Reviewerly/exHarmony huggingface dataset:
Variable Forgetting Factor-Based Adaptive Kalman Filter With Disturbance Estimation Considering Observation Noise Reduction

This paper addresses the influence reduction of quantization and observation noises in a disturbance observer (DOB) technique. DOB is a disturbance estimation method that makes control systems robust. However, in implementing low-resolution sensors, disturbance estimates from DOB are considerably influenced by observation and quantization noises. In this paper, a novel DOB design method for simultaneous estimation of state and unknown disturbances, including the reduction of noise influences, is proposed. The proposed method is divided into two components. The first component is a Kalman filter (KF)-based DOB for simultaneous estimation of state and unknown disturbances. To improve the estimation performance through the KF-based DOB, a forgetting factor-based adaptive KF (FAKF) was employed. The second component is an adaptive law for the forgetting factor in the FAKF. The adaptive law is used for balancing the estimation accuracy and observation noise reduction. Simulation results involving various types of noise environments demonstrate the effectiveness of the proposed method.
Survey of BERT (Bidirectional Encoder Representation Transformer) types

Abstract There are many algorithms used in Natural Language Processing( NLP) to achieve good results, such as Machine Learning (ML), Deep Learning(DL) and many other algorithms. In Natural Language Processing,the first challenges is to convert text to numbers for using by any algorithm that a researcher choose. So how can convert text to numbers? This is happen by using Word Embedding algorithms such as skip gram,bags of words,BERT and etc. Representing words as numerical vectors by relying on the contents has become one of the effective methods for analyzing texts in machine learning, so that each word is represented by a vector to determine its meaning or to know how close or distant this word from the rest of the other word. BERT(Bidirectional Encoder Representation Transformer) is one of the embedding methods. It is designed to pre-trained form left and right in all layer deep training. It is a deep language model that is used for various tasks in natural language processing. In this paper we will review the different versions and types of BERT.
Pitch-robust acoustic feature using single frequency filtering for children’s KWS

The pitch and speaking rate are the two significant factors that cause the acoustic mismatch in children’s keyword spotting (KWS) system. This paper proposes a pitch-robust acoustic feature based on single frequency filtering (SFF) for the development of children’s KWS system. In the proposed approach using SFF, the amplitude envelopes (AEs) of the speech data are computed at D-number of selected frequencies separated in Mel scale. The AEs are then averaged over short-time overlapping analysis frames and logarithmically compressed to represent the D-dimensional feature set per analysis frame, here termed as Mel spaced single frequency average log envelope (MSSF-ALE). By using the proposed MSSF-ALE feature, improved performance is observed for the deep neural network-hidden Markov model-based KWS system over the standard Mel-frequency cepstral coefficients (MFCC) and MFCC extracted from the smoothed spectra. The relative improvement of 104.44% in term-weighted value (TWV) for children’s KWS is observed over the MFCC by using MSSF-ALE. The performance of the KWS system is then evaluated with data-augmented training through explicit speaking rate modification of the training data set. The MSSF-ALE provides a relative improvement of 195.94% in TWV over MFCC with the data-augmented training. The MSSF-ALE also results in improved performance than the explored features in noisy test cases.
Hermes: Efficient Cache Management for Container-based Serverless Computing

Serverless computing systems are shifting towards shorter function durations and larger degrees of parallelism to eliminate intolerable latency. For container-based serverless computing, the state-of-the-art efforts fail to ensure low latency because on-demand container images reloading from remote storage can increase the data transmission rate and downgrades system performance.
Modified Chimp Optimization Algorithm Based On Classical Conjugate Gradient Methods

Abstract In this paper, a new hybrid algorithm was proposed for the chimp algorithm using another traditional efficient algorithm called the Conjugate Gradient Algorithm called (CGA). The algorithm CG works to optimize the randomly created elementary community as the basic community of chimp optimization algorithms by using the characteristic traditional algorithm above. The test was applied to (10) high-efficiency optimization functions with different dimensions and frequency. The results of the hybrid algorithm were excellent, encouraging, and superior to the original algorithm. The hybrid algorithm achieved optimal solutions by reaching to a minimum value ( f min ) for most of these functions.
Hybrid Feature Embedded Sparse Stacked Autoencoder and Manifold Dimensionality Reduction Ensemble for Mental Health Speech Recognition

Speech feature learning is the key to speech mental health recognition. Deep feature learning can automatically extract the speech features but suffers from the small sample problem. The traditional feature extract method is effective, but cannot find the inter-feature structure to generate the new high-quality features. This paper proposes an embedded hybrid feature deep sparse stacked autoencoder ensemble method to solve this problem. Firstly, the speech features are extracted based on prior knowledge and called original features. Secondly, the original features are embedded into the deep network (Sparse Stacked Autoencoder) to filter the output of the hidden layer, to enhance the complementarity between the deep features and the original features. Thirdly, the L1 regularized feature selection mechanism is designed to reduce the hybrid feature set formed by the combination of deep features and original features. Finally, a manifold projection classifier ensemble is designed to enhance the stability of classification. Besides, this paper for the first time proposes a speech collection scheme for mental health recognition. We construct a large-scale Chinese mental health speech database for verification of the proposed algorithm of mental health. In the experimental section, the proposed algorithm is verified and compared with the representative related algorithms. The experimental results show that the proposed algorithm has better classification accuracy than the other representative algorithms. The proposed method combines the advantages of deep feature learning and traditional feature extraction methods more efficiently to solve the small sample problem.
Expl(Ai)Ned: The Impact of Explainable Artificial Intelligence on Cognitive Processes

This paper explores the interplay of feature-based explainable AI (XAI) techniques, information processing, and human beliefs.Using a novel experimental protocol, we study the impact of providing users with explanations about how an AI system weighs inputted information to produce individual predictions (LIME) on users' weighting of information and beliefs about the task-relevance of information.On the one hand, we find that feature-based explanations cause users to alter their mental weighting of available information according to observed explanations.On the other hand, explanations lead to asymmetric belief adjustments that we interpret as a manifestation of the confirmation bias.Trust in the prediction accuracy plays an important moderating role for XAI-enabled belief adjustments.Our results show that feature-based XAI does not only superficially influence decisions but really change internal cognitive processes, bearing the potential to manipulate human beliefs and reinforce stereotypes.Hence, the current regulatory efforts that aim at enhancing algorithmic transparency may benefit from going hand in hand with measures ensuring the exclusion of sensitive personal information in XAI systems.Overall, our findings put assertions that XAI is the silver bullet solving all of AI systems' (black box) problems into perspective.
ViT-GAN: Using Vision Transformer as Discriminator with Adaptive Data Augmentation

These days, attention is thought to be an efficient way to recognize an image. Vision Transformer (ViT) uses a Transformer for images and has very high performance in image recognition. ViT has fewer parameters than Big Transfer (BiT) and Noisy Student. Therefore, we consider that Self-Attention-based networks are slimmer than convolution-based networks. We use a ViT as a Discriminator in a Generative Adversarial Network (GAN) to get the same performance with a smaller model. We name it ViT-GAN. Besides, we find parameter sharing is very useful to make parameter-efficient ViT. However, the performances of ViT heavily depend on the number of data samples. Therefore, we propose a new method of Data Augmentation. Our Data Augmentation, in which the strength of Data Augmentation varies adaptively, helps ViT for faster convergence and better performance. With our Data Augmentation, we show ViT-based discriminator can achieve almost the same FID but the number of the parameters of the discriminator is 35% fewer than the original discriminator.
Operationalizing Analytics with NewSQL

Operational data and analytical data are no longer two separate disciplines and discussions. Data Analysis is gaining more ground and more request from companies that begin to base their strategies - as well as decision intelligence and decision management - on factual information. In surveying the state of the data industry and the trends in data management technologies, NewSQL systems appear to be more present. They are able to answer the question of bridging operational data storage and administration with providing real-time access to analytical data. This paper aims to provide a structured look into the features and capabilities offered by NewSQL systems that can be leveraged to allow Data Analysis over a variety of data types. Furthermore, it provides an overview of Realtime Analytics offerings, Map Reduce capabilities and hybrid (transactional and analytical) features.
Bottom-boosting differential evolution based digital image security analysis

With the advancements in multimedia applications, many images are transferred over public networks. Therefore, these images are prone to various security threats. Recently, many image encryption algorithms have been designed to secure the communication of digital images. Generally, hyperchaotic maps are utilized to generate the secret keys for encrypting the images. However, these maps suffer from the attribute tuning issue. Therefore, in this paper, six-dimensional hyperchaotic map (SHM) is tuned using bottom-boosting differential evolution. Entropy and correlation coefficient are used as a fitness function. The proposed map provides six secret keys. These six keys are utilized to permute and diffuse the input images. Various security and attack analysis are considered to evaluate the performance of the proposed algorithm. Experimental results reveal that the proposed algorithm can resist various security threats.
Adaptive Spatial-Temporal Fusion of Multi-Objective Networks for Compressed Video Perceptual Enhancement

Perceptual quality enhancement of heavily compressed videos is a difficult, unsolved problem because there still not exists a suitable perceptual similarity loss function between two video pairs. Motivated by the fact that it is hard to design unified training objectives which are perceptual-friendly for enhancing regions with smooth content and regions with rich textures simultaneously, in this paper, we propose a simple yet effective novel solution dubbed "Adaptive Spatial-Temporal Fusion of Two-Stage Multi-Objective Networks" (ASTF) to adaptive fuse the enhancement results from networks trained with two different optimization objectives. Specifically, the proposed ASTF takes an enhancement frame along with its neighboring frames as input to jointly predict a mask to indicate regions with high-frequency textual details. Then we use the mask to fuse two enhancement results which can retain both smooth content and rich textures. Extensive experiments show that our method achieves a promising performance of compressed video perceptual quality enhancement.
Structured Context Enhancement Network for Mouse Pose Estimation

Automated analysis of mouse behaviours is crucial for many applications in neuroscience. However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours. Although deep learning based methods have made promising advances in human pose estimation, they cannot be directly applied to pose estimation of mice due to different physiological natures. Particularly, since mouse body is highly deformable, it is a challenge to accurately locate different keypoints on the mouse body. In this paper, we propose a novel Hourglass network based model, namely Graphical Model based Structured Context Enhancement Network (GM-SCENet) where two effective modules, i.e., Structured Context Mixer (SCM) and Cascaded Multi-level Supervision (CMLS) are subsequently implemented. SCM can adaptively learn and enhance the proposed structured context information of each mouse part by a novel graphical model that takes into account the motion difference between body parts. Then, the CMLS module is designed to jointly train the proposed SCM and the Hourglass network by generating multi-level information, increasing the robustness of the whole network. Using the multi-level prediction information from SCM and CMLS, we develop an inference method to ensure the accuracy of the localisation results. Finally, we evaluate our proposed approach against several baselines on our Parkinson’s Disease Mouse Behaviour (PDMB) and the standard DeepLabCut Mouse Pose datasets. The experimental results show that our method achieves better or competitive performance against the other state-of-the-art approaches.
Micro Computed Tomography Analysis of Four-Way Conversion Catalysts using Artificial Intelligence-Enabled Image Processing

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FURY: advanced scientific visualization

Free Unified Rendering in pYthon (FURY), is a community-driven, open-source, and highperformance scientific visualization library that harnesses the graphics processing unit (GPU) for improved speed, precise interactivity, and visual clarity.FURY provides an integrated API in Python that allows UI elements and 3D graphics to be programmed together.FURY is designed to be fully interoperable with most projects of the Pythonic ecosystem that use NumPy (C.R. Harris et al., 2020) for processing numerical arrays.In addition, FURY uses core parts of VTK (Schroeder et al., 1996) and enhances them using customized shaders.FURY provides access to the latest technologies such as raytracing, signed distance functionality, physically based rendering, and collision detection for direct use in research.More importantly, FURY enables students and researchers to script their own 3D animations in Python and simulate dynamic environments.
Advanced Driver Assistance System based on Machine Vision

In view of the frequent occurrence of traffic problems such as urban traffic congestion and traffic accidents, people pay more and more attention to traffic safety. This paper takes the key technical problems such as front vehicle detection and identification and pre-collision detection in the advanced driving assistance system as the research object, this paper puts forward a computer vision solution based on YOLOv5 algorithm and monocular camera distance calibration, which provides more comprehensive driving environment information for the advanced driving assistance system and improves the active safety of the vehicle. Finally, a case study is given to verify the superiority of the algorithm proposed in this paper.
Governance of Artificial Intelligence in Water and Wastewater Management: The Case Study of Japan

The integration of artificial intelligence into various aspects of daily life is developing at a rapid pace in Japan. Discussions to govern applications of artificial intelligence to the field of social infrastructure are also critical and need to match the rapid pace of development. However, the legal implications and risks of applying artificial intelligence to the management of lifelines such as drinking water supply and wastewater treatment have not yet been fully explored. This paper reviews the existing legislations and ongoing discussions on governance regarding applications of artificial intelligence to water and wastewater management. Based on the review, we discuss the ability of legislative frameworks in Japan to respond to the applications of artificial intelligence, as well as identifying potential gaps and challenges thereof, including access to accurate data, demarcation of rights and responsibilities, risk hedging and risk management, monitoring and evaluation, and handling of intellectual property rights. This paper concludes with key recommendations to national and local governments to support the application of artificial intelligence in the field of water and wastewater.
Do People Prefer to Give Interval-Valued or Point Estimates and Why?

Capturing interval-valued, as opposed to more conventional point-valued data, offers a potentially efficient method of obtaining richer information in individual responses.In turn, interval-valued data provide a strong foundation for subsequent fuzzy set based modelling-e.g., using the Interval Agreement Approach.In 2019, open-source software (DECSYS) was released to enable digital administration of interval-valued surveys using an ellipse response mode.This study follows on from an appraisal of this software and demonstration of practical value of the approach, reported last year, in one of many potential real-world applications (consumer preference research).A key ambition of ellipse-based interval elicitation is to maximise response efficiency-i.e., minimising workload and complexity in obtaining this richer information.User experience is therefore a vital consideration regarding potential for broader adoption.The present paper documents a direct empirical comparison between interval-valued response elicitation (using ellipses) and a conventional point-valued counterpart (using a Visual Analogue Scale), in terms of user experience during completion of a simple quantitative estimation task.We examine differences in perceived ease-of-use, unnecessary complexity and effective communication of desired responses, as well as overall liking-with positive outcomes for the interval-valued response mode in each case.We also report results of multiple regression analyses examining how the first three variables contribute to participants' overall liking of each response mode, as well as exploring differences driven by potentially important demographic factors (i.e., gender, age & native English speaking).
Multi-class Text Classification Model Based on Weighted Word Vector and BiLSTM-Attention Optimization

To address the problems that traditional multi-category text classification algorithms generally have high dimensionality of text vectorization representation, do not consider the importance of words to the overall text, and weak semantic feature information extraction. A multi-category text classification model based on Weighted Word2vec, BiLSTM and Attention mechanism (Weight-Text-Classification-Model, WTCM) is proposed. First, the text is vectorized by the Word2vec model; then the weight value of each word is calculated by the TF-IDF algorithm and multiplied with the word vector to construct a weighted text vector representation; then the semantic feature information is extracted by the context-dependent capability of BiLSTM; the Attention mechanism layer is incorporated after the BiLSTM layer to assign weights to the output of each moment After the BiLSTM layer, an Attention mechanism layer is incorporated to assign weights to the sequence information output at each moment; finally, it is input to the softmax classifier for multi-category text classification. The experimental results show that the classification accuracy, recall and F-value of the WTCM model are as high as 91.26%, 90.98% and 91.12%, which can effectively solve the multi-category text classification problem.
Geostatistical analysis of uranium concentrations in North-Western part of Ogun State, Nigeria

Several statistical techniques have been adopted in the analysis and interpretation of radiometric survey data set with little attention to the geostatistical approach. In this study, the geostatistical technique was used to analyse an airborne uranium data set in the North-western part of Ogun state to deduce the autocorrelation level of uranium with the corresponding geological composition through the geostatistical model fitting. Twelve uranium lithological (UL) units were identified and analysed using variogram and kriging. Three variogram models namely; spherical, exponential and gaussian, were utilised. The variogram results showed that the three models fit in the interpretation of uranium data in the order of Spherical < Gaussian < Exponential models for all the UL units except at UL6 which had range (R) values of 4227.3 m, 5318.2 m and 5636.4 m for Gaussian, Spherical and Exponential models, respectively. The Nugget-Sill ratios (NSR) were in the range of 3.57–18.97% for the three models across the 12 UL units. These results indicate strong spatial autocorrelation/dependency of uranium concentration on geological composition. The ordinary kriging estimator revealed that the weathered basement formation has the highest uranium concentration compared to basement and sedimentary formations across the study area. The coefficient of variation results also revealed homogeneity in the uranium source across the UL units. The geostatistics technique is a useful mathematical tool in the analysis and interpretation of uranium concentration in the North-western part of Ogun State.
Source camera identification for re-compressed images: A model perspective based on tri-transfer learning

Source Camera Identification (SCI) achieves high accuracy on matching identification, in which the training and testing sample sets are derived from the same statistical distribution. However, in practice the training and testing sets, namely, the source and test domains, may consist of digital images that are double compressed by various software and applications with different quantization tables. Unfortunately, existing methods are inadequate in performance under such circumstances, such that we aim to find an algorithm that can fill the gap between the training and testing sets. In this work, we propose an algorithm, tri-transfer Learning (TTL), which is a cross-pollination of transfer learning and tri-training. For TTL, the transfer learning module transfers the knowledge learned from the training sets to improve the identification performance on testing. Compared with other methods, TTL uses a semi-supervised approach requiring only a small number of training samples and has better performance than other methods. The tri-training module, which is a variation of the co-training, facilitates knowledge transferring by assigning pseudo-labels to unlabelled instances and adds target instances with labels to the training set in batches. Combining the two modules, our framework can gain higher efficiency and performance than other state-of-art methods on mismatched camera model identification which is supported by experiments based on the open-source Dresden Image Database.