Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Supply Chain Systems, Interoperability Model in a Pharmaceutical Case Study

The main purpose of supply chain systems based on blockchain technology is to take advantage of technology innovations to ensure that a tracked asset’s audit trail is immutable. However, the challenge lies in tracking the asset among different blockchain-based supply chain systems. The model proposed in this paper has been designed to overcome the identified challenges. Specifically, the proposed model enables: (1) the asset to be tracked among different blockchain-based supply-chain systems; (2) the tracked asset’s supply chain to be cryptographically verified; (3) a tracked asset to be defined in a standardized format; and (4) a tracked asset to be described with several different standardized formats. Thus, the model provides a great advantage in terms of interoperability between different blockchain-driven supply chains over other models in the literature, which will need to replicate the information in each blockchain platform they operate with, while giving flexibility to the platforms that make use of it and maintain the scalability of those logistic platforms. This work aims to examine the application of the proposed model from an operational point of view, in a scenario within the pharmaceutical sector.
Optimal Device Selection in Federated Learning for Resource-Constrained Edge Networks

Low latency, resource efficiency, and data privacy are some of the crucial requirements in modern communication networks. Federated learning can efficiently address these issues by utilizing the data at the network edge and processing massive amounts of data in parallel at the edge devices, thus ensuring data privacy and low latency. For a large-scale federated learning task spanning many devices, challenges arise due to device heterogeneity, data variability, and limited network resources. Optimal selection of edge devices participating in federated learning is essential to attaining resilient, reliable, and resource-efficient edge networks. In this context, this article proposes an optimal device selection method to minimize redundant data training and improve network resource utilization without affecting the performance of federated learning over resource-constrained edge networks. The proposed optimal device selection method aims to minimize network resource demands while maximizing data diversity within the aggregated model. The performance of the proposed federated learning framework is evaluated using a publicly available image data set of handwritten digits, EMNIST (an extended version of the MNIST data set). Experimental results indicate that the proposed framework can obtain accuracy convergence performance on par with conventional federated learning methods while significantly reducing device usage (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$50\%$ </tex-math></inline-formula> ) and resource utilization (up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$30\%$ </tex-math></inline-formula> ) while reaching 99% of achievable accuracy. The proposed method can therefore be effectively applied to resource-constrained edge networks.
Hidden Feature-Guided Semantic Segmentation Network for Remote Sensing Images

For semantic segmentation of remote sensing images, convolutional neural networks (CNNs) have proven to be powerful tools. However, the existing CNN-based methods have the problems of feature information loss, serious interference by clutter information, and ignoring the correlation between different scale features. To solve these problems, this article proposes a novel hidden feature-guided semantic segmentation network (HFGNet) for remote sensing images, which achieves accurate semantic segmentation by hierarchically extracting and fusing valuable feature information. Specifically, the hidden feature extraction module (HFE-M) is introduced to suppress the salient feature representation to mine more valuable hidden features. Meanwhile, the multifeature interactive fusion module (MIF-M) establishes the correlation between different features to achieve hierarchical feature fusion. The multiscale feature calibration module (MSFC) is constructed to enhance the diversity and refinement representation of hierarchical fusion features. Besides, the local-channel attention mechanism (LCA-M) is designed to improve the feature perception capability of the object region and suppress background information interference. We conducted extensive experiments on the widely used ISPRS 2-D Semantic Labeling dataset and the 15-Class Gaofen Image dataset. Experimental results demonstrate that the proposed HFGNet has advantages over several state-of-the-art methods. The source code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/darkseid-arch/RS-HFGNet</uri> .
Healthcare Monitoring Using Low-Cost Sensors to Supplement and Replace Human Sensation: Does It Have Potential to Increase Independent Living and Prevent Disease?

Continuous monitoring of health status has the potential to enhance the quality of life and life expectancy of people suffering from chronic illness and of the elderly. However, such systems can only come into widespread use if the cost of manufacturing is low. Advancements in material science and engineering technology have led to a significant decrease in the expense of developing healthcare monitoring devices. This review aims to investigate the progress of the use of low-cost sensors in healthcare monitoring and discusses the challenges faced when accomplishing continuous and real-time monitoring tasks. The major findings include (1) only a small number of publications (N = 50) have addressed the issue of healthcare monitoring applications using low-cost sensors over the past two decades; (2) the top three algorithms used to process sensor data include SA (Statistical Analysis, 30%), SVM (Support Vector Machine, 18%), and KNN (K-Nearest Neighbour, 12%); and (3) wireless communication techniques (Zigbee, Bluetooth, Wi-Fi, and RF) serve as the major data transmission tools (77%) followed by cable connection (13%) and SD card data storage (10%). Due to the small fraction (N = 50) of low-cost sensor-based studies among thousands of published articles about healthcare monitoring, this review not only summarises the progress of related research but calls for researchers to devote more effort to the consideration of cost reduction as well as the size of these components.
Learning from What is Already Out There: Few-shot Sign Language Recognition with Online Dictionaries

Today's sign language recognition models require large training corpora of laboratory-like videos, whose collection involves an extensive workforce and financial resources. As a result, only a handful of such systems are publicly available, not to mention their limited localization capabilities for less-populated sign languages. Utilizing online text-to-video dictionaries, which inherently hold annotated data of various attributes and sign languages, and training models in a few-shot fashion hence poses a promising path for the democratization of this technology. In this work, we collect and open-source the UWB-SL-Wild few-shot dataset, the first of its kind training resource consisting of dictionary-scraped videos. This dataset represents the actual distribution and characteristics of available online sign language data. We select glosses that directly overlap with the already existing datasets WLASL100 and ASLLVD and share their class mappings to allow for transfer learning experiments. Apart from providing baseline results on a pose-based architecture, we introduce a novel approach to training sign language recognition models in a few-shot scenario, resulting in state-of-the-art results on ASLLVD-Skeleton and ASLLVD-Skeleton-20 datasets with top- 1 accuracy of 30.97 % and 95.45 %, respectively.
On the finite representation of linear group equivariant operators via permutant measures

Abstract Recent advances in machine learning have highlighted the importance of using group equivariant non-expansive operators for building neural networks in a more transparent and interpretable way. An operator is called equivariant with respect to a group if the action of the group commutes with the operator. Group equivariant non-expansive operators can be seen as multi-level components that can be joined and connected in order to form neural networks by applying the operations of chaining, convex combination and direct product. In this paper we prove that each linear G -equivariant non-expansive operator (GENEO) can be produced by a weighted summation associated with a suitable permutant measure, provided that the group G transitively acts on a finite signal domain. This result is based on the Birkhoff–von Neumann decomposition of doubly stochastic matrices and some well known facts in group theory. Our theorem makes available a new method to build all linear GENEOs with respect to a transitively acting group in the finite setting. This work is part of the research devoted to develop a good mathematical theory of GENEOs, seen as relevant components in machine learning.
Novel medical image cryptogram technology based on segmentation and DNA encoding

This paper proposes a novel medical image cryptogram technology based on a fast and robust fuzzy C-means clustering image segmentation method and deoxyribonucleic acid encoding. In our method, first, the medical image is divided into background areas and regions of interest utilizing fuzzy C-means clustering image segmentation, which increases the encryption efficiency by about 60% when the background area is discarded. Second, some low-value pixels are also discarded in regions of interest to further reduce the encryption time. Third, a 4-dimensional hyperchaotic system has been improved. Furthermore, the hyperchaotic system and deoxyribonucleic acid encoding are utilized to encrypt the medical image. Finally, lossless encryption and fast encryption are done for different purposes. The experimental results demonstrate that the proposed algorithm has appealing encryption performance and the histogram and scatter graphs are governed by approximately uniform distribution. The NPCR and UACI of plaintext sensitivity and the key sensitivity are close to 99.6094% and 33.4635% respectively, which cause robustness against noise and clipping attacks.
Possibilities and human reasoning

This article describes how human reasoning differs from standard logics. It tells the story of three sorts of inference, for example: the possibility of rain implies the possibility of no rain; a forecast of rain or snow implies the possibility of rain and the possibility of snow; and a forecast of frost does not imply a forecast of frost or snow or both. People accept these inferences, which each violate the semantics of standard logics. But, they are predictions from the theory that assertions refer to mental models of possibilities that each hold in default of knowledge to the contrary. The moral of the story is that inconsistencies in human reasoning, unlike those in standard logics, have only local consequences.
AutoGen: An Automated Dynamic Model Generation Framework for Recommender System

Considering the balance between revenue and resource consumption for industrial recommender systems, intelligent recommendation computing has been emerging recently. Existing solutions deploy the same recommendation model to serve users indiscriminately, which is sub-optimal for total revenue maximization. We propose a multi-model service solution by deploying different-complexity models to serve different-valued users. An automated dynamic model generation framework AutoGen is elaborated to efficiently derive multiple parameter-sharing models with diverse complexities and adequate predictive capabilities. A mixed search space is designed and an importance-aware progressive training scheme is proposed to prevent interference between different architectures, which avoids the model retraining and improves the search efficiency, thereby efficiently deriving multiple models. Extensive experiments are conducted on two public datasets to demonstrate the effectiveness and efficiency of AutoGen.
Dressed vs. pairwise states, and the geometric phase of monopoles and charges

A bstract We construct the Faddeev-Kulish dressed multiparticle states of electrically and magnetically charged particles, incorporating the effects of real and virtual soft photons. We calculate the properties of such dressed states under Lorentz transformations, and find that they can be identified with the pairwise multi-particle states that transform under the pairwise little group. The shifts in the dressing factors under Lorentz transformations are finite and have a simple geometric interpretation. Using the transformation properties of the dressed states we also present a novel, fully quantum field theoretic derivation of the geometric (Berry) phase obtained by an adiabatic rotation of the Dirac string, and also of the Dirac quantization condition. For half integer pairwise helicity, we show that these multiparticle states have flipped spin-statistics, reproducing the surprising fact that fermions can be made out of bosons.
Efficient Integrity Auditing Mechanism With Secure Deduplication for Blockchain Storage

Massive nodes in a blockchain form an off-chain distributed storage network to provide storage resources for users to meet large data upload requirements. However, this storage approach introduces security and performance issues. Firstly, it is difficult to guarantee the integrity of the data uploaded, and these data may be easily corrupted or lost. Moreover, uploading excessive duplicate data leads to a waste of storage resources. In this study, to address these issues, with a double-copy storage model for blockchain off-chain storage, a novel public auditing scheme with client-side deduplication is proposed to reduce the storage overhead of nodes and check the integrity of the off-chain data. Based on smart contracts, our scheme could realize efficient user ownership and off-chain data integrity verification automatically. In addition, both data encryption and deduplication are achieved based on message-locked encryption and an improved authenticator generation algorithm. Security analysis and experimental comparisons show that the proposed scheme is effective and practical.
Feature selection on quantum computers

Abstract In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higher-quality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer, and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark data sets. We observe competitive performance.
Error-mitigated quantum computing of Heisenberg spin chain dynamics

Abstract We simulate the time-dependent dynamics of a three-site spin chain described by the Heisenberg XXX Hamiltonian. The quantum circuit representing the time-dependent wave function is constructed using the Suzuki-Trotter approximation, and is executed on the quantum computer ibm _ kawasaki . At each time step, the density matrix of the three-qubit state is reconstructed by state tomography. By applying four different mitigation methods, Clifford data regression, Pauli twirling, density matrix purification, and density matrix orthogonalization, we demonstrate that accurate time-dependent populations and density matrices can be calculated on noisy superconducting-qubit type quantum computers.
USE OF MOBILE TECHNOLOGY IN THE TEACHING/LEARNING PROCESS - OPPORTUNITIES AND BARRIERS

Nowadays, mobile devices are able to support a large diversity of tasks being used for communication, socialization, interaction, collaboration, sharing of information and resources. As already known, mobile phones became active parts of the lifestyle of many teachers and students. The powerful capabilities of mobile devices and the increasing number of educational applications opened new perspectives for the integration of cell phones in the classroom. Mobile learning provides various opportunities for teachers to increase students' motivation, individualize the teaching process, and extend it outside the classroom. However, there is a long journey for mobile devices to become common tools, successfully integrated into the teaching and learning process. Despite the new opportunities and perceived benefits, there are also barriers to integration. These barriers have been broadly classified as external and internal. A key issue takes is the teacher's willingness to incorporate mobile technology into their own teaching demarches. But this decision depends on each teacher's personality, attitude, and perception concerning the effectiveness of exploiting the mobile technology facilities in teaching and learning. In this respect, this paper aims to explore the perceptions of Romanian and Lithuanian science education teachers as regards the opportunities and barriers of using mobile technology in education. The results show that overall, the teachers from both countries have a positive perception concerning the opportunities provided by mobile technology and a similar concern as regards the potential of its inappropriate use. The results also revealed a diversity of perceptions related to the teacher's personality, individual teaching practices, and existing technical support.
Refined Knowledge Transfer for Language-Based Person Search

This paper proposes a novel method, named Refined Knowledge Transfer (RKT), for language-based person search. Existing state-of-the-art methods do not deal with knowledge imbalance between image and text. In detail, textual identity knowledge is limited, but the image contains more identity knowledge. We propose Cross-Modal Knowledge Transfer (CMKT) to enhance textual identity knowledge by image to address this problem. Besides, multiple texts of one image include more identity knowledge than a single text. Thus, we propose Intra-Modal Knowledge Transfer (IMKT) to enhance textual identity knowledge by other texts. These two types of knowledge transfer will enhance the identity knowledge in text. Additionally, by considering that identity-irrelevant knowledge is transferred to text, we propose Knowledge Refiner (KR) to refine the knowledge in text. KR is capable of preserving identity knowledge and discarding identity-irrelevant knowledge. By combining CMKT, IMKT, and KR, RKT makes textual identity knowledge more salient. Extensive experiments show the state-of-the-art performance of RKT on the CUHK-PEDES and our proposed PRWPEDES-CN datasets. In addition, the decent generalization ability of RKT is also validated on the Flickr30K, CUB, and Flowers datasets.
Validation of Peak Ground Velocities Recorded on Very-high rate GNSS Against NGA-West2 Ground Motion Models

Observations of strong ground motion during large earthquakes are generally made with strong-motion accelerometers. These observations have a critical role in early warning systems, seismic engineering, source physics studies, basin and site amplification, and macroseismic intensity estimation. In this manuscript, we present a new observation of strong ground motion made with very high rate (&gt;= 5 Hz) Global Navigation Satellite System (GNSS) derived velocities. We demonstrate that velocity observations recorded on GNSS instruments are consistent with existing ground motion models and macroseismic intensity observations. We find that the ground motion predictions using existing NGA-West2 models match our observed peak ground velocities with a median log total residual of 0.03-0.33 and standard deviation of 0.72-0.79, and are statistically significant following normality testing. We finish by deriving a Ground Motion Model for peak ground velocity from GNSS and find a total residual standard deviation 0.58, which can be improved by ~2% when considering a simple correction for Vs30.
Reduction of cybersickness in head mounted displays use: A systematic review and taxonomy of current strategies

This literature review examines the existing research into cybersickness reduction with regards to head mounted display use. Cybersickness refers to a collection of negative symptoms sometimes experienced as the result of being immersed in a virtual environment, such as nausea, dizziness, or eye strain. These symptoms can prevent individuals from utilizing virtual reality (VR) technologies, so discovering new methods of reducing them is critical. Our objective in this literature review is to provide a better picture of what cybersickness reduction techniques exist, the quantity of research demonstrating their effectiveness, and the virtual scenes testing has taken place in. This will help to direct researches towards promising avenues, and illuminate gaps in the literature. Following the preferred reporting items for systematic reviews and meta-analyses statement, we obtained a batch of 1,055 papers through the use of software aids. We selected 88 papers that examine potential cybersickness reduction approaches. Our acceptance criteria required that papers examined malleable conditions that could be conceivably modified for everyday use, examined techniques in conjunction with head mounted displays, and compared cybersickness levels between two or more user conditions. These papers were sorted into categories based on their general approach to combating cybersickness, and labeled based on the presence of statistically significant results, the use of virtual vehicles, the level of visual realism, and the virtual scene contents used in evaluation of their effectiveness. In doing this we have created a snapshot of the literature to date so that researchers may better understand what approaches are being researched, and the types of virtual experiences used in their evaluation. Keywords: Virtual reality cybersickness Simulator Sickness Visually induced motion sickness reduction Systematic review Head mounted display.