about 116,000 scholarly documents found for the search query “blockchain” and about 82,000 scholarly documents found for the search query “blockchain technology”. As of January 24, 2022, the market cap for all cryptocurrencies exceeds 1.65 trillion dollars. It is essential to secure the ledger of transactions in blockchain-based systems so that only authorized parties can access it. Furthermore, fine-grained role-based access control and secure data export for the ledger of transactions are highly desirable. In this paper, we propose a mechanism for encrypting the transaction ledger through the chaincode. The solution provides transaction data confidentiality in permissioned blockchain networks. We will show the viability of our approach by integrating it into IBM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Hyperledger <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Fabric 2.3 framework. It is an open-source project for a permissioned blockchain system that utilizes smart contracts to implement business logic whilst maintaining the core blockchain properties. We also propose using a mechanism for exporting transactions in a protected form from private permissioned blockchain systems to authorized external entities, supporting fine-grained role-based and attribute-based access control. This mechanism relies on encrypted spreadsheet files and provides confidentiality, integrity, and origin integrity for exported transaction data outside the permissioned blockchain network. The solution could stimulate a wider deployment of permissioned blockchain systems.
Fast Non-Uniform Quantization of Neural Networks

Neural Networks (NNs) have achieved state-of-the-art (SOTA) performance in a number of domains but suffer intensive complexity. Network quantization can effectively reduce computation and memory costs without modifying network structures, facilitating the deployment of NN s on cloud and edge devices. However, the low-bit quantization without time-consuming training or access to the full training set is still a challenging problem. Inspired by the traditional companding technique in the signal processing area, we propose a novel method to achieve fast non-uniform quantization of NNs with a few unlabeled samples. Extensive experiments on ImageNet2012 demonstrate that the proposed method can guarantee efficiency and accuracy simultaneously. We further show that the proposed method can extend to other computer vision tasks such as object detection and semantic segmentation.
Thangka Image Style Transfer Based on Improved CycleGAN

Aiming at the problems of artefacts, color loss, blurred outlines, etc. affecting the aesthetics of the overall artistic effect in the style transfer of thangka images, a style transfer method of CycleGAN digital thangka images based on attention mechanism is proposed. After extracting the style feature and content feature of the image, it is input into the generator based on the attention mechanism, and the attention mechanism is applied to assign probability distribution information to each area of the digital thangka image, so that the important style area can get more attention. The experimental results show that the method enhances the image structure and texture information, can highlight the style transfer effect of digital thangka images, and is beneficial to improve the integrity and artistry of the output image.
Significance detection based on deep convolutional neural network

Aiming at the limitations of the existing saliency detection algorithms in defining saliency features artificially and the poor robustness of the algorithm, a saliency detection algorithm based on deep convolutional neural network was proposed. Firstly, the VGG-16 model used for image classification is modified to make it applicable to significance detection. Then, the network is trained with super-pixel segmentation samples and the initial saliency graph is predicted. Finally, on the basis of preliminary significant image to join the regional contrast, increases significantly the difference of target and background region, further improve the quality of significant image. Compared with the existing algorithms, the proposed algorithm avoids the uncertainty of manually defining significant features, and has stronger robustness and generalization. The effectiveness of the proposed algorithm is demonstrated from two aspects of subjective qualitative observation and objective quantitative detection.
Application of Risk Assessment Model for Breast Cancer

Looking for a risk assessment model is of great significance for predicting, preventing and diagnosing breast cancer. This paper collects relevant data from SEER database(Survey, Epidemiology, and End Results), uses SVM (Support Vector Machine) and random forest in data mining to predict the possibility of breast cancer, and discusses the application value of Gail breast cancer risk assessment model. Finally, the prediction results based on three risk assessment models are analyzed. The analysis results show that the prediction accuracy rate of Gail model is more excellent.
Evaluation of the log-homotopy particle flow filter for localisation

Nonlinear state estimation related to systems with high dimensional state spaces is a difficult problem that has theoretical and practical relevance. The performance of the Kalman filter is consistent in any dimension, however the same does not hold for the particle filters, due to the curse of dimensionality and particle degeneracy. The log-homotopy based particle flow filter, introduced in recent years is a promising approach to hard state estimation problems. This paper examines the applicability of the Daum–Huang particle flow filter in a challenging multi-target tracking scenario involving a nonlinear measurement model and compares it with conventional estimation methods.
Multimodal Learning Approach for Multi-topic Twitter Summarization

Twitter summary is designed to filter out the content summary from lots of noisy tweets, which can be used for Twitter search, public opinion analysis, hot topic discovery, etc. But the existing Twitter summary methods mostly focus on individual topics or hot events, and there are few studies on Twitter summary with multi-topic posts. For the sake of resolve this problem, we propose a Twitter summary method based on social network information and manifold learning, which can extract tweets covering multiple topics, and better integrates the topic information and structural information of the tweets. First, the multi-modal manifold learning method is improved, and the social network information is integrated. Second, in order to test the relevance of the tweet summary and the topic, a test method of topic membership is designed. What&#x0027;s more, due to the lack of evaluation data sets, the author built a manual evaluation dataset. Our experiments on large-scale real tweets demonstrate the effectiveness of our framework.
Countermeasure of Telecom Network Fraud Investigation Based on Big Data

With the diversification of information data in the information age of big data and the integration of network technology and the development of different industries, criminals who carry out telecommunication fraud are also using the technical loopholes existing in the process of integration of big data with different industries as an opportunity to commit crimes. This paper studies the investigation process and countermeasures of telecom network fraud through big data technology. This paper first introduces the characteristics of big data, analyzes the challenge of personal information security under the background of big data, warns people to protect their personal information in the era of big data, puts forward the clustering algorithm based on big data, introduces the concrete steps based on big data clustering algorithm, and then puts forward the specific steps of big data clustering algorithm. The current situation of telecom network fraud is analyzed, and the telecommunication network fraud is clustered based on big data. The experimental results show that, based on the clustering analysis of telecommunication network fraud based on big data, it is found that through the information age of big data, as long as big data are used rationally, it can effectively suppress telecommunications fraud and reduce it by 80%.
Semantics Accommodated K-Anonymization (SAKA) Technique for Assorted Data

Techniques approaching k-anonymity for shielding the mini data privacy over data mining. Mini gatherings and abstraction are two conventional techniques for deploying the k-anonymity method. However, the above two methodologies lead to major errors over the anonymity of combined small data gathering. To approach this problem, we propose an efficient and new anonymity method SAKA that accommodates extra semantics than abstraction and mini data gatherings that can handle combined small data combinations. SAKA is the abbreviation for Semantics Accommodated K-Anonymity technique implied for anonymization of assorted data. The concept of SAKA is to merge the mean assorted vector of arithmetic data with abstraction values of classified data as a grouping centroid. It uses its epitome of tuples along with its equivalent clusters. Here we propose efficient algorithms to anonymize assorted data. An empirical result proves that SAKA can anonymize the assorted mini data persuasively and the algorithm implemented will provide good rapport between the quality of data and effectiveness of the algorithm thus it correlates anonymization algorithms with anonymous data.
Segmentation of 3D scenes using geometric features based on saliency prior for robotic applications

In recent years, there has been increasing interest in how to efficiently and quickly segment objects in robotic tasks. This work offers a supervoxel-based solution to object segmentation in cluttered scenes. Instead of using a uniformly distributed spatial seed resolution, our process uses a salient prior based on scene objects to drive supervoxel generation. Based on a saliency prior, the proposed method first over-segments the 3D scene into multiscale supervoxels with graph structure. The supervoxel maps that have been constructed are then used to distinguish between planar and non-planar structural features. Finally, in an energy optimization framework, we leverage the planar information gleaned from the scene to optimize the original segmentation results. We conducted experiments on OCID and TUW data to verify the effectiveness of our segmentation method.
Deep Intra-Class Similarity Measured Semi-Supervised Learning

Recently, how to handle the situation where only a few samples in the dataset are labeled has become a hot academic topic. Semi-Supervised Learning (SSL) has shown its great capacity and potential in this topic. However, existing methods tend to focus more on the relationship between unlabeled samples and labeled samples or focus on unlabeled samples' information while rarely exploring the hidden information between unlabeled data of the same category. To address this shortcoming, we use an intra-class similarity measure to exploit the information between unlabeled samples of the same class and, on this basis, introduce a new intra-class similarity loss term. In addition, to improve the accuracy of pseudo-labels in deep semi-supervised learning, we also propose an adaptive expansion of the Label Propagation algorithm. The proposed method outperforms many state-of-the-art results in CIFAR-10, CIFAR-100 and Mini-ImageNet. The experimental results show that adding the intra-class similarity loss term and the adaptive extension improvement to the deep semi-supervised learning model can effectively improve the model's performance.
Renewal of secret and shadows in secret image sharing

Secret image sharing (SIS) is an important method to protect the security of secret images. When part of the shadows generated by the secret sharing is leaked or when the secret image is restored, the shadows and the secret need to be renewed. Aiming at secret image and shadow image renewal, a scheme for renewing secret image and shadow images is proposed. The scheme is to multiply the original and renewed shadow of the corresponding serial number to get a new shadow over finite fields. Our scheme can realise the renewal of the secret image and shadows and has characteristics of the variable threshold, secure public transmission, variable size, and lossless recovery. This paper theoretically analyses and proves the security of the proposed scheme and verifies the effectiveness through a large number of experiments.
Siamese tracker based on motion potential energy

Siamese trackers have been widely concerned recently. Though many trackers have obtained better performance when the target encounter fast motion or changes the direction of motion, most trackers still utilize the previous frame&#x2019;s center to generate the center of the search box in the current frame, which may lead to loss of the tracked target. In this paper, we utilize the potential energy to the expended target detection region. Specially, we predict the search area of the current frame by analyzing the potential energy of the object&#x2019;s historical frame. Abundant experiments show that our tracker with better robustness than most trackers on tracking benchmarks. Especially, the compared results show our tracker has an excellent performance in the fast motion challenge.
Online Book Recommendation System using Custom Recommender

Recommendation systems can be considered as one of the most popular tools to raise the profit and retain users. In most of the fields Recommender systems are being used. The aim of the recommender system is that it suggests content for users based on their previous choices or what type of taste they are having. When there is a need for implementing an effective recommender system, it should always be diverse in content and it is not supposed to be biased towards the most popular content. In this perspective, the content-based filtering will provide well-suited results for the user. This research study attempts to propose a Recommender system for suggesting consecutive appropriate books for the user to read. The proposed recommender system is designed by using item-based collaborative filtering, content-based collaborative filtering (using Title, Author, Publisher, Category as features), Content-Based Collaborative Filtering (using Summary as a feature), Custom Recommender and at the end different recommenders are compared.
Dual-branch Approach for Tracking UAVs with the Infrared and Inverted Infrared Image

The imaging for small unmanned aerial vehicles (UAVs) in infrared imaging systems is easily disturbed when they shuttle between different surrounding regions in a large background, resulting in target contrast inversion. Existing methods have poor tracking performance in this scenario. A dual-branch object tracking framework is proposed to tackle this problem. Firstly, the features of original and inverted infrared images are extracted by different branches. Then, a cross-correlation feature fusion module is proposed, which fuses the features of the two branches to enhance the features of the target area to obtain a more accurate bounding box regression. The proposed method achieves top performance compared with the existing state-of-the-art tracking algorithms, reaching the success rate of 78.39% and precision rate of 80.07% on the 2nd anti-UAV datasets, which are 5.84% and 4.52% higher than the SuperDiMP baseline.
A real-time spectrum data compression method based on character encoding

Spectrum data is the main research object in the field of radio management, its storage and processing are the key process for radio monitoring. In view of the scanning frequency spectrum has high redundancy, we propose a real-time spectrum data compression method based on UTF-8 coding. This method aims at reducing storage consumption and excessive network load without affecting spectrum data analysis. Considering the similarity between spectrum data, a multi-threshold controlled matching algorithm is designed to pick out the typical spectrum sets represent all spectral data of each signal. Then replace all the original spectrum with a small amount of data to achieve the purpose of compressing. We have verified its effectiveness in several bands, and the results show that this method has good compression effect and low recovery distortion. In particular, the compression ratio is close to 1% at low frequency occupation.
Research of Sentiment Analysis Model Fusing Local and Global Information

BERT is widely used pre-trained language model in the field of sentiment analysis. The success of the BERT lies in the powerful role of the self-attention mechanism in Transformer. BERT has shown to be effective at capturing local contextual information in texts, but its ability to capture global information is more limited. In this paper, we propose LCF-GCN model, which uses Graph Convolutional Networks (GCN) to continuously fit adjacency information to capture global features and applies semantic distance calculation to improve local feature extraction based on BERT. The fusion and interaction of local information and global information of domain data are considered comprehensively through the above methods. We conduct comparative experiments on the housing review datasets. Experimental results show that LCF-GCN improves the accuracy of polarity extraction and f1 of aspect extraction task.
Impact of Smart Learning in the Satisfaction of Students in Logistics Engineering from a Private University in Peru

This research aims to evaluate the impact of smart learning on the academic satisfaction of Logistics and Transportation Engineering students at a Private University in Peru. The problem lies in improving the indicators of academic quality in the context of social isolation due to the pandemic. For this, the method of paired samples was applied with respect to the results, qualifications and perceptions of the students in the prepandemic phase (face-to-face modality) and during the pandemic (blended modality). The design of the intelligent learning process was carried out considering the achievements of specific competences of the students of the Engineering program. Obtaining as a result an adequate level of 75.52% in the achievement of competencies, equivalent to the upper limit of scale B, compared to the achievement before the application of the new intelligent model.