Reviewerly/exHarmony huggingface dataset:
Fourier Behind Entanglement: A Spectral Approach to the Quantum Internet

Abstract In this study, the bridge between quantum fourier transform (QFT) and the most important effect inside quantum physics—entanglement—is presented. Moreover, all the applications of entanglement are impregnated by this equivalence, which concludes in the first spectral approach to the quantum Internet of the literature, which will allow the development of configurations that are more immune to noise as well as fault‐tolerant. Several protocols used in the quantum Internet are implemented on a 5‐qubit international business machine quantum (IBM Q) platform, experimentally verifying the aforementioned equivalence.
Document-level medical relation extraction via edge-oriented graph neural network based on document structure and external knowledge

Relation extraction (RE) is a fundamental task of natural language processing, which always draws plenty of attention from researchers, especially RE at the document-level. We aim to explore an effective novel method for document-level medical relation extraction.We propose a novel edge-oriented graph neural network based on document structure and external knowledge for document-level medical RE, called SKEoG. This network has the ability to take full advantage of document structure and external knowledge.We evaluate SKEoG on two public datasets, that is, Chemical-Disease Relation (CDR) dataset and Chemical Reactions dataset (CHR) dataset, by comparing it with other state-of-the-art methods. SKEoG achieves the highest F1-score of 70.7 on the CDR dataset and F1-score of 91.4 on the CHR dataset.The proposed SKEoG method achieves new state-of-the-art performance. Both document structure and external knowledge can bring performance improvement in the EoG framework. Selecting proper methods for knowledge node representation is also very important.
Ångström-Prescott Models of Solar radiation over Earth surface

Abstract The current energy demand increases every day and the deficit of electricity generation in Colombia due to technical aspects such as the lack of generation infrastructure, cost overruns in the construction projects of new plants and environmental variability as a consequence of climate change, entail a high risk of energy rationing; for this reason some of the users, whether industrial or residential, have decided to seek multiple alternatives to reduce the costs of their billing in interconnected areas and even supply their energy needs. Among these alternatives is found solar energy in its thermal and photovoltaic use, a resource that must be characterized in order to determine the technical and budgetary aspects involved in the implementation of profitable energy generation projects. This article seeks to provide technical tools for the design and implementation of generation projects based on solar energy, providing physical-mathematical approach based in the classical Angström-Prescott model that allow engineers and researchers to model the global solar radiation on the Earth’s surface in order to maximize the use of this resource in a specific geographical location. Although there are different ways to characterize the solar resource on the earth’s surface, this paper presents a review study focused on the most important global solar radiation estimation models present in the literature.
Privacy-aware Distributed Diagnosis of Multi-Agent Plans

In many multi-agent systems (MAS), agents are assigned to perform tasks. These tasks require designing plans for the agents, which are called multi-agent plan (MAP). Such systems are prone to failures due to variety of reasons. When a failure occurs, there is a need to diagnose it and identify the faulty agents. This problem has been traditionally addressed in a centralized manner. However, in some systems, agents might not be able to share plans due to privacy or single point of failure reasons. To address this challenge, we propose Distributed Diagnosis of Multi-Agent Plans (DDMAP) algorithms, which identify faulty agents of a MAS in a distributed manner without sharing plans. The contributions of this paper are: (1) formalizing DDMAP as a model-based diagnosis problem, and (2) presenting synchronous, asynchronous and semi-asynchronous distributed algorithms to diagnose the faulty agents. Experiments show that the semi-asynchronous algorithm performs better in terms of run-time while the performance in terms of communication overhead is comparable.
WCL: Client Selection in Federated Learning with a Combination of Model Weight Divergence and Client Training Loss for Internet Traffic Classification

Internet traffic classification (TC) is a critical technique in network management and is widely applied in various applications. In traditional TC problems, the edge devices need to send the raw traffic data to the server for centralized processing, which not only generates a lot of communication overhead but also leads to the privacy leakage and information security issues. Federated learning (FL) is a new distributed machine learning paradigm that allows multiple clients to train a global model collaboratively without raw traffic data sharing. The TC in a FL framework preserves the user privacy and data security by keeping the raw traffic data local. However, because of the different user behaviours and user preferences, traffic data heterogeneity emerges. The existing FL solutions introduce bias in model training by averaging the local model parameters from all heterogeneous clients, which degrades the classification accuracy of the learnt global classification model. To improve the classification accuracy in heterogeneous data environment, this paper proposes a novel client selection algorithm, namely, WCL, in federated paradigm based on a combination of model weight divergence and local model training loss. Extensive experiments on the public traffic dataset QUIC and ISCX have proved that the WCL algorithm obtains, compared to CMFL, superior performance in improving model accuracy and convergence speed on low heterogeneous traffic data and high heterogeneous traffic data, respectively.
Generating Face Images Using VQGAN and Sparse Transformer

In this paper, we present a system for face image generation using VQGAN and sparse transformer. We explore the use of VQGAN models to learn visual tokens of image constituents and enhance the autoregressive priors to generate synthetic samples. We demonstrate that the routing transformer which learns sparse attention patterns over the visual tokens can generate samples with high-quality on face image datasets such as FFHQ and CelebA-HQ, while not suffering from mode collapse and lack of diversity.
AGR4BS: A Generic Multi-Agent Organizational Model for Blockchain Systems

Blockchain is a very attractive technology since it maintains a public, append-only, immutable and ordered log of transactions which guarantees an auditable ledger accessible by anyone. Blockchain systems are inherently interdisciplinary since they combine various fields such as cryptography, multi-agent systems, distributed systems, social systems, economy, and finance. Furthermore, they have a very active and dynamic ecosystem where new blockchain platforms and algorithms are developed continuously due to the interest of the public and the industries to the technology. Consequently, we anticipate a challenging and interdisciplinary research agenda in blockchain systems, built upon a methodology that strives to capture the rich process resulting from the interplay between the behavior of agents and the dynamic interactions among them. To be effective, however, modeling studies providing insights into blockchain systems, and appropriate description of agents paired with a generic understanding of their components are needed. Such studies will create a more unified field of blockchain systems that advances our understanding and leads to further insight. According to this perspective, in this study, we propose using a generic multi-agent organizational modeling for studying blockchain systems, namely AGR4BS. Concretely, we use the Agent/Group/Role (AGR) organizational modeling approach to identify and represent the generic entities which are common to blockchain systems. We show through four real case studies how this generic model can be used to model different blockchain systems. We also show briefly how it can be used for modeling three well-known attacks on blockchain systems.
TFHE-rs: A library for safe and secure remote computing using fully homomorphic encryption and trusted execution environments

Fully Homomorphic Encryption (FHE) and Trusted Execution Environ-ments (TEEs) are complementing approaches that can both secure computa-tions running remotely on a public cloud. Existing FHE schemes are, however, malleable by design and lack integrity protection, making them susceptible to integrity breaches where an adversary could modify the data and corrupt the output. This paper describes how both confidentiality and integrity of remote compu-tations can be assured by combining FHE with hardware based secure enclave technologies. We provide a software library for performing FHE within the Intel SGX TEE, written in the memory-safe programming language Rust to strengthen the internal safety of software and reduce its attack surface. We evaluate a sample application written with our library. We demonstrate that we can feasibly combine these concepts and provide stronger security guar-antees with a minimal development effort.
Implementations of Heralded Solid‐State SWAP and SWAP$\sqrt {SWAP}$ Gates through Waveguide‐Assisted Interactions

Abstract Some heralded schemes are presented for realizing solid‐state quantum gates, including SWAP and gates. They are achieved by emitter‐photon scattering in 1D waveguides, and the qubits are encoded on the degenerate ground states of the solid‐state emitters. The schemes for these two quantum gates feature a filtering mechanism that the faulty scattering events between quantum emitters and photons can be mapped into the heralded photon losses. That is, the protocols can turn physical errors caused by system imperfections into the detection of photon polarization, which is advantageous for quantum information and quantum computation. Furthermore, no auxiliary solid‐state qubits are needed in our schemes, which reduces not only quantum resource but also error. The calculations reveal that the success probabilities and fidelities of the two quantum gates are high with current experimental technologies.
An adaptive and general model for label noise detection using relative probabilistic density

We present a model, called relative probability density (RPD), to detect label noise by utilizing the contrasting characteristics in different classes. RPD has a natural ratio structure so that a powerful measurement, the Kullback–Leibler Importance Estimation Procedure (KLIEP), can be directly applied for its calculation instead of calculating the probability density in the numerator and denominator separately. In addition, the RPD model can be reduced to a new form that contains only P(Y|X) and can be calculated with only a probabilistic classifier and without relying on any other specific measurements, specific loss functions, noise estimation or other extra parameters. Furthermore, an RPD-based filter learning framework, which can adaptively optimize the threshold to accurately identify label noise, is proposed. The experimental results on synthetic and real data sets demonstrate that the RPD-based filter learning framework is more effective than some representative methods. The superior generality and adaptiveness, in addition to the simple design, make it a good replacement for traditional probabilistic classifiers on label-noisy data.
A Systematic Review for Sentiment Analysis of Arabic Dialect Texts Researches

People in the Arab world prefer to use their dialects in writing while using social media sites which results in generating a significant amount of texts. Sentiment analysis in texts is one of the most important applications of the Natural Language Processing field of science. Sentiment Analysis involves classifying texts based upon emotions or polarity. Different approaches have been utilized so far for Sentiment Analysis of Arabic Texts. In this systematic review, the author aims to explore the researches and studies that were in the field of Sentiment Analysis of Arabic Dialects to report the utilized approaches, constructed datasets, most common type of dialects explored by researchers, and reported results of Sentiment Analysis in term of accuracy, recall, precision, and F-Measure. Author reported the following findings for this systematic review: (1) in most of the included research papers the dialect type is not specified and mostly it was mentioned (Arabic Dialects) in general, however, some other dialects were explored specifically by authors such as Saudi Dialect (2) 50% of the utilized datasets in the included research papers are constructed by the authors themselves, moreover, the size of most of the utilized datasets are between 10,000 and 50,000, and very limit number of datasets had size above 100,000, (3) machine learning algorithms (classifiers) are the most common approach that were used for Sentiment Analysis of Arabic Dialects (4) the best result for Sentiment Analysis of Arabic Dialects achieved while different machine learning algorithms (classifiers) were used, and (5) among social media platforms, Twitter is the most common utilized online platform for constructing datasets for Arabic Dialects texts.
Multiscale residual fusion network for image denoising

Deep-learning methods have been developed in recent years and have achieved dramatic improvements for image denoising. The existing deep-learning methods can be conducted using two major models: Encoder–decoder and high-resolution, where the high-resolution model has superior resolution ability for detail description and restoration. In this study, a high-resolution-based network called multiscale residual fusion network (MRF-Net) is proposed, which employed the spatial and contextual information of images. In detail, dilated convolution layers are used to enlarge the network's receptive field and learned sufficient features in a multiscale feature extracting module. The function of dilated convolution is reinterpreted here and it is viewed as a complex downsampling operation. Therefore, multiscale feature analysis could be performed in the proposed network by dilated convolution. Multilevel feature maps are sequentially obtained through a residual projection module, where considerable contextual and spatial information was collected from the multiscale features. In a residual fusion module, all maps were aggregated to generate a residual image effectively for noise removal. Experiments demonstrated that the MRF-Net outperformed several state-of-the-art model-based and deep-learning methods in both blind and non-blind image denoising tests. Meanwhile, ablation studies were executed to verify the denoising performance of each module. Moreover, this method exhibited high computational efficiency, thus demonstrating its practicability.
Towards a Blockchain based Intelligent and Secure Voting

Blockchain and machine learning (ML) have emerged as two disruptive technologies that could transform various sectors. Blockchain is known as peer to peer decentralized, distributed ledger technology that enables to store and exchange anything of value, it is deterministic, permanent and immutable. On the other hand, ML is the ability of computers to learn without being programmed, and it is probabilistic. Hence, when blockchain and ML converge, they surely would benefit from each other. Blockchain can enhance security of ML platforms, and ML can provide automation and optimization to the blockchain solutions. In this work, we advocate the importance of enhancing blockchain with ML algorithms, as a proof of principle we address the issue of secure and intelligent e-voting.The use of blockchain technology has brought tremendous different application domains, e-voting is one of them. Most existing e-Voting systems require central authority during the process of authentication and verification of the voter. In this paper we propose a safe online voting approach based on blockchain and ML to provide a solution to this issue. We use blockchain to ensure integrity and transparency of the votes, and ML for automating the verification process of eligible voters based on AI-powered oracle platform for face authentication. The proposed solution offers automation, security, and mobility to the voting system.
Comparative Study of Handwritten Character Recognition System for Indian Languages

Handwritten Character Recognition is the emerging domain for the research in image processing and pattern recognition because a huge amount of handwritten and OCR documents need to be processed in daily needs of different systems. Based on surveys of Human Development very less amount of Indian population can write and read English language. It specifies the need for research in Indian script for OCR technology. The research in this area is actually needed for developing countries such as India and remains beneficial to inspire the human-machine interface system. HCR makes it ease the automatic processing of tax and admission forms, bank cheques, addresses and postal codes. In the comparison of printed character recognition, handwritten character recognition is difficult because it can be different from person to person based on the style of writing, curve, size, strokes and thickness of character. Sufficient research work has been performed in foreign handwritten characters such as Arabic, Chinese and Japanese but very less research work has been performed in Indian scripts. This paper provides a comparative study of famous and well-known Tamil, Telugu, Kannada and Gujarati Indian scripts. This paper provides a detailed study based on the used methodology, dataset and accuracy.
Information Security Management of Sharing Economy Based on Blockchain Technology

Based on the unprecedented development of current information technology, under the prevalence of third-party payment, in order to increase the utilization rate of idle resources and meet the diversified economic needs of today’s era, as an emerging economic model-the “sharing economy”, combined with Internet big data, it has effectively realized the perfect connection between supply and demand, and it has quickly swept through all walks of life. However, while promoting economic development, the lack of regulatory measures and the imperfect credit system have severely restricted the further development of the sharing economy. Blockchain technology (BT), as a new distributed infrastructure and computing method, has always been the core technology of Bitcoin. Based on the development of generalized economic model and BT, this paper analyzes the shortcomings of generalized economic model and blockchain technology through information fusion big data and proposes a kind of information based on information fusion big data+public BT. The security management system adapts to the status quo of the sharing economy and uses the functions of information fusion big data+blockchain technology to solve the current information security risks faced by the sharing economy. The experimental results of this article show that the sharing economy information security management system based on information fusion big data+blockchain technology has good protection capabilities. The interception rate of external information reached 95%, and the interception rate of system information reached 93%. Better protection of privacy leaks is needed.
Recovery of Lossy Compressed Music Based on CNN Super-Resolution and GAN

The lossy compression of music audio files through MP3 cause loss of sound quality, which results in the decline of auditory experience and cannot meet the requirements of high-quality music playback in a wide variety of occasions. To solve this problem, after long-term exploration, we proposed an approach of time-domain and frequency-domain bandwidth expansion based on CNN and GAN to support high-quality MP3 lossy compressed music recovery by analyzing the characteristics and correlation of human voice and different kinds of musical instruments in high-frequency and low-frequency portions of music. For bandwidth extension in the frequency domain, a method similar to image inpainting is designed; for bandwidth extension in the time domain, a super-resolution method is designed. We compared the proposed method with RNN, BPCNN, and other methods. The experimental results prove that the method proposed in this paper has the lowest spectral loss and the best reconstruction quality. The experimental results of human ear discrimination further prove the effectiveness of the audio enhancement algorithm.
Study on Military Regulations Knowledge Construction based on Knowledge Graph

Military regulations are a set of guidelines and constraints to guide and regulate military behavior. They contain many contents and have complex relationship with each other. This paper studies the knowledge modeling method of military regulations based on knowledge graphs, analyzes the element composition of military regulations, constructs the knowledge ontology of military regulations, gives the construction process of military regulations knowledge graph and the similarity analysis method of graph based on knowledge graph network, and analyzes the application of knowledge graph construction, it provides support for the overall grasp of military regulations and the construction and implementation of military regulations.
Real-Time Safety and Control of Robotic Manipulators with Torque Saturation in Operational Space

This paper presents a real-time safety and control for robot manipulators using control barrier functions and control Lyapunov functions in operational space. We first define the operational space in terms of system dynamics, jacobian, and torques and then ensure safety by designing Control Barrier Functions (CBF) around the body links of the robotic manipulator. The control barrier function provides provable collision-free behavior for the robotic manipulator by modifying the nominal control in a minimally invasive manner to formally satisfy the safety constraints. CBFs are formulated as a quadratic programming problem, which can be solved in real-time. We also design a controller based on Rapidly Exponentially Stabilizing Control Lyapunov Function (RESCLF) and quadratic programming to meet multiple objectives while ensuring exponential convergence. We then extend our formulation to solve RESCLF and CBF in a unified formulation to design the controller while ensuring the safety of manipulators and guaranteeing the torque saturation. The efficacy of the proposed approach is shown on 7 Degree of Freedom (DoF) KUKA LBR iiwa robot using Dynamic Animation and Robotics Toolkit (DART) physics engine.
Towards a Peer-to-Peer Federated Machine Learning Environment for Continuous Authentication

The in-depth consideration of security aspects in modern web infrastructures has become essential to stay competitive. In this context, continuous authentication is a promising approach to prevent the misuse of digital identities. To this end, machine learning (ML) models are well suited to analyze user behavior and to detect anomalies, due to their ability to identify complex patterns and trends that usually cannot be reflected by static rule-based approaches. However, the training of powerful ML models requires large amounts of data, which are often not available within a single organization. Consequently, a federated training of these models by cooperating organizations offers a promising solution, but leads to concerns about coordination, regulations, and quality assurance. To tackle these challenges, we present an approach that combines three research areas: (1) the establishment of continuous user authentication based on (2) a ML model trained by an organized peer-to-peer federation involving different organizations that is underpinned by (3) federated data governance ensuring regulatory compliance and quality of resulting artefacts.