Reviewerly/exHarmony huggingface dataset:
Research on Classification Method of Medical Ultrasound Image Processing Based on Neural Network

In clinical applications, the classification of ultrasound images needs to be processed as an aid to diagnosis. Based on this, a hybrid model of cascaded deep convolutional neural network consisting of two different CNNs and a new classification method are designed and evaluated for its feasibility and effectiveness in ultrasound image classification. A total of 1000 pathological slides of patients with thyroid nodular lesions kept in the Department of Pathology of the First Affiliated Hospital of Lanzhou University, China, were retrospectively collected. After image acquisition, the images were randomly divided into training set, validation set, and test set in the ratio of 4 : 3 : 3. Three convolutional neural network models (VGG 19 model, Inception V3 model, and DenseNet 161 model) with pretraining parameters acquired on the training set were trained, and the models were combined to construct an integrated learning model, and the performance of the models in recognizing pathological images was evaluated based on the test set data. The experimental results show that the VGG 19 model is less effective in classification, with a correct rate of 88.20%, which is lower than that of Inception V3 and DenseNet161 models (92.87% and 92.95%). InceptionV3 and DenseNet161 models have significant advantages in terms of accuracy, number of parameters, and training efficiency, where the DenseNet161 model has faster convergence and better generalization performance, but occupies more video memory in the operation; moreover, the DenseNet161 operation time (1986.48 s) and response time (16 s) are better than the other two models. In addition, the integrated learning of InceptionV3 and DenseNet161 can improve the recognition of pathological images by a single model. Compared with other methods, the performance of the cascaded CNNs proposed in this study is significantly improved, and the multiview strategy can improve the performance of cascaded CNNs. The experimental results demonstrate the potential clinical application of cascaded CNNs, which can provide physicians with an objective second opinion and reduce their heavy workload, in addition to making the diagnosis of thyroid nodules easy and reproducible for people without medical expertise.
Self-Super-Resolution of an MRI Image with Assistance of the DSTTD System

Motivation. In the modern world of information technology, the need for ensuring the safety of wireless transmissions while transiting through a given network is growing rapidly. The process of transmitting images via a wireless network is fraught with difficulty. There is a possibility that data may be corrupted while being transmitted, which would result in an image with low resolution. Both of these issues were investigated head-on in this research methodology using the aiding double space-time block coding (DSTTD) system and the self-super-resolution (SSR) method. Description. In recent times, medical image transmission over a wireless network has received a significant amount of attention, as a result of the sharing of medical images between patients and doctors. They would want to make sure that the image was sent in a risk-free and protected manner. Arnold cat map, often known as ACM, is a well-known and widely implemented method of image transmission encryption that has been in use for quite some time. At the receiver end, SSR is now being employed in order to view the transmitted medical image in the finest possible resolution. It is anticipated that in the near future, image transmission through wireless DSTTD will be technically feasible. This is performed in order to maximize the benefits that the system has to offer in terms of both spatial diversity and multiplexing as much as is possible. Conclusion. The SSR approach is used in order to represent the image in a document pertaining to human resources. ACM is used so that the image may be sent in a risk-free and protected way. The adoption of a DSTTD-based architecture for wireless communication is suggested. A comparison of the results is provided, and PSNR and SSIM values are detailed towards the results and discussion of the article.
Multi-Feature Integrated Concurrent Neural Network for Human Facial Expression Recognition

&lt;p&gt;Facial expression helps to communicate between the people for conveying abundant information about human emotions. Facial expression classification is applied in various fields such as remote learning education, medical care, and smart traffic. However, due to the complexity and diversity of the facial emotions, the present facial expression recognition model causes a low recognition rate and it is hard to extract the precise features that are related to facial expression changes. In order to overcome this problem, we proposed Multi-feature Integrated Concurrent Neural Network (MICNN) which is significantly different from the single neural network architectures. It aggregates the prominent features of facial expressions by integrating the three kinds of networks such as Sequential Convolutional Neural Network (SCNN), Residual Dense Network (RDN), and Attention Residual Learning Network (ARLN) to enhance the accuracy rate of facial emotions detection system. Additionally, Local Binary Pattern (LBP) and Principal Component Analysis (PCA) are applied for representing the facial features and these features are combined with the texture features identified by the Gray-level Co-occurrence Matrix (GLCM). Finally, the integrated features are fed into softmax layer to classify the facial images. The experiments are carried out on benchmark datasets by applying k-fold cross-validation and the results demonstrate the superiority of the proposed model.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Salt‐and‐pepper denoising method for colour images based on tensor low‐rank prior and implicit regularization

Most of the information obtained by humans comes from colour images. However, salt-and-pepper noise (SPN) during signal acquisition, encoding, transmission, and decoding easily interferes with the quality of colour images. Most existing SPN denoising methods decompose a colour image into three independent matrices according to the colour channel and then recover each channel signal independently, ignoring the strong data correlation between channels. In addition, most existing SPN denoising methods apply only a single model-driven or data-driven approach and fail to take the advantages of their combination fully. Therefore, we first regard a colour image contaminated by SPN as the sum of an SPN tensor and a tensor with missing data. In this manner, we transform the denoising problem into a low-rank tensor reconstruction problem. We then introduce a model-driven-based parallel matrix factorization low-rank tensor reconstruction algorithm and a data-driven-based FFDNet denoising network to restore the colour image better. The proposed method not only enhances the similarity of the colour image channels but also explores the deep prior of the colour image to capture the image details. Finally, the proposed method is compared with some advanced denoising methods. The results show that the proposed method achieves a competitive denoising performance.
Toponomic quantum computation

Holonomic quantum computation makes use of non-abelian geometric phases, associated to the evolution of a subspace of quantum states, to encode logical gates. We identify a special class of subspaces, for which a sequence of rotations results in a non-abelian holonomy of a topological nature, so that it is invariant under any SO(3)-perturbation. Making use of a Majorana-like stellar representation for subspaces, we give explicit examples of topological-holonomic (or toponomic) NOT and CNOT gates.
Statistical Analysis on Channel-wise Cross-correlation Features in Siamese Trackers

In this paper, we provide an analysis on channel-wise cross-correlation features on Siamese trackers. Channel statistics of the correlation feature is studied, where the correlation feature is computed from the features of the template and tracked target patches. From the study, a statistical pattern of the feature is observed regarding the tracking overlapping performance. The analysis is conducted on three frequently used tracking benchmark datasets.
RCRL: Replay-based Continual Representation Learning in Multi-task Super-Resolution

Super-resolution (SR) aims to recover high-resolution (HR) images from low-resolution (LR) images. Recently, various attempts, e.g., unsupervised SR models and domain-specific SR have achieved outstanding performance for various real-world applications. However, they significantly suffer from low generalization performance when trained on another domain dataset. Furthermore, they often exhibit performance degradation when the model continually learns multiple tasks; so-called catastrophic forgetting degrades the SR performance. In this paper, we are the first to propose a novel approach for continual multi-task SR named Replay-based Continual Representation Learning framework that can be applicable to GAN-based SR models, which utilizes feature memory for preserving the learned features from the previous task. Our experimental results demonstrate the effectiveness of RCRL in continual multi-task SR at improving generalization performance and alleviating catastrophic forgetting.
Accelerated Blind Deblurring Method via Video-based Estimation in Next Point Spread Functions for Surveillance

Blind deblurring has been attracting increased attention. In real-life problems, high-resolution images are needed to process and the blurring function, point spread function (PSF), is mostly unknown, especially in the surveillance systems such as camera integrated payload drop with a parachute. The PSFs are dependent on their previous functions, so we perform the deblurring process faster with our proposed model by integrating a previously prepared deep learning method. Our system consists of four phases: (i) enhancing images with an existing deep learning method, (ii) obtaining PSFs, (iii) predicting the next PSFs with our model, and (iv) enhancing the images with the wienerfiltering we developed. The number of PSFs to be estimated was experimentally found as the point at which the PSNR value began to decrease in the test images. Convolutional LSTM layers were used for our model which has been compared with other state-of-the-art models in terms of performance and running time.
Deep Network for Extremely Low-Resolution Human Action Recognition

Due to advancement in automated applications, privacy-preserving is an emerging concern. This concern is more significant in the case of human-centred surveillance application like human action recognition (HAR). Along with privacy concern, the computational complexity due to the huge size of video data is another major concern. To overcome these limitations, an attempt is made to examine the domain of human action recognition in low-resolution (LR) videos. The extremely LR video data ensures sufficient distortion in visual information to hide the identity of the person. Therefore, working with LR videos can resolve the above mentioned concerns of privacy preserving and computational complexity up to a certain extent. In this paper, a new generative adversarial network (GAN) based neural architecture is proposed for HAR in extremely low-resolution videos. The extensive results analysis with ablation study on the state-of-the-art datasets proves the effectiveness of the proposed method over the existing methods for LR-HAR.
Chaotic Oscillator for a Chaotic Oscillatory Neural Network Hardware Implementation

Neural networks have opened up a wide range of new abilities. However, increasing complexity narrows the range of application due to calculation time and power consumption growth. That is the reason why the world famous companies are starting to develop a hardware implementation for different types of neural networks. This article presents the results of creating models for hardware implementation of the chaotic oscillatory neural network. The hardware implementation needs small, fast, energy efficient, and tunable circuit for one-dimensional chaotic oscillator. This article proposes one-dimensional chaotic oscillator circuit with improved characteristics that is completely suitable for usage in chaotic oscillatory neural network hardware implementation. Using the proposed oscillator, the model of chaotic oscillatory neural network has been developed and tested.
Chatbots Analysis for the Creation of Automated Conversations in Real Time

In this paper we will be described some of the technologies use in chatbots to process the natural language and the creation of a basic Chatbot prototype based on Google technologies, also the economic impact of the implementation for the Business Process Outsourcing companies in El Salvador. The prototype was created with Dialogflow which is a natural language understanding platform that makes it easy to design and integrate a conversational user interface in different formats that can parse multiple types of input, including text or audio input. It can also respond in various ways, either through text or synthetic voice, and is also part of the conversational AI offering within GCP. The main objective of BPO companies is to reduce the costs of client companies and help them grow economically, through the optimization of time and resources of their business processes. Immersed in these business processes are customer service processes. Nowadays with existing technological advances, technology is part of people’s daily lives and is used to do work more efficiently and effectively. Currently there are machines that do activities that were previously performed by humans, with this it has been possible to improve productivity and existing processes within companies.
Strong nonlocality with genuine entanglement based on GHZ-like states in multipartite quantum systems

Abstract Strong nonlocality with genuine entanglement was first shown by Wang et al using sets of GHZ-like states in tripartite quantum systems (2021 Phys. Rev. A 104 , 012 424). However, it is an open problem whether there exists strong nonlocality with genuine entanglement in four or more partite systems. In this paper, we unify two different concepts of strong nonlocality introduced by Halder et al (2019 Phys. Rev. Lett. 122 , 040 403) and by Zhang et al (2019 Phys. Rev. A 99 , 062108). That is, we use a concept of k -strong nonlocality instead of these two different types of strong nonlocality. A set of orthogonal quantum states is k -strong nonlocal if it is locally irreducible in every k -partition. In fact, the strong nonlocality that is usually said is 2-strong nonlocality. The smaller the k is, the stronger the nonlocality will be. A set of states is k + -strong nonlocal if the strong nonlocality of this set is stronger than k -strong nonlocality but weaker than ( k − 1)-strong nonlocality. Based on these concepts, firstly, we show 2-strong nonlocality with genuine entanglement by some sets of GHZ-like states with weight d in tripartite systems. These sets are not necessarily complete bases. Secondly, we present 2-strong nonlocality with genuine entanglement for systems with four or more parties. These results solve the open problem raised by Wang et al Finally, we construct a set of GHZ-like states with n + -strong nonlocality in n -partite quantum systems.
Out of the Cave: An Immersive VR Comic About a Journey of Self-Healing

"Out of the Cave" is an immersive VR comic. Its story revolves around a girl struggling with the regrets of her past relationship and stepping on a journey to heal the scars. This work blends webcomics into a spatial medium to create a brand-new narrative experience. The audience can dive into the story world and complete the journey of pursuit and self-healing with the heroine.
Animal Pose Tracking: 3D Multimodal Dataset and Token-based Pose Optimization

Abstract Accurate tracking of the 3D pose of animals from video recordings is critical for many behavioral studies, yet there is a dearth of publicly available datasets that the computer vision community could use for model development. We here introduce the Rodent3D dataset that records animals exploring their environment and/or interacting with each other with multiple cameras and modalities (RGB, depth, thermal infrared). Rodent3D consists of 200 min of multimodal video recordings from up to three thermal and three RGB-D synchronized cameras (approximately 4 million frames). For the task of optimizing estimates of pose sequences provided by existing pose estimation methods, we provide a baseline model called OptiPose . While deep-learned attention mechanisms have been used for pose estimation in the past, with OptiPose , we propose a different way by representing 3D poses as tokens for which deep-learned context models pay attention to both spatial and temporal keypoint patterns. Our experiments show how OptiPose is highly robust to noise and occlusion and can be used to optimize pose sequences provided by state-of-the-art models for animal pose estimation.
Demonstration of RedirectedDoors: Manipulating User’s Orientation while Opening Doors in Virtual Reality

We present an installation demonstrating the applicability of RedirectedDoors, a redirection technique that occasionally manipulates the user's orientation during door-opening motions. In this demo, the player explores an indoor virtual environment containing doors while wearing a head-mounted display (HMD), and their orientation in reality is manipulated as a function of the door's opening angle. In addition, when the player opens the door by pushing or pulling the doorknob in virtual reality, the corresponding passive haptic feedback is provided by the self-actuated doorknob-type prop. When reaching the goal, they can see the manipulation results by comparing their virtual position with a real landmark position. Consequently, this demo both makes the player's experience more realistic and presents the virtual environment in a comparatively small physical space.
Trustworthiness of voice-based assistants: integrating interlocutor and intermediary predictors

Abstract When intelligent voice-based assistants (VBAs) present news, they simultaneously act as interlocutors and intermediaries, enabling direct and mediated communication. Hence, this study discusses and investigates empirically how interlocutor and intermediary predictors affect an assessment that is relevant for both: trustworthiness. We conducted a secondary analysis using data from two online surveys in which participants ( N = 1288) had seven quasi-interactions with either Alexa or Google Assistant and calculated hierarchical regression analyses. Results show that (1) interlocutor and intermediary predictors influence people’s trustworthiness assessments when VBAs act as news presenters, and (2) that different trustworthiness dimensions are affected differently: The intermediary predictors (information credibility; company reputation) were more important for the cognition-based trustworthiness dimensions integrity and competence. In contrast, intermediary and interlocutor predictors (ontological classification; source attribution) were almost equally important for the affect-based trustworthiness dimension benevolence.
Validating out of the box: Identifying a campaign of physical tests

The extent of the domain over which a model validation demonstrates the reliability of a model is discussed and a simple schematic diagram is used to illustrate the domain. The schematic diagram can also be used to optimise the physical test campaign required to demonstrate the reliability of a model for its context of use. The connections to existing validation approaches and procedures are discussed.
A three-dimensional B-spline wavelet finite element method for structural vibration analysis

In this study, a three-dimensional (3-D) B-spline wavelet finite element method (WFEM) is proposed by combining the wavelet theory and 3-D finite element method (FEM). Owing to the semi-orthogonality and multi-resolution analysis of spline wavelet over the solution domain, a cuboid with uniform distribution nodes is established for the 3-D B-spline wavelet on the interval (BSWI) element. The governing equations of vibration for the elastic structures are derived by introducing the 3-D B-spline wavelet theory into the displacement components of each node. A procedure for assembling the BSWI elements is presented by redefining the index element node functions. Numerical studies including the vibration analysis of solid and hollow beams and plates under different boundary conditions are performed to show the performance of the present method. A comprehensive comparison of the predicted vibration characteristics obtained by the present method, traditional FEM, and reference data is presented to verify the accuracy and efficiency of the 3-D B-spline WFEM. The core of the present method is involving the 3-D B-spline wavelet to replace the polynomial interpolation functions in the traditional FEM, which enhances computational accuracy during the vibration analysis of the elastic structures.
Recurrent attention convolutional neural network optimise track foreign body detection

Timely and accurate identification of track foreign body intrusion is of great significance for maintaining the safety of railway running. Object detection technology based on deep learning can be used to judge the foreign body in the track running area. However, the general target detection technology cannot recognise the foreign object information in complex environment, which has a certain hysteresis. In this study, adaptive adjustment is made to the algorithm according to the limited area in the railway special scene. A new Recurrent Attention Convolutional Neural Network and adaptive particle swarm fusion network is proposed. Produced in the process of image classification and regional sampling information loss, by introducing attention study under the multi-scale recursive fusion, each scale is a network of target recognition and attention network, starting from the full image, continuously put forward attention area, and as input to realise detection to the next dimension positioning; With the help of adaptive particle swarm optimisation algorithm, the feature information at different levels is fused, and the high-attention region is generated by continuous recursive clipping from rough to fine, The mutual enhancement of target area detection and fine-grained features is realised. Experiments conducted by the author in Railway data set showed that its target detection accuracy reached 84.2%. Compared with other existing models, the relative accuracy gain was 10.6% and 8.1%, respectively, and the maximum gain was 6.2% compared with the self-ablation experiment.