Reviewerly/exHarmony huggingface dataset:
Aggregating transformers and CNNs for salient object detection in optical remote sensing images

Salient object detection (SOD) in optical remote sensing images (RSIs) plays a significant role in many areas such as agriculture, environmental protection, and the military. However, since the difference in imaging mode and image complexity between RSIs and natural scene images (NSIs), it is difficult to achieve remarkable results by directly extending the saliency method targeting NSIs to RSIs. Besides, we note that the convolutional neural networks (CNNs) based U-Net cannot effectively acquire the global long-range dependency, and the Transformer doesn't adequately characterize the spatial local details of each patch. Therefore, to conduct salient object detection in RSIs, we propose a novel two-branch architecture based network for Aggregating the Transformers and CNNs, namely ATC-Net, where the local spatial details and the global semantic information are fused into the final high-quality saliency map. Specifically, our saliency model adopts an encoder-decoder architecture including two parallel encoder branches and a decoder. Firstly, the two parallel encoder branches extract global and local features by using Transformer and CNNs, respectively. Then, the decoder employs a series of feature-enhanced fusion (FF) modules to aggregate multi-level global and local features by interactive guidance and enhance the fused feature via attention mechanism. Finally, the decoder deploys the read out (RO) module to fuse the aggregated feature of FF module and the low-level CNN feature, steering the feature to focus more on spatial local details. Extensive experiments are performed on two public optical RSIs datasets, and the results show that our saliency model consistently outperforms 30 state-of-the-art methods.
Amorphous gallium oxide homojunction-based optoelectronic synapse for multi-functional signal processing

Abstract In the era of accelerated development in artificial intelligence as well as explosive growth of information and data throughput, underlying hardware devices that can integrate perception and memory while simultaneously offering the benefits of low power consumption and high transmission rates are particularly valuable. Neuromorphic devices inspired by the human brain are considered to be one of the most promising successors to the efficient in-sensory process. In this paper, a homojunction-based multi-functional optoelectronic synapse (MFOS) is proposed and testified. It enables a series of basic electrical synaptic plasticity, including paired-pulse facilitation/depression (PPF/PPD) and long-term promotion/depression (LTP/LTD). In addition, the synaptic behaviors induced by electrical signals could be instead achieved through optical signals, where its sensitivity to optical frequency allows the MFOS to simulate high-pass filtering applications in situ and the perception capability integrated into memory endows it with the information acquisition and processing functions as a visual system. Meanwhile, the MFOS exhibits its performances of associative learning and logic gates following the illumination with two different wavelengths. As a result, the proposed MFOS offers a solution for the realization of intelligent visual system and bionic electronic eye, and will provide more diverse application scenarios for future neuromorphic computing.
How does Metaverse affect the tourism industry? Current practices and future forecasts

ABSTRACTABSTRACTIn this study, the possible effects of current and probable developments in the Metaverse on tourism are examined by applying an exploratory research approach. In this context, from the definition of tourism to its characteristics, tourism businesses to employment, tourism experiences to satisfaction, the classical problems of tourism to its sustainability, the opinions and evaluations of the participants about the Metaverse-tourism interaction were presented. Interview techniques from qualitative research methods were used. Interviews were conducted with 26 participants determined by the purposive sampling method, within the framework of a semi-structured interview form. According to the participants, the Metaverse has the potential to change the traditional concepts and characteristics of tourism, as well as to create a tourism literature and sector of its own. It was emphasized by the participants that Metaverse will positively affect and support tourism with its potential to create sales and marketing, participation of disadvantaged groups, creating new jobs and employment, education and virtual experiences that are closest to reality. In addition, legal and personal concerns regarding the Metaverse, reservations and issues about the possible effects of sustainability and tourism's structural problems were other prominent emphases.KEYWORDS: Metaversetourismtechnologyvirtual realitysustainabilitytourism development Disclosure statementNo potential conflict of interest was reported by the author(s).
Scalable and Security Framework to Secure and Maintain Healthcare Data using Blockchain Technology

The technological advent of Blockchain, Bigdata, Cloud, and AI made it easier to be stored and retrieved Electronic Health Record (EHR) data and then used it by patients, doctors, and research groups for treatments and analytics. Blockchain security ensures the security of patients' records data with smart contracts due to the most reliable and secure platform for many industries such as finance, IoT, healthcare, and logistics management. The innovative use of smart contracts in the healthcare industry enables a secure system to provide privacy and complete protection with patients' record data and to be able to integrate with various entities in the healthcare sector. The criticality of acquiring, storing, and securing EHR is essential in the healthcare industry to provide faster services. Integrating EHR data with multiple healthcare providers accrues various benefits such as quick insurance approvals and dealing with emergencies and critical situations. It expedites medical records access to patients, doctors, insurance companies, primary healthcare providers, pharmaceuticals, and laboratories to reap healthcare benefits efficiently. The challenges with EHR data are security and storage, which can be addressed with Blockchain security, and integrating it with Bigdata provides huge benefits of data storage, scalability, and performance to store and maintain data efficiently. Often acquiring, storing, securing, and maintaining healthcare data involves excessive administrative and operative costs. Most of the research work is done on Blockchain technology frameworks and implementation but the most critical part of data storage and maintenance is yet to be focused area, as how to manage this data in the long term. This paper intends to research the best Blockchain based architectural framework to store EHR data in Bigdata storage systems at a lesser cost compared to other Infrastructure facilities by utilizing inexpensive hardware grouped into clusters and assigned resources to share across the network and easy to maintain even after procuring the data across the entities securely.
Real Time Fake Currency Note Detection using Deep Learning

Great technological advancement in printing and scanning industry made counterfeiting problem to grow more vigorously. As a result, counterfeit currency affects the economy and reduces the value of original money. Thus it is most needed to detect the fake currency. Most of the former methods are based on hardware and image processing techniques. Finding counterfeit currencies with these methods is less efficient and time consuming. To overcome the above problem, we have proposed the detection of counterfeit currency using a deep convolution neural network. Our work identifies the fake currency by examining the currency images. The transfer learned convolutional neural network is trained with two thousand, five hundred, two hundred and fifty Indian currency note data sets to learn the feature map of the currencies. Once the feature map is learnt the network is ready for identifying the fake currency in real time. The proposed approach efficiently identifies the forgery currencies of 2000, 500, 200, and 50 with less time consumption.
Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends

In the recent past, more than 5 years or so, DL especially the large language models (LLMs) has generated extensive studies out of a distinctly average downturn field of knowledge made up of a traditional society of researchers. As a result, DL (DL) is now so pervasive that its use is widespread across the body of research related to machine learning computing. The rapid emergence and apparent dominance of DL architectures over traditional machine learning techniques on a variety of tasks have been truly astonishing to witness. DL models outperformed in a variety of areas, including natural language processing (NLP), image analysis, language understanding, machine translation, computer vision, speech processing, audio recognition, style imitation, and computational biology. In this study, the aim is to explain the rudiments of DL, such as neural networks, convolutional neural networks, deep belief networks, and various variants of DL. The study will explore how these models have been applied to NLP and delve into the underlying mathematics behind them. Additionally, the study will investigate the latest advancements in DL and NLP, while acknowledging the key challenges and emerging trends in the field. Furthermore, it will discuss the core component of DL, namely embeddings, from a taxonomic perspective. Moreover, a literature review will be provided focusing on the application of DL models for six popular pattern recognition tasks: speech recognition, question answering, part of speech tagging, named entity recognition, text classification, and machine translation. Finally, the study will demystify state-of-the-art DL libraries/frameworks and available resources. The outcome and implication of this study reveal that LLMs face challenges in dealing with pragmatic aspects of language due to their reliance on statistical learning techniques and lack of genuine understanding of context, presupposition, implicature, and social norms. Furthermore, this study provides a comprehensive analysis of the current state-of-the-art advancements and highlights significant obstacles and emerging developments. The article has the potential to enhance readers' understanding of the subject matter.
Ensemble of kernel extreme learning machine based elimination optimization for multi-label classification

Multi-label learning is a class of machine learning algorithms that study the classification problem of data associated with multiple labels simultaneously. Ensemble-based method is one of the representative methods in multi-label learning. In the existing ensemble-based multi-label classification methods, the essence of its base learner is still a binary or multi-class classifier. At present, there is no ensemble-based multi-label method that uses a multi-label classifier based on algorithm adaptation as a base learner. Multi-label algorithms suffer from expensive time costs, and ensemble-based methods further exacerbate the time cost. To address these issues, we propose an efficient multi-label classification method based on kernel extreme learning machine and ensemble learning. Firstly, to solve the problem of high time complexity of multi-label classifiers, we use a Gaussian kernel function to generate a kernel extreme learning machine based multi-label classifier (KELM_ML). Then, based on random sampling, we construct the base classifier, i.e., KELM_ML, in ensemble learning. Finally, to build an ensemble model of multi-label classifiers, an elimination optimization ensemble strategy is proposed by defining an encoding vector of the base classifiers and updating it. Although our method is an ensemble model, it inherits the advantages of good time complexity of ELM, i.e., fast learning time. The experimental and statistical results show that compared with baseline methods and other ensemble-based multi-label methods, our proposed method has better classification performance and stable results.
Experimental investigation of wire arc additively manufactured steel T-stub connections

This paper presents an experimental investigation into the structural response of wire arc additively manufactured (WAAM) steel T-stub connections subjected to tension. A total of eighty T-stub specimens featuring different printing strategies, bolt arrangements and geometric configurations were tested to failure. 3D laser scanning was employed to determine the specimen geometry, while digital image correlation (DIC) was used to monitor the strain and displacement fields during testing. Utilising the DIC data, a new method was introduced for the determination of the failure mode and key points on the load-deformation response of T-stub connections. The structural response of the WAAM T-stubs generally followed the anticipated trends, though the geometric irregularities resulted in a delay in the development of prying action in some specimens and the printing strategy was shown to have a clear influence on the load-carrying capacity. Finally, comparisons between the test results and the capacity predictions yielded by existing design equations were made; overall, reasonable agreement was achieved, but further research is required to establish reliable design rules that are specific to this form of manufacture.
The Principles of Data-Centric AI

Uniting data-centric perspectives and concepts to trace the foundations of DCAI.
CrowdMLP: Weakly-supervised crowd counting via multi-granularity MLP

Currently, state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire. When only weak supervisory signals at the count level are available, it is arduous and error-prone to regress total counts due to the lack of explicit spatial constraints. To address this issue, we propose a novel and efficient counter, CrowdMLP, which explores the modelling of global dependencies of embeddings and regresses total counts by designing a multi-granularity MLP regressor. Specifically, a locally-focused pre-trained frontend is used to extract crude feature maps with intrinsic spatial cues, preventing the model from collapsing into trivial outcomes. The crude embeddings, along with the raw crowd scenes, are tokenized at different granularity levels. Next, the multi-granularity MLP mixes tokens at the dimensions of cardinality, channel, and spatial for mining global information. We also propose an effective proxy task called Split-Counting to overcome the limited samples and the lack of spatial hints in a self-supervised manner. Extensive experiments demonstrate that CrowdMLP significantly outperforms existing weakly-supervised counting algorithms and performs better than state-of-the-art location-level supervised approaches.
FAIR for AI: An interdisciplinary and international community building perspective

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.
An n-dimensional modulo chaotic system with expected Lyapunov exponents and its application in image encryption

One-dimensional (1D) chaotic maps have many shortcomings, including simple structures, uncomplicated chaotic behaviors, and narrow chaotic ranges. Multi-dimensional (MD) chaotic maps certainly have more complex constructions and more abundant chaotic phenomena. However, it is still challenging to artificially customize its Lyapunov exponents (LEs), which are characterization indicators of the chaotic state. This paper proposed a novel n-dimensional (nD) modulo chaotic system (nD-MCS) based on preceding works that can generate nD chaotic maps by using existing 1D chaotic maps as seed chaotic maps. The proposed nD-MCS can customize its LEs by setting system parameters. Three 2D and three 3D chaotic maps are presented as examples. Performance evaluations demonstrated that they both exhibit hyperchaotic behaviors, broad chaotic ranges, and large complexities. We present an encryption scheme to confirm the application of the developed chaotic maps to image encryption. Finally, a hardware experiment utilizing the field-programmable gate array (FPGA) is adapted to verify the proposed cryptosystem.
Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier

Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology to address this problem and enable accurate identification and authentication in masked scenarios. In this paper, we propose a novel method that utilizes a combination of deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) for masked face recognition. Specifically, we use pretrained ssd-MobileNetV2 for detecting the presence and location of masks on a face and employ landmark and oval face detection to identify key facial features. The proposed method also utilizes RPCA to separate occluded and non-occluded components of an image, making it more reliable in identifying faces with masks. To optimize the performance of our proposed method, we use particle swarm optimization (PSO) to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. Our proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion.
Exact electronic states with shallow quantum circuits from global optimisation

Abstract Quantum computers promise to revolutionise molecular electronic simulations by overcoming the exponential memory scaling. While electronic wave functions can be represented using a product of fermionic unitary operators, the best ansatz for strongly correlated electronic systems is far from clear. In this contribution, we construct universal wave functions from gate-efficient, spin symmetry-preserving fermionic operators by introducing an algorithm that globally optimises the wave function in the discrete ansatz design and continuous parameter spaces. Our approach maximises the accuracy that can be obtained with near-term quantum circuits and provides a practical route for designing ansätze in the future. Numerical simulations for strongly correlated molecules, including water and molecular nitrogen, and the condensed-matter Hubbard model, demonstrate the improved accuracy of gate-efficient quantum circuits for simulating strongly correlated chemistry.
Enhancing IIR system identification: Harnessing the synergy of gazelle optimization and simulated annealing algorithms

In the field of digital filter design and system identification, accurately modeling Infinite Impulse Response (IIR) systems is of utmost importance. This paper introduces a new adaptive algorithm that combines the gazelle optimization algorithm with simulated annealing to achieve superior optimization performance for digital IIR filters used in system identification. To evaluate the algorithm's effectiveness, extensive experiments were conducted on two fronts: the challenging CEC2020 benchmark functions and a fifth order IIR system identification problem. The proposed algorithm proves its effectiveness by achieving optimal solutions on the CEC2020 benchmark functions when compared to other well-known optimization algorithms such as arithmetic optimization, particle swarm optimization, differential evolution, sine cosine algorithm, grey wolf optimizer, biogeography-based optimization, salp swarm algorithm, and the original gazelle optimization algorithm. The algorithm's performance is measured by its ability to find the best solutions for these benchmark functions. For the IIR system identification problem, the study considers both the same order and reduced order cases. The system to be identified is represented by a fifth order plant. The algorithm's statistical analysis and convergence behavior are compared to the original gazelle optimization algorithm, artificial hummingbird algorithm, mountain gazelle optimization algorithm, and the widely used artificial bee colony algorithm. These comparisons demonstrate the proposed algorithm's superior capability and efficiency in accurately identifying the parameters of IIR systems.
Nuclear shell-model simulation in digital quantum computers

The nuclear shell model is one of the prime many-body methods to study the structure of atomic nuclei, but it is hampered by an exponential scaling on the basis size as the number of particles increases. We present a shell-model quantum circuit design strategy to find nuclear ground states by exploiting an adaptive variational quantum eigensolver algorithm. Our circuit implementation is in excellent agreement with classical shell-model simulations for a dozen of light and medium-mass nuclei, including neon and calcium isotopes. We quantify the circuit depth, width and number of gates to encode realistic shell-model wavefunctions. Our strategy also addresses explicitly energy measurements and the required number of circuits to perform them. Our simulated circuits approach the benchmark results exponentially with a polynomial scaling in quantum resources for each nucleus. This work paves the way for quantum computing shell-model studies across the nuclear chart and our quantum resource quantification may be used in configuration-interaction calculations of other fermionic systems.