Reviewerly/exHarmony huggingface dataset:
Continual learning classification method with human-in-the-loop based on the artificial immune system

Currently, most classification algorithms may suffer from serious misclassification when faced with unbalanced or new data, for lacking feedback and update capability. Many existing classification methods learn from the continuous learning mechanism in the biological immune system to realize the learning of new data. However, these methods in classifying new data, because of the lack of feedback, lead to a search for a long time, even may deviate from the correct results. Humans can make the immune response proceed to a certain extent as they wish through a variety of intervention techniques. Motivated by this, this paper proposes a continual learning classification method with human-in-the-loop (H-CLCM) based on the artificial immune system. H-CLCM adjusts parameters online corresponding to misidentified data by integrating human experience during the testing phase. This enables it not only to converge to an accurate prediction model at minimal cost but also to have the capability to learn new classes of data without retraining the classifier. Experiments on 10 benchmark datasets demonstrate the performances and advantages of the proposed method. The experimental results show that H-CLCM has superior classification performance than the other approaches under the same data.
A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT

Currently, algorithms to embed watermarks into digital images are increasing exponentially, for example in image copyright protection. However, when a watermarking algorithm is applied, the preservation of the image’s quality is of utmost importance, for example in medical images, where improper embedding of the watermark could change the patient’s diagnosis. On the other hand, in digital images distributed over the Internet, the owner of the images must also be protected. In this work, an imperceptible, robust, secure, and hybrid watermarking algorithm is presented for copyright protection. It is based on the Hermite Transform (HT) and the Discrete Cosine Transform (DCT) as a spatial–frequency representation of a grayscale image. Besides, it uses a block-based strategy and a perfectibility analysis of the best embedding regions inspired by the Human Vision System (HVS), giving the imperceptibility of the watermark, and a Singular-Value Decomposition (SVD) approach improved robustness against attacks. In addition, the proposed method can embed two watermarks, a digital binary image (LOGO) and information about the owner and the technical data of the original image in text format (MetaData). To secure both watermarks, the proposed method uses the Jigsaw Transform (JST) and the Elementary Cellular Automaton (ECA) to encrypt the image LOGO and a random sequence generator and the XOR operation to encrypt the image MetaData. On the other hand, the proposed method was tested using a public dataset of 49 grayscale images to assess the effectiveness of the watermark embedding and extraction procedures. Furthermore, the proposed watermarking algorithm was evaluated under several processing and geometric algorithms to demonstrate its robustness to the majority, intentional or unintentional, attacks, and a comparison was made with several state-of-the-art techniques. The proposed method obtained average values of PSNR = 40.2051 dB, NCC = 0.9987, SSIM = 0.9999, and MSSIM = 0.9994 for the watermarked image. In the case of the extraction of the LOGO, the proposal gave MSE = 0, PSNR ≫ 60 dB, NCC = 1, SSIM = 1, and MSSIM = 1, whereas, for the image MetaData extracted, it gave BER = 0% and Berror=0. Finally, the proposed encryption method presented a large key space (K=1.2689×1089) for the LOGO image.
A Cryo-CMOS DAC-based 40 Gb/s PAM4 Wireline Transmitter for Quantum Computing Applications

State-of-the-art quantum computers already comprise hundreds of cryogenic quantum bits (qubits), and prototypes with over 10k qubits are currently being developed. Such large-scale systems require local cryogenic electronics for qubit control and readout, leaving the digital controllers for algorithm execution and quantum error correction (QEC) at room temperature due to the limited cryogenic cooling budget. The entire process, including qubit readout, data transmission, QEC, and algorithm execution, should be completed well within the qubit decoherence time, thus requiring a low-power high-speed communication link between the cryogenic quantum processor and classical processor located at room temperature. To this end, this paper presents the first cryo-CMOS high-speed 4-level pulse amplitude modulation (PAM4) wireline transmitter. Thanks to a power-efficient serializing architecture driving a 6-bit digital-to-analog converter (DAC), the 40-nm CMOS chip achieves a data rate of 40 Gb/s PAM4 with an efficiency of 2.46pJ/b and a ratio of level mismatch (RLM) of 97.8% at 4.2 K. While demonstrating an energy efficiency comparable to state-of-the-art transmitters in more advanced CMOS nodes, the extremely wide temperature operating range (4.2 K - 300 K) will enable future large-scale quantum computers.
Alerting the Globe of Consequential Earthquakes

Abstract The primary ingredients on the hazard side of the equation include the rapid characterization of the earthquake source and quantifying the spatial distribution of the shaking, plus any secondary hazards an earthquake may have triggered. On the earthquake impact side, loss calculations require the aforementioned hazard assessments—and their uncertainties—as input, plus the quantification of the exposure and vulnerability of structures, infrastructure, and the affected inhabitants. Lastly, effectively communicating uncertain estimates of the resulting impacts on society requires careful consideration of its function and form. All these aspects of rapid earthquake information delivery entailed wide‐ranging collaborative research and development among seismologists, earthquake engineers, geographers, social scientists, Information Technology professionals, and communication experts, leveraging diverse components and ingredients not achievable without extensive collaboration. I was very fortunate to be able to work on interesting and useful projects with many colleagues who got involved with them. Advances in content, its rapid delivery, and our ability to better communicate uncertain loss estimates greatly expanded the range of users and critical decision‐makers who could directly benefit from rapid post‐earthquake information. Moreover, in the critical user–developer feedback loop, we have intently followed requests from users to develop new ways of delivering the most‐requested post‐earthquake information within the limitations of the science and technology. Such new avenues and tools then motivated and prioritized additional research directions and developments.
Attention-Based 1D CNN-BiLSTM Hybrid Model Enhanced with FastText Word Embedding for Korean Voice Phishing Detection

In the increasingly complex domain of Korean voice phishing attacks, advanced detection techniques are paramount. Traditional methods have achieved some degree of success. However, they often fail to detect sophisticated voice phishing attacks, highlighting an urgent need for enhanced approaches to improve detection performance. Addressing this, we have designed and implemented a novel artificial neural network (ANN) architecture that successfully combines data-centric and model-centric AI methodologies for detecting Korean voice phishing attacks. This paper presents our unique hybrid architecture, consisting of a 1-dimensional Convolutional Neural Network (1D CNN), a Bidirectional Long Short-Term Memory (BiLSTM), and Hierarchical Attention Networks (HANs). Our evaluations using the real-world KorCCVi v2 dataset demonstrate that the proposed architecture effectively leverages the strengths of CNN and BiLSTM to extract and learn contextually rich features from word embedding vectors. Additionally, implementing word and sentence attention mechanisms from HANs enhances the model’s focus on crucial features, considerably improving detection performance. Achieving an accuracy score of 99.32% and an F1 score of 99.31%, our model surpasses all baseline models we trained, outperforms several existing solutions, and maintains comparable performance to others. The findings of this study underscore the potential of hybrid neural network architectures in improving voice phishing detection in the Korean language and pave the way for future research. This could involve refining and expanding upon this model to tackle increasingly sophisticated voice phishing strategies effectively or utilizing larger datasets.
A patent recommendation method based on KG representation learning

The number of patents is constantly increasing, providing an invaluable knowledge base. But retrieving this knowledge becomes ever more challenging with the increase in data. In this context, this study proposes a patent recommendation method based on knowledge graph (KG) representation. Although KG-based recommendation systems received broad attention in the literature, there is still a lack of literature that explores how it can be used for patent recommendations. First, and in terms of KG construction, the existing methods encounter issues when it comes to simultaneously accounting for accuracy and recall rate. This study proposes a combination of rule-based and deep learning methods for KG construction to solve this problem. Second, existing methods have performance and semantic association limitations when learning KG representations. This study therefore uses high-performance graph neural networks for encoding and models the semantic association limitations in graphs using traditional KG representation learning methods. Finally, existing recommendation systems usually focus on the semantic correlation between interaction items in recommendations, ignoring their direct relationships. This study therefore applies the KG representation learning method for the patent recommendation task, using KG representation learning to model the semantic association and constraints in the product–patent heterogeneous graph. Experiments based on a practical case example demonstrate that the proposed method outperforms baseline methods, and that it can guide innovative design processes. Findings have important implications for research and practice.
Generative Adversarial Networks and Other Generative Models

Abstract Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially been meant not to be an image analysis tool but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods and has proven to be highly successful—though by no means from the first attempt. This chapter gives a basic introduction into the motivation for generative adversarial networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, as well as typical signs for poor convergence and their reasons. Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.
Evaluation of Existing Virtual Tour Studies and Their Applicability to Jordanian Universities

Several studies were conducted in the past for evaluating the benefits and shortcomings of virtual tours in various sites including university campuses. The previous studies addressed numerous issues such as providing initial perceptions, guiding visitors, etc. Most of these issues can be related, up to some extent, to Jordanian universities in general. However, there are issues specific to Jordan that need to be addressed such as the availability of fast internet connection and suitable data caps. Furthermore, new demands rose recently such as the need for social distancing under COVID-19 restrictions, thus limiting the sizes of physical tour groups. This paper reviews previous studies on virtual tours and evaluates their applicability to Jordanian university and their present practicality. Then, the paper outlines the motivations and limitations of virtual campus tours in Jordan.
Graph-based density peak merging for identifying multi-peak clusters

Density peaks clustering (DPC), which is short for clustering by fast search-and-find of density peaks, is a recently developed density-based clustering method that is widely used because of its effective detection of isolated high-density regions. However, it often fails to identify true cluster structures from data owing to its intrinsic assumption that a cluster has a unique and high-density center, because a single cluster can contain several peaks. We call this the “multi-peak problem”. To overcome this, we propose a peak merging method for clustering. In the proposed algorithm, a valley and its local density are defined to identify the intersection between two adjoined peaks. These are used to construct directed and connected subgraphs, using which we merge multiple peaks if needed. Unlike DPC and its variants, the proposed method is capable of identifying highly complex shaped clusters with no interpretation of the decision graph. Numerical experiments based on synthetic and real datasets demonstrated that our method outperformed the benchmarking methods.
Data augmentation guided breast tumor segmentation based on generative adversarial neural networks

Breast tumor segmentation built on Dynamic Contrast-Enhanced Magnetic Resonance Imaging is a noteworthy phase for the quantifiable radiomics study of breast cancer. Manual tumor explanation is an inefficient method and encompasses medical associates, influenced, persuaded to error, and inter-user divergence. The quantities of contemporary preparations have exposed the competence of deep learning depictions in image segmentation. At this point, we designate a 3D Connected-AUNets for tumor segmentation from 3D MRIs built on encoder–decoder design. Owing to a constrained training dataset size, a generative adversarial networks channel is complementary to modernizing the input image itself to recognize the shared decoder and implement added controls on its layers. Based on the preliminary segmentation of Connected-AUNets, a fully connected 3D condition random field is used to improve segmentation results by determining 2D neighbor areas and 3D volume statistics. Furthermore, 3D connected components assessment is used to sustain around large components and reduce segmentation noise. The proposed system has been estimated on two regularly available datasets, apparently INbreast and the Curated Breast Imaging Subset of Digital Database for Screening Mammography. The proposed system has also been evaluated using a private dataset. The experimental significances show that the proposed model outperforms the state-of-the-art processes for breast tumor segmentation.
Geo Science-Based Optimization Algorithms: A New Paradigm

Various problems in science and engineering may be formulated as optimization problems with intricate nonlinear constraints. Despite the fact that naturally-inspiring systems feature a multitude of intricate underlying mechanisms. Various Nature-Inspired Optimization Algorithms (NIOAs) have been created recently, drawing inspiration from nature. Likewise, a new paradigm is presented that draws its inspiration from geoscience, which studies the surface of the earth and related natural phenomena. Highly nonlinear challenges typically call for more advanced optimization algorithms, which ordinary algorithms may find difficult to solve. Due to their adaptability and efficiency, geoscience-based optimization algorithms are currently popular. However, there are several significant problems with geoscience and computation inspired by nature. An in-depth analysis of certain new geoscience-based optimization algorithms is presented in this paper.
Rabble Based Autonomous Assistance Using Machine Learning Algorithms

In order to properly control rabbles, we shall investigate computer vision and mechanical aid technologies with lending devices here. Machine intelligence, commonly referred to as AI, or algorithms, enables software to improve prediction performance while requiring a deliberate goal to do so. Machine-learning algorithms anticipate new output using historical data as their input. Since information is so crucial, creating novel approaches for effectively administering the neighbourhood, pervasive data gathering systems of today is a crucial next stage for actors and thus is entirely independent. Therefore, deep learning and its autonomous rabbel control support network will be discussed here. With the availability of information expanding through time, education for application in numerous sectors has become more widespread. Many fields have benefited from the use of machine vision, encompassing research and academia database and process visualisation, biomedical imaging, and more. These methods have been applied to safety, computer components, internet websites, and mobile applications. Thus, we can draw an opinion regarding an autonomous assistance system for effective kernel maintenance that is based upon deep learning.Currently, COVID-19 is causing mayhem throughout the world. There have found over than 110 million cases, or the number grows daily. Due to the outbreak, or more 4 million people died. The three most affected nations are the Usa, India, and Brazil, and many agencies are working to develop a vaccine. The world is seeking get back to normal after a string of shutdowns while upholding safety precautions. As a consequence, it is necessary to keep an eye on anyone's hat and temperature within public spaces like malls, hospitals, offices, shrines, and the like. Costs increase as a consequence of the lengthy process and extensive contact with people. Consequently, a system is required that can detect respirators, gauge temperatures, and manage the crowd that enters the building.
Improving multi‐object tracking by full occlusion handle and adaptive feature fusion

Abstract Occlusion has always been a challenging research topic in the field of multi‐target tracking. The invisibility of the target in full occlusion increases the difficulty of continuous tracking, which makes the recovery failure when the target is re‐visible, and ultimately leads to a decrease in tracking accuracy. To address full occlusion problem, an effective multi‐object tracking algorithm with full occlusion handle and adaptive fusion features is proposed. Firstly, a spatio‐temporal model is established for full occlusion, and a simple, efficient and training‐free method is proposed to find full occluded targets. Secondly, local high discrimination features with better stability and independence is proposed to realize effective correlation between targets before and after the full occlusion. Finally, an adaptive feature fusion mechanism is proposed, which can adjust feature structure dynamically according to the occlusion state. The experimental results show that most evaluation metrics of the proposed algorithm are superior to those of some typical algorithms proposed in recent years under full occlusion tracking scenes. The proposed algorithm can realize accurate occluded targets identification and improve tracking robustness under short‐term, long‐term and frequent full occlusion.
A Detailed Analysis for the Performance of Artificial Intelligence System in Voice Recognition and its Classifications

Artificially intelligent machines possess cognitive capabilities that are comparable to those of human beings. Voice detection algorithms and methods have been developed for the purpose of identifying, differentiating, and verifying speakers. The amalgamation of speech recognition technology and artificial intelligence has facilitated advancements on a global scale. The implementation of conversational artificial intelligence (AI) holds the capacity to significantly transform the market. Law enforcement agencies could potentially employ this technology to identify perpetrators based on their speech patterns. Large-scale social gatherings can provide numerous benefits to researchers across various domains. This technology exhibits the capacity to benefit both the educational sector and the medical and healthcare domain.
Vulnerability Identification and Assessment for Critical Infrastructures in the Energy Sector

Vulnerability identification and assessment is a key process in risk management. While enumerations of vulnerabilities are available, it is challenging to identify vulnerability sets focused on the profiles and roles of specific organizations. To this end, we have employed systematized knowledge and relevant standards (including National Electric Sector Cybersecurity Organization Resource (NESCOR), ISO/IEC 27005:2018 and National Vulnerability Database (NVD)) to identify a set of 250 vulnerabilities for operators of energy-related critical infrastructures. We have elaborated a “double-mapping” scheme to associate (arbitrarily) categorized assets, with the pool of identified Physical, Cyber and Human/Organizational vulnerabilities. We have designed and implemented an extensible vulnerability identification and assessment framework, allowing historized assessments, based on the CVSS (Common Vulnerability Scoring System) scoring mechanism. This framework has been extended to allow modelling of the vulnerabilities and assessments using the Structured Threat Information eXpression (STIX) JSON format, as Cyber Threat Intelligence (CTI) information, to facilitate information sharing between Electrical Power and Energy Systems (EPES) and to promote collaboration and interoperability scenarios. Vulnerability assessments from the initial analysis of the project in the context of Research and Technology Development (RTD) projects have been statistically processed, offering insights in terms of the assessment’s importance and distribution. The assessments have also been transformed into a dynamic dataset processed to identify and quantify correlation and start the discussion on the interpretation of the way assessments are performed.