Reviewerly/exHarmony huggingface dataset:
Detecting disturbances in network-coupled dynamical systems with machine learning

Identifying disturbances in network-coupled dynamical systems without knowledge of the disturbances or underlying dynamics is a problem with a wide range of applications. For example, one might want to know which nodes in the network are being disturbed and identify the type of disturbance. Here, we present a model-free method based on machine learning to identify such unknown disturbances based only on prior observations of the system when forced by a known training function. We find that this method is able to identify the locations and properties of many different types of unknown disturbances using a variety of known forcing functions. We illustrate our results with both linear and nonlinear disturbances using food web and neuronal activity models. Finally, we discuss how to scale our method to large networks.
Rule Extraction using Machine Learning Classifiers for Complex Event Processing

The large volume of data processing is always challenging for real-time applications. These applications need an optimal framework for handling large scale data and correlating these streams in real time to make better decision making. Complex event processing has emerged as a novel methodology for handling event streams based on atomic events or complex events to find useful patterns by predefined rules.Rules play a major role in these systems and the streams are matched with rules created through a decision tree and machine learning classifier algorithms. In this research work, we propose a complex event processing based framework for rule extraction as well as a comparative analysis of rule-based classifier algorithms for automatic extraction of rules, and since event rules are based on human expertise, sometimes they fail due to a static approach, therefore there is a need for an automatic rule extraction framework.The comparative analysis is performed using a case study of an air quality dataset that outperformed traditional approaches to rule extraction for stream data. Decision tree fetched most number of rules with an accuracy of 99%.The classifier’s learning rate show how efficiently the rule are fetched.
A Comprehensive Survey on Sentiment Analysis Techniques

Sentiment analysis is a natural language processing (NLP) technique used to decide if the underlying sentiment is positive, negative, or neutral. Subjective information from the text can be extracted using sentiment analysis by recognizing its context and position. Data from a variety of sources, like social network comments, news stories, consumer reviews, and more, can be used for sentiment analysis. Sentiment analysis uses different algorithms to analyze words, phrases, and context available in text and different procedures to determine the overall sentiment communicated. There are various ways in which sentiment analysis is performed, ranging from rule-based methods that use lists of positive and negative terms as labeled data for training machine learning algorithms to building classifiers. Understanding social sentiment, underlying intents, and responses to various characteristics of humans can be done with the help of sentiment analysis, which helps in decision-making. The primary goal of this work is to provide the audience with the knowledge needed to understand sentiment analysis, highlight potential opportunities and challenges, and investigate recent studies that have been published in reputable resources focusing on the field of sentiment analysis in NLP.
Operating Coupled VO<sub>2</sub>-based Oscillators for Solving Ising Models

Coupled nano-oscillators are attracting increasing interest because of their potential to perform computation efficiently, enabling new applications in computing and information processing. The potential of phase transition devices for such dynamical systems has recently been recognized. This paper investigates the implementation of coupled VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -based oscillator networks to solve combinatorial optimization problems. The target problem is mapped to an Ising model, which is solved by the synchronization dynamics of the system. Different factors that impact the probability of the system reaching the ground state of the Ising Hamiltonian and, therefore, the optimum solution to the corresponding optimization problem, are analyzed. The simulation-based analysis has led to the proposal of a novel Second-Harmonic Injection Locking (SHIL) schedule. Its main feature is that SHIL signal amplitude is repeatedly smoothly increased and decreased. Reducing SHIL strength is the mechanism that enables escaping from local minimum energy states. Our experiments show better results in terms of success probability than previously reported approaches. An experimental Oscillatory Ising Machine (OIM) has been built to validate our proposal.
Deep Reinforcement Learning With Explicit Context Representation

Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state using contextual key frames (CKFs), which can then be used to extract a function that represents the affordances of the state; in addition, two loss functions are introduced with respect to the affordances of the state. The novelty of the IECR framework lies in its capacity to extract contextual information from the environment and learn from the CKFs' representation. We validate the framework by developing four new algorithms that learn using context: Iota deep Q-network (IDQN), Iota double deep Q-network (IDDQN), Iota dueling deep Q-network (IDuDQN), and Iota dueling double deep Q-network (IDDDQN). Furthermore, we evaluate the framework and the new algorithms in five discrete environments. We show that all the algorithms, which use contextual information, converge in around 40 000 training steps of the neural networks, significantly outperforming their state-of-the-art equivalents.
Asymmetric Graph Contrastive Learning

Learning effective graph representations in an unsupervised manner is a popular research topic in graph data analysis. Recently, contrastive learning has shown its success in unsupervised graph representation learning. However, how to avoid collapsing solutions for contrastive learning methods remains a critical challenge. In this paper, a simple method is proposed to solve this problem for graph representation learning, which is different from existing commonly used techniques (such as negative samples or predictor network). The proposed model mainly relies on an asymmetric design that consists of two graph neural networks (GNNs) with unequal depth layers to learn node representations from two augmented views and defines contrastive loss only based on positive sample pairs. The simple method has lower computational and memory complexity than existing methods. Furthermore, a theoretical analysis proves that the asymmetric design avoids collapsing solutions when training together with a stop-gradient operation. Our method is compared to nine state-of-the-art methods on six real-world datasets to demonstrate its validity and superiority. The ablation experiments further validated the essential role of the asymmetric architecture.
New multiparty measurement-device-independent quantum secret sharing protocol based on entanglement swapping

A new multiparty measurement-device-independent quantum secret sharing (MDI-QSS) protocol is proposed based on entanglement swapping. All the measurement tasks are delegated to an untrusted party, eliminating all detector-side-channel loopholes. Only Einstein–Podolsky–Rosen (EPR) pairs, single photons and Bell-state measurements are required irrespective of the number of participants. Compared with existing MDI-QSS protocols, no additional unitary operations or Greenberger–Horne–Zeilinger (GHZ)-state measurements are needed. Our protocol is feasible experimentally and holds inspiring potential for the application in practical multiparty quantum communication networks.
3D Multimodal Visualization of Medical Data: Applied to Perfusion-Weighted MRI

Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging method used in radiology to create detailed images of anatomy and provide physiological data about the brain. Perfusion-weighted magnetic resonance imaging (PW-MRI) is one of MRI techniques used to study blood flow in the brain. This technique aims to estimate the parameters that characterize cerebral blood flow in a regular grid formed by large volume voxels, called the acquisition grid. One of the primary metrics assessed in PW-MRI is cerebral blood volume (CBV), which quantifies the amount of blood flowing through a specific brain region within a defined time frame. The accuracy of the quantification of CBV in each voxel of the acquisition grid is affected by the partial volume effect (PVE), which introduces a significant bias in cerebral perfusion measurements. In this research, we introduce an innovative multimodal technique aimed at enhancing the modeling of cerebral blood volume (CBV). This method integrates diverse tissue characteristics into the acquisition volume, effectively tackling and rectifying the issue of partial volumes of brain substances within the acquisition area. Our novel approach effectively mitigates the partial volume effect within the acquisition grid, ultimately providing a high-resolution 3D visualization of CBV. The first results show that we can qualify with more relevance whether a region contains a tumor or not.
An Efficient Violence Detection Approach for Smart Cities Surveillance System

Detecting violence in surveillance videos is crucial in activity recognition, with wide-ranging applications in unmanned aerial vehicles (UAVs), internet video filtering, and related domains. This study proposed a highly effective deep learning architecture that employs a two-stream approach, combining a 3D convolution network with a merging module for violence detection. One stream analyzes RGB frames with suppressed background, while the other focuses on the optical flow between corresponding frames. These inputs are precious in identifying violent actions often characterized by distinctive body movements. To ensure robust long-range feature extraction with fewer parameters, we replace the 3D depth-wise convolution operation at each layer instead of the conventional 3D. Our model outperforms existing methods on challenging datasets such as RWF2000, Real-Life Violence Situation (RLVS), and Movie Fight, securing state-of-the-art results. Our experiments demonstrate that the proposed model is well-suited for edge devices, offering computational efficiency and precise detection capabilities.
Application of the metauniverse in cities, its concept, advantages and disadvantages

In the second half of 2021, companies around the world decided to make a massive digital transition into the virtual space. The development of metaverse poses a lot of questions for the society, but so far, few people think about how the appearance and structures of cities will change if the majority of residents go into virtual reality. The article discusses the latest trends and directions in society and science, which is known to us as the metaverse, attempts to discuss this trend and its connection with architecture and urban planning through scientific literature, their analysis and the actual stage of development of this trend.&#x0D; The purpose of study is to provide a basic understanding of the metaverse and its application analyses to cities at this stage.&#x0D; The tasks of the study are: definition and understanding of the metaverse and its working systems; application of this direction/trend in cities; reveal the main advantage and disadvantage of using this direction for cities. To achieve the proposed result, it is necessary to use the method of analysis of scientific literature and documents related to the metaverse concept and its application in cities to reach and form conclusions. This study concluded in studying the latest direction and application of metaverse in urban planning and identifying some of its advantages and disadvantages.&#x0D; The scientific novelty of the study lies in the study and application of the metaverse concept in cities and the identification of its main advantages and disadvantages. The author emphasizes that this direction does not actually reconstruct cities, but on the contrary, a new function of reality appears in the city that people want to see and, as a result, should make cities more livable.
Application and use of telepresence robots in libraries and information center services: prospect and challenges

Purpose The purpose of this paper is to explore how robots are being used in libraries and information centers to transform their services and what are the future possibilities and trends in the application of robots in libraries. Design/methodology/approach Through a review of the literature, this paper analyzes various library websites and consults literature relating to the use of telepresence robots in libraries; the current application of robots in libraries has been enumerated along with case studies of libraries currently adopting telepresence robots. Findings With the practical examples of libraries using different types of robots, this study summarizes diverse activities of artificial intelligence-mediated robots. The uses of telepresence technology in libraries help to enhance library services, reach new users and provide a more inclusive and accessible library experience. Telepresence robots enhance the quality and accessibility of library services, expand library outreach and provide new opportunities for virtual engagement and programming. The application of telepresence robots in libraries can offer many benefits, but there are also several challenges that libraries must address to ensure successful implementation. Social implications This study will motivate libraries and library professionals to take advantage of adopting telepresence robots in library and information center services and further accelerate library operations in the right direction. Originality/value This paper highlights how the introduction of telepresence robots in libraries improves services and productivity and creates a more engaging environment for the user group. The benefits and challenges of using robots in the library and the future trend in the application of telepresence robots in libraries are also discussed.
ChatGPT What does it mean for Language Centres

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A Modular Ontology for MODS – Metadata Object Description Schema

The Metadata Object Description Schema (MODS) was developed to describe bibliographic concepts and metadata and is maintained by the Library of Congress. Its authoritative version is given as an XML schema based on an XML mindset which means that it has significant limitations for use in a knowledge graphs context. We have therefore developed the Modular MODS Ontology (MMODS-O) which incorporates all elements and attributes of the MODS XML schema. In designing the ontology, we adopt the recent Modular Ontology Design Methodology (MOMo) with the intention to strike a balance between modularity and quality ontology design on the one hand, and conservative backward compatibility with MODS on the other.
Data Encryption and Anonymization Techniques for Enhanced Information System Security and Privacy

The rapid evolution of digital technology has ushered in a new era of data-driven information systems, bringing both unprecedented convenience and complex challenges to the forefront. This research delves into the realm of data encryption and anonymization techniques to enhance the security and privacy of information systems. The study encompasses a comprehensive exploration of diverse encryption and anonymization methods, evaluating their effectiveness through qualitative and quantitative analysis. A bibliometric analysis, facilitated by VOSviewer, unveils influential authors, research trends, and collaborative networks in the field. The research sheds light on the practical implications of encryption and anonymization techniques through real-world case studies and offers insights into the multidimensional landscape of information system security and user privacy.
Why Batch Normalization Damage Federated Learning on Non-IID Data?

As a promising distributed learning paradigm, federated learning (FL) involves training deep neural network (DNN) models at the network edge while protecting the privacy of the edge clients. To train a large-scale DNN model, batch normalization (BN) has been regarded as a simple and effective means to accelerate the training and improve the generalization capability. However, recent findings indicate that BN can significantly impair the performance of FL in the presence of non-i.i.d. data. While several FL algorithms have been proposed to address this issue, their performance still falls significantly when compared to the centralized scheme. Furthermore, none of them have provided a theoretical explanation of how the BN damages the FL convergence. In this article, we present the first convergence analysis to show that under the non-i.i.d. data, the mismatch between the local and global statistical parameters in BN causes the gradient deviation between the local and global models, which, as a result, slows down and biases the FL convergence. In view of this, we develop a new FL algorithm that is tailored to BN, called FedTAN, which is capable of achieving robust FL performance under a variety of data distributions via iterative layer-wise parameter aggregation. Comprehensive experimental results demonstrate the superiority of the proposed FedTAN over existing baselines for training BN-based DNN models.
Multi-objectives reinforcement federated learning blockchain enabled Internet of things and Fog-Cloud infrastructure for transport data

For the past decade, there has been a significant increase in customer usage of public transport applications in smart cities. These applications rely on various services, such as communication and computation, provided by additional nodes within the smart city environment. However, these services are delivered by a diverse range of cloud computing-based servers that are widely spread and heterogeneous, leading to cybersecurity becoming a crucial challenge among these servers. Numerous machine-learning approaches have been proposed in the literature to address the cybersecurity challenges in heterogeneous transport applications within smart cities. However, the centralized security and scheduling strategies suggested so far have yet to produce optimal results for transport applications. This work aims to present a secure decentralized infrastructure for transporting data in fog cloud networks. This paper introduces Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for Transport Infrastructure. MORFLB aims to minimize processing and transfer delays while maximizing long-term rewards by identifying known and unknown attacks on remote sensing data in-vehicle applications. MORFLB incorporates multi-agent policies, proof-of-work hashing validation, and decentralized deep neural network training to achieve minimal processing and transfer delays. It comprises vehicle applications, decentralized fog, and cloud nodes based on blockchain reinforcement federated learning, which improves rewards through trial and error. The study formulates a combinatorial problem that minimizes and maximizes various factors for vehicle applications. The experimental results demonstrate that MORFLB effectively reduces processing and transfer delays while maximizing rewards compared to existing studies. It provides a promising solution to address the cybersecurity challenges in intelligent transport applications within smart cities. In conclusion, this paper presents MORFLB, a combination of different schemes that ensure the execution of transport data under their constraints and achieve optimal results with the suggested decentralized infrastructure based on blockchain technology.
The potential of Large Language Models in language education

This editorial explores the potential of Large Language Models (LLMs) in language education. It discusses the role of LLMs in machine translation, the concept of ‘prompt programming’, and the inductive bias of LLMs for abstract textual reasoning. The editorial also highlights using LLMs as creative writing tools and their effectiveness in paraphrasing tasks. It concludes by emphasizing the need for responsible and ethical use of these tools in language education.
Sanity Checks for Saliency Methods Explaining Object Detectors

Saliency methods are frequently used to explain Deep Neural Network-based models. Adebayo et al.’s work on evaluating saliency methods for classification models illustrate certain explanation methods fail the model and data randomization tests. However, on extending the tests for various state of the art object detectors we illustrate that the ability to explain a model is more dependent on the model itself than the explanation method. We perform sanity checks for object detection and define new qualitative criteria to evaluate the saliency explanations, both for object classification and bounding box decisions, using Guided Backpropagation, Integrated Gradients, and their Smoothgrad versions, together with Faster R-CNN, SSD, and EfficientDet-D0, trained on COCO. In addition, the sensitivity of the explanation method to model parameters and data labels varies class-wise motivating to perform the sanity checks for each class. We find that EfficientDet-D0 is the most interpretable method independent of the saliency method, which passes the sanity checks with little problems.
Toward Better SSIM Loss for Unsupervised Monocular Depth Estimation

Unsupervised monocular depth learning generally relies on the photometric relation among temporally adjacent images. Most of previous works use both mean absolute error (MAE) and structure similarity index measure (SSIM) with conventional form as training loss. However, they ignore the effect of different components in the SSIM function and the corresponding hyperparameters on the training. To address these issues, this work proposes a new form of SSIM. Compared with original SSIM function, the proposed new form uses addition rather than multiplication to combine the luminance, contrast, and structural similarity related components in SSIM. The loss function constructed with this scheme helps result in smoother gradients and achieve higher performance on unsupervised depth estimation. We conduct extensive experiments to determine the relatively optimal combination of parameters for our new SSIM. Based on the popular MonoDepth approach, the optimized SSIM loss function can remarkably outperform the baseline on the KITTI-2015 outdoor dataset.