Reviewerly/exHarmony huggingface dataset:
Radio Frequency Fingerprint Identification for LoRa Using Deep Learning

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on the intrinsic hardware characteristics of wireless devices. This paper designs a deep learning-based RFFI scheme for Long Range (LoRa) systems. Firstly, the instantaneous carrier frequency offset (CFO) is found to drift, which could result in misclassification and significantly compromise the stability of the deep learning-based RFFI system. CFO compensation is demonstrated to be effective mitigation. Secondly, three signal representations for deep learning-based RFFI are investigated in time, frequency, and time-frequency domains, namely in-phase and quadrature (IQ) samples, fast Fourier transform (FFT) results and spectrograms, respectively. For these signal representations, three deep learning models are implemented, i.e., multilayer perceptron (MLP), long short-term memory (LSTM) network and convolutional neural network (CNN), in order to explore an optimal framework. Finally, a hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy. The CFO will not change dramatically over several continuous days, hence it can be used to correct predictions when the estimated CFO is much different from the reference one. Experimental evaluation is performed in real wireless environments involving 25 LoRa devices and a Universal Software Radio Peripheral (USRP) N210 platform. The spectrogram-CNN model is found to be optimal for classifying LoRa devices which can reach an accuracy of 96.40% with the least complexity and training time.
Human-centered XAI: Developing design patterns for explanations of clinical decision support systems

Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing transparency of machine learning models. More recently, the focus on human-centered approaches to XAI has increased. Yet, there is a lack of practical methods and examples on the integration of human factors into the development processes of AI-generated explanations that humans prove to uptake for better performance. This paper presents a case study of an application of a human-centered design approach for AI-generated explanations. The approach consists of three components: Domain analysis to define the concept & context of explanations, Requirements elicitation & assessment to derive the use cases & explanation requirements, and the consequential Multi-modal interaction design & evaluation to create a library of design patterns for explanations. In a case study, we adopt the DoReMi-approach to design explanations for a Clinical Decision Support System (CDSS) for child health. In the requirements elicitation & assessment, a user study with experienced paediatricians uncovered what explanations the CDSS should provide. In the interaction design & evaluation, a second user study tested the consequential interaction design patterns. This case study provided a first set of user requirements and design patterns for an explainable decision support system in medical diagnosis, showing how to involve expert end users in the development process and how to develop, more or less, generic solutions for general design problems in XAI.
Communication-Efficient Federated Learning With Compensated Overlap-FedAvg

While petabytes of data are generated each day by a number of independent computing devices, only a few of them can be finally collected and used for deep learning (DL) due to the apprehension of data security and privacy leakage, thus seriously retarding the extension of DL. In such a circumstance, federated learning (FL) was proposed to perform model training by multiple clients' combined data without the dataset sharing within the cluster. Nevertheless, federated learning with periodic model averaging (FedAvg) introduced massive communication overhead as the synchronized data in each iteration is about the same size as the model, and thereby leading to a low communication efficiency. Consequently, variant proposals focusing on the communication rounds reduction and data compression were proposed to decrease the communication overhead of FL. In this article, we propose Overlap-FedAvg, an innovative framework that loosed the chain-like constraint of federated learning and paralleled the model training phase with the model communication phase (i.e., uploading local models and downloading the global model), so that the latter phase could be totally covered by the former phase. Compared to vanilla FedAvg, Overlap-FedAvg was further developed with a hierarchical computing strategy, a data compensation mechanism, and a nesterov accelerated gradients (NAG) algorithm. In Particular, Overlap-FedAvg is orthogonal to many other compression methods so that they could be applied together to maximize the utilization of the cluster. Besides, the theoretical analysis is provided to prove the convergence of the proposed framework. Extensive experiments conducting on both image classification and natural language processing tasks with multiple models and datasets also demonstrate that the proposed framework substantially reduced the communication overhead and boosted the federated learning process.
DocFormer: End-to-End Transformer for Document Understanding

We present DocFormer - a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). VDU is a challenging problem which aims to understand documents in their varied formats (forms, receipts etc.) and layouts. In addition, DocFormer is pre-trained in an unsupervised fashion using carefully designed tasks which encourage multi-modal interaction. DocFormer uses text, vision and spatial features and combines them using a novel multi-modal self-attention layer. DocFormer also shares learned spatial embeddings across modalities which makes it easy for the model to correlate text to visual tokens and vice versa. DocFormer is evaluated on 4 different datasets each with strong baselines. DocFormer achieves state-of-the-art results on all of them, sometimes beating models 4x its size (in no. of parameters).
DeRF: Decomposed Radiance Fields

With the advent of Neural Radiance Fields (NeRF), neural networks can now render novel views of a 3D scene with quality that fools the human eye. Yet, generating these images is very computationally intensive, limiting their applicability in practical scenarios. In this paper, we propose a technique based on spatial decomposition capable of mitigating this issue. Our key observation is that there are diminishing returns in employing larger (deeper and/or wider) networks. Hence, we propose to spatially decompose a scene and dedicate smaller networks for each decomposed part. When working together, these networks can render the whole scene. This allows us near-constant inference time regardless of the number of decomposed parts. Moreover, we show that a Voronoi spatial decomposition is preferable for this purpose, as it is provably compatible with the Painter’s Algorithm for efficient and GPU-friendly rendering. Our experiments show that for real-world scenes, our method provides up to 3× more efficient inference than NeRF (with the same rendering quality), or an improvement of up to 1.0 dB in PSNR (for the same inference cost).
Blending asynchronous and synchronous digital technologies and instructional approaches to facilitate remote learning

This two-stage qualitative-dominant sequential mixed-method study, using an online survey of elementary and secondary school English language teachers (N = 73) and follow-up interviews (N = 10), collectively explores how teachers in Hong Kong adapted their instruction to online teaching in responses to COVID-19. The findings indicate that teachers used a variety of asynchronous and synchronous digital technologies and instructional approaches to facilitate students’ learning, assess learning, and communicate with students and parents remotely. The findings suggest that a blend of asynchronous and synchronous modes are seen as optimum to support student learning online. A model is proposed on how teachers can blend asynchronous and synchronous digital technologies and instructional approaches within a sequence of learning.
Exploring the Factors Affecting Mobile Learning for Sustainability in Higher Education

Mobile learning (M-learning) has become an important instructional technology component in higher education. The goal of this research is to determine how Malaysian university students use M-learning in higher education. The technology acceptance model (TAM) concept was used to construct a theoretical model of M-learning acceptability. In theory, five independent criteria were discovered as contributing to the actual usage of M-learning for educational sustainability by influencing students’ attitudes towards M-learning and their intention to use it. A questionnaire survey based on the technology acceptance model (TAM) was used as the primary data collection technique, with 200 students from UTHM University of Malaysia participating. The data were analyzed using SPSS and Structural Equation Modeling (SEM-Amos). The results of the students’ attitudes towards using M-learning and their behavioral intentions to use M-learning show a beneficial impact on the actual use of M-learning as well as the long-term sustainability of M-learning in higher education. In addition, both male and female students were satisfied with perceived usefulness, perceived ease of use, perceived enjoyment, attitude towards use, task-technology fit, behavioral intention to use, perceived resources and actual use of mobile learning for educational sustainability. This study contributes to the validation of the extended TAM for M-learning by demonstrating that the predicted model predicts students’ attitudes towards using M-learning and their behavioral intentions in Malaysian higher education.
Preliminary assessment of heavy metals in surface water and sediment in Nakuvadra-Rakiraki River, Fiji using indexical and chemometric approaches

River water and sediment embody environmental characteristics that give valuable environmental management information. However, indexical and chemometric appraisal of heavy metals (HMs) in river water and sediment is very scarce in Island countries including Fiji. In this research, forty-five sediment and fifteen water samples from the Nakuvadra-Rakiraki River, Fiji were analyzed for appraising spatial distribution, pollution, and source identification of selected heavy metals (HMs) using the coupling tools of self-organizing map (SOM), compositional data analysis (CDA), and sediment and water quality indices. The mean concentration of HMs increased in the order of Cd < Co < Pb < Cu < Zn < Ni < Cr < Mn < Fe for sediment and Cd < Pb < Cu < Ni < Zn < Co < Cr < Fe < Mn for water, respectively. The outcomes of the enrichment factor, geo-accumulation index and contamination factor index varied spatially and most of the sediment samples were polluted by Pb, Mn, and Cu. The potential ecological risk recognized Cd, and Pb as ecological and public health risks to the surrounding communities. Based on SOM and CDA, three potential sources (e.g., point, nonpoint and lithological sources) of HMs for sediment and two sources (e.g., geogenic and human-induced sources) of HMs for water were identified. The spatial patterns of EWQI values revealed that the northern and northeast zones of the studied area possess a high degree of water pollution. The entropy weight indicated Ni and Cd as the main pollutants degrading the water quality. This study gives a baseline dataset for combined eco-environmental measures for the river's water and sediment pollution as well as contributes to an inclusive appraisal of HMs contamination in global rivers.
Explainable Deep Reinforcement Learning for UAV autonomous path planning

Autonomous navigation in unknown environment is still a hard problem for small Unmanned Aerial Vehicles (UAVs). Recently, some neural network-based methods are proposed to tackle this problem, however, the trained network is opaque, non-intuitive and difficult for people to understand, which limits the real-world application. In this paper, a novel explainable deep neural network-based path planner is proposed for quadrotor to fly autonomously in unknown environment. The navigation problem is modelled as a Markov Decision Process (MDP) and the path planner is trained using Deep Reinforcement Learning (DRL) method in simulation environment. To get better understanding of the trained model, a novel model explanation method is proposed based on the feature attribution. Some easy-to-interpret textual and visual explanations are generated to allow end-users to understand what triggered a particular behaviour. Moreover, some global analyses are provided for experts to evaluate and improve the trained network. Finally, real-world flight tests are conducted to illustrate that our path planner trained in the simulation is robust enough to be applied in the real environment directly.
A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.
Is semi‐selfish mining available without being detected?

Selfish mining attacks get a high prize due to the additional rewards unproportionate to their mining power (mining pools have particular advantages). Generally, this category of attacks stresses decreasing the threshold to maximize the rewards toward the view of attackers. Semi-selfish mining falls into the family of selfish mining attacks, where the threshold value is approximately 15%. However, it gets little attention to implement these attacks in practical. In this paper, we focus on the validity of semi-selfish mining attacks considering the probability of being detected. More specifically, we discuss mining strategies through backward deduction. That is to say that the attacking states derived from the observable states, which with normal forking rate, just as without semi-selfish mining attacks, toward the view of the honest miners. Rewards distribution is further investigated concerning these strategies. The simulation results indicate that it does not necessarily bring rewards advantage over large pools. Instead, the small pools have an advantage over the additional rewards. However, the probability for small pools to successfully implement these strategies is pretty low. That is, it is impossible for the pools, although profitable for them, to sponsor semi-selfish mining attacks without being detected.
Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query.Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing (NLP) applications.This book provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) is the best-known example.The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in NLP, information retrieval (IR), and beyond.This book provides a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area.It covers a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly.Two themes pervade the book: techniques for handling long documents, beyond typical sentence-bysentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size).Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques.However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this book also attempts to prognosticate where the field is heading.
Understanding digital bubbles amidst the COVID-19 pandemic: Evidence from DeFi and NFTs

This paper investigates digital financial bubbles amidst the COVID-19 pandemic. Using a sample of 9 DeFi tokens, 3 NFTs, Bitcoin, and Ethereum, we detect several bubbles overlapping the examined cryptoassets. We also uncover DeFi and NFT-specific bubbles in Summer 2020 suggesting distinct driving factors for this class of assets. We document that DeFi and NFTs bubbles are less recurrent but have higher magnitudes than cryptocurrencies' bubbles. We also find that COVID-19 and trading volume exacerbate bubble occurrences, while Total Value Locked (TVL) is negatively associated with cryptoassets' bubbles. Our results suggest that TVL can be used as a tool for market monitoring.
Scalable blockchain model using off-chain IPFS storage for healthcare data security and privacy

Traditional healthcare systems in the present scenario follow centralized client-server architecture to store and process patient-health related information. Data stored in each of the healthcare institution remain in silos which cannot be easily shared with other institutions due to technical and infrastructural constraints. Hospitals do not have an effective and secure data sharing mechanism leading to monetary and resource loss in the case of a person visiting different hospitals. Blockchain, a disruptive technology with secure and reliable decentralized framework, and can be used to circumvent problems in traditional healthcare architecture for secure storage, sharing and retrieval of Electronic Health Records (EHR). A blockchain-based framework integrated with InterPlanetary File System (IPFS) for EHR in healthcare management has been proposed in this paper. This proposed framework will enable healthcare institutions to maintain fail-safe and tamper-proof healthcare ledgers in a decentralized manner. Hospitals and doctors act as lightweight nodes, whereas patient nodes can be full or lightweight nodes. The model proposes two-factor authentication and multi-factor authentication for preventing fake node attacks. Patient-centric access model allows the patients to act as digital stewards for their health data, allowing access to doctors and hospitals on demand and revoking it after stipulated time. Symmetric key encryption (AES-128) is used for encrypting data before storing into IPFS. Asymmetric encryption (RSA-4096) is used for generating digital envelopes to pass on symmetric key to authorized entities. Digital signatures (RSA-1024) make sure that the transactions are valid and from authorized nodes. Hashing of the encrypted data is done using SHA-256 algorithm. Multiple layers of security implemented in this model makes sure that adversaries cannot obtain data stored in IPFS; even if they retrieve the data, it will not be meaningful since it is encrypted. The proposed framework for off-chain storage of health data using IPFS saves blockchain structure from scalability issues. Further the proposal for blockchain integration with IPFS helps preserve privacy in the healthcare system, making it highly secure, scalable and robust.
An enhanced hybrid arithmetic optimization algorithm for engineering applications

Arithmetic optimization algorithm (AOA) is a newly well-developed meta-heuristic algorithm that is inspired by the distribution behavior of main arithmetic operators in mathematics. Although the original AOA has shown well competitive performance with popular meta-heuristic algorithms, it still faces the issues of insufficient exploitation ability, ease of falling into local optima and low convergence accuracy in large-scale applications. In order to ameliorate these deficiencies, an enhanced hybrid AOA named CSOAOA, integrated with point set strategy, optimal neighborhood learning strategy and crisscross strategy, is developed in this paper. First, a good point set initialization strategy is added to obtain a higher-quality initial population, which improves the convergence speed of the algorithm. Then, the optimal neighborhood learning strategy is adopted to guide the individual’s search behavior and avoid the algorithm falling into the current local optimum, which boosts the search efficiency and calculation accuracy. Finally, by combining AOA with the crisscross optimization algorithm, the exploration and utilization ability of the crisscross algorithm are integrated into the CSOAOA. These strategies collaborate to enhance AOA in accelerating overall performance. The superiority of the proposed CSOAOA is comprehensively verified by comparing with the original AOA, six improved AOA and numerous celebrated and newly developed algorithms on the well-known 23 classical benchmark functions, IEEE Congress on Evolutionary Computation (CEC) 2019 test suite and IEEE CEC 2020 benchmark functions, respectively. Meanwhile, the practicability of CSOAOA is also highlighted by solving eight real-world engineering design problems. Furthermore, the statistical testing of CSOAOA has been conducted to validate its significance. Experimental results and statistical comparisons manifest the superior performance of CSOAOA over the comparison algorithms in terms of precision, convergence rate and solution quality. Therefore, CSOAOA is potentially a powerful and competitive meta-heuristic algorithm for solving complex engineering optimization problems.