Reviewerly/exHarmony huggingface dataset:
Kernel-based clustering via Isolation Distributional Kernel

Clustering has become one of the widely used automatic data-labeling techniques applied in a variety of disciplines. Kernel-based clustering is a technique designed to identify non-linearly separable clusters with irregular shapes. However, existing kernel-based clustering algorithms usually produce weaker clustering outcomes than density-based clustering, and they have high computational cost which limits their applications to small datasets only. In this paper, we contend that these limitations are mainly due to the use of (a) the Expectation-and-Maximization algorithm as an optimization procedure, and (b) a non-adaptive kernel. In addition, to address the limitations of current kernel-based algorithms, we propose the first clustering algorithm that employs an adaptive distributional kernel without any optimization, while achieving a similar optimization objective function. We demonstrate its superior performance of identifying complex clusters on massive datasets under different real-world application scenarios.
Analyzing the performances of squash functions in capsnets on complex images

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Joint-product representation learning for domain generalization in classification and regression

Abstract In this work, we study the problem of generalizing a prediction (classification or regression) model trained on a set of source domains to an unseen target domain, where the source and target domains are different but related, i.e , the domain generalization problem. The challenge in this problem lies in the domain difference, which could degrade the generalization ability of the prediction model. To tackle this challenge, we propose to learn a neural network representation function to align a joint distribution and a product distribution in the representation space, and show that such joint-product distribution alignment conveniently leads to the alignment of multiple domains. In particular, we align the joint distribution and the product distribution under the $$L^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> -distance, and show that this distance can be analytically estimated by exploiting its variational characterization and a linear variational function. This allows us to comfortably align the two distributions by minimizing the estimated distance with respect to the network representation function. Our experiments on synthetic and real-world datasets for classification and regression demonstrate the effectiveness of the proposed solution. For example, it achieves the best average classification accuracy of 82.26% on the text dataset Amazon Reviews, and the best average regression error of 0.114 on the WiFi dataset UJIIndoorLoc.
Sentiment Analysis of Social Network Comment Text Based on LSTM and Bert

This paper has the aim of solving problems in research studies on the analysis tasks of text emotion; the problems are the low utilization of text, the difficulty of effective information extraction, the failure of recognizing word polysemy with effectiveness. Thus, based on LSTM and Bert, the method of sentiment analysis on text is adopted. To be precise, word embedding of dataset in view of the skip-gram model is used for training course. In each sample, the word embeddings combine matric with the two-dimensional feature to be neural network input. Next, construction of analysis model for text sentiment combines Bert pre-training language model and long short-term memory (LSTM) network, using the word vector pre-trained by Bert instead of that trained in the traditional way to dynamically generate the semantic vector according to the word context. Finally, the semantic representation of words from text is improved by effectively identifying the polysemy of words, and the semantic vector is input into the LSTM to capture the semantic dependencies, thereby enhancing the ability to extract valid information. The Accuracy, Precision, Recall and F-Measure for the method of Bert–LSTM based on analysis of text sentiment are 0.89, 0.9, 0.84 and 0.87, indicating high value than the compared ones. Thus, the proposed method significantly outperforms the comparison methods in text sentiment analysis.
Research on the Application of Sound in Virtual Reality

Immersive sound is an essential element of any Virtual Reality (VR) experience. In recent years, increasing popularity of VR devices and numerous advancements in hardware and software technologies, the development of immersive interactive sound has become more potent. This article explores VR through the sonic perception, presents a review of the Method of combining and applying sound and VR, with the goal of providing more references and suggestions for the sound of VR. This includes the creative and technical work of music and spatial sound in practical projects in recent years. Some examples of musical VR are provided in this article to compare their characteristics. A review of the state of the art of sound simulations, focusing on the various factors that ensure a complete and immersive audio experience, while trying to strike an appropriate balance between realism and accuracy. This article finally discusses the future and challenges of sound in VR.
AI in Video Recommendation System

Short videos are very popular all over the world. Video recommendation system is an essential part in it. It can help people to watch the video that they are interested in. This paper is written for study the specific principle of the video recommendation system. The result was getting through relative literatures and actual test. Short video recommendation systems typically use collaborative filtering and deep learning techniques to achieve this. Collaborative filtering comes in two types: user-based and content-based. User-based collaborative filtering recommends videos to new users based on the viewing behavior of similar users. Content-based collaborative filtering uses video features and similarity to recommend similar videos. Finally, this paper shows how the video web set can learn what is the user’s interest.
Insulator defect detection algorithm based on improved YOLOv5

Aiming at the problems such as small target scale, complex background, difficult detection, false detection and leakage detection of aerial insulators in transmission lines, this paper proposes an insulator defect detection algorithm based on improved YOLOv5. Firstly, CBAM attention module is added to the backbone network of YOLOv5 to improve the feature extraction capability of insulator pictures. Secondly, in the feature extraction part, PANet structure is replaced by BiFPN structure to make full use of the underlying feature information. Finally, the improved K-means algorithm is used to determine the prior frame and improve the defect detection accuracy of the insulator. Experimental results show that this method can improve the identification accuracy of insulator defect detection in transmission lines.
Robust federated learning under statistical heterogeneity via Hessian spectral decomposition

Federated Learning (FL) is a collaborative machine learning paradigm in which a global model is learned via aggregating local ones. Although statistical heterogeneity of the local training data is necessary for the generalisability of the global model, it also introduces local model “drift” that slows down the convergence. Thus, how to optimally aggregate local models in FL remains an open problem. Recognising that training data lends varying evidential credence to different parts of a local model, we propose a novel approach to exploit such evidential asymmetry in FL aggregation in not independent and identically distributed (non-IID) data by applying a unique weight coefficient to each of the local parameter updates. To this end, we measure the parameter-level evidential credence making use of the eigenvalues of the Hessian of the local likelihood function, which are theoretically connected to the observed Fisher information. We employ these eigenpairs to propose a novel aggregation method, which we name FedHess. Our experiments show FedHess achieves smoother and faster convergence to a more accurate global model when compared with popular baselines such as Federated Average (FedAvg), FedProx, SCAFFOLD, Federated Curvature (FedCurv) and FedDF across different types of heterogeneous training data drawn from a number of benchmark datasets.
Trace element variations of pyrite in orogenic gold deposits: Constraints from big data and machine learning

Orogenic gold deposits represent key global gold resources, yet its internal differences are poorly constrained. Pyrite is ubiquitous in orogenic gold ores, and its trace element geochemistry has significant implications for the ore-forming processes. However, the diffusion regularity and controlling factors of pyrite trace element geochemistry of orogenic gold deposits lack effective constraints. To address these issues, pyrite trace element geochemical data (4092 sets of data) from 67 orogenic gold deposits were compiled for big- data and machine learning analyses. The principal component analysis (PCA) divides the pyrite data into four groups according to trace element correlations: I) Au-As; II) Sb-Cu; III) Ag-Pb-Bi; and IV) Co-Ni-Te-Se-Mo. This division is likely ascribed to the element occurrence in pyrite. Partial least squares-discriminant analysis (PLS-DA) and Random Forests classification were used to identify pyrite trace elemental variations among orogenic deposits of different mineralization ages, wallrocks, and tectono-metallogenic settings. The results show that the influence on pyrite geochemistry from temporal variation is not obvious, and only Triassic and Jurassic orogenic-type auriferous pyrite shows enrichments of Cu, Pb, and Sb, which may reflect the multiple mineralization events in these deposits as Pb commonly occurs as cation in the fluids, while Sb usually occurs in the melt. The spatial difference is more distinct with less overlapping of confidence circles in PLS-DA and higher accuracy in Random Forests, which may have been associated with fluid source variation influenced by the geodynamic setting (mantle model, supposed by distinctive geodynamic variation from metallogenic belts in China) and source strata (metamorphic model, supposed by Archean deposits mostly hosted in green-schist phase besides China). Pyrite in orogenic gold ores hosted in different wallrocks can be differentiated by the different As, Pb, and Cu contents, as the contents of these elements vary widely in the wallrocks and participate in pyrite lattice through fluid-rock reactions. Random Forests regression reveals that pyrite As-Sb contents in pyrite show distinct negative correlations with ore-forming temperature, but Se content shows a non-linear influence. This suggests that the pyrite As-Sb-Se contents in orogenic gold ores can be used as a geothermometer. Furthermore, we suggest that the pyrite Co-Ni contents are not good indicators for the ore-forming temperature, as the low salinity fluid in orogenic gold systems makes Co-Ni difficult to form chloride complexes which is sensitive to temperature variation.
Phase-based fine-grained change detection

Detecting fine-grained changes among a set of images taken at different times is a challenging problem, which is important for the applications such as high-value scene monitoring and structural inspection. Existing methods use a geometry registration and lighting compensation pipeline to alleviate the ingredients that affect change detection. However, lighting compensation may introduce a lot of artifacts, e.g., inaccurate local lighting conditions and fine-grained change disappearance. The distortion introduced by general registration methods also makes the process of lighting correction more challenging. To solve these problems, in this paper, we propose a new phase-based change detection method, called as pb-FGCD, which first aligns two observation of different times and then extract structure information to handle lighting difference. Benefiting from the proposed weighted amplitude filter and iterative alignment strategy, we could obtain a better registration performance. The structure information extracts the lighting irrelevant components of an image and is more sensitive to structure changes compared with the lighting compensation based method, which is vital for detecting the changes with low intensity difference. Furthermore, we contribute a new change detection dataset, which contains 100 groups of the real-world data of the ancient Mogao murals and the Terra-Cotta. It is the first fine-grained change detection dataset focusing on the ancient mural. Our approach significantly improves the accuracy by more than 190% higher F1-measure than the state-of-the-art methods.
HSP-MFL: A High-level Semantic Property driven Multi-task Feature Learning Network for unsupervised person Re-ID

Unsupervised person re-identification aims to distinguish different pedestrians from discriminative representations on the basis of unlabeled data. Currently, most unsupervised Re-ID approaches explore visual representations to generate pseudo-labels for model’s training, which may suffer from background noise and semantic loss. To tackle this problem, this paper proposes a High-level Semantic Property driven Multi-task Feature Learning Network (HSP-MFL) to firstly introduce three high-level semantic properties for unsupervised person Re-ID. Technically, we design a novel Multiple Feature Fusion Module (MFFM) to deeply explore the complex correlation among multiple semantic and visual features to capture the discriminative feature cues, as well as a multi-task training scheme to generate robust fusion features. The architecture is quite simple and does not consume extra labeling costs. Extensive experiments on three datasets demonstrate that both high-level semantic properties and multi-task learning are effective in performance improvement, yielding SOTA mAPs for unsupervised person Re-ID.
Density-based reliable and robust explainer for counterfactual explanation

As an essential post-hoc explanatory method, counterfactual explanation enables people to understand and react to machine learning models. Works on counterfactual explanation generally aim at generating high-quality results, which means providing close and detailed explanations to users. However, the counterfactual explainer trained on data is fragile in practice, i.e., even a small perturbation to samples can lead to large differences in explanation. In this work, we address this issue by analyzing and formalizing the robustness of counterfactual explainer with practical considerations. An explainer is considered robust if it can generate relatively stable counterfactuals under various settings. To this end, we propose a robust and reliable explainer for searching counterfactuals of classifier predictions by using density gravity. To evaluate the performance, we provide metrics that allow comparison of our proposed explainer with others and further demonstrate the importance of density in enhancing robustness. Extensive experiments on real-world datasets show that our method offers a significant improvement in explainer reliability and stability.
The design and evaluation of adaptive biometric authentication systems: Current status, challenges and future direction

Biometric authentication systems may suffer from decreasing recognition performance due to varying environmental conditions and sample ageing, which cause intra-class variability. Adaptive biometric authentication systems have been proposed to address these deficiencies by dynamically changing their sampling and recognition processes in response to changes in the operating environment. This paper provides the complete discussion on the design of an adaptive biometric authentication system through a systematic review of the existing literature to date. The review shows that further investigation is required to target mobile and/or wearable devices as well as to consider a continuous authentication methodology. In addition to this, the evaluation of adaptive biometric authentications with large-scale datasets is required to validate the feasibility of the system and its scalability in real-world with a comprehensive study of evaluation metrics.
Identifying the Utilization of ICT-Based Interactive Media in School during Pandemic Covid-19

The Covid-19 pandemic has had a very significant impact on paradigm shifts and learning activities. Currently, ICT has a great opportunity to be utilized and contribute optimally to facilitate students during learning activities during the Covid-19 pandemic. The main purpose of this research is to analyze specifically the teacher's views regarding the implementation and use of ICT-based learning media that takes place during learning from home during the covid-19 pandemic. This research design includes qualitative research. The subjects in this study were subject teachers in high schools. Data collection techniques used questionnaires, observations and interviews, with the instruments being questionnaire items, observation sheets and interview guidelines. Data from the questionnaire were analyzed by triangulation, through data reduction, data presentation, and drawing conclusions. The results showed that so far teachers use media as learning aids in the form of printed modules, and assignment sheets, besides the lecture method and presentation media also dominate in the continuity of learning activities, teachers still persist not to use ICT in the learning process, especially to support the independent learning process during the covid-19 pandemic. Innovation through the integration of ICT in the learning process is an important thing for teachers have to do, especially now that students are carrying out learning from home, so that students will remain enthusiastic and motivated to carry out a series of independent learning activities.
UAV 3D path planning based on improved grey wolf optimization algorithm

In this paper, an improved grey wolf optimization algorithm is proposed for the research of UAV path planning in a complex 3D environment. Firstly, a new nonlinear convergence factor is proposed to balance the performance of global search and local development. Secondly, a cubic chaotic mapping is adopted to initialize the wolf population, diversifying the population while improving the uniformity of the population distribution. Finally, a mutation operation is introduced to mutate the individual gray wolf, which enhances the ability of the algorithm to jump out of the local optimum. Three-dimensional environment model is established by elevation data. The simulation results show that the optimal fitness of the improved algorithm is improved by 2.34% compared with that before the improvement, which proves the effectiveness of the algorithm in this paper.
Research on Obstacle Avoidance Control of Multiple UAV Formation based on Genetic Algorithm

The UAV (Unmanned Aerial Vehicle) group cooperative formation flight technology has the advantages of wide coverage, large activity radius, strong overall search ability and high efficiency of the aircraft group. Therefore, it is suitable for various complex tasks in the military field such as battlefield environment reconnaissance, tactical attack and cooperative search. This paper proposes a consistency control strategy based on GA (Genetic Algorithm) and applies it to multi-UAV formation obstacle avoidance, which can effectively solve the collision between UAVs and between UAV formation and obstacles. Demodulate the received ground desired control command, and introduce an additional auxiliary traction acceleration through GA to avoid the local optimal solution. The auxiliary traction acceleration is related to the speed and relative position of UAV and obstacles. It can be used as a disturbance to solve the local optimal solution, and also as an auxiliary acceleration to improve the speed of avoiding moving obstacles. Finally, the rapid formation and obstacle avoidance of UAV fleet during flight are realized, and the survivability of the fleet in the battlefield environment is improved.
Sanitizable signature scheme with privacy protection for electronic medical data sharing

Electronic medicine has received more and more attention because of its ability to provide more efficient and better medical services. However, the characteristics of electronic medical networks make them more vulnerable to security threats such as data integrity and user privacy leakage. Traditional digital signatures cannot meet the diversity and privacy requirements of medical data applications. Sanitizable signatures incorporate sanitization capabilities into signatures to allow designated sanitizers to modify variable parts of a message in a controlled manner without the cooperation of the original signer. This paper uses the key-exposure free chameleon hash function to convert the data sanitization operation into using trapdoor keys to find collisions in the key-exposure free chameleon hash function, and builds a privacy-preserving sanitizable signature scheme. Security analysis and performance evaluation demonstrate that our new scheme achieves public verifiability, which greatly reduces computing costs while effectively ensuring data security and user privacy, and is especially suitable for electronic medical data sharing scenarios.
Proactive AI Enhanced Consensus Algorithm with Fraud Detection in Blockchain

The security and transparency provided to the data in blockchain are unmatchable, with the least instances of system hack or failure reported. With a number of consensus algorithms used in the past and the presence of leader nodes in many of them, it is important to check the leader node’s activities. As the system is large, the usage of artificial intelligence and deep learning methodologies seems the right choice to monitor the leader node’s activities. Hence in this chapter, an algorithm is proposed as to how should the consensus algorithm be modified while adding deep learning techniques to keep track of the leader node’s selection behaviors. It also explains how the system detects and moves back to stability once such a scenario is encountered. Hence in this work, the artificial neural network is used to learn the node selection behavior of the leader node by taking in 5 input parameters: sender ID, receiver ID, transaction amount, sender’s balance, and receiver’s balance. Output is either 0 (not selected into the chain) or 1 (selected into the chain) once trained neurons (each input parameter) are tested for it’s sensitivity to the selection. If it exceeds a threshold value, it is assumed to be biased upon that parameter/s, and further consensus occurs.
Trust Management in Cyber-Physical Systems: Issues and Challenges

The industry has recently experienced the development of a new industrial system called the Cyber-Physical System. Cyber entities must collaborate and communicate to command and manage physical entities to accomplish the latter. To make the actions and physical commands that need to be carried out into an intelligent and autonomous activity, the Cyber-Physical System also includes a number of technologies (Sensor Networks, the Internet of Things, Artificial Intelligence, etc.). In this regard, Cyber-Physical Systems benefit the industry in several advantages. However, this diversity of technologies and the establishment of collaboration between the different entities lead to the appearance of several attacks. For this, a security level is essential in all aspects of Cyber-Physical Systems. In this chapter, we are interested in presenting Cyber-Physical Systems and the technologies involved. While focusing on their security, we list the various attacks threatening Cyber-Physical Systems and focus on Trust Management as an approach to secure Cyber-Physical Systems collaborations and interactions.