Reviewerly/exHarmony huggingface dataset:
Enhanced LDR Detail Rendering for HDR Fusion by TransU-Fusion Network

High Dynamic Range (HDR) images are widely used in automotive, aerospace, AI, and other fields but are limited by the maximum dynamic range of a single data acquisition using CMOS image sensors. High dynamic range images are usually synthesized through multiple exposure techniques and image processing techniques. One of the most challenging task in multiframe Low Dynamic Range (LDR) images fusion for HDR is to eliminate ghosting artifacts caused by motion. In traditional algorithms, optical flow is generally used to align dynamic scenes before image fusion, which can achieve good results in cases of small-scale motion scenes but causes obvious ghosting artifacts when motion magnitude is large. Recently, attention mechanisms have been introduced during the alignment stage to enhance the network’s ability to remove ghosts. However, significant ghosting artifacts still occur in some scenarios with large-scale motion or oversaturated areas. We proposea novel Distilled Feature TransformerBlock (DFTB) structure to distill and re-extract information from deep image features obtained after U-Net downsampling, achieving ghost removal at the semantic level for HDR fusion. We introduce a Feature Distillation Transformer Block (FDTB), based on the Swin-Transformer and RFDB structure. FDTB uses multiple distillation connections to learn more discriminative feature representations. For the multiexposure moving scene image fusion HDR ghost removal task, in the previous method, the use of deep learning to remove the ghost effect in the composite image has been perfect, and it is almost difficult to observe the ghost residue of moving objects in the composite HDR image. The method in this paper focuses more on how to save the details of LDR image more completely after removing the ghost to synthesize high-quality HDR image. After using the proposed FDTB, the edge texture details of the synthesized HDR image are saved more perfectly, which shows that FDTB has a better effect in saving the details of image fusion. Futhermore, we propose a new depth framework based on DFTB for fusing and removing ghosts from deep image features, called TransU-Fusion. First of all, we use the encoder in U-Net to extract image features of different exposures and map them to different dimensional feature spaces. By utilizing the symmetry of the U-Net structure, we can ultimately output these feature images as original size HDR images. Then, we further fuse high-dimensional space features using Dilated Residual Dense Block (DRDB) to expand the receptive field, which is beneficial for repairing over-saturated regions. We use the transformer in DFTB to perform low-pass filtering on low-dimensional space features and interact with global information to remove ghosts. Finally, the processed features are merged and output as an HDR image without ghosting artifacts through the decoder. After testing on datasets and comparing with benchmark and state-of-the-art models, the results demonstrate our model’s excellent information fusion ability and stronger ghost removal capability.
A Discrete JAYA Algorithm Based on Reinforcement Learning and Simulated Annealing for the Traveling Salesman Problem

The JAYA algorithm is a population-based meta-heuristic algorithm proposed in recent years which has been proved to be suitable for solving global optimization and engineering optimization problems because of its simplicity, easy implementation, and guiding characteristic of striving for the best and avoiding the worst. In this study, an improved discrete JAYA algorithm based on reinforcement learning and simulated annealing (QSA-DJAYA) is proposed to solve the well-known traveling salesman problem in combinatorial optimization. More specially, firstly, the basic Q-learning algorithm in reinforcement learning is embedded into the proposed algorithm such that it can choose the most promising transformation operator for the current state to update the solution. Secondly, in order to balance the exploration and exploitation capabilities of the QSA-DJAYA algorithm, the Metropolis acceptance criterion of the simulated annealing algorithm is introduced to determine whether to accept candidate solutions. Thirdly, 3-opt is applied to the best solution of the current iteration at a certain frequency to improve the efficiency of the algorithm. Finally, to evaluate the performance of the QSA-DJAYA algorithm, it has been tested on 21 benchmark datasets taken from TSPLIB and compared with other competitive algorithms in two groups of comparative experiments. The experimental and the statistical significance test results show that the QSA-DJAYA algorithm achieves significantly better results in most instances.
Natural Language Processing for Sentiment Analysis in Social Media Marketing

In recent years, more and more people have been using social media as a way to market their business. But because social media platforms generate so much data, it can be hard for businesses to analyze and use this data in the best way. Natural Language Processing (NLP) techniques can be used to figure out how people feel about posts on social media and get useful information for marketing on social media. This research paper gives an overview of how NLP techniques are used in social media marketing to analyze how people feel about things. The paper talks about the different NLP techniques, such as those based on dictionaries, rules, and machine learning. The paper also shows a case study of how NLP can be used to figure out how people feel about a certain product or brand in social media posts. The results of the case study show that NLP works well for analyzing sentiment and has the potential to help social media marketing in important ways. At the end of the paper, we talk about some of the problems with using NLP for sentiment analysis in social media marketing and where it could go in the future.
Josephson parametric amplifiers for rapid, high-fidelity measurement of solid-state qubits

Superconducting quantum circuits are a leading platform for building a large-scale quantum processor [1]. A crucial component necessary for the high-fidelity readout of qubits in this framework, is an ultra-low-noise microwave amplifier. Thus, over the past two decades, superconducting Josephson parametric amplifiers (JPAs) have been developed that achieve near quantum-limited noise performance, an order of magnitude lower than competing microwave amplifiers based on semiconductor transistors [2]. Nevertheless, JPAs suffer from drawbacks such as limited bandwidth and compression power that makes them difficult to use in an eventual large-scale quantum processor. Thus, an ongoing theme in JPA development is to optimize the circuit to improve these specifications without sacrificing other desirable properties such as the noise performance, ease of construction and operation, etc. Here, we review some results of optimizing a specific JPA circuit called the SNAIL parametric amplifier [3]–[5], shown in Fig. 1, where the acronym SNAIL refers to the Superconducting Nonlinear Asymmetric Inductive eLement. The SNAIL is a composite element made from conventional Al/AlOx/Al Josephson junctions, which has a nonlinear current-phase relation that can be approximated as a nonlinear inductance. By applying a strong microwave frequency pump to modulate the nonlinear inductance, parametric amplification can be realized. By device and circuit engineering we have demonstrated two orders of magnitude improvement in compression power (Fig. 2) in such devices without sacrificing other desirable characteristics.
MLStar: A System for Synthesis of Machine-Learning Programs

This paper introduces our auto-ML system, MLStar, which uses genetic programming to create scikit-learn and Keras-based Python programs to perform supervised learning. MLStar leverages our own genetic programming system (GPStar4) and provides a greater search space compared to traditional genetic programming frameworks.
Development of an obstacle avoiding autonomous vehicle by using stereo depth estimation and artificial intelligence based semantic segmentation

In this study, an autonomous vehicle that can avoid obstacles has been developed by using stereo imaging systems and artificial intelligence applications together. An integrated stereo camera module and NVIDIA Jetson Nano developer kit were used as computer vision system. Checkerboard calibration was performed to prevent camera distortions. The images of the cameras were rectified and the difference costs between the left and right image pairs on the same epipolar plane were calculated. These difference costs were passed through the weighted least squares (WLS) filter, thus a depth map of the left camera image was created. The rectified left camera view was also processed by artificial intelligence-based semantic segmentation. Segmentation was carried out using a previously trained artificial intelligence network (SegNet). These semantic segmentation outputs were passed through the HSV color mask and a mask image was hereby obtained. Using the mask image; movable ground, obstacle, and background information was extracted. Useful data analysis was performed on the depth map and semantic segmentation outputs of the same frame. This information is transmitted to the 2-wheeled vehicle which is designed based on ROS that provides the movement, and decisions are made within the scope of the avoidance algorithm. This study's novel contribution involves the integration of a passive depth sensing system and artificial intelligence based semantic segmentation, in tandem with a real-time obstacle avoidance algorithm that utilizes these combined technologies. Consequently, the autonomous vehicle is capable of making semantic inferences about its environment while effectively avoiding obstacles.
ML Based Automated Assistance System for Efficient Crowd Control A detailed investigation

This section covers ML and automated assistance systems for crowd control. ML-based artificial intelligence (AI) lets software programmes enhance prediction accuracy without being designed to. ML techniques forecast correct output using past data. The next step towards truly automated beings is to develop methods for understanding and managing the currently pervasive community info gadgets since data is so important. In this part, we'll discuss ML's automatic crowd control. Data availability is expanding learning for application across sectors. Computer vision has helped medical diagnosis, data presentation and processing, science, and research. Computer hardware, electronics, internet content, and cyber security have used these methods. The machine learning-based automatic assistance system for efficient cored management can now be concluded.
Deep learning insights and methods for classifying wildlife

With the introduction of low-cost and widely accessible sensors such as cellphones, drones, satellites, voice recorders, and bio-logging equipment, the amount of information collected about animals has expanded. Meanwhile, modern data processing systems prohibit them from collecting, digesting, and condensing data into usable information. We think that machine learning, especially deep learning algorithms, will be able to tackle this analytical difficulty by enhancing our understanding, monitoring capacities, and animal welfare. By merging machine learning with ecological processes, it may be feasible to expand the inputs to population and behavior models, resulting in integrated hybrid modeling tools where machine learning models give data-supported insights and ecological models act as constraints. Animal ecologists may basically profit from the quantity of data created by contemporary sensor technologies by integrating cutting-edge machine learning methods with ecological domain expertise. This will enable them to assess population abundances more precisely, research animal behavior, and reduce human-wildlife conflicts.
A Hybrid Approach of Cryptography

This article represents a dual security mechanism based on compressive sensing, quantum chaos map, random pixel exchange, and frequency division approach. It also provides the digital data authentication system based on combined approach of DWT and SVD. Two watermarks are used for dual authentication purposes and compressive sensing is used to provide parallel compression and data encryption. The detail algorithm and approach is explained in the next section. This approach is more effective than other conventional approachs and takes less time and space complexity to the others i.e. AES, DES RSA type data encryption mechanism. This article also provides the parallel compression mechanism which reduced the required memory size. This mechanism also provides the parallel digital data security as well as authentication system which provided more secure communication on the social network and also provides a secure authentication mechanism based on frequency division, compressive sensing, random pixel exchange, etc. The results are shown in the form of images and tables.
A Hybrid Retina Net Classifier for Thermal Imaging

Thermal imaging is a cutting-edge technology which has the capability to detect objects in any environmental conditions, such as smoke, fog, smog, etc. This technology finds its importance mainly during nighttime since it does not require light to detect the objects. Applications of this technology span into various sectors, most importantly in border security to detect any incoming hazards. Object detection and classification are generally difficult with thermal imaging. In this paper, a one-stage deep convolution network-based object detection and classification called retina net is introduced. Existing surveys are based on object detection using infrared information obtained from the objects. This research is focused on detecting and identifying objects from thermal images and surveillance data.
An analysis of medical images using deep learning

The use of AI models in health care system and the life sciences is expanding. In this paper, we will take a look at the present state of the art and address the unanswered issues regarding the development of Ai technologies as clinical decision support tools. A review, which included a critical examination of papers published from 1990 and 2022, led the study's most challenging aspects.First, we demonstrate the structural distinction between ML and DL methods. Methods for training, validating, and testing ML models, as well as feature extraction, are described. In DL, models are provided as multi-layered artificial neural networks for direct image analysis. Data management includes technical stages like as image labelling, picture annotation, data standardization, and federated learning. After that, we divide the following into their own subsections: sample size computation, including frequent trials in AI methods; data augmentation strategies for coping with limited or unequal data; and the understandability of AI models. Finally, the advantages and disadvantages of ML and DL in introducing AI applications to diagnostic imaging are compared and contrasted. Biomedical and healthcare systems rank high on the list of important topics for AI applications, with medical imaging ranking as one of the most relevant and promising fields in which to apply such technology. Gaining insight into the specific difficulties associated with developing and deploying such systems in healthcare situations is helpful.
The Emergence Of The Metaverse In The Digital Blockchain Economy: Applying The Esg Framework For A Sustainable Future

The world is witnessing a paradigm shift towards digital blockchain transformation; disrupting businesses, industries, governments, institutions, cultures and nations. Breakthrough developments in the fields of machine learning, artificial intelligence (AI), intelligent data, virtual and augmented reality, and the internet of things, many others as well, have driven the extraordinary rise of the so-called a "digital economy." The Metaverse is anticipated to thrive when the distributed ledger economy transitions into digital technology. But as it grows, it presents ecological sustainability with both fresh possibilities and difficulties. In this paper, we use the Environmental, Social, and Governance (ESG) model to analyse these potential and constraints. The metaphysical phenomena was critically analysed using an organised study of literature and the ESG system, which aims to illustrate community, energy, and governance problems in order to ensure a sustainable future. For the objective of choosing, evaluating, and synthesising pertinent papers on the topic, resources such as EBSCO, Scholar by Google, Scopus, Web of Science, and Sciences Direct were used. Our analysis shows that organizations engaged in the Metaverse blockchainphenomena are more likely to achieve higher levels of sustainability when they adopt solid governance practices in relation to the ESG framework. While the Metaverse holds the promise of significant sustainability benefits in the social, environmental, and governance arenas, several challenges and concerns on these dimensions ought to be acknowledged and addressed by decision makers. Adopting the ESG framework to the Metaverse phenomena allows stakeholders to better understand how to manage challenges and risks pivoting around sustainability matters.
Performance Analysis of a Repetition Cat Code Architecture: Computing 256-bit Elliptic Curve Logarithm in 9 Hours with 126 133 Cat Qubits

Cat qubits provide appealing building blocks for quantum computing. They exhibit a tunable noise bias yielding an exponential suppression of bit flips with the average photon number and a protection against the remaining phase errors can be ensured by a simple repetition code. We here quantify the cost of a repetition code and provide valuable guidance for the choice of a large scale architecture using cat qubits by realizing a performance analysis based on the computation of discrete logarithms on an elliptic curve with Shor's algorithm. By focusing on a 2D grid of cat qubits with neighboring connectivity, we propose to implement 2-qubit gates via lattice surgery and Toffoli gates with off-line fault-tolerant preparation of magic states through projective measurements and subsequent gate teleportations. All-to-all connectivity between logical qubits is ensured by routing qubits. Assuming a ratio between single- and two-photon losses of 1e-5 and a cycle time of 500 ns, we show concretely that such an architecture can compute a 256-bit elliptic curve logarithm in 9 h with 126133 cat qubits and on average 19 photons by cat state. We give the details of the realization of Shor's algorithm so that the proposed performance analysis can be easily reused to guide the choice of architecture for others platforms.
The Use of Blockchain Technology and OCR in E-Government for Document Management: Inbound Invoice Management as an Example

The field of electronic government (e-government) is gaining prominence in contemporary society, as it has a significant influence on the wider populace within the context of a technologically advanced world. E-government makes use of information and communication technologies (ICTs) at various levels and domains within government agencies and the public sector. ICT reduces manual labour, potential fraud points, errors, and process lapses. The Internet’s quick accessibility and the widespread adoption of modern technologies and disciplines, such as big data, the Internet of Things, machine learning, and artificial intelligence, have accelerated the need for e-government. However, these developments raise a number of data reliability and precision concerns. The adoption of blockchain technology by researchers demonstrates its efficacy in addressing such issues. The present study proposes the SECHash system model, which integrates blockchain and Optical Character Recognition (OCR) technologies for the purpose of regulating the processing of incoming documents by governmental agencies. As a case study to assess the proposed system paradigm, the study uses a document containing incoming invoices. The proposal seeks to maintain the integrity of document data by prohibiting its modification after acceptance. Additionally, SECHash guarantees that accepted documents will not be destroyed or lost. The analysis demonstrates that using the SECHash model system will decrease fraudulent transactions by eradicating manual labour and storing documents on a blockchain network.
The Theory of Planned Behavior Regarding Artificial Intelligence in Recommendations and Selection of YouTube News Content

Increased accessibility to YouTube-based news content is further accelerated by the incorporation of artificial intelligence (AI). Notably, the AI in the recommendations and selection of YouTube videos contains specific omnipresent algorithms. This study focuses on the increasing popularity of YouTube-based news collection and on AI in further facilitating and making the content appealing to the audience. The study applied an experimental approach and collected data from 280 respondents in Jordan. Mainly supported by the theory of planned behavior, we found that attitudes and subjective norms (p > 0.000) are strongly associated with the selection process of YouTube content. Moreover, AI is a strong mediator of the attitudes, subjective norms, and content selection of users of YouTube-based news (p > 0.000). Thus, we conclude that AI is a potential variable in further strengthening the intention to use YouTube in searching, selecting, and watching news content. Additionally, we have highlighted the limitations of the study.
A Systematic Review on MODI Script Character Recognition

Character recognition is simple, but creating software that recognizes characters is relatively difficult. MODI is a prehistoric language. It isn’t easy to create a viable Character recognition system that can retain high recognition accuracy without compromising input document quality. MODI handwritten character recognition is used in this review work. Handwritten character identification has been complex in recent years because each person’s writing style is unique. Character recognition is just the conversion of human handwritten text into machine language. Until now, several studies on various Indian languages have been completed. Many historical texts in India are written in several Indian script languages. However, the MODI script receives less attention. Different identification algorithms for various languages have been developed to preserve such records for the future, and research on Indian scripts is continuing. The various writing styles of each HOCR have been a significant issue in recent decades. The world respected India for its unity in diversity. The geographical and cultural diversity of the nation has led to the development of several spoken languages and written scripts that are used daily. The diversity and structure of Indian characters have led to the development of character recognition systems being acknowledged as an ongoing research topic. This investigation looks at earlier work on handwritten character recognition using MODI.