Reviewerly/exHarmony huggingface dataset:
Pre-processing and enhancement techniques for COVID-19 X-ray and CT-scan medical images

In medical diagnosis, medical imaging plays an important role, also plays a role in diagnosis and detection of chest diseases. The Pre-Processing techniques are an important step required to increase quality for Chest X-ray and CT medical images. These Techniques are used of improving the quality of the Covid19 x-ray and CT scan images. Medical images contain many unnecessary noise components in the scanned images’ actual format. Some of the image pre-processing techniques are needed to eliminate certain irritating sections of an image to properly visualize the images until specifically finding the diseases.The main aim of this paper is to apply pre-processing on the images of the lungs which includes images for Covid-19, Normal and pneumonia to improve the quality of the images. Image quality improvement is accomplished by the application of filtering techniques, noise reduction and contrast enhancement techniques. The proposed technique is evaluated by using Peak signal-to-noise ratio (PSNR), Mean Square Error (MSE), and Absolute Mean Brightness Error (AMBE) to evaluate the contrast enhancement of the image that has been processed. The results show that the used technique gives better images than original images.
Sentiment and Semantic Hierarchical Transformer for Utterance-level Emotion Recognition

In natural language processing, utterance-level emotion recognition (ULER) is a fundamental task which requires comprehensive analysis of words and contexts. Since one word can exhibit opposite emotion polarities in different contexts, even large scale deep learning models cannot accurately predict the emotions of the utterances, especially in daily dialogues. Thus, in this paper, a novel Semantic and Sentiment Hierarchical Transformer (SS-HiTransformer) has been proposed, which predicts the emotion label of a response utterance based on its preceding contextual utterances. At word-level processing, each utterance token is represented as matrices with both semantic and sentiment word embeddings and fused together as token features. Then, context token features are fed into transformer encoders to further capture the long dependent utterance-level information. The final prediction is made by a transformer decoder which combines the features from the contextual and the response utterances with multi-head attention mechanism. Experiment results showed that the proposed SS-HiTransformer outperforms the state-of-the-art models on Friends, EmotionPush, EmoryNLP, IEMOCAP and MELD data sets.
DIJE: Dense Image Jacobian Estimation for Robust Robotic Self-Recognition and Visual Servoing

For robots to move in the real world, they must first correctly understand the state of its own body and the tools that it holds. In this research, we propose DIJE, an algorithm to estimate the image Jacobian for every pixel. It is based on an optical flow calculation and a simplified Kalman Filter that can be efficiently run on the whole image in real time. It does not rely on markers nor knowledge of the robotic structure. We use the DIJE in a self-recognition process which can robustly distinguish between movement by the robot and by external entities, even when the motion overlaps. We also propose a visual servoing controller based on DIJE, which can learn to control the robot's body to conduct reaching movements or bimanual tool-tip control. The proposed algorithms were implemented on a physical musculoskeletal robot and its performance was verified. We believe that such global estimation of the visuomotor policy has the potential to be extended into a more general framework for manipulation.
DeepEnReg: Joint Enhancement and Affine Registration for Low-contrast Medical Images

Affine registration aims to find the low-dimensional parametric transformation that best aligns one data to another. However, existing registration methods, either classic energy optimization or deep learning are mainly designed for adult brain images and have limited performance on infant brain images with widely varied intensity distributions and low-contrast issues. To achieve fast and robust registration on low-contrast infant brain images, we propose an unsupervised deep registration framework DeepEnReg with a deep enhancement module and a deep affine registration module. Our affine registration module leverages a multi-resolution loss to guarantee consistency on sparsely sampled infant brain images. Our DeepEnReg achieves reasonable and reliable performance on the affine registration tasks of infant brain images and synthetic data and significantly reduces irregular registration results compared to other mainstream methods. Our proposed method significantly improves the computation efficiency over the mainstream medical image processing tools (from 13 to 0.570 s for a 3D image pair on affine registration) and outperforms state-of-the-art approaches.
Operation Design of Comprehensive Scoring System for Classical Dance Training Based on Information Technology

In the teaching of Chinese classical dance, body rhyme teaching not only improves the training system of Chinese classical dance, perfectly unifies the training, creation and performance, but also makes the training of Chinese classical dance more standardized and diversified. This study first analyzes the importance and artistic characteristics of body rhyme teaching, then analyzes the influence of body rhyme teaching on the development of Chinese classical dance, then expounds the evaluation criteria of the comprehensive scoring system, and then discusses the comprehensive scoring system from four aspects: the establishment of the evaluation index dimension, the analysis and processing of the evaluation index, the weight ratio of the evaluation index dimension, the conversion of the evaluation index and the comprehensive calculation At last, the operation and application of the comprehensive scoring system are introduced.
Dirt Detection and Segmentation Network for Autonomous Washing Robots

In the field of industrial, robots are becoming a modern method to perform automatic washing on large industrial components while detecting dirt. Therefore, the detection and segmentation of dirt have a great impact on optimizing the effects and improving quality of washing by modern washing robots. We propose DDSN (Dirt Detection and Segmentation Network) in this paper which improves the SVDD (Support Vector Data Description), using neural networks to obtain the optimal kernel function of SVDD, so that training images with no dirt can be mapped to the smallest hypersphere in the feature space. In dirt detection and segmentation, the distance between the test images and the centers of the corresponding hypersphere is defined as the feature value, and the dirt scores of the pixels can be obtained. Before training of dirt dataset, we use a larger anomaly detection dataset named MVTecAD which is also the one-class classification to pretrain the feature extraction network, which makes up for the lack of samples in the dirt dataset and speeds up the convergence of the model. Afterwards, we transfer the feature extraction network to training the dirt dataset of large industrial components. The results show that the methods proposed in this paper performs well in detection and segmentation of both MVTecAD dataset and dirt dataset of large industrial components.
CiraaS

Cloud computing reduces provider and user costs by multiplexing workloads. Advantages of cloud computing include high utilization by temporal and spatial sharing computing resources and a subscription model that charges only for the resources and time used[1]. As recent cloud computing has evolved to maximize these advantages, microservices [11], function-as-a-service (FaaS) [10], and other more granular and short-lived cloud services have emerged. Today's FaaS are oriented towards fast provisioning, fine-grained billing times, tight memory constraints, stateless processing, and real-time processing [15]. However, since the context switching in CPUs is the bottleneck [8], their processing demands are no longer satisfactorily met. Therefore, a shift is expected toward a more efficient system architecture for cloud computing [10].
Brain Tumor Detection Based on Multilevel 2D Histogram Image Segmentation Using DEWO Optimization Algorithm

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners. Multilevel image segmentation is a computationally simple and efficient approach for segmenting brain MR images. Conventional image segmentation does not consider the spatial correlation of image pixels and lacks better post-filtering efficiency. This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors. Further, to validate the efficiency of the proposed hybrid algorithm, it is compared with some prominent metaheuristic algorithms in recent past using between-class variance and the Tsallis entropy functions. The proposed hybrid algorithm for image segmentation is able to achieve better results than all the other metaheuristic algorithms in every entropy-based segmentation performed on brain MR images.
A Sequential Recommender System with Embeddings Based on GraphSage Aggregators

Recommender systems help users by filtering a large amount of content to which they are exposed daily, in order to recommend products in e-commerce, music, movies, people on social networks and others in a personalized way, thus being a complex task. Currently, sequential recommender systems have been increasingly used in these tasks. They receive behavior trajectories as input, that is, items accessed during a given time, and use recommendation algorithms to suggest the next item. In a graph-based representation of these sequences, an initial step involves learning representations for the items, vectors of real numbers called embeddings. In this work we propose to change the module responsible for building these item representations, which originally employs the GGNN technique in the SR-GNN Sequential Recommendation System, by the Graph-Sage technique with its aggregators: Mean, Maxpooling and LSTM. We validated our proposal with the datasets: yoochoose, diginetica, aotm and 30music. The results indicate that, with the Mean aggregator, it is possible to reduce the execution time in all tested scenarios, maintaining the original effectiveness.
Theoretical analysis of QBER for Quantum Key Distribution in 5G multi-site networks

This paper presents theoretical analysis of Quantum Bit Error Rate (QBER) for Quantum Key Distribution (QKD) in mobile networks (5G/6G). The considered configuration of the underlying optical fiber network that is available for the establishment of the QKD in multi-site 5G networks assumes that the 5G sites are connected by a single optical fiber. Thus, multiplexing of both quantum and classical channels in a single optical fiber is thus necessary. This, in turn, results in a cross talk between classical and quantum channels, which increases QBER and imposes limitations on the maximum distance that can be reached. The approach followed in this paper provides an estimate of the maximum quantum link budget for the targeted QBER. The results of the analysis show that the QKD solution considered here can be potentially deployed using current Operators’ 5G infrastructures to replace currently used asymmetric key exchange algorithms if needed.
Low-latency Hardware Architecture for VDF Evaluation in Class Groups

The verifiable delay function (VDF), as a kind of cryptographic primitives, has recently been adopted quite often in decentralized systems. Highly correlated to the security of VDFs, the fastest implementation for VDF evaluation is generally desired to be publicly known. In this paper, for the first time, we propose a low-latency hardware implementation for the complete VDF evaluation in the class group by jointly exploiting optimizations. On one side, we reduce the required computational cycles by decreasing the hardware-unfriendly divisions and increase the parallelism of computations by reducing the data dependency. On the other side, we provide low-latency large-number divisors, multipliers, and adders, respectively, while those operators are generally very hard to be accelerated. Besides, we carefully schedule the sub-modules and devise the low-latency architecture for the complete VDF evaluation. Finally, the proposed design is coded and synthesized under the TSMC 28-nm CMOS technology. The experimental results show that our design can achieve a speedup of 3.5x compared to the optimal C++ implementation for the VDF evaluation over an advanced CPU. Moreover, compared to the state-of-the-art hardware implementation for the squaring, a key step of VDF, we achieve about 2x speedup.
Analysis on Semantic categorization technique of analogous label by using Deep Learning Technique

In current era, by the arrival mobile Internet, the quantity of statistics and available data in the system is increasing drastically, particularly moderately structured and partially structured data also increasing. This manuscript introduce the idea model of usual language to resolve the elevated mode of presenting and semantic opening in the conventional 3 dimensional space model and method. In this Research work the fastest technique known as deep learning is used for analyzing and selecting the methods and parameters needed for training. Meanwhile in similar occasion, in conditions of keyword characteristic withdrawal, the technique based on huffman tree is projected to resolve the characteristic removal replica of natural language text and to optimize the early weight stuck between words and in this to influence transport technique of expressions nodes. Based on the various results done it was witnessed that the investigational consequences explain that future proposed technique is sensible and helpful in text multilabel categorization technique.
A Distortion-Guided Strategy for Efficient Underwater Image Restoration

Underwater images often suffer from serious degradations in view of complex and changeable marine environment. The current quality improvement strategies for degraded underwater images can be divided into enhancement and restoration methods according to whether they are based on degrading models. However, the existing algorithms often miss the distorted area or have poor performance when dealing with multi-type distorted images. To overcome the above limitation, we propose a Distortion-Guided strategy for efficient Underwater Image Restoration (DGUIR). DGUIR splits the restoration task into two stages: distorted area detection and targeted restoration. In addition, the knowledge generated in the distortion detection stage will guide the subsequent targeted restoration. It is worth mentioning that DGUIR is able to address multiple types of distortions with only one model, and exhibits superior performance in comparison with existing algorithms.
On the Subcode-Support-Weight Distributions of Some Classes of Optimal Codes

Based on the concept of the value assignment and the enumeration techniques in the projective space, we determine the subcode-support-weight distributions for several classes of linear optimal codes which were constructed recently by using the so-called down-sets. Particularly, the generalized Hamming weights of these classes of optimal codes can be determined from their subcode-support-weight distributions. By using the value assignment, we also determine the trellis complexity of each class of optimal codes.
Machine Learning Approaches with Multilingual Bibliographic, Quotation, and Terminology Databases for the Study of the Chinese Buddhist Canon

This paper describes machine learning approaches for the study of the Chinese Buddhist Canon with bibliographic, quotation, and terminology databases. The use of these techniques brings traditional artifacts of academic study for the Chinese Buddhist canon to scholars’ digital desktops as well as English translations of modern Chinese literature discussing the canon. The bibliographic database includes English translations of titles, references to modern translations, parallels in Sanskrit and other languages, and general references. Machine learning can be used to help the scholar discover relevant information in full text search with key bibliographical information surfaced for context. The goals of this are to make study and translation of Chinese Buddhist texts easier and faster with bibliographical content and presented to a reader in an easily discoverable way, with user interface elements such as tooltips with mouse-over, popovers, and search snippets. The machine learning approach in this case is based on a logistic regression statistical model for relevance using document similarity. Examples will be given from the Taishō Tripitaka.A database of multilingual quotations found in Buddhist literature will be briefly described. This paper will describe the use of a decision tree classifier on a corpus of bilingual phrases trained for relevance. The explainability and simplicity of the model and smaller amount of training material will be contrasted with the needs of deep learning models. Examples will be given from the Blue Cliff Record.Thirdly, use of machine translation of modern Chinese text into English with the Fo Guang Shan Glossary of Humanistic Buddhism embedded will be described. This approach uses deep learning with the Google Translate API. Although many of the translations are surprisingly good and the overall result is helpful, the challenges relating to the accuracy of the results will be examined critically. The need for modifications to the glossary to improve translation quality will be explained. Examples of translations with Venerable Master Hsing Yun’s works comparing machine and human translation will be given.
Multilingual Contemplation for Social Media data by Hybrid Classifier

Sentiment analysis procure substantial contemplation for abandon growth of the web and social media. The goal is to identify opinions, sensation, or conviction of multilingual tweet. A large set of manually labeled tweets in different languages, use them as training data, and construct sentiment analysis model. A machine-learning algorithm is used to identify and extract characteristics such as adjectives, adverbs, and abstract nouns from each cleaned tweet. If the input text is not in English, the Google Machine Translator is utilized to convert it into English. A hybrid model called voting-based ensemble model which integrates Naive Bayes, Multinomial Naive Bayes, Bernoulli’s Naive Bayes, Logistic Regression, Stochastic Gradient Descent, Support Vector Machines, and Maximum Entropy classifiers to resolve the ambiguous language. If, same word appears in both English and non-English it utilizes a hybrid model retrieved features to translate a tweet /string. The classifiers identify the sentiment of translated multilingual tweets and extended with prediction.
Air xylophone Using OpenCV

Writing and drawing within the air with computer vision is one among the foremost fascinating areas in image processing and pattern recognition. OpenCV may be a widely used image processing and computer vision technology. It is used for tasks like automatic face recognition, seeing, hand gestures so on. Object detection is employed for detecting things of a particular class (such as individuals, buildings, or cars) in image or video. Everything goes computerized nowadays, including musical instruments. This project is about creating a virtual xylophone instrument using openCV. this may allow the user to virtually play the xylophone within the air without touching any of the keys or the screen and internet. The keys were displayed on the screen. By installing the necessities and using openCV, mallets (xylophone sticks) are automatically identified and therefore the user can play the virtual xylophone by just waving the mallet. It works in three steps. First, detect the required colour object, then track its movement from key to key, and at last, generate the required output/audio.
Hybrid Arabic classification techniques based on naïve Bayes algorithm for multidisciplinary applications

Text classification is a major data-mining application that holds a high weight in the modern digitized world; it assists many areas such as renewable energy systems, manufacturing applications, email filtering, digital libraries, and security threats, among many other areas. In Arabic text classification area, the current works remain limited, which creates a huge possibility for new work. The Arabic language is one of the top five most spoken languages in the world, therefore, the availability of Arabic text is far from limited. In this chapter, three algorithms including support vector machine (SVM), artificial neural network (ANN), and J48, are combined with the naïve Bayes algorithm to create new hybrid algorithms, namely, Vote NBSVM, Vote NBANN, and Vote NBJ48. These algorithms are applied on three Arabic text datasets with a total of 32,262 documents, and their performance is measured and compared. The results showed that the voting NBSVM obtained better results than other algorithms. When combined with the naïve Bayes algorithm using the voting method, the accuracy levels dropped by 2.25% for the ANN algorithm, 6.62% for the SVM algorithm, and 2.52% for the J48 algorithm, where the new algorithm NBJ48 showed superior outstanding results that are highly competitive and increased the accuracy of the J48 by 5.72%. This is especially important taking into account that this algorithm has never been applied to Arabic text.
Leaning-Based Control of an Immersive-Telepresence Robot

In this paper, we present an implementation of a leaning-based control of a differential drive telepresence robot and a user study in simulation, with the goal of bringing the same functionality to a real telepresence robot. The participants used a balance board to control the robot and viewed the virtual environment through a head-mounted display. The main motivation for using a balance board as the control device stems from Virtual Reality (VR) sickness; even small movements of your own body matching the motions seen on the screen decrease the sensory conflict between vision and vestibular organs, which lies at the heart of most theories regarding the onset of VR sickness. To test the hypothesis that the balance board as a control method would be less sickening than using joysticks, we designed a user study (N=32, 15 women) in which the participants drove a simulated differential drive robot in a virtual environment with either a Nintendo Wii Balance Board or joysticks. However, our pre-registered main hypotheses were not supported; the joystick did not cause any more VR sickness on the participants than the balance board, and the board proved to be statistically significantly more difficult to use, both subjectively and objectively. Analyzing the open-ended questions revealed these results to be likely connected, meaning that the difficulty of use seemed to affect sickness; even unlimited training time before the test did not make the use as easy as the familiar joystick. Thus, making the board easier to use is a key to enable its potential; we present a few possibilities towards this goal.