Reviewerly/exHarmony huggingface dataset:
Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images

Microscopic tissue analysis is the key diagnostic method needed for disease identification and choosing the best treatment regimen. According to the Global Cancer Observatory, approximately two million people are diagnosed with colorectal cancer each year, and an accurate diagnosis requires a significant amount of time and a highly qualified pathologist to decrease the high mortality rate. Recent development of artificial intelligence technologies and scanning microscopy introduced digital pathology into the field of cancer diagnosis by means of the whole-slide image (WSI). In this work, we applied deep learning methods to diagnose six types of colon mucosal lesions using convolutional neural networks (CNNs). As a result, an algorithm for the automatic segmentation of WSIs of colon biopsies was developed, implementing pre-trained, deep convolutional neural networks of the ResNet and EfficientNet architectures. We compared the classical method and one-cycle policy for CNN training and applied both multi-class and multi-label approaches to solve the classification problem. The multi-label approach was superior because some WSI patches may belong to several classes at once or to none of them. Using the standard one-vs-rest approach, we trained multiple binary classifiers. They achieved the receiver operator curve AUC in the range of 0.80–0.96. Other metrics were also calculated, such as accuracy, precision, sensitivity, specificity, negative predictive value, and F1-score. Obtained CNNs can support human pathologists in the diagnostic process and can be extended to other cancers after adding a sufficient amount of labeled data.
Computational capacity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>L</mml:mi><mml:mi>R</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:math> , memristive, and hybrid reservoirs

Reservoir computing is a machine learning paradigm that uses a high-dimensional dynamical system, or \emph{reservoir}, to approximate and predict time series data. The scale, speed and power usage of reservoir computers could be enhanced by constructing reservoirs out of electronic circuits, and several experimental studies have demonstrated promise in this direction. However, designing quality reservoirs requires a precise understanding of how such circuits process and store information. We analyze the feasibility and optimal design of electronic reservoirs that include both linear elements (resistors, inductors, and capacitors) and nonlinear memory elements called memristors. We provide analytic results regarding the feasibility of these reservoirs, and give a systematic characterization of their computational properties by examining the types of input-output relationships that they can approximate. This allows us to design reservoirs with optimal properties. By introducing measures of the total linear and nonlinear computational capacities of the reservoir, we are able to design electronic circuits whose total computational capacity scales extensively with the system size. Our electronic reservoirs can match or exceed the performance of conventional "echo state network" reservoirs in a form that may be directly implemented in hardware.
Digital Entrepreneurs' Strategic Responses to the Incomplete Global Policy Framework for Blockchain-Based Business

Adopting blockchain is a strategic priority of many companies worldwide. However, utilization of this technology remains hindered by the diversity of policy responses and lack of a global policy framework. We investigated blockchain-based entrepreneurs in North America, Europe, and Asia and analyzed 12 reputable cases in the industry to discover how they implemented their strategies. We provide an overview of the global policy framework for blockchain-based businesses and identify four tactics that can be adopted by digital pioneers to thrive within the formal and informal institutional voids (IV). We contribute to the international business literature by exploring IV as a multilevel opportunity and by deepening the understanding of IV outside traditionally considered geographical boundaries.
Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

Abstract Collision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.
How immersive virtual reality can become a key tool to advance research and psychotherapy of eating and weight disorders

Immersive virtual reality technology (VR) still waits for its wide dissemination in research and psychotherapy of eating and weight disorders. Given the comparably high efforts in producing a VR setup, we outline that the technology's breakthrough needs tailored exploitation of specific features of VR and user-centered design of setups. In this paper, we introduce VR hardware and review the specific properties of immersive VR versus real-world setups providing examples how they improved existing setups. We then summarize current approaches to make VR a tool for psychotherapy of eating and weight disorders and introduce user-centered design of VR environments as a solution to support their further development. Overall, we argue that exploitation of the specific properties of VR can substantially improve existing approaches for research and therapy of eating and weight disorders. To produce more than pilot setups, iterative development of VR setups within a user-centered design approach is needed.
Multi-Channel EMG Signal analysis for Italian Sign Language Interpretation

Sign language elevates the communication gap between special and ordinary people. It allows them to connect with society. Many studies have proposed image-based or sensor-based models for sign language whereas this study develops a sign language recognition (SLR) system for Italian sign language through Myo armband Electromyography (EMG) sensors only. The sensor keeps the subject comfortable during its utilization. In the proposed methodology, raw EMG from the Myo armband was denoised using an infinite impulse response (IIR) filter. A combination of 11 features was extracted from various domains after preprocessing of EMG signals. Subsequently, some prominent classifiers were trained and tested for accurate system design. The system has achieved the highest accuracy of 93.5% using the Linear Discriminant technique. The system authentication was performed on a 10-folds cross-validation
TeRFF: Temperature-aware Radio Frequency Fingerprinting for Smartphones

In recent years, radio frequency (RF) fingerprinting has attracted more and more attention. Many different types of RF fingerprints have been proposed, such as carrier frequency offset (CFO), sampling frequency offset and error vector magnitude. Among them, the CFO fingerprint is recognized as a promising RF fingerprint. However, for commonly used smartphones, we find that its CFO fingerprint is unstable, because the temperature of crystal oscillator varies greatly and large fluctuations of temperature significantly affect its CFO fingerprint. Therefore, the solutions of CFO-based fingerprinting will no longer be effective for smartphones if the temperature of crystal oscillator is not involved. To this end, we propose a more reliable and applicable CFO-based fingerprinting approach called temperature-aware radio frequency fingerprinting (TeRFF). First, we construct a dataset by extracting crystal oscillator's temperature and the corresponding CFO value on multiple smartphones over a period. In the dataset, the extracted temperature values constitute a set of temperature values, and each registered temperature value corresponds to a group of CFO samples. On this basis, we train multiple Naive Bayes models, each tagged with a registered temperature value. Moreover, since there are many temperature values which are not in the temperature set, we design a CFO estimation method to estimate the CFO fingerprint at the unregistered temperature. Finally, the experimental results demonstrate that our proposed solution TeRFF makes the CFO fingerprinting still effective for smartphone identification, and its performance is better than other existing RF fingerprinting schemes.
On the Accuracy of Open Video Quality Metrics for Local Decision in AV1 Video Codec

VMAF is a popular objective quality metric used for video quality evaluation. The power of VMAF has been demonstrated for a wide variety of video scales and encoding processes. However, its ability to evaluate the quality of small video patches has not yet been tested, despite its importance for encoding algorithms. We applied Maximum Likelihood Difference Scaling (MLDS) methodology to estimate supra-threshold perceptual differences in localized sections in videos, also known as tubes, encoded using AV1. We further used the results to assess the performance of VMAF in this scenario and proposed a recalibration of the algorithm to improve its agreement with the subjective data.
Digitale Kompetenzen in Deutschland

Abstract Based on the National Educational Panel Study (NEPS), the study provides an overview of the distribution of digital literacy in Germany up to the beginning of the Covid pandemic. Already in childhood and adolescence, there are systematic differences in digital literacy depending on socio-economic background. Children with a migration background and those with unemployed parents show particularly low digital literacy. Gender-specific differences in digital literacy are small in childhood and adolescence, but clearly pronounced among adults. In addition, people with little formal education and people with a migration background have systematically lower digital competences in adulthood. The education sector should therefore promote the digital competences of children and young people at an early stage in order to compensate for the apparently low level of competence development outside the formal education sector. Educational opportunities for digitally less competent adults should also be strengthened to enable older generations to continue to participate in the changing spheres of life, education, and work.
Implementation of a Blockchain-enabled Federated Learning Model that Supports Security and Privacy Comparisons

The rapid development of the information technology era has resulted in increased awareness of data privacy by the general public, and the corresponding laws and regulations regarding data protection are gradually enacted, which significantly impacts the development of data-dependent artificial intelligence. The federated learning model is used to fully protect users' privacy and maximize the value of the data created through the process of data use. However, the traditional federated learning algorithm is easily intercepted during use, resulting in data leakage and distortion. Suppose the federated learning model can be combined with the blockchain. In that case, the blockchain structure can be fully utilized, and the characteristics of the blockchain provide credible security to the federated learning algorithm, which will ensure that the algorithm fully protects the user's data privacy. This paper presents a federated learning security and privacy model enabled by blockchain technology and analyzes its data algorithm architecture in detail. This paper aims to optimize the above algorithm to ensure that it can be used normally for the protection of user data.
Identification and Visualization of Key Topics in Scientific Publications with Transformer-Based Language Models and Document Clustering Methods

With the rapidly growing number of scientific publications, researchers face an increasing challenge of discovering the current research topics and methodologies in a scientific domain. This paper describes an unsupervised topic detection approach that utilizes the new development of transformer-based GPT-3 (Generative Pretrained Transformer 3) similarity embedding models and modern document clustering techniques. In total, 593 publication abstracts across urban study and machine learning domains were used as a case study to demonstrate the three phases of our approach. The iterative clustering phase uses the GPT-3 embeddings to represent the semantic meaning of abstracts and deploys the HDBSCAN (Hierarchical Density-based Spatial Clustering of Applications with Noise) clustering algorithm along with silhouette scores to group similar abstracts. The keyword extraction phase identifies candidate words from each abstract and selects keywords using the Maximal Marginal Relevance ranking algorithm. The keyword grouping phase produces the keyword groups to represent topics in each abstract cluster, again using GPT-3 embeddings, the HDBSCAN algorithm, and silhouette scores. The results are visualized in a web-based interactive tool that allows users to explore abstract clusters and examine the topics in each cluster through keyword grouping. Our unsupervised topic detection approach does not require labeled datasets for training and has the potential to be used in bibliometric analysis in a large collection of publications.
Comparative Analysis of Convolutional Neural Network in Object Detection

Deep learning has proven to be a vital technique over the last few decades based on its potential to manage a huge amount of data. It is an artificial intelligence function that mimics the functioning of the human brain in accessing information and generating patterns to be used in problem solving. Using its methodologies, complex computer vision tasks like object detection including facial recognition and image classification can achieve cutting-edge results. With the help of deep learning object detection algorithms, we can instantly recognize and localize objects of interest once we look at pictures and videos with a meaningful result. The primary objective of object detection is to mimic this intellectual ability or intelligence using a computer. Object detection can be accomplished using a variety of deep learning techniques like YOLO and R-CNN including FASTER R-CNN and FAST R-CNN, and all these techniques use convolutional neural networks (CNN). We implemented these deep learning techniques to object detection in this paper as well as analyzed the results to choose which one performs better with the highest accuracy and we found that YOLO works better among four of them. However, the performance of these algorithms varies according to the scenarios in which they are used.
PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems

Federated learning (FL) has been recognized as a promising collaborative on-device machine learning method in the design of Internet of Things (IoT) systems. However, most existing FL methods fail to deal with IoT applications that contain a variety of IoT devices equipped with different types of neural network (NN) models. This is because traditional FL methods assume that local models on devices should have the same architecture as the global model on cloud. To address this problem, we propose a novel framework named PervasiveFL that enables efficient and effective FL among heterogeneous IoT devices. Without modifying original local models, PervasiveFL installs one lightweight NN model named modellet on each device. By using the deep mutual learning (DML) and our entropy-based decision gating (EDG) method, modellets and local models can selectively learn from each other through soft labels using locally captured data. Meanwhile, since modellets are of the same architecture, the learned knowledge by modellets can be shared among devices in a traditional FL manner. In this way, PervasiveFL can be pervasively applied to any heterogeneous IoT system. Comprehensive experimental results on four well-known datasets show that PervasiveFL can not only pervasively enable FL among heterogeneous devices within a large-scale IoT system, but also significantly enhance the inference accuracy of heterogeneous IoT devices with low communication overhead.
A Quantum-Behaved Particle Swarm Optimization Algorithm on Riemannian Manifolds

The Riemannian manifold optimization algorithms have been widely used in machine learning, computer vision, data mining, and other technical fields. Most of these algorithms are based on the geodesic or the retracement operator and use the classical methods (i.e., the steepest descent method, the conjugate gradient method, the Newton method, etc.) to solve engineering optimization problems. However, they lack the ability to solve non-differentiable mathematical models and ensure global convergence for non-convex manifolds. Considering this issue, this paper proposes a quantum-behaved particle swarm optimization (QPSO) algorithm on Riemannian manifolds named RQPSO. In this algorithm, the quantum-behaved particles are randomly distributed on the manifold surface and iteratively updated during the whole search process. Then, the vector transfer operator is used to translate the guiding vectors, which are not in the same Euclidean space, to the tangent space of the particles. Through the searching of these guiding vectors, we can achieve the retracement and update of points and finally obtain the optimized result. The proposed RQPSO algorithm does not depend on the expression form of a problem and could deal with various engineering technical problems, including both differentiable and non-differentiable ones. To verify the performance of RQPSO experimentally, we compare it with some traditional algorithms on three common matrix manifold optimization problems. The experimental results show that RQPSO has better performance than its competitors in terms of calculation speed and optimization efficiency.
A Hybrid Support Vector Machine Algorithm for Big Data Heterogeneity Using Machine Learning

Big data technology has gained attention in all fields, particularly with regard to research and financial institutions. This technology has changed the world tremendously. Researchers and data scientists are currently working on its applicability in different domains such as health care, medicine, and the stock market, among others. The data being generated at an unexpected pace from multiple sources like social media, health care contexts, and Internet of things have given rise to big data. Management and processing of big data represent a challenge for researchers and data scientists, as there is heterogeneity and ambiguity. Heterogeneity is considered to be an important characteristic of big data. The analysis of heterogeneous data is a very complex task as it involves the compilation, storage, and processing of varied data based on diverse patterns and rules. The proposed research has focused on the heterogeneity problem in big data. This research introduces the hybrid support vector machine (H-SVM) classifier, which uses the support vector machine as a base. In the proposed algorithm, the heterogeneous Euclidean overlap metric (HEOM) and Euclidean distance are introduced to form clusters and classify the data on the basis of ordinal and nominal values. The performance of the proposed learning classifier is compared with linear SVM, random forest, and k-nearest neighbor. The proposed algorithm attained the highest accuracy as compared to other classifiers.
A comparative study of several metaheuristic algorithms for optimization problems

This article presents a study of a recent metaheuristic optimization method, the search and rescue algorithm (SAR), against four known metaheuristic optimization algorithms, the Salp Swarm Algorithm (SSA), the Cuckoo Search Algorithm (CSA), the Firefly Algorithm (FA), and the Grey Wolf Optimization Algorithm (GWO). An evaluation of its performance against the other algorithms will be performed by the means of thirteen mathematical benchmarks functions, afterwards a study of the optimization of five multi-dimensional mathematical problems will be investigated, the optimization of the Dejoung function, the Cosine Mixture function, the Griewank function, the Rastrigin function, and the Rosenbrok function, while the dimension of these problems increases from five to thirty. Furthermore, a discussion and a conclusion about the results obtained by each algorithm face to the resolution of these complex multi-dimensional problems will be drawn.
Stroking trajectory shapes velocity effects on pleasantness and other touch percepts.

Research has identified an inverted u-shaped relationship between the pleasantness of arm stroking and stroking velocity. However, the generalizability of this relationship is questionable as much of the work relied on the rotary tactile stimulator (RTS), which strokes skin with force varying along an arc and confounds stimulus velocity with duration. We explored how these parameters shape the subjective evaluation of touch. In Study 1, one group of participants was stroked by the RTS, while two other groups were stroked by a new robot capable of different stroking trajectories. Participants were stroked at five velocities and rated pleasantness, humanness, intensity, and roughness. In Study 2, participants were stroked by the new robot imitating the trajectory of the RTS exactly, imitating it while controlling stimulus duration, or moving linearly or ovally with both constant force and duration. Participants rated pleasantness and humanness. Although stroke velocity was related to both pleasantness and humanness in an inverted u-shaped manner, stimulus trajectory modulated this relationship and the association between velocity and the other ratings. Together, our results clearly link stroking velocity to the perception of touch but highlight that this relationship is shaped by other physical parameters including touch duration and spatial pattern. (PsycInfo Database Record (c) 2023 APA, all rights reserved).