Reviewerly/exHarmony huggingface dataset:
LMFFNet: A Well-Balanced Lightweight Network for Fast and Accurate Semantic Segmentation

Real-time semantic segmentation is widely used in autonomous driving and robotics. Most previous networks achieved great accuracy based on a complicated model involving mass computing. The existing lightweight networks generally reduce the parameter sizes by sacrificing the segmentation accuracy. It is critical to balance the parameters and accuracy for real-time semantic segmentation. In this article, we propose a lightweight multiscale-feature-fusion network (LMFFNet) mainly composed of three types of components: split-extract-merge bottleneck (SEM-B) block, feature fusion module (FFM), and multiscale attention decoder (MAD), where the SEM-B block extracts sufficient features with fewer parameters. FFMs fuse multiscale semantic features to effectively improve the segmentation accuracy and the MAD well recovers the details of the input images through the attention mechanism. Without pretraining, LMFFNet-3-8 achieves 75.1% mean intersection over union (mIoU) with 1.4 M parameters at 118.9 frames/s using RTX 3090 GPU. More experiments are investigated extensively on various resolutions on other three datasets of CamVid, KITTI, and WildDash2. The experiments verify that the proposed LMFFNet model makes a decent tradeoff between segmentation accuracy and inference speed for real-time tasks. The source code is publicly available at https://github.com/Greak-1124/LMFFNet.
Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations

Artificial intelligence (AI) has shown promising results when used on retrospective data from mammographic screening. However, few studies have explored the possible consequences of different strategies for combining AI and radiologists in screen-reading.A total of 122,969 digital screening examinations performed between 2009 and 2018 in BreastScreen Norway were retrospectively processed by an AI system, which scored the examinations from 1 to 10; 1 indicated low suspicion of malignancy and 10 high suspicion. Results were merged with information about screening outcome and used to explore consensus, recall, and cancer detection for 11 different scenarios of combining AI and radiologists.Recall was 3.2%, screen-detected cancer 0.61% and interval cancer 0.17% after independent double reading and served as reference values. In a scenario where examinations with AI scores 1-5 were considered negative and 6-10 resulted in standard independent double reading, the estimated recall was 2.6% and screen-detected cancer 0.60%. When scores 1-9 were considered negative and score 10 double read, recall was 1.2% and screen-detected cancer 0.53%. In these two scenarios, potential rates of screen-detected cancer could be up to 0.63% and 0.56%, if the interval cancers selected for consensus were detected at screening. In the former scenario, screen-reading volume would be reduced by 50%, while the latter would reduce the volume by 90%.Several theoretical scenarios with AI and radiologists have the potential to reduce the volume in screen-reading without affecting cancer detection substantially. Possible influence on recall and interval cancers must be evaluated in prospective studies.• Different scenarios using artificial intelligence in combination with radiologists could reduce the screen-reading volume by 50% and result in a rate of screen-detected cancer ranging from 0.59% to 0.60%, compared to 0.61% after standard independent double reading • The use of artificial intelligence in combination with radiologists has the potential to identify negative screening examinations with high precision in mammographic screening and to reduce the rate of interval cancer.
A correlation guided genetic algorithm and its application to feature selection

Traditional feature selection methods based on genetic algorithms randomly evolve using unguided crossover operators and mutation operators. This leads to many inferior solutions being generated and verified using costly fitness functions. In this paper, we propose a new feature selection method based on a correlation-guided genetic algorithm. It first roughly checks the quality of the potential solutions to reduce the possibility of producing inferior solutions. Then more potentially superior solutions can be verified by the classifier to improve the efficiency of the evolutionary process. It is theoretically proven that the proposed method converges to the optimal solution with a very weak precondition. Numerical results on 4 artificial datasets and 6 real datasets show that compared with other existing methods, the proposed method is a competitive feature selection method with higher classification accuracy and a more efficient evolutionary process.
On sampled metrics for item recommendation

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B , not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate.
Does unit of account affect willingness to pay? Evidence from metaverse LAND transactions

LAND is a non-fungible token (NFT) representing virtual real estate ownership in The Sandbox metaverse, a digital world built on the Ethereum blockchain. The open nature of a blockchain-based metaverses like The Sandbox allows users to transact in multiple denominations/currencies. We analyze more than 71,000 transactions and find that when transactions are settled in The Sandbox's native utility token (SAND), users pay 3.4% more compared to ETH (in effective USD prices). There are also evidences that users pay less when transactions are settled in wETH and USD-pegged stablecoins (USDC). Thus, the cryptocurrency/token settlement choice (i.e., unit of account) matters for willingness to pay.
Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential

Modern cities worldwide are undergoing radical changes to foster a clean, sustainable and secure environment, install smart infrastructures, deliver intelligent services to residents, and facilitate access for vulnerable groups. The adoption of new technologies is at the heart of implementing many initiatives to address critical concerns in urban mobility, healthcare, water management, clean energy production and consumption, energy saving, housing, safety, and accessibility. Given the advancements in sensing and communication technologies over the past few decades, exploring the adoption of recent and innovative technologies is critical to addressing these concerns and making cities more innovative, sustainable, and safer. This article provides a broad understanding of the current urban challenges faced by smart cities. It highlights two new technological advances, edge artificial intelligence (edge AI) and Blockchain, and analyzes their transformative potential to make our cities smarter. In addition, it explores the multiple uses of edge AI and Blockchain technologies in the fields of smart mobility and smart energy and reviews relevant research efforts in these two critical areas of modern smart cities. It highlights the various algorithms to handle vehicle detection, counting, speed identification to address the problem of traffic congestion and the different use-cases of Blockchain in terms of trustworthy communications and trading between vehicles and smart energy trading. This review paper is expected to serve as a guideline for future research on adopting edge AI and Blockchain in other smart city domains.
Multi-population-based adaptive sine cosine algorithm with modified mutualism strategy for global optimization

The sine cosine algorithm (SCA) is a population-based metaheuristic strategy that has been demonstrated competitive performance and has received significant attention from scientists in various fields. Regardless, like other population-based techniques, SCA also has a tendency to get stuck in adjacent optima and uneven exploitation. Given the shortcomings of SCA, a new modified SCA variation, MAMSCA, with a balanced global and local search, is presented in this work. The new method divides the population into two equal halves for updating using a sine or cosine strategy. To provide additional variation to the population, a modified mutualism phase is adopted. To increase convergence speed and accuracy of the solution, a one-to-one mapping between the individuals of each half is maintained. The new algorithm is compared with state-of-the-art algorithms and modified algorithms using classical benchmark functions and IEEE CEC 2019 functions. The method’s real-world relevance is demonstrated by tackling five engineering design challenges. The comparison of numerical results and analysis of performance measures of the algorithm from the statistical aspect, time complexity, and speed of generating the solution demonstrates the considerable improvement of the proposed algorithm to solve real-world challenges.
Deep Learning-Based Automatic Detection of Ships: An Experimental Study Using Satellite Images

The remote sensing surveillance of maritime areas represents an essential task for both security and environmental reasons. Recently, learning strategies belonging to the field of machine learning (ML) have become a niche of interest for the community of remote sensing. Specifically, a major challenge is the automatic classification of ships from satellite imagery, which is needed for traffic surveillance systems, the protection of illegal fisheries, control systems of oil discharge, and the monitoring of sea pollution. Deep learning (DL) is a branch of ML that has emerged in the last few years as a result of advancements in digital technology and data availability. DL has shown capacity and efficacy in tackling difficult learning tasks that were previously intractable. Specifically, DL methods, such as convolutional neural networks (CNNs), have been reported to be efficient in image detection and recognition applications. In this paper, we focused on the development of an automatic ship detection (ASD) approach by using DL methods for assessing the Airbus ship dataset (composed of about 40 K satellite images). The paper explores and analyzes the distinct variations of the YOLO algorithm for the detection of ships from satellite images. A comparison of different versions of YOLO algorithms for ship detection, such as YOLOv3, YOLOv4, and YOLOv5, is presented, after training them on a personal computer with a large dataset of satellite images of the Airbus Ship Challenge and Shipsnet. The differences between the algorithms could be observed on the personal computer. We have confirmed that these algorithms can be used for effective ship detection from satellite images. The conclusion drawn from the conducted research is that the YOLOv5 object detection algorithm outperforms the other versions of the YOLO algorithm, i.e., YOLOv4 and YOLOv3 in terms accuracy of 99% for YOLOv5 compared to 98% and 97% respectively for YOLOv4 and YOLOv3.
The influence of chatbot humour on consumer evaluations of services

Abstract Technological advances have enabled firms to automate customer service by employing artificial intelligence (AI) chatbots. Despite their many potential benefits, interactions with chatbots may still feel machine‐like and cold. The current study proposes the use of humour by chatbots as a gateway to humanizing them and thereby enhancing the customer experience. Across three experimental studies, the results reveal that (i) the use of humour enhances service satisfaction when it is used by a chatbot but not when it is used by a human agent, (ii) this chatbot humour effect is serially mediated by enhanced perceptions of anthropomorphism and interestingness of the interactions with the chatbot, and (iii) whilst both positively and negatively valenced chatbot humour may enhance the interestingness of the interactions, socially appropriate (i.e., affiliative) humour as opposed to inappropriate (i.e., aggressive) humour leads to enhanced service satisfaction. This study extends the understanding of the humanization processes of chatbots and provides guidelines for how firms should use chatbot humour to positively influence consumers' service satisfaction.
Learning Feature Recovery Transformer for Occluded Person Re-Identification

One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e. , the interference of noise during feature matching and the loss of pedestrian information brought by the occlusions. In this paper, we propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously, which mainly consists of visibility graph matching and feature recovery transformer. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its k -nearest neighbors in the gallery to recover the complete features. Extensive experiments across different person Re-ID datasets, including occluded, partial and holistic datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly outperforms state-of-the-art results by at least 6.2% Rank- 1 accuracy and 7.2% mAP scores on the challenging Occluded-Duke dataset.
An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers

Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high.Its effects will be reduced if diagnosed early.BC's early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients.It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately.Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets.In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers.First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms.We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection.We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets.We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others.The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy.Because of high performance, the proposed system is of great importance to the medical industry and relevant research community.The comparison shows that the proposed method outperformed other state-of-the-art methods.
YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection

Military target detection technology is the foundation and key to perceive and analyze the battlefield situation, and it is also the premise of target tracking technology. Aiming at the task of military target detection, the detection performance of traditional detection algorithms is poor in complex environment. We realize automatic detection of military targets in complex environment through deep learning. In this research, we improve the components of YOLOv3 and propose a novel military target detection algorithm (YOLO-G). We have built a military target dataset composed of armed men with different weapons, which provides a test environment for various object detection algorithms. In the YOLOv3 network structure, by introducing the lightweight convolutional neural network GhostNet as the feature extraction network, the accuracy and speed of military target detection are improved. Then, the attention mechanism based on the coordinate attention block is introduced to enhance the representation ability of target features, suppress interference and improve the detection accuracy. Finally, the loss function of the target detector is redesigned by using DIOU loss function and Focal loss function, which further improves the detection accuracy of our detection model for military targets. We tested YOLO-G on the military target dataset. Experimental results show that our method improves the mAP by 2.9% and the detection speed by 25.9 frames/s compared with the original YOLOv3 algorithm, and the size of the proposed model is reduced to 1/6 of that of YOLOv3. In addition, we also compare our method with state-of-the-art object detection algorithms. The results show that YOLO-G also has superior detection performance, and the mAP index obtained by our method is 1.2% higher than that of the latest YOLOv5 on the premise of meeting the application requirements, which plays an important auxiliary role in the generation and analysis of battlefield situation.
PVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework

Over the past years, the increasingly severe data island problem has spawned an emerging distributed deep learning framework—federated learning, in which the global model can be constructed over multiple participants without directly sharing their raw data. Despite its promising prospect, there are still many security challenges in federated learning, such as privacy preservation and integrity verification. Furthermore, federated learning is usually performed with the assistance of a center, which is prone to cause trust worries and communicational bottlenecks. To tackle these challenges, in this paper, we propose a privacy-preserving and verifiable decentralized federated learning framework, named PVD-FL, which can achieve secure deep learning model training under a decentralized architecture. Specifically, we first design an efficient and verifiable cipher-based matrix multiplication (EVCM) algorithm to execute the most basic calculation in deep learning. Then, by employing EVCM, we design a suite of decentralized algorithms to construct the PVD-FL framework, which ensures the confidentiality of both global model and local update and the verification of every training step. Detailed security analysis shows that PVD-FL can well protect privacy against various inference attacks and guarantee training integrity. In addition, the extensive experiments on real-world datasets also demonstrate that PVD-FL can achieve lossless accuracy and practical performance.
The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems

With the development of the social Internet of Things (IoT) and multimedia communications, our daily lives in computational social systems have become more convenient; for example, we can share shopping experiences and ask questions of people in an ad hoc network. Relation extraction focuses on supervised learning with adequate training data, and it helps to understand the knowledge behind the observed information. However, if only some social data in an unknown area can be used, how to obtain the related knowledge and information is a key topic for supporting social intelligence. This article proposes the joint method of triple attention and novel loss function for entity relation extraction by few-shot learning in computational social systems. We consider using a prototypical network as the base model to acquire support set prototypes and to compare queries with the prototypes for classification. First, triple attention is employed to make the query instances and support set share interactive information in a global and instancewise manner, highlighting the important features. Second, we combine a weighted Euclidean distance function with a multilayer perceptron (MLP) to perform class matching, which maps the generated features to their proper classifications, emphasizing the prominent dimensions in the feature space and relieving data sparsity. Third, triplet loss and uniformity regularization are used to solve the inconsistency problem faced by the support set, where the features of the support set in the same class are often far apart in different characteristic dimensions. Finally, the experimental results demonstrate the improved performance of our model on the FewRel dataset.
Blockchain Oracles: State-of-the-Art and Research Directions

Blockchain interoperability is an innovative feature of blockchain technology that is rapidly gaining momentum in various fields.The mass adoption of enterprise blockchains has not yet been achieved because blockchain networks act as isolated islands that cannot connect or exchange assets and/or information.In addition, the invocation of smart contracts is restricted only to on-chain executions because of the lack of connectivity between the blockchains.This survey paper aims to conduct a comprehensive and thorough literature review regarding blockchain interoperability, with special highlight on blockchain Oracles being state-of-the-art.Oracles have shown potential as an emerging technology that has revolutionized the blockchain ecosystem by acting as agents that fetch external information into the blockchain ecosystem.A detailed comparative discussion of different blockchain interoperability techniques is presented, showing the strengths and weaknesses of each technique.Being overlooked in the literature, the shortcomings of these techniques in comparison to Oracles are identified, addressing how Oracles have succeeded in overcoming many of these limitations.In addition, the literature lacks a focus on the latest market solutions adopting blockchain Oracles, and only a few studies have considered them in detail.This gap has been addressed through an in-depth assessment of the latest market solutions adopting Oracles in the past few years.Finally, design issues trying to achieve the best practices of Oracles, future directions, and identified research gaps are highlighted. .