Reviewerly/exHarmony huggingface dataset:
Face photo-line drawings synthesis based on local extraction preserving generative adversarial networks

Facial photo-to-sketch synthesis is crucial for entertainment and criminal investigations, yet challenges persist, including local detail blurring and identity feature loss. To mitigate these problems in face sketch synthesis, We propose a technique for synthesizing photographic sketches of faces using generative adversarial networks that focus on image localization to extract and preserve image identity features – namely LEPGAN. Our method introduces an image local extraction module, an attention mechanism in the generator, and an extended U-Net structure to enhance critical image features and preserve identity information. Multi-scale perceptual and local loss functions further enhance synthetic image quality. LEPGAN outperforms existing methods, producing images with crisper facial details that closely align with human visual perception, effectively preserving unique identity characteristics. Our approach represents a significant advancement in face sketch synthesis for various applications.
Elk herd optimizer: a novel nature-inspired metaheuristic algorithm

Abstract This paper proposes a novel nature-inspired swarm-based optimization algorithm called elk herd optimizer (EHO). It is inspired by the breeding process of the elk herd. Elks have two main breeding seasons: rutting and calving. In the rutting season, the elk herd splits into different families of various sizes. This division is based on fighting for dominance between bulls, where the stronger bull can form a family with large numbers of harems. In the calving season, each family breeds new calves from its bull and harems. This inspiration is set in an optimization context where the optimization loop consists of three operators: rutting season, calving season, and selection season. During the selection season, all families are merged, including bulls, harems, and calves. The fittest elk herd will be selected for use in the upcoming rutting and calving seasons. In simple words, EHO divides the population into a set of groups, each with one leader and several followers in the rutting season. The number of followers is determined based on the fitness value of its leader group. Each group will generate new solutions based on its leader and followers in the calving season. The members of all groups including leaders, followers, and new solutions are combined and the fittest population is selected in the selection season. The performance of EHO is assessed using 29 benchmark optimization problems utilized in the CEC-2017 special sessions on real-parameter optimization and four traditional real-world engineering design problems. The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used. Statistical testing using Friedman’s test post-hocked by Holm’s test function confirms the superiority of the proposed EHO when compared to other methods. In a nutshell, EHO is an efficient nature-inspired swarm-based optimization algorithm that can be used to tackle several optimization problems.
In defense of local descriptor-based few-shot object detection

State-of-the-art image object detection computational models require an intensive parameter fine-tuning stage (using deep convolution network, etc). with tens or hundreds of training examples. In contrast, human intelligence can robustly learn a new concept from just a few instances (i.e., few-shot detection). The distinctive perception mechanisms between these two families of systems enlighten us to revisit classical handcraft local descriptors (e.g., SIFT, HOG, etc.) as well as non-parametric visual models, which innately require no learning/training phase. Herein, we claim that the inferior performance of these local descriptors mainly results from a lack of global structure sense. To address this issue, we refine local descriptors with spatial contextual attention of neighbor affinities and then embed the local descriptors into discriminative subspace guided by Kernel-InfoNCE loss. Differing from conventional quantization of local descriptors in high-dimensional feature space or isometric dimension reduction, we actually seek a brain-inspired few-shot feature representation for the object manifold, which combines data-independent primitive representation and semantic context learning and thus helps with generalization. The obtained embeddings as pattern vectors/tensors permit us an accelerated but non-parametric visual similarity computation as the decision rule for final detection. Our approach to few-shot object detection is nearly learning-free, and experiments on remote sensing imageries (approximate 2-D affine space) confirm the efficacy of our model.
Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

Abstract In the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating constitutive models straight from data, capable of describing complex material behavior in a more flexible way than classical constitutive models. Yield functions, which serve as foundation of constitutive models for plasticity, can be properly described in a data-oriented manner using ML methods. However, the quality of these descriptions heavily relies on the availability of sufficient high-quality and representative training data that needs to be generated by fundamental numerical simulations, experiments, or a combination of both. The present paper addresses the issue of data selection, by introducing an active learning approach for Support Vector Classification (SVC) and its application in training an ML yield function with suitable data. In this regard, the Query-By-Committee (QBC) algorithm was employed, guiding the selection of new training data points in regions of the feature space where a committee of models shows significant disagreement. This approach resulted in a marked reduction in the variance of model predictions throughout the active learning process. It was also shown that the rate of decrease in the variance went along with an increase in the quality of the trained model, quantified by the Matthews Correlation Coefficient (MCC). This demonstrated the effectiveness of the approach and offered us the possibility to define a dynamic stopping criterion based on the variance in the committee results.
Correlation enhanced distribution adaptation for prediction of fall risk

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different data sources with labeled and unlabeled samples to predict a patient's condition poses a significant challenge. Traditional machine learning models assume that data for new patients follow a similar distribution. If the data does not satisfy this assumption, the trained models do not achieve the expected accuracy, leading to potential misdiagnosing risks. To address this issue, we utilize domain adaptation (DA) techniques, which employ labeled data from one or more related source domains. These DA techniques promise to tackle discrepancies in multiple data sources and achieve a robust diagnosis for new patients. In our research, we have developed an unsupervised DA model to align two domains by creating a domain-invariant feature representation. Subsequently, we have built a robust fall-risk prediction model based on these new feature representations. The results from simulation studies and real-world applications demonstrate that our proposed approach outperforms existing models.
Dominant‐pole placement for predictor synthesis

Abstract This article analyzes the high‐gain prediction approach for nonlinear input‐delay systems. The problem is discussed in the light of weighted homogeneity and input‐to‐state stability. The canonical form for uniformly observable nonlinear systems allows tuning the linear‐part spectrum by multiplicity‐induced dominancy and ensuring closed‐loop system input‐to‐state stability using the descriptor method of the Lyapunov–Krasovskii approach. Due to the trade‐off between delay and gain margin, the limitation of high gain that results for time‐delay systems is overcome using a cascade of subpredictors. A comparative analysis shows that our proposal achieves a better trade‐off between delay and gain margin, resulting in a reduced number of subpredictors.
Autonomous multicopter landing on a moving vehicle based on RSSI

Abstract Currently, most of the studies on unmanned aerial vehicle (UAV) automatic landing systems mainly depend on image information to determine the landing location. However, the system requires a camera, a gimbal system and a separate image-processing device, which increases the weight and power consumption of the UAV, resulting in a shorter flight time. In addition, a large amount of computation and slow reaction speed can cause the camera to miss a proper landing moment. To solve these problems, in this study, the moving direction and relative distance between an object and the automatic landing system were measured using a receive signal strength indicator of the radio-frequency (RF) signal. To improve the movement direction and relative distance estimation accuracy, the noise in the RF signal was minimised using a low pass filter and moving average filter. Based on the filtered RF signal, the acceleration of the multicopter to reach the object was estimated by adopting the proportional navigation algorithm. The performance of the proposed algorithm for precise landing on a moving vehicle was demonstrated through experiments.
Unmasking the oligarchs - Using open source data to detect sanctions violations

Russia’s invasion of Ukraine in February 2022 led Western governments to impose extensive sanctions on the Russian economy and the people who maintain the regime - the so-called oligarchs. The enforcement of these sanctions and the manoeuvres of these oligarchs to evade them have created new challenges for the authorities, industry and society at large. This study aims to unmask sanctioned oligarchs who are beneficial owners of companies in the UK. The approach is based on the assumption that the actions of oligarchs leave detectable traces in the UK company register. Inspired by simple tools developed by volunteers for citizens doing their own intelligence work, software has been developed to achieve three different research objectives: to develop indicators of sanctions evasion, to demonstrate that it is possible to do useful intelligence work using only the resources available to the average citizen, and to develop a software tool to identify officers in the UK company register who are suspected of being linked to sanctioned oligarchs. The findings underscore the importance of open source investigations, reflecting a burgeoning intelligence phenomenon introduced as ‘citizen intelligence’, which has the potential to enhance the protection of society by building a more democratic and diverse intelligence community. This study opens the door for further research into the actions and tactics of sanctioned individuals, providing insights to protect global financial integrity and security.
A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.
Research and Development of a Traffic Sign Recognition Module in Vietnam

Automatic traffic sign recognition is essential in researching and developing driver assistance systems and autonomous vehicles. This paper presents the research and development of an automated traffic sign recognition module in Vietnam. The recognition model is developed based on the deep learning model YOLOv5 and incorporates architectural modifications to reduce computational complexity, increase inference speed, and meet real-time requirements for embedded system applications. The model is trained using a custom dataset collected by the research team from real-world street environments in Vietnam, encompassing diverse locations, times, and weather conditions. The trained recognition model is deployed on the Jetson embedded system, yielding high-quality recognition results and meeting real-time recognition needs.
Analytical framework using remote sensing and water geochemistry to assess changes in landscapes with high ecological and social value

Abstract The Brazilian Mata Atlântica coastal rainforest suffered a dramatic reduction in size due to agricultural and mining activities since the 1500s. Most relevant fragments are in mountainous areas assigned for environmental conservation, where there is increasing market pressure to open these areas for mining operations. In this study, we created a broader framework for water basin study on mountainous regions in tropical areas of high ecological importance. Remote sensing and hydrochemical analysis were used to create a more encompassing evaluation and a prognosis of environmental changes caused by mining operations. Our results showed that land use and land cover variations affect the physical and chemical behavior of rivers, and that seasonality plays a compounding role in their levels of particulate and chemical elements. Applying these combined techniques proved to be a good strategy in the evaluation of land cover change on the physical and chemical variations found in smaller watersheds.
Mammography with deep learning for breast cancer detection

X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.
AI-based real-time hand gesture-controlled virtual mouse

Hand gesture control technology is an emerging alternative to conventional input devices like keyboards and mice, enabling users to interact with computers through natural hand movements. Although, many researchers worked on gesture recognition and its challenges, yet wide range of gestures has not been addressed. In this direction, the author proposed a novel gesture-controlled virtual mouse system that uses hand gestures and hand tip detection with a webcam or internal camera for computer navigation that accurately recognizes various gestures, including cursor movement, left and right clicks, double clicks, scrolling, drag and drop, multiple item selection, volume control. To deal with this real-world problem, the authors proposed a solution for limited space situations and assist individuals with hand impairments who cannot use a physical mouse. Furthermore, it surpasses the limitations of traditional mice, such as mechanical wear and tear, adaptability to different environments, and restricted functionalities. By leveraging hand gestures and tip detection through a camera proposed system offers an efficient and adaptable approach to operating a PC mouse. The proposed methodology uses computer vision and AI for the accurate detection of gestures, even in challenging environments. It also offers a comprehensive set of gestures for various actions and functions.
Application of Preference Selection Index in Recruitment of Search Engine Optimization Specialist

Search Engine Optimization Specialist is one of the important factors that can help digital agencies improve services to all their customers. This research aims to develop a decision-making system in the recruitment process of Search Engine Optimization Specialists by applying the Preference Selection Index method. The Search Engine Optimization Specialist recruitment decision-making system in this study uses 5 (five) criteria, namely educational background, ability to use SEO tools, work experience as a Search Engine Optimization Specialist, digital marketing skills, and age. The research sample data collection technique related to the recruitment of Search Engine Optimization Specialists used in this research is Literature Study. After the author succeeds in collecting research sample data, at the next stage the author analyzes the application of the Preference Selection Index method in the recruitment of Search Engine Optimization Specialists. The results of this study show that alternative A02 (0.855641604) has the highest value and gets the first ranking position. Meanwhile, the alternatives that get the second to fifth ranking positions are A05 (0,834620739), A03 (0,741086734), A04 (0,716537597), and A01 (0,708920064). So that the most recommended alternative based on the calculation of the Preference Selection Index on the recruitment of Search Engine Optimization Specialists in this study is alternative A02 (0,855641604).
The long mean-life-time-controlled and potentially scalable qubits composed of electric dipolar molecules based on graphene

Abstract We propose a new kind of qubits composed of electric dipolar molecules. The electric dipolar molecules in an external electric field will take simple harmonic oscillations, whose quantum states belonging to the two lowest energy levels act as the states $|0\rangle$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>|</mml:mo> <mml:mn>0</mml:mn> <mml:mo>〉</mml:mo> </mml:math> , $|1\rangle$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>|</mml:mo> <mml:mn>1</mml:mn> <mml:mo>〉</mml:mo> </mml:math> of a qubit. The qubits’ excited states have a very long controlled mean life time about several seconds. We can perform quantum computations by manipulating the qubits of electric dipolar molecules just like those of neutral atoms. When the qubits are used for quantum computations, the dipolar moments’ orientations will harmonically oscillate along an external electric field and they will not change the directions: along or against the electric field, so the qubits can be large-scalely manufactured in graphene system. The radius of Rydberg blockade is about 100 nm.
Interdisciplinary technical perspective: transformation of blockchain and opportunities for organizational training

This study explores how blockchain technology can be applied in the area of training in organizations, the benefits it brings, and the management challenges that blockchain technology raises. By exploring the decentralized, transparent, and intelligent contract nature of blockchain technology, we analyzed the application of blockchain technology to organizational training. The results show that organizations can achieve training resource sharing, supply and demand balancing, training certification, and intelligent contract enforcement based on blockchain technology. However, it also presents potential challenges, mainly regarding engagement across sectors, support from all members, global technology rollout, staff competency, data confidentiality, and return on investment. Case and industry analysis further validates the content of our research and demonstrates the potential and challenges of blockchain technology in the field of organizational training.
Identification of Panoramic Photographic Image Composition Using Fuzzy Rules

Making panoramic images has gradually become an essential function inside personal intelligent devices because panoramic images can provide broader and richer content than typical images. However, the techniques to classify the types of panoramic images are still deficient. This paper presents novel approaches for classifying the photographic composition of panoramic images into five types using fuzzy rules. A test database with 168 panoramic images was collected from the Internet. After analyzing the panoramic image database, the proposed feature model defined a set of photographic compositions. Then, the panoramic image was identified by using the proposed feature vector. An algorithm based on fuzzy rules is also proposed to match the identification results with that of human experts. The experimental results show that the proposed methods have demonstrated performance with high accuracy and this can be used for related applications in the future.
Multifractal signal generation by cascaded chaotic systems and their analog electronic realization

Multifractality, that is, self-similarity where in scaling follows a continuous spectrum of exponents, is a ubiquitous property of the morphology and dynamics of large-scale complex systems. However, to date, the prerequisites for the generation of multifractal time series by dynamical systems remain an open issue. This work demonstrates that multifractality can emerge in the time series spontaneously generated by a small ensemble of cascaded nonlinear oscillators, which are deterministic, autonomous and delayless. Namely, a chain of four Rössler systems with directed couplings is investigated numerically and realized experimentally in the form of analog electronic circuits. The observation of multifractality is established using the detrended fluctuation analysis and confirmed through surrogate tests, wavelet-based analyses and cascade structure visualization. Multifractality consistently arises when the oscillation frequencies of the coupled oscillators span a sufficient range along the chain, and the couplings have an intermediate strength which engenders partial entrainment between the adjacent nodes. These results affirm that neither external entropy injection nor driving are indispensable, and provide a blueprint for the design of self-contained generative circuits toward diverse applications across physiology modeling and unconventional computing.