Reviewerly/exHarmony huggingface dataset:
Binarization Based on Maximum and Average Gray Values

Many image processing techniques use binarization for object detection in images, where the objects and background are well distinct by their brightness values, where, the threshold level is globally assigned, on the other hand, if it’s adaptive, the threshold level is locally calculated. In order to determine the optimal binarization threshold, from an image with the mean gray values and extreme gray values, exchanging the mean gray values relating to automatic analisis for a standard histogram equalization, which can evaluate a wide range of image features, even when the gray values in both the object of interest and background of the image are not uniform.
A convergence analysis of Nesterov’s accelerated gradient method in training deep linear neural networks

As the training process of deep neural networks involves expensive computational cost, speeding up the convergence is of great importance. Nesterov's accelerated gradient (NAG) is one of the most popular accelerated optimizers in the deep learning community, which often exhibits improved convergence performance over gradient descent (GD) in practice. However, theoretical investigations of NAG mainly focus on the convex setting. Since the optimization landscape of the neural network is non-convex, little is known about the convergence and acceleration of NAG. Nowadays, some works make progress towards understanding the convergence of NAG in training over-parameterized neural networks, where the number of the parameters exceeds that of the training instances. Nonetheless, previous studies are limited to the two-layer neural network, which are far from explaining the remarkable success of NAG in optimizing deep neural networks. In this paper, we investigate the convergence of NAG in training two architectures of deep linear networks: deep fully-connected linear neural networks and deep linear ResNets. Based on the over-parameterization regime, we first analyze the residual dynamics induced by the training trajectory of NAG for a deep fully-connected linear neural network under random Gaussian initialization. Our results show that NAG can converge to the global minimum at a (1-O(1/κ))t rate when the width is near-linear in the depth of the network, where t is the number of iterations and κ>1 is a constant depending on the condition number of the feature matrix. Compared to the (1-O(1/κ))t rate of GD, NAG achieves an acceleration over GD. For deep linear ResNets, we utilize the same analytical approach and obtain a similar convergence result, while the width requirement is independent of the depth. To the best of our knowledge, these are the first theoretical guarantees for the convergence and acceleration of NAG in training deep neural networks. Numerical results show the acceleration of NAG compared to GD in terms of iterations. In addition, we conduct experiments to evaluate the effect of the depth on the convergence rate of NAG, which validate our derived conditions of the width. We hope our results may shed light on understanding the optimization behavior of NAG for modern deep neural networks.
Multi-stage Knowledge Enhancement for Ultra-fine-grained Entity Typing

Fine-grained entity typing is referred to as identifying types of a target entity mention with context from a large number of fine-grained labels. Existing approaches depend on the quality of exogenous features or suffer from the distant supervision noise caused by multi-source inconsistency, and they usually ignore potential reasoning information provided by contextual entities. In this paper, we propose a multi-stage method that injects knowledge into both pre-trained contextualized word representations and entity typing structure. To deal with inconsistency of distantly labeled data from different knowledge sources, we propose a task with a novel contrastive objective. Then two plug-in modules for RoBERTa backbone are designed and pre-trained based on the task to inject knowledge into semantic representations. To capture contextual entity information and enhance the typing task by reasoning, a type graph is constructed to encode embeddings and relations of soft type labels. We conduct experiments on the Ultra- Fine Entity Typing dataset and observe state-of-the-art performance on several benchmarks.
Multi-Feature Multi-Sensor Fusion for Emitter Identification Based on a Modified DS Application

Emitter identification is a crucial task in electronic countermeasure technology area, which deeply affects the accuracy of subsequent threat estimation. In emitter identification system, sensors (transmitter and receiver) have inevitable inconsistency and fuzziness, along with possible ambiguity and instability under interference and malfunction. To manage the uncertainty in emitter identification system, we propose a multi-feature multi-sensor fusion algorithm based on a modified DS application. The modified DS application for emitter identification system is accomplished by two parts—multi-feature fusion based on the improved proximity approach to obtain basic probability assignment ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>B</mi> <mi>P</mi> <mi>A</mi> </math> ) and multi-sensor fusion based on the combination of two revised evidences to solve potential evidence conflicts. Firstly, the multi-feature fusion method based on the improved proximity approach is raised to produce the identification result for each receiver, which simultaneously build <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>B</mi> <mi>P</mi> <mi>A</mi> <mi>s</mi> </math> for DS application. Four entropies are extracted to establish the multi-feature description of received signal. Then, in order to solve the inconsistency among different receivers and realize multi-sensor fusion for emitter identification, the multi-sensor fusion method based on the combination of two revised evidences is proposed. Two revised evidences are put forward, respectively, by the introduction of Lance distance function and spectral angle cosine function before applying DS combination. Experiments and analyses comprehensively demonstrate the great uncertainty management performance and favorable emitter identification effect of the proposed algorithm.
Security‐based teleradiology in <scp>DICOM</scp> used two‐level <scp>DWT</scp> based optimization watermarking

Abstract The electronic transmission of radiographic images from one geographical location to another location is known as teleradiology. Radiological image processing issues occurred during the transmission across various medical image workstations have been handled by this approach, in order to provide various health care activities. (i.e., diagnosis, medical practicing, consulting second opinion, or treatment and monitor a patient). The major difficulties of remote transmission process are privacy, safety, image retention, etc. A noise‐tolerant signal is being implanted with a digital watermark furtively (e.g., an image, symbol, audio, video, etc.), concerning the typical identification of the signal's patent. The intention of utilizing digital watermarks is either to validate the carrier signal's authenticity and ethics or to reveal its patent holder's identity, to provide protected transmission. In this work, watermarking approach is implemented to integrate biometrics with cryptography. The entropy‐based fingerprint feature has been extracted for generating the key to process the cryptography, and digital imaging and communications in medicine (DICOM) images. To obtain secure transmission, two‐level discrete wavelet transform method is employed for watermarking the cover image with encrypted image. Subsequently, the receiver can extract the watermarked image by using watermarking keys. This method is proposed to access the diagnostic image with privacy, accessibility, and reliability. The anticipated outputs of the proposed approach have been assessed based on factors such as structural similarity index measures (SSIM), normalized correlations (NC), and peak‐signal‐to‐noise ratio (PSNR).
[DC] Improving Multi-User Interaction for Mixed Reality Telecollaboration

Mixed reality (MR) approaches offer merging of real and virtual worlds to create new environments and visualizations for real-time interaction. Existing MR systems, however, do not utilise user real environment, lack detail in dynamic environments, and often lack multi-user capabilities. This research focuses on exploring multiuser aspects of immersive collaboration, where an arbitrary number of co-located and remotely located users can collaborate in a single or merged collaborative MR space. The aim is to enable users to experience VR/AR together, irrespective of the type of HMD, and facilitate users with their collaborative tasks. The main goal is to develop an immersive collaboration platform in which users can utilize the space around them and at the same time collaborate and switch between different perspectives of other co-located and remote users.
Development of a Novel CNN Architecture for Improved Diagnosis from Liver Ultrasound Tumor Images

Malignant liver tumors are considered as one of the most common cancers and a leading cause of cancer death worldwide. While using convolutional neural networks (CNNs) for feature extraction from ultrasound (US) images and tasks thereafter, most works focus on pre-trained architectures using transfer learning which can sometimes cause negative transfer and reduced performance in medical domain. A new method based on Pascal’s Triangle was developed for feature extraction using CNN. The convolutions and the kernels in Pascal’s Triangle based CNN (PT-CNN) are according to the coefficients at each level of Pascal’s Triangle. Due to the fuzzy nature of US images, the input layer takes a combination of the image and its neutrosophically pre-processed components as a single unit to improve noise robustness. The proposed CNN when implemented as a backbone for feature extraction for binary classification, gave validation accuracy &gt; 90% and test accuracy &gt;95% than other state-of-the-art CNN architectures. For tumor segmentation using Mask R-CNN framework, the aggregated feature maps from all convolutional layers were given as the input to the region proposal network for multiscale region proposals and to facilitate the detection of tumors of varying sizes. This gave an F1 score &gt; 0.95 when compared with other architectures as backbone on Mask R-CNN framework and simple U-Net based segmentation. It is suggested that the promising results of PT-CNN as a feature extraction backbone could be further investigated in other domains.
Collaborative Filtering Recommendation Algorithm Based on Cloud Model Envelope Curve Distance Similarity and User Preference Correlation

Aiming at the shortcomings of high time complexity and insufficient discrimination of existing cloud model similarity calculation, we propose a similarity calculation method based on cloud model envelope curve distance; Based on the different effects of cold and hot items on user preferences, we propose a modified user preference correlation coefficient, and then obtain a collaborative filtering recommendation algorithm based on the similarity of cloud model envelope distance and user preference correlation. Experiments show that the proposed algorithm has higher recommendation accuracy and lower time complexity.
Person Re-identification Based on Deep Learning Algorithms with Manual Extracted Colour and Texture Features

Person re-identification technology is an important research direction in the field of computer vision and intelligent transportation. Deep learning algorithms have better performance than traditional feature extraction algorithms in recent years. The paper proposes a hybrid deep learning algorithm with manual colour and texture features. The proposed hybrid model in the paper has 3-5 % higher accuracy than the single deep learning algorithms in the two mainstream databases. The experiment proves that the combination between deep learning algorithms and traditional algorithms has great significance.
Learning-based fuzzy c-means clustering using mixtures of Student’s-t distributions with missing information

In this paper is proposed a clustering algorithm, based on a fuzzy treatment of finite mixtures of multivariate Student&#x2019;s-t distributions, using Learning-based fuzzy c-means (LB-FCM) algorithm as well as missing data. We construct a robust LB-FCM framework for handling missing data assuming the finite mixture Student&#x2019;s-t distributions. Comparisons between LB-FCM and EM-type algorithms are made. Experimental results and comparisons actually demonstrate the advantage of the proposed LB-FCM.
Photovoltaic Performance Using Modify Back Propagation Neural Network Based On 3 Different Solar Panels

Abstract Depletion of natural resources and increasing demand of power, sustainable energy sources such as photovoltaics (PV) are becoming increasingly more reliable and important. However, the production PV output power is unable to meet in terms of supply and demand due to irregular of solar irradiance and temperature daily. Therefore, a proper method/tool of prediction is very crucial to manage photovoltaic performance system and optimize the corresponding energy management system. In this paper, the modified Back Propagation Neural Network (BPNN) architecture is investigated. The investigating was conducted to provide more accurate prediction and error for short-term period prediction. The networkcompromises of 2 inputs parameter; the module temperature and solar irradiance and 1 output parameter; AC Power. Early research was conducted based on forecast time period on May 2021 starting from 7am to 8pm (14 hour) daily from 3 different panels; monocrystalline, polycrystalline and thin film, which are located on the rooftop of PEARL lab building. The result obtained shows that polycrystalline panel performed the best compare to monocrystalline and thin film panels.
Blockchain Technology as A Guarantee of Transparency in The Supply Chain of Commercial Enterprises

Traceability has become an essential element of supply chain management, especially in safety-sensitive sectors such as food, pharmaceuticals, etc. Upstream (manufacturers, producers, etc.) and downstream (distributors, wholesalers, etc.) supply chain actors need to store and process traceability information in order to provide proof of regulatory compliance to national authorities as well as the most demanding customers. Studies have shown that developing countries, especially on the African continent, are particularly prone to the commercialization of counterfeit products and the use of diverted consumables, thus reducing the confidence in the various products and services offered by commercial enterprises, not only towards consumers but also towards companies and other actors of the concerned sectors. Therefore, the main objective of this paper is to examine the characteristics and functionalities of blockchain technology and to identify blockchain-based solutions to address consumer product traceability issues, as well as to highlight the benefits of implementing blockchain-based traceability systems by proposing a blockchain architecture to ensure traceability and transparency.
The copper deposit of the Zamin Hossein district, Dehaj- Sarduiyeh metallogenic belt, SE Iran: Constraints on ore mineralization, alteration, and fluid inclusions

The copper deposit of the Zamin Hossein district, Dehaj- Sarduiyeh metallogenic belt, SE Iran: Constraints on ore mineralization, alteration, and fluid inclusions
Mishathi - A Smart Vision System

Visual impairments curtail the day to day lives of a large section of the population. They not only restrict one&#x0027;s quality of life but also have vast psychological impacts. Visual disablement stems from damage or disease to the optical system and can occur anywhere along the visual pathway. Other factors such as hereditary disease or injury can also lead toloss of vision or visual processing. There exist a myriad of instances when a visually impaired individual requires assistancein day to-day activities. &#x2018;Mishathi&#x2019; means to open your eyes and is of Sanskrit origin and encompasses the essence of our aspiration: to allow people with visual disabilities to lead their lives without hindrances. Our main goal is to ensure that visuallyimpaired individuals can utilize our system to overcome everyday impediments. The current methodology focuses on creating an embedded system comprising of a Raspberry Pi, a Pi camera and a Bluetooth headset. The system will employ machine learning models for text processing, object detection, face recognition and gesture perception and be used to guide the vision impaired to lead comfortable lives. This work aims to impart knowledge on the technological advancements in pastoral regions and provide an economical product to ameliorate the standard of living of individuals.
Research on the Generation of Creative Animation Driven by Deep Learning Model

It is a very interesting and practical task to transform real-world images such as portraits or scenery into creative animation images. Since this concept was put forward, it has aroused extensive research interest in the field of computer vision. The generative adversarial networks (GAN) model is widely used in this field. Depth convolution GAN (DCGAN) and Wasserstein GAN (WGAN) improve the original GAN, but there are still problems existing in creative animation generation such as model collapse. To solve these problems, the Wasserstein distance is introduced to replace the JS divergence in the GAN model to measure the gap between the sample distribution generated by the generator and the real distribution, and the loss function is improved. In order to achieve a better animation generation effect, the training of the model is further optimized through the adjustment of the network model structure and the setting of parameters. Through the comparison with DCGAN and WGAN models in the animation data set and CelebA data set and the quantitative analysis and comparison of the generation effects of different models, the effectiveness and generalization of the improved GAN model are verified.
Real-Time QoE Estimation for DASH Video Using Active Network Probing

Video on Demand (VoD) accounts for a significant amount of traffic on IP networks. To meet users&#x0027; expectations, network operators need means to monitor and to identify when service quality is degraded in order to take actions to avoid customer churn. Most solutions cannot monitor end-to-end conditions without modification on video player applications or require deep packet inspection techniques, which may raise privacy issues. In this demonstration, we use active network probing to measure end-to-end network Quality of Service (QoS) conditions and use a Machine Learning model to infer users&#x0027; Quality of Experience (QoE) in real-time. The results show that the method allows us to identify whether the network conditions allow video sessions with high QoE, or situations in which the user&#x0027;s QoE is degraded.
Minimaps for Impossible Spaces: Improving Spatial Cognition in Self-Overlapping Virtual Rooms

Natural walking in virtual reality is constrained by the physical boundaries defined by the size of the user&#x0027;s tracking space. Impossible spaces, a redirected walking technique, enlarge the virtual environment by creating overlapping architecture and letting multiple locations occupy the same physical space. Minimaps, which are small representations of the user&#x0027;s surroundings, are a common method to assist with wayfinding and navigation. Unfortunately, in a naive implementation of such minimaps for an environment with impossible spaces, the overlap would be obvious. We investigated approaches to displaying impossible spaces on minimaps without attracting users&#x0027; attention to the overlapping parts of the VE. In this paper, we present a study that compared spatial cognition in a short gamified VR experience between three different conditions: (i) without minimap, (ii) with basic minimap, and (iii) with full minimap. Our results show significant improvements on accuracy in direction estimation tasks, suggesting that minimaps help users acquire spatial knowledge in the presence of impossible spaces.
Double Spatial Attention for Convolution Neural Networks

In recent years, attention technology has attracted a lot attention in improving the performance of convolution neural networks (CNNs). However, most existing methods mainly consider the global information including spatial and channel dimensions, which ignore the selectively augmenting the key features like human eyes. This paper proposes an efficient Double Spatial Attention (DSA) mechanism, which does not involve convolution operation, avoiding non-linear operation. DSA includes two parts: (1) calculating the spatial attention coefficients of foreground and background respectively; (2) dynamic pruning the redundant spatial pixels according to foreground and background attention coefficients. The proposed DSA module not only can effectively focus on the main features in feature maps to improve the performance, but also maximally remove the spatial feature map redundancy and reduces the redundant computation. Experiments show that our method could improve 0.03%~2.49% accuracy on MobileNetV2 without increasing additional parameters.
yadg: yet another datagram

The management of scientific data is a crucial aspect of modern data science.Four simple guiding principles combined under the FAIR data moniker define the current "gold standard" in data quality: the data has to be Findable by anyone, Accessible without barriers, Interoperable with other programs, and Reusable after analysis (Wilkinson et al., 2016).Yet, many scientific data formats do not conform to these principles.This is especially true for proprietary formats, often associated with expensive lab instrumentation.The yadg package helps to resolve this issue by parsing raw data files into a standardised, annotated and timestamped format readable by both humans and machines.Various raw data formats are supported, including chromatograms, electrochemical cycling protocols, reflection coefficient traces, spectroscopic data, and tabulated data.The parsed files include information about data provenance, units of measure, and experimental uncertainties by default.Finally, several common data processing steps, such as applying calibration functions, integration of chromatographic traces, or fitting of reflection coefficients, are available in yadg.
Multi-Modal Sparse Tracking by Jointing Timing and Modal Consistency

In this paper, we propose a multi-modal sparse tracking by jointing timing and modal consistency to locate the target location with the similarity of multiple local appearances. First, we propose an alignable patching strategy for red-green-blue (RGB) color mode and thermal infrared mode to adapt to the local changes of the target. Second, we propose a consistency expression of the corresponding aligned patches between the modes and the correlation of the gaussian mapping within mode to reconstruct the target judgment likelihood function. Finally, we propose an updating scenario based on timing correlation and mode sparsity to fit with the target changes. According to the experimental results, significant improvement in terms of tracking accuracy can be achieved on average compared with the state-of-the-art algorithms. The source code of our algorithm is available on https://github.com/Liincq/tracker .