Reviewerly/exHarmony huggingface dataset:
Image Enhancement Techniques Applied to Magnetic Resonance Images: Multiple sclerosis

As the most crucial step in image preprocessing techniques, Contrast enhancement (CE) is utilized to improve low contrast images quality with the aim to ameliorate the image&#x2019;s visual appearance for later automated processing (detection, segmentation and recognition). Due to this importance, several techniques were proposed to treat this topic. The present manuscript proposes comparative research across some contrast enhancement methods mainly: Global Histogram Equalization (GHE), Adaptive Gamma Correction (AGC) and knee function and gamma correction based on Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) method (KGC-DWT-SVD). These methods had been applied on brain and spinal cord MR (Magnetic Resonance) images of patients affected with multiple sclerosis. The obtained results had been evaluated according to the following measurement metrics: the entropy (H), peak signal to noise ratio (PSNR), mean squared error (MSE), structure similarity index measurement (SSIM) and feature similarity index measurement (FSIM).
Project Averroes: An Effective, Durable, and Consumer-Oriented Solution to the Locomotion Problem in VR Environments

The virtual reality (VR) industry is gaining more and more traction, especially in domains pertaining to experiential education, data visualization, high-risk and scenario-based simulations as well as in gaming and entertainment. Locomotion in VR can be said to be an important part of the ‘immersion’ experience, as it bridges the gap between the physical and virtual environments and is key to developing or training the user's spatial performance in the VR realm. Yet, it is an aspect that does not currently possess a holistic, well-rounded approach to adequately answer all locomotion-related concerns. This paper proposes an effective, durable, and consumer-oriented solution to the locomotion problem in the VR environments. This paper also surveys related works and represents inspiration from various locomotion approaches to achieve a low-cost, feasible and consumer-friendly platform that leverages the best features of all surveyed works and mitigates their respective trade-offs.
Explainability of Methods for Critical Information Extraction From Clinical Documents: A survey of representative works

The accumulation and integration of electronic health records have opened up opportunities for their new uses over the course of a patient’s care. In artificial intelligence (AI), natural language processing (NLP) methods that can extract important information from clinical documents are gaining success. For clinicians to consider using automatically extracted information in their decision making, the information should be explainable to support a reasoning process that is traceable and understandable. We review the state of the art in several types of information extraction methods designed for use in health care. Our focus is on comparing the approaches based on the explainability of the extracted results. We highlight the advantages and challenges faced by the adopted methods in conveying support for the extracted information.
A hybrid deep learning technique based integrated multi-model data fusion for forensic investigation

Digital evidence is an integral part of any trial. Data is critical facts, encrypted information that requires explanation in order to gain meaning and knowledge. The current process of digital forensic research cannot effectively address the various aspects of a complex infrastructure. Therefore, digital forensics requires the optimal processing of a complex infrastructure that differs from the current process and structure. For a long time, digital forensic research has been utilized to discuss these issues. In this research, we offer a forensic investigation hybrid deep learning approach based on integrated multi-model data fusion (HDL-DFI). First, we concentrate on digital evidence collection and management systems, which can be achieved by an integrated data fusion model with the help of an improved brain storm optimization (IBSO) algorithm. Here, we consider several multimedia data’s for evidence purposes, i.e. text, image, speech, physiological signals, and video. Then, we introduce a recurrent multiplicative neuron with a deep neural network (RM-DNN) for data de-duplication in evidence collection, which avoids repeated and redundant data. After that, we design a multistage dynamic neural network (MDNN) for sentimental analysis to decide what type of crime has transpired and classify the action on it. Finally, the accuracy, precision, recall, F1-score, G-mean, and area under the curve of our proposed HDL-DFI model implemented with the standard benchmark database and its fallouts are compared to current state-of-the-art replicas (AUC). The results of our experiments show that the computation time of the proposed model HDL-DFI is 20% and 25% lower than the previous model’s for uploading familiar and unfamiliar files, 22% and 29% lower for authentication generation, 23% and 31% lower for the index service test scenario, and 24.097% and 32.02% lower for familiarity checking.
Enhanced Deep Animation Video Interpolation

Existing learning-based frame interpolation algorithms extract consecutive frames from high-speed natural videos to train the model. Compared to natural videos, cartoon videos are usually in a low frame rate. Besides, the motion between consecutive cartoon frames is typically nonlinear, which breaks the linear motion assumption of interpolation algorithms. Thus, it is unsuitable for generating a training set directly from cartoon videos. For better adapting frame interpolation algorithms from nature video to animation video, we present AutoFI, a simple and effective method to automatically render training data for deep animation video interpolation. AutoFI takes a layered architecture to render synthetic data, which ensures the assumption of linear motion. Experimental results show that AutoFI performs favorably in training both DAIN and ANIN. However, most frame interpolation algorithms will still fail in error-prone areas, such as fast motion or large occlusion. Besides AutoFI, we also propose a plug-and-play sketch-based post-processing module, named SktFI, to refine the final results using user-provided sketches manually. With AutoFI and SktFI, the interpolated animation frames show high perceptual quality.
The Contributions of Public Libraries to Satisfy Intellectual Curiosity of Rural Inhabitants in West Bengal: An Indian Case Study

Abstract The purpose of this study is to investigate the contributions of public libraries to the satisfaction of the intellectual needs of rural inhabitants in West Bengal, India. The study applied a quantitative survey method with the help of a well-structured questionnaire. Questionnaires were distributed among to the users (students, officers/professionals, self-employed individuals, businessmen, jobseekers, retired persons, and housewives) of 45 rural public libraries in five selected districts of West Bengal. The study found that the largest group of respondents belonged to the students and jobseekers categories, who visit the rural public libraries for seeking academic and job-related information. Many students from humble backgrounds were solely dependent on public libraries because they have procured some textbooks from middle school up to graduation level. Jobseekers were mostly consulting the Career Guidance Section of the library to prepare for various competitive exams. The study also revealed that public libraries have been trying to satisfy the intellectual and informational needs of the rural inhabitants by providing various services and facilities. Respondents have further reported that public libraries should adopt modern technologies and provide ICT-based library services as well. The outcome of this study would help to develop and upgrade the public library collection, services, and facilities in developing countries in a way that reflects patrons’ stated needs.
Comparing Strategies for Visualizing the High-Dimensional Exploration Behavior of CPS Design Agents

The design of cyber-physical systems often involves search within high-dimensional design spaces. When evaluating the performance of algorithms in tasks such as these, the patterns of exploration are often informative and can help support algorithm selection. However, accurately representing these patterns in a way that is human understandable while still preserving the nuanced search complexities in the high-dimensional space is nontrivial. This work specifically examines approaches for visualizing the search trajectories of reinforcement learning agents. We assess trajectories on two exemplar problems: the design of a racecar and the design of an aerial vehicle. We compare and contrast the visualizations produced using PCA, t-SNE, UMAP, TriMap, and PaCMAP. Future work should extend this comparison to a wider variety of exemplar design problems and consider the additional challenges posed by set-based design algorithms (e.g., genetic algorithms, particle swarm optimization).
New and optimal set up for 3D-based stereovision system for manufacturing inspection in 4.0 industry

The manufacturing industry faces unprecedented progress accompanied with numerous challenges. For instance, Inspections of castings and forgings require the acquisition of dimensions on parts of various shapes and complexity, potentially performed in any kind of environment. Imagine how much time manual inspection could take while inspecting a complex machine part on the assembly lines. Here reveals the importance of Optics and Computer vision (CV) which are embedded together as 3D scanning system to make an unexceptional contribution in tackling these challenges and to meet customers&#x2019; needs and requirements.Indeed, Optical systems are currently integrated in industry field to enable operators control more castings and forgings with more information and without surface preparation direct contact with objects.Basically, a 3D scanning system comprises high-resolution cameras, optics, and AI algorithms integration to provide multiple benefits to large manufacturing units. Indeed, unlike touch probing, 3D scanning is one of most relevant computer vision application which provides an overall view of the inspected part. 3D scanners can validate whether the material is sufficient to proceed with machining or not. This work is an attempt to design and test a fully automated and low-cost scanner for producing three-dimensional models with millimeter-scale resolution for object measurement and inspection. Hence, a stereovision system is set up where pair of cameras is mounted to acquire image pairs of a centered object to perform a scan. Promising and potential result is achieved by performed configuration.
Human Action Recognition With Raw Millimeter Wave Radar Data

Prediction of human movements is a current hot topic that researchers are working on. The most common technologies such as image or wearable inertial sensors are not preferable due to privacy issues or subject discomfort. In this study, we propose to use FMCW (Frequency Modulated Continuous Waves) Millimeter Wavelength (MmWave) Radar technology in raw format for this task. We recorded human movements using FMCW MmWave, and fed our CNN network by transforming a multi-channel 2D structure with our new method called Video Of Radar (VIDAR). We tested our model with both real-time human movements and offline test datasets. The results were promising with over 90% accuracy.
Increasing Cyber Defense in the Music Education Sector Using Blockchain Zero-Knowledge Proof Identification

Music creation and its promotion are encouraged both in music education and through activities organized in the context of artistic creation as part of the education in question. Although copyright registration is the primary way authors protect their rights, this is not feasible in most cases, as the processes take a long time to complete and incur high costs. We utilize modern innovative technologies and their developments in copyright protection matters to increase security and trust in music education. In particular, an advanced model of ensuring the methods and innovation produced in music education processes is proposed, using blockchain technology and smart contracts. But given that, even in an advanced system like the proposed one, authentication evidence can be easily intercepted, this work proposes a single and robust identification scheme based on an innovative zero-knowledge proof (ZNP) system, which allows one side of communication to convince the other of its validity.
Implementation of Efficient Teaching Scheme of Human Anatomy and Physiology Based on Multimedia Information Processing Technologies

An application scheme based on the teaching of human anatomy and physiology, namely, PBL, is proposed. In particular, 95 medical students were randomly divided into two groups: classes 2 and 3 were the experimental classes (48 students), and then, the teaching practice was carried out according to the machine learning route; year 1 and class 4 were the control classes (47 students), and the traditional teaching method was used. The teaching effectiveness was evaluated by performance analysis, the mean score of the overall evaluation of the experimental class was (93.23 ± 2.01), the mean score of the control group was (91.51 ± 2.89), and the difference was statistically significant ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> <mo>&lt;</mo> <mn>0.01</mn> </math> ). The survey showed that more than 93% of the students in the experimental class thought that the deep learning model helped stimulate their interest in learning, enhance their sense of teamwork, and improve their overall ability.
Novel Extreme Learning Machine and Chaotic in-Built Opposition Based – Quantum Ruddy Turnstone Optimization Algorithms for Real Power Loss Dwindling and Voltage Consistency Enhancement

This paper proposes Ruddy turnstone optimization (RTO) algorithm, Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Chaotic based Ruddy turnstone optimization (CRTO) algorithm, Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, Opposition based Ruddy turnstone optimization (ORTO) Algorithm and chaotic in-built Opposition based – Quantum Ruddy turnstone optimization (COQRTO) algorithm for genuine loss lessening. Important goals of the paper are Power fidelity extension, power eccentricity minimization and genuine loss lessening. The leading stimulus is in the sculpting of Repositioning –Peripatetic and argumentative actions of Ruddy turnstone. Ruddy turnstone will guzzle wiretaps, young insect and it subsists in bundling style. The constellation of Ruddy turnstone, which mobile from one place to alternate in the sequence of repositioning and the fresh investigation agent position is to evade the smash amongst their contiguous Ruddy turnstone. In the proposed Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Ruddy turnstone Optimization Algorithm approach enhances Extreme Learning Machine features to determine an optimal skeleton of Extreme Learning Machine for enhanced canons. In Chaotic based Ruddy turnstone optimization (CRTO) algorithm Exploration and Exploitation are augmented. In Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, features emulate the analogous performance with the certain stage as they route in a credible powdered of median. Opposition based Ruddy turnstone optimization (ORTO) Algorithm employs Laplace distribution to enhance the exploration skill. Then examining the prospect to widen the exploration, a new method endorses stimulating capricious statistics used in formation stage regulator factor in Ruddy turnstone Optimization Algorithm. In the projected chaotic in-built Opposition based – Quantum Ruddy turnstone optimization (COQRTO) algorithm, the transaction of erratic figures is completed with the irrational digits enthused by Laplace distribution to amplify the support of the probability of formation level inside the exploration zone. Proposed Ruddy turnstone optimization (RTO) algorithm, Extreme Learning Machine based Ruddy turnstone Optimization (ELMRTO) Algorithm, Chaotic based Ruddy turnstone optimization (CRTO) algorithm, Quantum based Ruddy turnstone Optimization (QRTO) Algorithm, Opposition based Ruddy turnstone optimization (ORTO) Algorithm and chaotic in-built Opposition based – Quantum Ruddy turnstone optimization (COQRTO) algorithm are corroborated in Garver’s 6-bus test system, IEEE 30, 57, 118, 300, 354 bus test systems and Practical system - WDN 220 KV (Unified Egyptian Transmission Network (UETN)). Loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained.
Comparison of the Neural Networks with Crosscorrelation Algorithm for the Displacements on Images Estimation

In this paper three methods for determining displacements on images are compared. Two of them are neural networks designed for Particle Image Velocimetry images processing. The third method is classic cross-correlation approach. For estimation the accuracy of the algorithms synthetic data were used. Based on the obtained simulation results, it can be concluded that the use of neural networks is promising both for fluid dynamics and solid mechanics applications.
AxBy-ViT: Reconfigurable Approximate Computation Bypass for Vision Transformers

Vision Transformers, evolved from the attention based transformer architectures, have obtained elevated attention as a competitive alternative to convolutional neural networks in computer vision learning tasks. Although showing promising accuracy, vision transformers face challenges due to large model size and intensive computation workload with millions of parameters, inhibiting their practical deployment on resource constrained platforms.In this paper, we investigate trivial computations - a set of computations the results of which can be determined without actual computations - in vision transformers. Typical examples include multiplication with 0, +1/-1 and addition with 0. Existing studies have leveraged the existence of trivial computations to save energy or accelerate execution. Based on this, we present AxBy-ViT, a reconfigurable approximate computation bypass scheme for vision transformers that leverages approximate matching of bit representations of operands. Case study on vision transformers using the ImageNet dataset shows that with 2.4% accuracy loss, AxBy-ViT can match up to 28.69% of the operand for computation bypass. The code repository of AxBy-ViT can be found at https://github.com/VU-DETAIL/AxBy-ViT.
Privacy-preserving Hybrid Cryptosystem Based on Chaos Theory and ECC Algorithm

Data encryption is a practical approach to protect biomedical image information security. Towards the massive amount of data generated in the medical field, chaotic model cryptosystems, with high sensitivity to initial conditions and overall stability and randomness of the system, are inevitably becoming an appropriate platform for communication protection. This paper proposes a privacy-preserving Hybrid encryption scheme based on Chaos theory and ECC Algorithm (HCEA) for the secure delivery of medical data. Specifically, we employ the logistic map with randomness characteristics to protect published data privacy against the complex network environment and other non-subscribers. The proposed cryptosystem can essentially achieve the secrecy effect of a “one-time pad.” To achieve double encryption of textual information and keys, we propose an improved image steganography technique with secondary scrambling encryption of carrier images by the MLNCML system, enhancing encryption efficiency and security. Different from existing standard LSB image encryption methods, the HCEA can encrypt initial parameters of logistic and MLNCML with the asymmetric encryption ECC algorithm while guaranteeing the security of key distribution in complex network environments. The security proof and performance evaluation show that the proposed HCEA scheme is secure in medical data transmission and effective in practice.
Video Important Shot Detection Based on ORB Algorithm and FLANN Technique

A shot is continuous frames taken by a single camera. A shot is the essential unit in the analysis of video files for many applications, such as, online video advertising and e-commerce. Therefore, the important shot determines how much a shot influences the video analysis process. Because of their low substance, some video shots should be discarded in order to reduce video raw data using the summarization technique. A robust method for important shot detection and its key frame is proposed in this paper. Many parameters that indicate the important shot and its key frame are taken into account by the proposed strategy. These parameters (which include the shot&#x2019;s interest points based on ORB(Oriented Rotated Brief Very Fast Binary Descriptor) algorithm and FLANN(Fast Library For Approximate Nearest Neighbors) technique, entropy metrics for video summary, shot length, and shot activity factors) have been used to determine shot relevance. Each of these elements has a different impact on the importance score. The robustness of the proposed method was demonstrated by the results.
OneDetect: A Federated Learning Architecture for Global Soft Biometrics Prediction

Following the amount of threats and increased number of terrorism reported over the years, the development of security technologies is becoming a crucial subject, in recent era. Moreover, the paradigm of security technologies is shifting from concept of multiple representations to one and unique identity for each individual - like there are several different identity verification systems exist in the world, developed by different countries. On the other hand, security is a broad subject with its applications at open places like markets and roads and verification during online sessions, etc., however, border control is one of the most important point. Right now, biometrics like finger prints and facial scans are most widely used technology, termed as traditional or non-intrusive biometrics. To provide a single representation based unique identity verification system, we are going to propose a federated learning architecture called OneDetect, which uses intrusive features from human body to predict three most common global soft biometrics, e.g., gender, age and ethnicity. The purpose of using federated architecture is to ensure privacy of data from each client, i.e., country or region, while developing a more generalized verification system where each verified instance improves the verification system overall. Additionally, the use of global soft biometrics in such kind of architecture will provide seamless recognition whether, it is an airport, seaport or any public place. To achieve this goal, we recorded our own dataset called MMV Pedestrian dataset, in an airport like walking corridor and EfficientNetB3 architecture is used for training and prediction by each client.
Comparison of LeNet-5, AlexNet and GoogLeNet models in handwriting recognition

The aim of the study was to compare the accuracy of handwriting recognition and the time needed to classify data from the test sets. The Lenet-5, AlexNet and GoogLeNet architectures were used for the research. They are all models of convolutional neural networks. The research was carried out with the use of image databases, handwritten digits MNIST and handwritten letters EMNIST. After the tests, it was found that the GoogLeNet model showed the highest accuracy, and the LeNet-5 the lowest. However, the LeNet-5 model needed the least time to complete the task, and GoogLeNet the most. On the basis of the obtained results, it was found that increasing the complexity of the model positively influences the accuracy of object classification, but significantly increases the demand for computer re-sources.