Reviewerly/exHarmony huggingface dataset:
Well-calibrated confidence measures for multi-label text classification with a large number of labels

We extend our previous work on Inductive Conformal Prediction (ICP) for multi-label text classification and present a novel approach for addressing the computational inefficiency of the Label Powerset (LP) ICP, arrising when dealing with a high number of unique labels. We present experimental results using the original and the proposed efficient LP-ICP on two English and one Czech language data-sets. Specifically, we apply the LP-ICP on three deep Artificial Neural Network (ANN) classifiers of two types: one based on contextualised (bert) and two on non-contextualised (word2vec) word-embeddings. In the LP-ICP setting we assign nonconformity scores to label-sets from which the corresponding p-values and prediction-sets are determined. Our approach deals with the increased computational burden of LP by eliminating from consideration a significant number of label-sets that will surely have p-values below the specified significance level. This reduces dramatically the computational complexity of the approach while fully respecting the standard CP guarantees. Our experimental results show that the contextualised-based classifier surpasses the non-contextualised-based ones and obtains state-of-the-art performance for all data-sets examined. The good performance of the underlying classifiers is carried on to their ICP counterparts without any significant accuracy loss, but with the added benefits of ICP, i.e. the confidence information encapsulated in the prediction sets. We experimentally demonstrate that the resulting prediction sets can be tight enough to be practically useful even though the set of all possible label-sets contains more than $1e+16$ combinations. Additionally, the empirical error rates of the obtained prediction-sets confirm that our outputs are well-calibrated.
Sizing optimization of truss structures using the political optimizer (PO) algorithm

The challenge that structural optimization tasks pose by dint of their highly non-linear design constraints and complex solution domains has spurred both the implementation and development of many novel metaheuristic algorithms over the past two decades. In this study, the very recently proposed political optimizer algorithm has been implemented in the context of structural optimization. Inspired by the multi-phased political process in parliamentary democracies, the algorithm efficiently balances between exploration and exploitation by logically dividing the population of search agents into political parties that compete for constituency (electoral district) domination. This competitive-based population partitioning scheme ensures that enough of the search space is thoroughly investigated for the global optima all the while maintaining algorithmic speed and efficiency. Moreover, a local search mechanism known as the recent past-based position updating strategy (RPPUS) provides for an effective exploitation strategy where every search agent is allowed to learn from its previous behavior and hence collectively guide the population towards better solutions and away from worse ones. To quantitatively assess the performance of the algorithm, three planar trusses (10 bar, 18 bar, and 200 bar) and four space trusses (22 bar, 25 bar, 72 bar, and 942-bar) with multiple loading conditions and design constraints have been considered. Results show that for small/medium-scale structural systems, the PO algorithm outperforms all previously proposed state-of-the-art optimization methodologies in all aspects may it be final optimized weight, algorithmic stability, or convergence speeds, and that for larger structures excellent performance is still maintained but a certain, yet acceptable, extent of algorithmic instability is manifest. Based off these findings, future research into the PO algorithm as an efficient structural optimizer is strongly recommended.
Convergence of Federated Learning Over a Noisy Downlink

We study federated learning (FL), where power-limited wireless devices utilize their local datasets to collaboratively train a global model with the help of a remote parameter server (PS). The PS has access to the global model and shares it with the devices for local training using their datasets, and the devices return the result of their local updates to the PS to update the global model. The algorithm continues until the convergence of the global model. This framework requires downlink transmission from the PS to the devices and uplink transmission from the devices to the PS. The goal of this study is to investigate the impact of the bandwidth-limited shared wireless medium on the performance of FL with a focus on the downlink. To this end, the downlink and uplink channels are modeled as fading broadcast and multiple access channels, respectively, both with limited bandwidth. For downlink transmission, we first introduce a digital approach, where a quantization technique is employed at the PS followed by a capacity-achieving channel code to transmit the global model update over the wireless broadcast channel at a common rate such that all the devices can decode it. Next, we propose analog downlink transmission, where the global model is broadcast by the PS in an uncoded manner. We consider analog transmission over the uplink in both cases, since its superiority over digital transmission for uplink has been well studied in the literature. We further analyze the convergence behavior of the proposed analog transmission approach over the downlink assuming that the uplink transmission is error-free. Numerical experiments show that the analog downlink approach provides significant improvement over the digital one with a more notable improvement when the data distribution across the devices is not independent and identically distributed. The experimental results corroborate the convergence analysis, and show that a smaller number of local iterations should be used when the data distribution is more biased, and also when the devices have a better estimate of the global model in the analog downlink approach.
Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

U.S. President Joe Biden took his oath after being victorious in the controversial U.S. elections of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic following delays of the announcement of the election’s results. Donald J. Trump claimed that there was potential rigging against him and refused to accept the results of the polls. The sentiment analysis captures the opinions of the masses over social media for global events. In this work, we analyzed Twitter sentiment to determine public views before, during, and after elections and compared them with actual election results. We also compared opinions from the 2016 election in which Donald J. Trump was victorious with the 2020 election. We created a dataset using tweets’ API, pre-processed the data, extracted the right features using TF-IDF, and applied the Naive Bayes Classifier to obtain public opinions. As a result, we identified outliers, analyzed controversial and swing states, and cross-validated election results against sentiments expressed over social media. The results reveal that the election outcomes coincide with the sentiment expressed on social media in most cases. The pre and post-election sentiment analysis results demonstrate the sentimental drift in outliers. Our sentiment classifier shows an accuracy of 94.58% and a precision of 93.19%.
A novel adaptive weighted kernel extreme learning machine algorithm and its application in wind turbine blade icing fault detection

The conventional weighted kernel extreme learning machine (WKELM) is a typical cost-sensitive learning algorithm, which can provide a solution for the data imbalance problem in wind turbine blade icing fault detection. However, WKELM algorithm uses a fixed weighting strategy that only considers the number of samples to achieve imbalanced learning, which leads to its insufficient performance in data imbalance classification and fault detection. In this study, the support vector data description (SVDD) method is used to obtain sample distribution information to construct a novel adaptive weighting strategy. The adaptive weighting strategy is combined with the conventional fixed weighting strategy to construct an adaptive weighted kernel extreme learning machine (AWKELM) algorithm. The effectiveness and superiority of the AWKELM algorithm is verified through three benchmark cases, it is demonstrated that AWKELM algorithm can effectively solve the problem of data imbalance and further improve the effect of wind turbine blade icing detection.
Multi-scale adversarial network for vehicle detection in UAV imagery

Vehicle detection in Unmanned Aerial Vehicle (UAV) imagery plays a crucial role in a variety of applications. However, UAVs are usually small, very maneuverable, and can take images from a variety of viewpoints and heights, leading to large differences in vehicle appearance and size. To address the vehicle detection challenge with such diversity in UAV images, we seek to align features between different viewpoints, illumination, weather, and background using remote sensing imagery as an anchor. Following this domain adaptation concept, we propose a multi-scale adversarial network, consisting of a deep convolutional feature extractor, a multi-scale discriminator, and a vehicle detection network. Specifically, the feature extractor is a Siamese network with one path for the UAV imagery and another for the satellite imagery. The shared weights in this sub-network allow us to exploit the large collections of labeled remote sensing imagery for improved vehicle detection in UAV imagery. Experimental results suggest that our proposed algorithm improves the vehicle detection accuracy in the UAVDT dataset and VisDrone dataset. The proposed model achieves great performance in images taken from different perspectives, at different altitudes, and under different imaging situations.
Elastic Smart Contracts in Blockchains

In this paper, we deal with questions related to blockchains in complex Internet of Things (IoT)-based ecosystems. Such ecosystems are typically composed of IoT devices, edge devices, cloud computing software services, as well as people, who are decision makers in scenarios such as smart cities. Many decisions related to analytics can be based on data coming from IoT sensors, software services, and people. However, they are typically based on different levels of abstraction and granularity. This poses a number of challenges when multiple blockchains are used together with smart contracts. This work proposes to apply our concept of elasticity to smart contracts and thereby enabling analytics in and between multiple blockchains in the context of IoT. We propose a reference architecture for Elastic Smart Contracts and evaluate the approach in a smart city scenario, discussing the benefits in terms of performance and self-adaptability of our solution.
A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks

Message exchange among vehicles plays an important role in ensuring road safety.Emergency message dissemination is usually carried out by broadcasting.However, high vehicle density and mobility lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception.This paper proposes a federated learning based blockchainassisted message dissemination solution.Similar to the incentivebased Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proofof-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain.Both theoretical and practical analysis of the proposed solution are provided.Specifically, the proposed blockchain based federated learning results in more vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain.It also outperforms other blockchain approaches in reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbor vehicle more efficiently.The economic model to incentivize vehicles participating in federated learning and message dissemination is further analyzed using Stackelberg game.The analysis of asymptotic complexity proves PoFL as the most scalable solution compared to other consensus algorithms in vehicular networks.
A fast and efficient image watermarking scheme based on Deep Neural Network

In this work, a robust image watermarking method is proposed based on the LWT (lifting wavelet transform) and DNN (Deep Neural Network). Watermark embedding uses wavelet transforms that help in maintaining a high value of imperceptibility and robustness. Different frequency bands are tested to find the optimum balance between robustness and imperceptibility. To check the robustness of proposed work, various attacks like compression attacks, noise attacks and filtering attacks are used. Deep Neural Network is trained to identify the changes made by these attacks on the different frequency bands. The use of high-frequency sub-band (LH (low-high)/LH1/HL (high-low) 2) for watermark insertion enhances the invisibility of scheme. On the contrary, various sub-bands perform differently (in terms o/f robustness) against the different types of attacks. The proposed scheme is tested on 600 images and the average PSNR (peak signal to noise ratio) is found to be 44.1148 dB with the variance of 0.5146 and standard deviation of 0.7173. Also, time analysis shows it to suitable for real-time applications. Comparative analysis suggests that the proposed method depicts improved performance over state-of-the-art techniques in almost all the parameters for most of the cases.
A text GAN framework for creative essay recommendation

Automated essay scoring is one of the most exciting tasks in natural language processing, reducing massive workloads and speeding up the learning process and its effectiveness. Many researchers have made momentous efforts in this matter. However, as far as we know, most AES works have concentrated on the AES technique; no relevant paper has been seen on finding creative essays while performing automated scoring. One of the reasons is that creativity is difficult to judge. This paper explores this concern: we assume that a creative essay is more challenging to write than a common essay; if we mask part of a creative essay, then it is difficult to predict or train the masked part of the essay, while a common essay is relatively easy to predict or train. We build a generative adversarial network framework to predict (train) the hidden parts. By calculating the distance between the generated essay and the original essay, the proposed method gives a judgment of whether or not an essay is creative. We developed a small-scale dataset based on the ASAP dataset for creative essay training. The experimental results show that the proposed method is feasible for finding creative essays among the datasets.
Blockchain-Based Secure Deduplication and Shared Auditing in Decentralized Storage

Data deduplication and public auditing are significant for providing secure and efficient network storage services. However, the existing data deduplication schemes supporting auditing not only cannot effectively alleviate the threats of the single point of failure and duplicate-faking attack, but also have to bear the massive waste of computation and storage resources caused by metadata redundancy and repetitive audit tasks. In this article, we propose a blockchain-based secure deduplication and shared auditing scheme in decentralized storage. Specifically, our scheme utilizes a novel deduplication protocol based on the double-server storage model to achieve efficient space-saving while protecting data users from losing data under a single point of failure and duplicate-faking attack. Besides, it sharply reduces the computation and storage costs of metadata by introducing a lightweight authenticator generation algorithm and update protocol. On this basis, our scheme further adopts a blockchain-based two-way shared auditing mechanism to achieve decentralized public auditing without the third-party auditor, in which the audit authenticators and results of outsourced data are shared among its users to avoid repetitive audit tasks. Security and performance analysis indicates the practicability of our scheme.
OFDM-Guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels

We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map the source images to complex-valued baseband samples for OFDM transmission. The multipath channel and OFDM are represented by non-trainable (deterministic) but differentiable layers so that the system can be trained end-to-end. Furthermore, our JSCC decoder further incorporates explicit channel estimation, equalization, and additional subnets to enhance the performance. The proposed method exhibits 2.5 – 4 dB SNR gain for the equivalent image quality compared to conventional schemes that employ state-of-the-art but separate source and channel coding such as Better Portable Graphics (BPG) and Low-Density Parity-Check (LDPC) schemes. The performance further improves when the system incorporates the channel state information (CSI) feedback. The proposed scheme is robust against OFDM signal clipping and parameter mismatch for the channel model used in training and evaluation.
Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback

Recommender systems are commonly trained on centrally-collected user interaction data like views or clicks. This practice however raises serious privacy concerns regarding the recommender’s collection and handling of potentially sensitive data. Several privacy-aware recommender systems have been proposed in recent literature, but comparatively little attention has been given to systems at the intersection of implicit feedback and privacy. To address this shortcoming, we propose a practical federated recommender system for implicit data under user-level local differential privacy (LDP). The privacy-utility trade-off is controlled by parameters ϵ and k, regulating the per-update privacy budget and the number of ϵ-LDP gradient updates sent by each user, respectively. To further protect the user’s privacy, we introduce a proxy network to reduce the fingerprinting surface by anonymizing and shuffling the reports before forwarding them to the recommender. We empirically demonstrate the effectiveness of our framework on the MovieLens dataset, achieving up to Hit Ratio with K=10 ([email protected]) 0.68 on 50,000 users with 5,000 items. Even on the full dataset, we show that it is possible to achieve reasonable utility with [email protected]>0.5 without compromising user privacy.
Graph Neural Network (GNN) in Image and Video Understanding Using Deep Learning for Computer Vision Applications

Graph neural networks (GNNs) is an information - processing system that uses message passing among graph nodes. In recent years, GNN variants including graph attention network (GAT), graph convolutional network (GCN), and graph recurrent network (GRN) have shown revolutionary performance in computer vision applications using deep learning and artificial intelligence. These neural network model extensions, collect information in the form of graphs. GNN may be divided into three groups based on the challenges it solves: link prediction, node classification, graph classification. Machines can differentiate and recognise objects in image and video using standard CNNs. Extensive amount of research work needs to be done before robots can have same visual intuition as humans. GNN architectures, on the other hand, may be used to solve various image categorization and video challenges. The number of GNN applications in computer vision not limited, continues to expand. Human-object interaction, actin understanding, image categorization from a few shots and many more. In this paper use of GNN in image and video understanding, design aspects, architecture, applications and implementation challenges towards computer vision is described. GNN is a strong tool for analysing graph data and is still a relatively active area that needs further researches attention to solve many computer vision applications.
Survey of crosschain communications protocols

Crosschain communications allows information to be communicated between blockchains. Consensus in the context of crosschain communications relates to how participants on one blockchain are convinced of the state of a remote blockchain. It describes how parties associated with a source blockchain come to agreement and communicate with a destination blockchain such that information from the source blockchain can be trusted. This paper surveys crosschain communications protocols, presenting them based on the top-level usage scenarios they are trying to meet: value swapping, crosschain messaging, and blockchain pinning. It analyses how each protocol achieves crosschain consensus, what trust assumptions are made, their ability to operate successfully in permissionless and permissioned blockchains contexts, and whether the protocol delivers atomic updates across blockchains. • Comparison and analysis of crosschain communications protocols. • Analysis of how they achieve consensus. • Analysis of their applicability to permissionless and permissioned blockchains. • Analysis of whether they deliver atomic updates across blockchains.
Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging

Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures, the diversity of scanners, and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates, or models become outdated due to these domain shifts. We propose a continual learning approach to deal with such domain shifts occurring at unknown time points. We adapt models to emerging variations in a continuous data stream while counteracting catastrophic forgetting. A dynamic memory enables rehearsal on a subset of diverse training data to mitigate forgetting while enabling models to expand to new domains. The technique balances memory by detecting pseudo-domains, representing different style clusters within the data stream. Evaluation of two different tasks, cardiac segmentation in magnetic resonance imaging and lung nodule detection in computed tomography, demonstrate a consistent advantage of the method.