Reviewerly/exHarmony huggingface dataset:
Adaptive differential evolution with fitness-based crossover rate for global numerical optimization

Abstract Differential evolution (DE) is one of the most efficient evolution algorithms (ES) for dealing with nonlinear, complicated and difficult global optimization problems. The main contribution of this paper can be summarized in three directions: Firstly, a novel crossover rate (CR) generation scheme based on the zscore value of fitness, named fcr , is introduced. For a minimization problem, the proposed CR generation strategy always assigns a smaller CR value to individual with smaller fitness value. Therefore, the parameters of individuals with better fitness are inherited by their offspring with high probability. In the second direction, the control parameters are adjusted by unused bimodal settings in which each parameter setting is selected according to the evolution status of individual. The third direction of our work is introducing the L1 norm distance as the weights for updating the mean value of crossover rate and scale factor. Theoretically, compared with L2 norm, L1-norm is more efficient to suppress outliers in the difference vector. These modifications are first integrated with the mutation strategy of JADE, then a modified version, named JADEfcr, is proposed. In addition, to improve the optimization ability further, another variant LJADEfcr by using a linear population reduction mechanism is considered. So as to confirm and examine the performance of JADEfcr and LJADEfcr, numerical experiments are conducted on 29 optimization problems defined by CEC2017 benchmark. For JADEfcr, its experimental results are made a comparison with twelve state-of-the-art algorithms. The comparative study demonstrates that in terms of robustness, stability and solution quality, JADEfcr are better and highly competitive with these well-known algorithms. For LJADEfcr, its results are compared with JADEfcr and other nine powerful algorithms including four recent algorithms and five top algorithms on CEC2017 competition. Experimental results indicate that LJADEfcr is superior and statistically competitive with these excellent algorithms in terms of robustness, stability and the quality of the obtained solutions.
A novel fusion maximum correntropy Kalman/UFIR filter for state estimation with uncertain non-Gaussian noise statistics

Despite the higher estimation accuracy of the maximum correntropy Kalman filter (MCKF) algorithm in system with non-Gaussian noise, its estimation performance will decrease when the noise source changes due to disturbances from external uncertainties. Fortunately, the unbiased finite impulse response predictor (UFIR) can overcome the MCKF problems by automatically ignoring the noise related to both the process and measurements. Meanwhile, the MCKF could resolve the UFIR problems, such as non-optimal estimation performance and risk of dead zones. Therefore, a novel fusion maximum correntropy Kalman/UFIR filter is proposed to achieve a more advantageous estimation effect, in which specific probability weights are assigned to the two filters to implement the fusion of two filters. The simulation results demonstrate the superiority of the presented fusion filter algorithm when compared to the fusion Kalman/UFIR filter regarding the estimation performance under the non-Gaussian noise system.
Enhancing Robustness of Viewpoint Changes in 3D Skeleton-Based Human Action Recognition

Previous research on 3D skeleton-based human action recognition has frequently relied on a sequence-wise viewpoint normalization process, which adjusts the view directions of all segmented action sequences. This type of approach typically demonstrates robustness against variations in viewpoint found in short-term videos, a characteristic commonly encountered in public datasets. However, our preliminary investigation of complex action sequences, such as discussions or smoking, reveals its limitations in capturing the intricacies of such actions. To address these view-dependency issues, we propose a straightforward, yet effective, sequence-wise augmentation technique. This strategy enhances the robustness of action recognition models, particularly against changes in viewing direction that mainly occur within the horizontal plane (azimuth) by rotating human key points around either the z-axis or the spine vector, effectively creating variations in viewing directions. We scrutinize the robustness of this approach against real-world viewpoint variations through extensive empirical studies on multiple public datasets, including an additional set of custom action sequences. Despite the simplicity of our approach, our experimental results consistently yield improved action recognition accuracies. Compared to the sequence-wise viewpoint normalization method used with advanced deep learning models like Conv1D, LSTM, and Transformer, our approach showed a relative increase in accuracy of 34.42% for the z-axis and 10.86% for the spine vector.
Cleaning Big Data Streams: A Systematic Literature Review

In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overcome these challenges, such as cleaning big data in real time. This systematic literature review presents recently developed techniques that have been used for the cleaning process and for each data cleaning issue. Following the PRISMA framework, four databases are searched, namely IEEE Xplore, ACM Library, Scopus, and Science Direct, to select relevant studies. After selecting the relevant studies, we identify the techniques that have been utilized to clean big data streams and the evaluation methods that have been used to examine their efficiency. Also, we define the cleaning issues that may appear during the cleaning process, namely missing values, duplicated data, outliers, and irrelevant data. Based on our study, the future directions of cleaning big data streams are identified.
Binary-Convolution Data-Reduction Network for Edge–Cloud IIoT Anomaly Detection

Industrial anomaly detection, which relies on the analysis of industrial internet of things (IIoT) sensor data, is a critical element for guaranteeing the quality and safety of industrial manufacturing. Current solutions normally apply edge–cloud IIoT architecture. The edge side collects sensor data in the field, while the cloud side receives sensor data and analyzes anomalies to accomplish it. The more complete the data sent to the cloud side, the higher the anomaly-detection accuracy that can be achieved. However, it will be extremely expensive to collect all sensor data and transmit them to the cloud side due to the massive amounts and distributed deployments of IIoT sensors requiring expensive network traffics and computational capacities. Thus, it becomes a trade-off problem: “How to reduce data transmission under the premise of ensuring the accuracy of anomaly detection?”. To this end, the paper proposes a binary-convolution data-reduction network for edge–cloud IIoT anomaly detection. It collects raw sensor data and extracts their features at the edge side, and receives data features to discover anomalies at the cloud side. To implement this, a time-scalar binary feature encoder is proposed and deployed on the edge side, encoding raw data into time-series binary vectors. Then, a binary-convolution data-reduction network is presented at the edge side to extract data features that significantly reduce the data size without losing critical information. At last, a real-time anomaly detector based on hierarchical temporal memory (HTM) is established on the cloud side to identify anomalies. The proposed model is validated on the NAB dataset, and achieves 70.0, 64.6 and 74.0 on the three evaluation metrics of SP, RLFP and RLFN, while obtaining a reduction rate of 96.19%. Extensive experimental results demonstrate that the proposed method achieves new state-of-the-art results in anomaly detection with data reduction. The proposed method is also deployed on a real-world industrial project as a case study to prove the feasibility and effectiveness of the proposed method.
A Cryo-CMOS, Low-Power, Low-Noise, Phase-Locked Loop Design for Quantum Computers

This paper analyzes the performance requirements that need to be met by a clock generator applied to a low-temperature quantum computer and analyzes the negative effects on the clock generator circuit under low-temperature conditions. In order to meet the performance requirements proposed in this paper and suppress the negative effects brought about by the low temperature, a clock generator for ultra-low-temperature quantum computing is designed. This clock generator is designed by using F-CLASS Voltage Controlled Oscillator (VCO), power filter, tail resistor, differential charge pump, and other techniques. And the noise characteristics of the clock generator are analyzed by Impulse Sensitive Function (ISF) and simulation results. After simulation tests, the average power consumption of the clock generator designed in this paper is 7 mW, the phase noise is −121 dBc/Hz@1 MHz, and the jitter is 62 fs. The performance of the clock generator meets the performance requirements proposed in this paper, and the reduction in the corner frequency proves that the circuit will have better performance at low temperatures.
Research on Unmanned Surface Vessel Aggregation Formation Based on Improved A* and Dynamic Window Approach Fusion Algorithm

The traditional A* and DWA fusion algorithm has three problems in the task of aggregation formation: one is the lack of meeting coordination strategy, the other is the inability to unify the terminal course, and the third is too many turning points in the global path. To solve the above problems, an improved algorithm is proposed. Firstly, by smoothing the global planning path, the stability of a USV heading in the navigation is improved. Then, by adding the key points of the global path, a guide path is formed to unify the terminal heading range. Finally, by adding an encounter coordination strategy, aggregation efficiency is improved. Simulation experiments were carried out in the Python environment based on this algorithm. The results show that the improved algorithm can improve the navigation obstacle avoidance ability of USVs and guide multiple USVs to finish the task of aggregation formation.
Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords

The paper addresses issues concerning secure authentication in computer systems. We focus on multi-factor authentication methods using two or more independent mechanisms to identify a user. User-specific behavioral biometrics is widely used to increase login security. The usage of behavioral biometrics can support verification without bothering the user with a requirement of an additional interaction. Our research aimed to check whether using information about how partial passwords are typed is possible to strengthen user authentication security. The partial password is a query of a subset of characters from a full password. The use of partial passwords makes it difficult for attackers who can observe password entry to acquire sensitive information. In this paper, we use a Siamese neural network and n-shot classification using past recent logins to verify user identity based on keystroke dynamics obtained from the static text. The experimental results on real data demonstrate that keystroke dynamics authentication can be successfully used for partial password typing patterns. Our method can support the basic authentication process and increase users' confidence.
RepC-MVSNet: A Reparameterized Self-Supervised 3D Reconstruction Algorithm for Wheat 3D Reconstruction

The application of 3D digital models to high-throughput plant phenotypic analysis is a research hotspot nowadays. Traditional methods, such as manual measurement and laser scanning, have high costs, and multi-view, unsupervised reconstruction methods are still blank in the field of crop research. It is challenging to obtain a high-quality 3D crop surface feature composition for 3D reconstruction. In this paper, we propose a wheat point cloud generation and 3D reconstruction method based on SfM and MVS using sequential wheat crop images. Firstly, the camera intrinsics and camera extrinsics of wheat were estimated using a structure-from-motion system with feature maps, which effectively solved the problem of camera point location design. Secondly, we proposed the ReC-MVSNet, which integrates the heavy parametric structure into the point cloud 3D reconstruction network, overcoming the difficulty of capturing complex features via the traditional MVS model. Through experiments, it was shown that this research method achieves non-invasive reconstruction of the 3D phenotypic structure of realistic objects, the accuracy of the proposed model was improved by nearly 43.3%, and the overall value was improved by nearly 14.3%, which provided a new idea for the development of virtual 3D digitization.
Exploring the impact of design technique on usability: A case study on designing the eHealth websites using card sorting and interactive dialogue model

Abstract The design and info‐architecture of the web content contribute significantly to achieving better usability and user experience as well as to adopting a website to its intended end‐users. Although several design techniques exist, choosing a suitable one for designing a particular type of website is difficult and needs further investigation. Thus, the objective of this research is to explore the impact of a design technique on web usability and to find out the best suitable design technique for a particular type of website. To achieve these objectives, two separate websites for providing electronic health services named Citizen Health Service portal were developed using the two mostly used design techniques, namely card sorting and interactive dialogue model (IDM). The usability of both websites was evaluated through a laboratory‐based usability testing approach with 20 participants. The study result showed that the IDM performs better in designing such an information‐intensive website.
Cross-modal knowledge guided model for abstractive summarization

Abstract Abstractive summarization (AS) aims to generate more flexible and informative descriptions than extractive summarization. Nevertheless, it often distorts or fabricates facts in the original article. To address this problem, some existing approaches attempt to evaluate or verify factual consistency, or design models to reduce factual errors. However, most of the efforts either have limited effects or result in lower rouge scores while reducing factual errors. In other words, it is challenging to promote factual consistency while maintaining the informativeness of generated summaries. Inspired by the knowledge graph embedding technique, in this paper, we propose a novel cross-modal knowledge guided model (CKGM) for AS, which embeds a multimodal knowledge graph (MKG) combining image entity-relationship information and textual factual information (FI) into BERT to accomplish cross-modal information interaction and knowledge expansion. The pre-training method obtains rich contextual semantic information, while the knowledge graph supplements the textual information. In addition, an entity memory embedding algorithm is further proposed to improve information fusion efficiency and model training speed. We elaborately conducted ablation experiments and evaluated our model on the Visual Genome, FewRel, MSCOCO, and CNN/DailyMail datasets. Experimental results demonstrate that our model can significantly improve the FI consistency and informativeness of generated summaries.
Hybrid RNN-FFBPNN Optimized with Glowworm Swarm Algorithm for Lung Cancer Prediction

Computer-aided diagnosis in health centres detects lung cancer at an early period. The feature set dimensions and over fitting of lung cancer features make it extremely difficult to achieve accuracy in cancer detection. To overcome these issues, in this article, a hybrid recurrent neural network and feed-forward back propagation neural network (Hyb-RNN-FFBPNN) optimized with the glow worm swarm algorithm (GWSA) is proposed for lung cancer prediction (LCP). Initially, input pictures are taken from the dataset of the National Institute of Health (NIH) chest X-ray image using the Kaggle repository for predicting lung cancer. The images are pre-processed utilizing contrast-limited adaptive histogram equalization filtering (CLAHEF) approach for removing sounds and enhancing the image quality. These pre-processed images are given to feature extraction. The empirical wavelet transform (EWT) is applied during feature extraction. Then, the hybrid FFBPNN is constructed for classifying the extracted features into benign, malignant, and normal. The proposed Hyb-RNN-FFBPNN approach does not exhibit any optimization methods adoption to determine optimal parameters and assure proper lung cancer prediction. Therefore, the hyperparameter of Hyb-RNN-FFBPNN is tuned along glowworm swarm optimization approach (GSOA), which classifies lung cancer exactly. The proposed technique is activated in MATLAB; its performance is analysed to the other existing techniques.
Consistent graph embedding network with optimal transport for incomplete multi-view clustering

Existing incomplete multi-view learning models focus on reconstructing the latent variables of multiple views by exploring complementary and consistent information among diverse views. However, filling the missing information for views results in a loss of consistency, while fusion and reconstruction between views face over-fitting problems. The optimal transport algorithm delicately measures the distance of two distributions, resulting in decreased reconstruction errors and guaranteeing consistency and complementarity across multiple views of the data. In light of this, this paper proposes the incorporation of the optimal transport algorithm into the framework of incomplete multi-view clustering. The proposed consistent graph embedding network (CGEN-OT) via optimal transport combines the adversarial module and fusion module to learn a completed latent graph embedding. Specifically, CGEN-OT utilizes an adversarial module to generate complete views and fuses them into a consistent embedding, and introduces reconstruction loss and Sinkhorn loss to jointly optimize the proposed network and obtain superior latent graph embedding and clustering performance. To further validate the clustering accuracy and convergence of the CGEN-OT, experimental evaluation was conducted on six distinct incomplete datasets. A comparison with existing state-of-the-art models highlights the superiority of the proposed framework.
Efficient deep-narrow residual networks using dilated pooling for scene recognition

This paper aims to address the challenges posed by complex scenery image classification. Most of the existing deep learning networks are trained and evaluated using ImageNet. However, when these models are applied to scenery images, dramatic performance degradation is observed due to the change in data characteristics. To challenge the prevailing practices in network design, we investigate the impact of altering data on the performance of deep networks. Specifically, we introduce a novel data-oriented network design to emphasize the importance of considering the unique characteristics of the data. Our proposed approach is a Deep-Narrow Network, which incorporates a Dilated Pooling module built upon the ResNet architecture. Compared to ResNet, our approach achieves a significant reduction of floating-point operations by 51.5% and in the number of parameters by 54.5%. Remarkably, despite the reduction in computational complexity and model size, our design exhibits a 0.4% increase in overall accuracy. This approach offers an efficient and effective means of scaling the network according to the data characteristics while maintaining highly competitive performance.
Risky? So, why people are getting back to invest in cryptocurrencies? The United Arab Emirates as a case

Purpose The purpose of this paper is to investigate the determinants of users' intention to continue to invest in cryptocurrencies. The paper also aims to examine the impact of hedonic motivation and the legal environment on perceived value in cryptocurrencies. Design/methodology/approach A questionnaire was designed to obtain data from 258 respondents in UAE. The Structural Equation Modeling – Partial Least Squares (SEM-PLS) was used to evaluate the research model and test the hypotheses. Findings The results of smart PLS path analysis showed that perceived value, hedonic motivation, gambling attitude, and price volatility were significant determinants of the continued intention to invest in cryptocurrency. This study also revealed that hedonic motivation enhances perceived value and improves the perception of cryptocurrencies value from user's perspective. Originality/value This study provides new insights into the literature on cryptocurrencies adoption, and delivers advanced understanding about the determinants of user's intention to continue investing in cryptocurrencies. In addition, the study provides important practical implications for cryptocurrencies companies to promote this financial technology to users by enhancing the knowledge of policy makers about how investors think and get motivated towards a continued investment of cryptocurrencies.
Toward a Unified Framework for Verifying and Interpreting Learning-Based Networking Systems

There has been a growing interest in applying machine learning to real-world tasks. However, due to the blackbox nature of machine learning models, it is crucial to (1) verify important properties of a model and (2) understand the reasons behind a model's prediction before deploying them in a production environment. Existing approaches typically handle them as two separate and sometimes orthogonal topics. In this paper, we show that the verification and interpretability of machine learning models are tightly related and can be unified by satisfiability modulo theories (SMT). Our key insight is: not only a wide range of properties of machine learning models can be formulated as SMT problems and verified accordingly, but many commonly studied interpretability questions can also be answered by iteratively checking the satisfiability and related properties of multiple SMT problems. Leveraging this insight, we design UINT, a general verification and interpretability framework for learning-based networking systems. UINT (1) allows operators to specify verification and interpretability problems as SMT formulas, (2) encodes the target machine learning models into SMT constraints, and (3) automatically solves the corresponding verification and interpretability problems using commodity SMT solvers. We implement a prototype of UINT and evaluate it on real-world learning-based networking systems. Results demonstrate the efficiency and efficacy of UINT in verifying and interpreting key questions for these systems.