Reviewerly/exHarmony huggingface dataset:
Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging

Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system’s decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.
FuzzyNet: Medical Image Classification based on GLCM Texture Feature

In recent study has found that high-precision monitoring, big data, and medical diagnosis continue to be hampered by transit delays. We designed and implemented an unique fuzzy logic-based method to help with this problem. Currently, a high-accuracy automated approach for detecting anomalies in X-ray images is being developed. Pre-processing image technologies are used to improve the quality of medical pictures in order to achieve high accuracy while utilizing a small number of system resources. Noise reduction and contrast enhancement are two procedures engaged in image pre-processing that assist to the delivery of a rapid anomaly detecting system. We suggested a Fuzzy Net model Classification Based on GLCM Feature and particle swam optimization technique in this study. This method categorizes the X-ray pictures. The musculoskeletal radiographs (MURA) dataset is classified as normal or abnormal. We also examined at performance indicators and loss over epochs, which we compared using a confusion matrix and displayed plots of the model’s learned membership functions.
Cctv customer location data processing on webgis based a &amp; s security system

Geographic Information System (GIS) is a computer-based information system, designed to work with data that has spatial information (spatial reference). Currently the information data about the location of CCTV Customers on the A &amp; S Security System in Medan City has not been inventoried in a spatial-based web information system, the mapping data for CCTV Customer locations on the A &amp; S Security System in Medan City still uses Microsoft Word and not programmed. For this reason, the authors created a solution with a Geographic Information System, which is an information system that is used to enter, process, and produce geographically or geospatially referenced data, to support decision making in planning. This information system will later have a visualization in the form of a web that is used to map the location of CCTV Customers on the A &amp; S Security System in Medan City. In the development of the CCTV customer location processing system at the A &amp; S Security System in Medan City, PHP and MySql programming languages ​​were used as database systems.
Phase-Space Geometry and Optimal State Preparation in Quantum Metrology with Collective Spins

We revisit well-known protocols in quantum metrology using collective spins and propose a unifying picture for optimal state preparation based on a semiclassical description in phase space. We show how this framework allows for quantitative predictions of the timescales required to prepare various metrologically useful states, and that these predictions remain accurate even for moderate system sizes, surprisingly far from the classical limit. Furthermore, this framework allows us to build a geometric picture that relates optimal (exponentially fast) entangled probe preparation to the existence of separatrices connecting saddle points in phase space. We illustrate our results with the paradigmatic examples of the two-axis countertwisting and twisting-and-turning Hamiltonians, where we provide analytical expressions for all the relevant optimal timescales. Finally, we propose a generalization of these models to include p-body collective interaction (or p-order twisting), beyond the usual case of p=2. Using our geometric framework, we prove a no-go theorem for the local optimality of these models for p>2.Received 8 November 2022Revised 1 February 2023Accepted 28 March 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020314Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum metrologyQuantum sensingTechniquesPhase space dynamicsQuantum InformationNonlinear DynamicsAtomic, Molecular & Optical
An Exploratory Study about the Effect of COVID-19 on the Intention to Adopt Virtual Reality in the Tourism Sector

During the health crisis caused by COVID-19, virtual reality (VR) proved to be useful for the tourism industry, allowing this industry to continue working despite the restrictions imposed. However, it remains to be seen if the impact of this sanitary crisis in the tourism industry influenced managers’ intention to adopt this technology in the post-pandemic period. To fill this gap, a qualitative methodological approach was adopted, using the MAXQDA20 software and interviews with managers of tourism enterprises. The results show that the willingness to invest in technology, the perception of VR as a business strategy, and the perception of the impact of the pandemic are factors that regulate the intention of companies to adopt VR. In addition, prior experience with VR and the perception of technical support are also important for its adoption. Thus, it was concluded that VR can be a valuable sustainable strategy for tourism companies to address the challenges imposed by the pandemic. However, adopting the technology depends on factors such as financial availability, business strategy, and previous experience with VR. Furthermore, tourism companies must also receive adequate technical support to ensure its correct implementation.
Two-Qutrit Quantum Algorithms on a Programmable Superconducting Processor

Processing quantum information using quantum three-level systems or qutrits as the fundamental unit is an alternative to contemporary qubit-based architectures with the potential to provide significant computational advantages. We demonstrate a fully programmable two-qutrit quantum processor by utilizing the third energy eigenstates of two transmons. We develop a parametric coupler to achieve excellent connectivity in the nine-dimensional Hilbert space enabling efficient implementations of two-qutrit gates. We characterize our processor by realizing several algorithms like Deutsch-Jozsa, Bernstein-Vazirani, and Grover's search. Our hardware-efficient protocols allow us to show that two stages of Grover's amplification can improve the success rates of an unstructured search with quantum advantage. Our results pave the way for building fully programmable ternary quantum processors using transmons as building blocks for a universal quantum computer.
Systematic Literature Review on Virtual Electronics Laboratories in Education: Identifying the Need for an Aeronautical Radar Simulator

The objective of this work is to propose the development of a virtual electronics laboratory with an aeronautical radar simulator using immersive technologies to help students learn. To verify whether this proposal was viable, the systematic literature review (SLR) methodology was used, whose objective was to verify whether immersive technologies were being used effectively in education and, also, what challenges, opportunities, and benefits they bring to Education 4.0. For this, eight Research Questions (RQs) were formulated to be answered by articles based on the highest SLR scores. The results presented by SLR were as follows: there was an increase in the use of immersive technologies in education, but virtual reality (VR) is still more used in education than AR, despite VR being more expensive than AR; the use of these new technologies brings new challenges, opportunities, and benefits for education; there was an increase in the quality of teaching for complex subjects; and there was an increase in students’ interest in the content presented.
Bioinspired In‐Sensor Reservoir Computing for Self‐Adaptive Visual Recognition with Two‐Dimensional Dual‐Mode Phototransistors

Abstract Artificial visual systems that dynamically process spatiotemporal optoelectronic signals under complex real‐life environments bear a wide spectrum of edge applications. Despite significant progress in optoelectronic sensors and neuromorphic computing algorithms, developing visual systems that can adapt to a broad illumination range while retaining high performance, high efficiency, and low training costs remains a challenge. Here, this work reports a bioinspired in‐sensor reservoir computing (RC) for self‐adaptive visual recognition. By leveraging voltage‐tunable photoresponses of the MoS 2 ‐based phototransistor array, the RC system demonstrates both scotopic and photopic adaptation functions and maintains a recognition accuracy of 91%. The horizontal modulation (HM) block enables the reservoir to adapt automatically in real‐time under changing illumination conditions, yielding a 90.64% recognition accuracy (14.21% improvement over conventional RC systems). These results pave the way for the emergence of a reconfigurable in‐sensor RC system with broad applications and enhanced performance for an efficient artificial vision system at the edge.
BERT-ERC: Fine-Tuning BERT Is Enough for Emotion Recognition in Conversation

Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pretrained language models (PLMs) and then analyzing contextual information and dialogue structure information among the extracted features. However, we discover that this paradigm has several limitations. Accordingly, we propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy. Furthermore, we develop our model BERT-ERC according to the proposed paradigm, which improves ERC performance in three aspects, namely suggestive text, fine-grained classification module, and two-stage training. Compared to existing methods, BERT-ERC achieves substantial improvement on four datasets, indicating its effectiveness and generalization capability. Besides, we also set up the limited resources scenario and the online prediction scenario to approximate real-world scenarios. Extensive experiments demonstrate that the proposed paradigm significantly outperforms the previous one and can be adapted to various scenes.
2D and 3D object detection algorithms from images: A Survey

Object detection is a crucial branch of computer vision that aims to locate and classify objects in images. Using deep convolutional neural networks (CNNs) as the primary framework for object detection can efficiently extract features, which is closer to real-time performance than the traditional model that extracts features manually. In recent years, the rise of Transformer with powerful self-attention mechanisms has further enhanced performance to a new level. However, when it comes to specific vision tasks in the real world, it is necessary to obtain 3D information about the spatial coordinates, orientation, and velocity of objects, which makes research on object detection in 3D scenes more active. Although LiDAR-based 3D object detection algorithms have excellent performance, they are difficult to popularize in practical applications due to their high price. Hence, we summarize the development process, different frameworks, contributions, advantages, disadvantages, and development trends of image-based 2D and 3D object detection algorithms in recent years to help more researchers better understand this field. Besides, representative datasets,evaluation metrics,related techniques and applications are introduced, and some valuable research directions are discussed.
The Opportunities and challenges in virtual reality for virtual laboratories

The use of extended reality (XR) technologies, namely Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality(MR) in education, has attracted much attention in recent years. Since the middle of the 20th century, virtual reality (VR) has been a part of our lives. Today, technology has made virtual reality accessible to everyone. In the last ten years, virtual reality equipment has become suitable for individual use and has started to be used by large numbers of people. Virtual reality can now be used for educational purposes, including education virtual laboratories. The studies were carried out to reveal the advantages of virtual reality compared to traditional media tools, as well as the conclusion that a successful instructional design process plays a major role in the success of technology in education. In line with what has been mentioned, we review in this paper the historical development of virtual reality technologies, followed by the use of Extended reality technology in the field of education, including virtual laboratories. This paper is to argue and present evidence from extensive research that VR can be a valuable tool in engineering, such as virtual laboratories. As a result of the review we conducted, we were able to conclude that VR has a positive pedagogical and cognitive impact on engineering education, thus increasing students' understanding of the subjects, their grades and performance, and overall satisfaction with their education. Furthermore, VR, as a replacement for physical laboratories, can reduce university liability, infrastructure, and costs. It was also discovered that VR applications for education currently lack the integration of learning theories and objectives in engineering education. In this research, we aim to provide recommendations and guidelines for researchers who want to work in the field of virtual reality and to offer suggestions for virtual laboratories.
Scalable Quantum Error Correction for Surface Codes using FPGA

A fault-tolerant quantum computer must decode and correct errors faster than they appear. The faster errors can be corrected, the more time the computer can do useful work. The Union-Find (UF) decoder is promising with an average time complexity slightly higher than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(d^{3})$</tex> . We report a distributed version of the UF decoder that exploits parallel computing resources for further speedup. Using an FPGA-based implementation, we empirically show that this distributed UF decoder has a sublinear average time complexity with regard to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> , given <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(d^{3})$</tex> parallel computing resources. The decoding time per measurement round decreases as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> increases, a first time for a quantum error decoder. The implementation employs a scalable architecture called Helios that organizes parallel computing resources into a hybrid tree-grid structure. Using Xilinx's cycle-accurate simulator, we present cycle-accurate decoding time for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> up to 15, with the phenomenological noise model with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p=0.1\%$</tex> . We are able to implement <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> up to 7 with a Xilinx ZC106 FPGA, for which an average decoding time is 120 ns per measurement round. Since the decoding time per measurement round of Helios decreases with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> , Helios can decode a surface code of arbitrarily large <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> without a growing backlog.
Metaverse: Literature Review, Synthesis and Future Research Agenda

ABSTRACTMetaverse has been a hot topic of discussion among academics and practitioners with the advancements of VR, AR, blockchain, 5G, as well as non-face-to-face communications. This paper intends to give a synthesis and systematic review of the current status of metaverse-related research. The Systematic Literature Review (SLR) has been employed in this study. The review reveals the importance to (i) enhance the amount of comprehensive academic studies on metaverse (ii) have a clear definition of metaverse, (iii) keep up with technologies advancements, (iv) be more detailed on promotion of the metaverse application, (v) increase studies relating to engagement of metaverse users, (vi) social and personal impacts of metaverse be further explored, (vii) pay more attention to studies on deviant behaviors resulting from metaverse. This paper offers a summary of metaverse definitions, what theories and the underlying technologies are needed for the metaverse, how and where metaverse is employed in specific areas, and challenges faced in the use of metaverse (e.g., psychological effects metaverse brings about). Based on the findings, this study demonstrates the dynamics of metaverse research and discusses future research directions in the six in-depth research agenda relating to technological and social issues.KEYWORDS: Metaversesystematic literature reviewresearch agenda AcknowledgmentThis paper is supported by NSFC 72150610503.Disclosure statementNo potential conflict of interest was reported by the author(s).
Revisiting Parameter-Efficient Tuning: Are We Really There Yet?

Parameter-Efficient Tuning (PETuning) methods have been deemed by many as the new paradigm for using pretrained language models (PLMs).By tuning just a fraction amount of parameters comparing to full model finetuning, PETuning methods claim to have achieved performance on par with or even better than finetuning.In this work, we take a step back and re-examine these PETuning methods by conducting the first comprehensive investigation into the training and evaluation of them.We found the problematic validation and testing practice in current studies, when accompanied by the instability nature of PETuning methods, has led to unreliable conclusions.When being compared under a truly fair evaluation protocol, PETuning cannot yield consistently competitive performance while finetuning remains to be the best-performing method in medium-and high-resource settings.We delve deeper into the cause of the instability and observed that the number of trainable parameters and training iterations are two main factors: reducing trainable parameters and prolonging training iterations may lead to higher stability in PETuning methods.
Recent advances in Grey Wolf Optimizer, its versions and applications: Review

The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO’s appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.
One-way information reconciliation schemes of quantum key distribution

With the rapid improvement of quantum computing technology, quantum key distribution(QKD) is a hot technology. Information reconciliation is a key step of QKD which is useful for correcting key error. Classical message interaction is necessary in a practical information reconciliation scheme, which makes the efficiency of these protocols decreased. Therefore, some one-way information reconciliation schemes based on low-density parity-check(LDPC) codes and polar codes are proposed. Here we propose a concatenated method of IR schemes which can achieve any given error rate level without the need of interactions. Compared with the one-way IR schems based on LDPC codes and polar codes, the IR schemes based on the proposed concatenated method can get lower bit error rates after error correction, which can also reduce the communication delay and system complexity of QKD, improve the final key generation rate and enhance the practicability of QKD system.