Reviewerly/exHarmony huggingface dataset:
An iterative adaptive fuzzy filter using alpha trimmed mean elimination of high density salt and pepper noise in images

This work deals with the removal of pepper and salt noise in two stages (detection and denoising).The detection stage uses an adaptive fuzzy detector. This is trailed by the denoising stage using slanted mean filter. The key steps include the initialization of parameters namely the window half size parameter (M) and number of good pixels (N).Once initialized Gaussian Membership function is applied. Then if the value obtained so is less than the threshold it is assumed to be noisy. The cardinality of the good pixels around the noisy pixel is found. If the cardinality so obtained is less than the previously initialized parameter N, then relaxation is done on the parameters namely chosen window size and then the initially set N good pixel demand. The denoising uses weighting function based on the inverse distance weighting. Here lesser heaviness is allocated to the pixel far away from the center pixel and higher weight for the pixelearlier to the centerpixel
Berklee Method

The Berklee methodBerklee method originated as the music theory that was being taught at Berklee School of Music.
Multi-sensor weighted combination fusion algorithm considering time registration

This article addresses the problem of multi-sensor data fusion and time registration in the case of large data transmission delay. Kalman filter method has good filtering and prediction performance, so based on the time registration method of Kalman filter, a prediction-fusion-update-fusion target location algorithm combined with weighted combination fusion method is proposed. Firstly, the algorithm aligns the sampling time through the Kalman filter registration algorithm, and uses the weighted fusion to get the fusion estimation at the sampling time. Then, the fusion estimation is updated to the current time by using the Kalman filter. Finally, the real-time fusion estimation and the real-time prediction estimation of a single sensor are combined with weights to realize the high-precision real-time positioning of the target. Simulation results show that the algorithm can effectively reduce the positioning error of the target under the condition of large time delay, and has good robustness to improve the positioning accuracy of the target in the case of complex maneuvering.
A novel teaching model based on Web Mining and Data Mining

Aiming at the characteristics of current distance teaching sites with few interactive means and inability to teach students in accordance with their aptitude, a remote teaching model based on Web mining and data mining is proposed. And the realization algorithm is given. This model makes full use of the various information accumulated on the site to help course designers and managers to redesign courses and reconstruct sites. so that the design of courses is more reasonable and in line with teaching regulations. Learners can also give full play to their own personality. The model, compared with the WWW-based one, is more beneficial to distance education, and has broad development and application prospects.
A Dual Transformer Super-Resolution Network for Improving the Definition of Vibration Image

Visual measurement methods are gaining more and more attention in the field of structural body health monitoring due to the advantages of long-range, noncontact, and multipoint monitoring. However, the imaging system is usually affected by many factors, such as distortion, blurring, and noise, which lead to displacement measurement errors after the degradation of the acquired image quality. Therefore, in this article, we propose a structural body image super-resolution network based on a dual transformer architecture to improve the clarity of the collected structural body vibration displacement image to better capture the vibration displacement information of the target. Meanwhile, we design a dual transformer block based on an encoder–decoder architecture for the characteristics of vision-based structural body vibration displacement measurement tasks to better extract structural body image details and edge feature information. In this module, we introduce two different transformers. In addition, modules based on the encoder–decoder architecture focus more on the input and output image information and often ignore the feature information in different layers. Therefore, we introduce an attention mechanism in the network and interact with the feature information in different layers of the encoder–decoder architecture to obtain a better structural body image super-resolution effect. After comparison tests with the rest of the latest and most classical networks as well as the current optimal networks, it is shown that our network obtains excellent image reconstruction results under different structural body vibration image datasets (SETs), which also provides a strong guarantee for the task of accurate vision-based structural body vibration displacement measurement.
Unsupervised Feature Learning in Activity Recognition using Convolutional Denoising Autoencoders with Squeeze and Excitation Networks

Sensor modalities have recently been used to acquire activity recognition data, especially wearable sensors, due to their relative advantages over other modalities. Datasets obtained using wearable sensors are often in time-series format; therefore, there is a need to segment and extract only salient features from the raw sensor data. Generally, the overall performance of activity recognition relies heavily on the quality of these extracted features. For this reason, various feature extraction approaches have been proposed in the literature, ranging from manual to automatic feature extraction methods. However, most of the existing feature extraction methods are time-consuming and supervised. Since wearable sensor data is quite tedious and time-consuming to label, there is a need to propose unsupervised feature extraction methods capable of extracting features from fully unlabelled wearable sensor datasets. Several autoencoder models have been proposed for unsupervised feature learning using fully connected and convolutional neural networks. However, these methods cannot effectively learn discriminative features since they treat all features as equally important. To tackle this, this research introduced a feature weighting mechanism based on squeeze and excitation networks in a convolutional denoising autoencoder to act as channel-wise attention to reweigh feature channels accordingly. This allowed more discriminative activity features to be extracted from fully unlabelled activity data. The proposed model was benchmarked on two publicly available datasets, including a dataset which consists of transitional activities (WISDM and HAPT). Results showed that the proposed method exploited more discriminative features while learning inherent features of unlabelled activity recognition data and outperformed the existing methods with improved recognition performance on the two benchmark datasets.
Component-Tree Simplification Through Fast Alpha Cuts

Tree-based hierarchical image representations are commonly used in connected morphological image filtering, segmentation and multi-scale analysis. In the case of component trees, filtering is generally based on thresholding single attributes computed for all the nodes in the tree. Alternatively, so-called shapings are used, which rely on building a component tree of a component tree to filter the image. Neither method is practical when using vector attributes. In this case, more complicated machine learning methods are required, including clustering methods. In this paper I present a simple, fast hierarchical clustering algorithm based on cuts of $$\alpha $$ -trees to simplify and filter component trees.
Towards Positive Jacobian: Learn to Postprocess for Diffeomorphic Image Registration with Matrix Exponential

We present a postprocessing layer for deformable image registration to make a registration field more diffeomorphic by encouraging Jacobians of the transformation to be positive. Diffeomorphic image registration is important for medical imaging studies because of the properties like invertibility, smoothness of the transformation, and topology preservation/non-folding of the grid. Violation of these properties can lead to destruction of the neighbourhood and the connectivity of anatomical structures during image registration. Most of the recent deep learning methods do not explicitly address this folding problem and try to solve it with a smoothness regularization on the registration field. In this paper, we propose a differentiable layer, which takes any registration field as its input, computes exponential of the Jacobian matrices of the input and reconstructs a new registration field from the exponentiated Jacobian matrices using Poisson reconstruction. Our proposed Poisson reconstruction loss enforces positive Jacobians for the final registration field. Thus, our method acts as a post-processing layer without any learnable parameters of its own and can be placed at the end of any deep learning pipeline to form an end-to-end learnable framework. We show the effectiveness of our proposed method for a popular deep learning registration method Voxelmorph and evaluate it with a dataset containing 3D brain MRI scans. Our results show that our post-processing can effectively decrease the number of non-positive Jacobians by a significant amount without any noticeable deterioration of the registration accuracy, thus making the registration field more diffeomorphic. Our code is available online at https://github.com/Soumyadeep-Pal/Diffeomorphic-Image-Registration-Postprocess
Pattern Matching and Information-Aware Between Reviews and Ratings for Recommendation

Real recommendation scenarios have diverse and multi-modal features, which are often high-dimensional, sparse, and heterogeneous, making it difficult to learn. To improve model performance, many studies jointly model the semantic information of user and item reviews. However, due to the inherent changes in natural language processing, it has become a challenge to more accurately model the semantic interaction and matching relationship between user and item reviews. In addition, most of the previous work directly connects text representations and numerical features without considering their natural gap. In view of this, we propose a novel Pattern Matching and Information-aware Network (MIAN). Specifically, we design a matching network composed of global matching and specific matching, which is used to model the matching information between user and item reviews. The specific matching module uses the item description as a bridge to assist in modeling more fine-grained matching relationships. In addition, we construct an information perception layer to align the information between reviews and numerical features. Furthermore, we adopt a joint learning manner for better model training by employing a matching task fusion, which benefits from the prior matching knowledge. Comprehensive experiments on five real-world datasets show that MIAN outperforms the state-of-the-art methods.
Dynamic Bayesian Networks for Reduced Uncertainty in Underwater Operations

This paper presents a novel framework for modelling dynamic Bayesian belief networks (BBNs) for online risk assessment in underwater operations. Existing frameworks spans from commercial software with restricted code access to non-profit open source frameworks. Frameworks with restricted code access often provides general user interfaces and visualization tools, while open source frameworks provides access to code for developers. The model presented in this paper pursues a best of both worlds scenario, where the model implementation should be uncomplicated while also providing visualization and verification to provide the user with a clear perception of the implemented model. The presented method is an expansion of the Bayesian model of the pomegranate python library, and simplifies the procedure of building, verifying and utilizing BBN models. The method is applied to a conceptual design of an underwater scenario case study with a model for an underwater vehicle manipulator system.
Language Translation for Impaired People

Gesture-based communication is a visual language utilized by both discourse disabled and hearing-impeded individuals as their first language. Individuals experiencing hearing and communicating in handicaps utilize communication via gestures as a straightforward method of correspondence among one another and furthermore with others, yet tragically few out of every odd one of the ordinary individuals can comprehend communication via gestures consequently. It brings about an absence of correspondence and seclusion. To the extent both discourse disabled and hearing-weakened individuals are concerned, approaching gesture-based communication is extremely fundamental for their social, passionate, etymological, and social development. Objective of this proposed work to overcome any barrier between both discourse disabled and hearing-weakened individuals with the coming of new innovations. Proposed application will change the voice of the client over communication with the assistance of normal language preparing semantics.
Adaptive Differential Evolution Algorithm with Multiple Gaussian Learning Models

The search efficiency of the differential evolution (DE) algorithm significantly depends on its mutation strategies and control parameters. The multiple Gaussian learning model-based parameter adaptation mechanism is useful in choosing suitable parameters. Naturally, it is worth studying whether this mechanism is also beneficial for the automatic selection of the appropriate mutation strategies. To this end, this paper proposes an adaptive Differential Evolution algorithm with Multiple Gaussian Learning Models, known as MGLMDE, which includes two adaptation mechanisms, i.e., the multiple Gaussian learning model-based parameter adaptation mechanism (MGLMP) and multiple Gaussian learning model-based strategy adaptation mechanism (MGLMS). MGLMP and MGLMS determine the future mutation strategies and control parameters, respectively, using multiple Gaussian models to learn from successful historical memories. The linear population size reduction (LPSR) mechanism is used to control the population size. The proposed algorithm is evaluated via comparisons with some powerful DE methods on lots of test problems. The experimental results demonstrate that the proposed method is better than or at least highly competitive with the state-of-the-art DE algorithms.
IoT and Cloud based Face detection application design for Surveillance systems using Deep Learning

Semantic Index, Human Action Detection, and Event Detection are video surveillance packages that assist automate surveillance tasks. Video surveillance structures have entered the generation of virtual surveillance structures in which virtual video is used to traverse the course of different virtual facts. Advances in storage, telecommunications, and facts compression have enabled the increase of more than one technology in virtual surveillance structures. Using more than one video surveillance fashion in risky conditions extends the competencies of rule implementation organizations. In addition, video surveillance enables the form of a motion to carry out in one-of-a-kind conditions. In addition, spotting a particular man or woman in a video is essential for added security, multimedia, and multimedia packages, including offline, seek, and online monitoring of involved humans within the video. The proposed IoT- Cloud based Face detection application is designed to find a person from a huge size dataset and that can generate more accurate results. This application uses Deep Learning methods to find exactness when classifying the images. IoT here is used for liveness detection by comparing all the images present in cloud systems. For results comparison, we used Haar Cascade and DenseNet architectures.
Development of automatic performance Tublum mechanism to efficiently convert blows and sounds

Music is a tool that enriches our lives. Recorded electronic sound sources can be listened to on a daily basis due to the development of CDs and portable music players. However, listening to live music requires a performer, and there are few opportunities to listen to live music compared to electronic sound sources. As a method of listening to live music unattended, automatic performance devices such as music boxes have existed in the past. However, most automatic performances are often limited to playing melodies. Percussion instrument automatic performance devices require high-speed movement when playing drums, and it is difficult to create the speed at which the drums are struck. Therefore, percussion instrument automatic performance devices are limited to those that are relatively easy to produce sound, such as keyboard percussion instruments. Has been done. Therefore, in this research, instead of using a large drum as in the past, we used a pipe drum that uses a vinyl chloride pipe that has a relatively small diameter and is easy to produce sound, and whose pitch can be changed by the length of the pipe. Develop an automatic performance device for Tubulum.
Beyond Post-Truth: Critical Theory and the Possibility of Radical Enlightenment

In this chapter, the idea of post-truth is diagnosed as a flawed concept. It serves as a catch-all phrase able to explain the existence of the present social malaise through a liberal lens. The chapter draws on Horkheimer’s classic distinction between traditional and critical theory, and proposes that many of the theoretical responses to the phenomena associated with a ‘post-truth’ condition can be understood as instances of traditional theory. This is due to the degree to which they tacitly accept and fail to question aspects of the social whole that engenders these phenomena. On this basis, and whilst referring to Adorno, the chapter then goes on to show that there are, in consequence, elements of ‘truth’ buried within aspects of ‘post-truth’ discourse.
Solving Linear Equations with Disturbance Rejection

This paper aims to solve a system of linear equations with measurement noises. Different from existing noise-free or stochastic formulations, we assume the noisy data is subject to some deterministic and periodic disturbances. We develop an observer-based dynamic to estimate and reject these disturbances. This combined with some optimization method is proven to be able to recover the least squares solution to the nominal linear equations in both centralized and distributed settings. The effectiveness of this design is verified by a numerical example.
CMB: A Novel Structural Re-parameterization Block without Extra Training Parameters

Structural re-parameterization is a raising field, which aims at improving the performance of convolutional neural networks (CNNs) through training an over-parameterization model and transferring it into a compact inference model. However, the performance improvements of prior structural re-parameterization works often come at the cost of heavy extra training resources, which increases carbon emissions and limits the potential applications on large-scale industrial tasks. To this end, first, we conduct experiments with a series of blocks composed of multiple identical branches to investigate the mechanism behind the structural re-parameterization, and then provide an interpretation. Moreover, motivated by the studies of effective receptive fields in the biological visual systems and neural networks, we propose a novel compact block named circular mask block (CMB). Given a neural network, we replace the regular convolutional layer with CMB to construct a training architecture, which can be trained to gain an accuracy boost with No extra training parameters and limited extra training FLOPs. After training, the training architecture can be transformed into the original architecture for inference. Extensive experiments are performed on CIFAR-10 and ImageNet to evaluate the effectiveness of our method. For example, we improve 0.85% top-1 accuracy of ResNet-50 on ImageNet without extra training parameters and only 11.32M extra training FLOPs, which saves 434x training FLOPs compared with prior works.
Design and Implementation of Database Query System of Clinical Chinese Medicine Knowledge

To facilitate the query and application of Chinese medicine knowledge, a database query system of clinical Chinese medicine knowledge is established. Based on the database, we designed and developed a query system of clinical Chinese medicine knowledge based on browser/server architecture using Java programming language, MVC development model, and Eclipse integrated development environment. The database query system was developed to realize the query of original Chinese medicine information and Chinese medicine codes. The system provides a new way of querying Chinese medicine knowledge, which helps users understand and learn Chinese medicine knowledge.
Closing the Gap Between Privacy Policies and Privacy Preferences with Privacy Interfaces

Privacy is an important topic regarding the modern and interconnected world. However, there is a tendency to ignore own intentions regarding customization of privacy policies in favor of saving time and using a specific service. To tackle this problem, a wide range of formal languages, expressing user preferences or privacy policies, have been developed by the scientific community. However, due to the variety of these languages, interoperability is only given between approaches designed to complement each other. As a result, as long as there is no standard in this domain, there will not be a broadly accepted solution. The aim of this paper is to provide a mechanism to counteract the lack of compatibility. To contribute to that very goal, this paper introduces the Privacy Interfaces mechanism which allows a Unified Privacy Policy Negotiation. It enables compatibility between otherwise incompatible preference languages and privacy languages, by creating standardized Language Mappings, which serve as a baseline for negotiation.
Detection of Dangerous Driving Behavior Based on CG-YOLOv5

In order to avoid huge traffic accidents caused by dangerous driving behavior on the way, it is very important to detect this behavior in real time. In view of the high loss of the existing methods and the interference of the data set background, an improved YOLOv5 detection method is proposed. Firstly, the convolution block attention module mechanism is integrated in the network to enhance the feature expression ability and suppress the background interference in the process of feature fusion, so as to reduce the loss of the algorithm. Secondly, Ghost convolution is used instead of ordinary convolution operation to realize the lightweight of the detection model. The experimental results show that the recognition accuracy of this method is 99.9%, and the reasoning time under the same test data is as low as 3.895s. At the same time, the model is much smaller than the original network model, and can be effectively applied to the real-time accurate monitoring of dangerous driving behavior of drivers in the real environment.