Reviewerly/exHarmony huggingface dataset:
An Extensive Study on Gravitational Search Algorithm

Gravitational search algorithm is a naturally occurring algorithm based on Newton's mathematical model of the law of gravitation and motion. Over the course of a decade, researchers have provided many variants of the gravitational search algorithm by modifying its parameters to effectively solve complex optimization problems. This paper conducts a comparative analysis of ten types of gravity search algorithms that modify the three parameters of optimum, speed and position. Tests are conducted on two sets of benchmark types, namely standard functions and issues belonging to different types such as CEC2015 functions, univocal, multimodal and unrestricted optimization functions. Performance comparison is evaluated and statistically validated based on the average exercise value and concentration graph. In trials, IGSA has achieved excellent accuracy through a balanced trade between exploration and exploitation. Furthermore, three negative breast cancer datasets were considered to analyze the efficacy of GSA variants for the black section. Different performance analyzes were performed based on both quality and quantity with the integrated jacquard index as a performance measure. Tests confirm that the IGSA based method worked better than other methods.
FGFL: A blockchain-based fair incentive governor for Federated Learning

Federated Learning is a framework that coordinates a large amount of workers to train a shared model in a distributed manner, in which the training data are located on the workers' sides in order to preserve data privacy. There are two challenges in the crowdsourcing of FL, the workers who participant in training need to consume computing and communication resources, so that they are reluctant to participate in the training process if they can not get reasonable rewards. Moreover, there may be attackers who send arbitrary updates to get undeserving compensation or even destroy the model, thus, effective prevention of malicious workers is also critical. An incentive mechanism is urgently required in order to encourage high-quality workers to participate in FL and to punish the attackers. In this paper, we propose FGFL, a blockchain-based incentive governor for Federated Learning. In FGFL, we assess the participants with reputation and contribution indicators. Then the task publisher rewards workers fairly to attract efficient ones while the malicious ones are punished and eliminated. In addition, we propose a blockchain-based incentive management system to manage the incentive mechanism. We evaluate the effectiveness and fairness of FGFL through theoretical analysis and comprehensive experiments. The evaluation results show that FGFL fairly rewards workers according to their corresponding behavior and quality. FGFL increases the system revenue by 0.2% to 3.4% in reliable federations compared with baselines. And in the unreliable scenario where contains attackers, the system revenue of FGFL outperforms the baselines by more than 46.7%.
Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach

Service composition as an important technique for combining multiple services to construct a value-added service is a major research issue in cloud manufacturing. Highly dynamic environments present great challenges to cloud manufacturing service composition (CMfg-SC). Most of previous studies employ heuristic algorithms to solve service composition issues in cloud manufacturing, which, however, are designed for specific problems and lack adaptability necessary to dynamic environment. Hence, CMfg-SC calls for new adaptive approaches. Recent advances in deep reinforcement learning (DRL) provide a new means for solving this issue. Based on DRL, we propose a Deep Deterministic Policy Gradient (DDPG)-based service composition approach to cloud manufacturing, with which optimal service composition solutions can be learned through repeated training. Performance of DDPG in solving CMfg-SC in both static and dynamic environments is examined. Results obtained with another DRL algorithm - Deep Q-Networks (DQN) and the traditional Ant Colony Optimization (ACO) are also presented. Comparison indicates that DDPG has better adaptability, robustness, and extensibility to dynamic environments than ACO, although ACO converges faster and its steady QoS value of the service composition solution is higher than that of DDPG by 0.997%. DDPG outperforms DQN in convergence speed and stability, and the QoS value of the service composition solution of DDPG is higher than that of DQN by 3.249%.
Finite key effects in satellite quantum key distribution

Abstract Global quantum communications will enable long-distance secure data transfer, networked distributed quantum information processing, and other entanglement-enabled technologies. Satellite quantum communication overcomes optical fibre range limitations, with the first realisations of satellite quantum key distribution (SatQKD) being rapidly developed. However, limited transmission times between satellite and ground station severely constrains the amount of secret key due to finite-block size effects. Here, we analyse these effects and the implications for system design and operation, utilising published results from the Micius satellite to construct an empirically-derived channel and system model for a trusted-node downlink employing efficient Bennett-Brassard 1984 (BB84) weak coherent pulse decoy states with optimised parameters. We quantify practical SatQKD performance limits and examine the effects of link efficiency, background light, source quality, and overpass geometries to estimate long-term key generation capacity. Our results may guide design and analysis of future missions, and establish performance benchmarks for both sources and detectors.
MAF: A General Matching and Alignment Framework for Multimodal Named Entity Recognition

In this paper, we study multimodal named entity recognition in social media posts. Existing works mainly focus on using a cross-modal attention mechanism to combine text representation with image representation. However, they still suffer from two weaknesses: (1) the current methods are based on a strong assumption that each text and its accompanying image are matched, and the image can be used to help identify named entities in the text. However, this assumption is not always true in real scenarios, and the strong assumption may reduce the recognition effect of theMNER model; (2) the current methods fail to construct a consistent representation to bridge the semantic gap between two modalities, which prevents the model from establishing a good connection between the text and image. To address these issues, we propose a general matching and alignment framework (MAF) for multimodal named entity recognition in social media posts. Specifically, to solve the first issue, we propose a novel cross-modal matching (CM) module to calculate the similarity score between text and image, and use the score to determine the proportion of visual information that should be retained. To solve the second issue, we propose a novel cross-modal alignment (CA) module to make the representations of the two modalities more consistent. We conduct extensive experiments, ablation studies, and case studies to demonstrate the effectiveness and efficiency of our method.The source code of this paper can be found in https://github.com/xubodhu/MAF.
Length-Controllable Image Captioning

The last decade has witnessed remarkable progress in the image captioning task; however, most existing methods cannot control their captions, e.g., choosing to describe the image either roughly or in detail. In this paper, we propose to use a simple length level embedding to endow them with this ability. Moreover, due to their autoregressive nature, the computational complexity of existing models increases linearly as the length of the generated captions grows. Thus, we further devise a non-autoregressive image captioning approach that can generate captions in a length-irrelevant complexity. We verify the merit of the proposed length level embedding on three models: two state-of-the-art (SOTA) autoregressive models with different types of decoder, as well as our proposed non-autoregressive model, to show its generalization ability. In the experiments, our length-controllable image captioning models not only achieve SOTA performance on the challenging MS COCO dataset but also generate length-controllable and diverse image captions. Specifically, our non-autoregressive model outperforms the autoregressive baselines in terms of controllability and diversity, and also significantly improves the decoding efficiency for long captions. Our code and models are released at https://github.com/bearcatt/LaBERT .
Efficient spam and phishing emails filtering based on deep learning

Nowadays, spam emails represent a severe threat to security and cause a big waste in transmission time and users’ time spent in browsing unsolicited bulk emails (UBE). This is in addition to a lot of bandwidth and large severs storage consumed by these spam emails, resulting in financial losses for organizations and annoying individual users. Another type of malicious emails is phishing emails, which aim to get sensitive information from users leading to credential theft. This forms a challenging threat in the cyberspace. Many machine learning (ML) approaches are used to classify emails as ham or spam emails. In this paper, a deep learning model is introduced that showed improvement in performance compared to state-of-the-art related studies. Three benchmark datasets are employed in our experiments, which include content-based features rather than text analysis techniques that may consume more time. Our classifier is used to discriminate between three classes for more general spam filtering. Different performance measures are used for model validation and testing. Moreover, the time consumed in both offline training and online detection stages is reported. The proposed classifier is designed with an eye on the validation accuracy achieving fast and competitive performance promoting its use in practical applications. A comparative study is presented to show that our work outperforms recent related studies.
Temporal segment graph convolutional networks for skeleton-based action recognition

Different actions usually emphasize on different parts of a skeleton, even for a specific action, different action stages have the corresponding emphases. Previous studies generally construct the human skeletons as predefined, thus lacking the adaptability to different action modes. In addition, these methods simply employ the padding or truncation operation on the skeleton sequence to fix the sequence length, resulting in additional temporal misalignment problem. In this work, we propose a novel temporal segment graph convolutional networks (TS-GCN) for skeleton-based action recognition. Our model divides the whole sequence into several subsequences. Then GCNs are applied on each subsequence to capture the dynamic information stage by stage, which can align the motion features in temporal domain. Besides, in order to explore the intrinsic features contained in each subsequence, our model introduces a graph-adaptive method to construct an individual graph that can be learned and updated from skeleton data for each subsequence, which increases the generality of graph construction to adapt to different sequences. Extensive experiments are conducted on two standard datasets, NTU-RGB+D and Kinetics. The experimental results demonstrate the effectiveness of the proposed method.
A Model of Semantic-Based Image Retrieval Using C-Tree and Neighbor Graph

The problems of image mining and semantic image retrieval play an important role in many areas of life. In this paper, a semantic-based image retrieval system is proposed that relies on the combination of C-Tree, which was built in our previous work, and a neighbor graph (called Graph-CTree) to improve accuracy. The k-Nearest Neighbor (k-NN) algorithm is used to classify a set of similar images that are retrieved on Graph-CTree to create a set of visual words. An ontology framework for images is created semi-automatically. SPARQL query is automatically generated from visual words and retrieve on ontology for semantics image. The experiment was performed on image datasets, such as COREL, WANG, ImageCLEF, and Stanford Dogs, with precision values of 0.888473, 0.766473, 0.839814, and 0.826416, respectively. These results are compared with related works on the same image dataset, showing the effectiveness of the methods proposed here.
Mixing up contrastive learning: Self-supervised representation learning for time series

The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing high quality representations could be of great value. It is key to enabling transfer learning, which is especially beneficial for medical applications, where there is an abundance of data but labeling is costly and time consuming. We propose an unsupervised contrastive learning framework that is motivated from the perspective of label smoothing. The proposed approach uses a novel contrastive loss that naturally exploits a data augmentation scheme in which new samples are generated by mixing two data samples with a mixing component. The task in the proposed framework is to predict the mixing component, which is utilized as soft targets in the loss function. Experiments demonstrate the framework's superior performance compared to other representation learning approaches on both univariate and multivariate time series and illustrate its benefits for transfer learning for clinical time series.
The effects of domain knowledge on trust in explainable AI and task performance: A case of peer-to-peer lending

Increasingly, artificial intelligence (AI) is being used to assist complex decision-making such as financial investing. However, there are concerns regarding the black-box nature of AI algorithms. The field of explainable AI (XAI) has emerged to address these concerns. XAI techniques can reveal how an AI decision is formed and can be used to understand and appropriately trust an AI system. However, XAI techniques still may not be human-centred and may not support human decision-making adequately. In this work, we explored how domain knowledge, identified by expert decision makers, can be used to achieve a more human-centred approach to AI. We measured the effect of domain knowledge on trust in AI, reliance on AI, and task performance in an AI-assisted complex decision-making environment. In a peer-to-peer lending simulator, non-expert participants made financial investments using an AI assistant. The presence or absence of domain knowledge was manipulated. The results showed that participants who had access to domain knowledge relied less on the AI assistant when the AI assistant was incorrect and indicated less trust in AI assistant. However, overall investing performance was not affected. These results suggest that providing domain knowledge can influence how non-expert users use AI and could be a powerful tool to help these users develop appropriate levels of trust and reliance.
A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images

Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to develop new computational techniques that can automatically and rapidly diagnose a large amount of specimens without delay, which would be of great benefit for clinical laboratories and hospitals. The goal of this study was to investigate the use of a deep learning model for the classification of WSIs of LBC specimens into neoplastic and non-neoplastic. To do so, we used a dataset of 1605 cervical WSIs. We evaluated the model on three test sets with a combined total of 1468 WSIs, achieving ROC AUCs for WSI diagnosis in the range of 0.89-0.96, demonstrating the promising potential use of such models for aiding screening processes.
Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI): An approach for learning from imbalanced data streams

Learning from imbalanced data streams is one of the challenges associated with classification algorithms and learning classifiers. The goal of this paper is to propose and validate a new approach for learning from data streams, with reference to the problem of class-imbalanced data. A hybrid approach for changing the class distribution towards a more balanced dataset using over-sampling and instance selection techniques is discussed. The proposed approach is based on the integration of a weighted ensemble classification and a technique to deal with the problem of class imbalance, and is called Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI). Our approach assumes that classifiers are induced from incoming blocks of instances, called data chunks. These data chunks consist of incoming instances from different classes, and a balance between them is reached through our hybrid approach. These data chunks are then used to induce classifier ensembles. The proposed approach is validated experimentally using several selected benchmark datasets, and the results of computational experiments are presented and discussed. The results show that the proposed approach for eliminating class imbalance in data streams can help increase the performance of online learning algorithms. This is an extended version of a paper that was presented at the International Conference on Computational Science in 2021 (ICCS-2021) (Czarnowski, 2021). This version has been enriched with a deeper analysis of the results obtained from a modified variant of the originally proposed approach.
Learning Fast Sample Re-weighting Without Reward Data

Training sample re-weighting is an effective approach for tackling data biases such as imbalanced and corrupted labels. Recent methods develop learning-based algorithms to learn sample re-weighting strategies jointly with model training based on the frameworks of reinforcement learning and meta learning. However, depending on additional unbiased reward data is limiting their general applicability. Furthermore, existing learning-based sample re-weighting methods require nested optimizations of models and weighting parameters, which requires expensive second-order computation. This paper addresses these two problems and presents a novel learning-based fast sample re-weighting (FSR) method that does not require additional reward data. The method is based on two key ideas: learning from history to build proxy reward data and feature sharing to reduce the optimization cost. Our experiments show the proposed method achieves competitive results compared to state of the arts on label noise robustness and long-tailed recognition, and does so while achieving significantly improved training efficiency. The source code is publicly available at https://github.com/google-research/google-research/tree/master/ieg.
Task-aware Part Mining Network for Few-Shot Learning

Few-Shot Learning (FSL) aims at classifying samples into new unseen classes with only a handful of labeled samples available. However, most of the existing methods are based on the image-level pooled representation, yet ignore considerable local clues that are transferable across tasks. To address this issue, we propose an end-to-end Task-aware Part Mining Network (TPMN) by integrating an automatic part mining process into the metric-based model for FSL. The proposed TPMN model enjoys several merits. First, we design a meta filter learner to generate task-aware part filters based on the task embedding in a meta-learning way. The task-aware part filters can adapt to any individual task and automatically mine task-related local parts even for an unseen task. Second, an adaptive importance generator is proposed to identify key local parts and assign adaptive importance weights to different parts. To the best of our knowledge, this is the first work to automatically exploit the task-aware local parts in a meta-learning way for FSL. Extensive experimental results on four standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art FSL methods.
Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model

Purpose This study aims to extend the technology acceptance model (TAM) to examine whether the introduction of robots influences employees’ behavioral intentions to use robots and awareness of robots to promote human–robot collaboration (HRC). Besides, the role of strategic human resource management (HRM) involvement as a moderator in the perception of robots as a team member was investigated. Design/methodology/approach Data were collected from 500 respondents via the Amazon Mechanical Turk platform. After data cleaning, 329 valid responses were analyzed. Partial least squares structural equation modeling was applied using Smart PLS Ver. 3.0 to test the study’s measurement and proposed research model. Findings The study results show that robots’ perceived usefulness and ease of use positively influence employees’ behavioral intentions to use robots. In addition, the advantages and disadvantages of robots have a positive impact on robot awareness. Employees’ behavioral intentions and awareness contribute positively to HRC. On the other hand, the moderating role of strategic human resources (HR’s) involvement in the relationships was insignificant. Research limitations/implications This study provides an exclusively applied understanding of robot presence and embodiment relevant to real-world HRC. In the travel, tourism and hospitality (TTH) industry, employees’ intention to use robots and robot awareness are significant factors. However, HRM involvement without the acceptance of robots could not enhance HRC. Originality/value Based on the literature review, to the best of the authors’ knowledge, this study is one of the first on this topic and extends TAM with new antecedents related to robot use, robot awareness and HRC in the TTH industry. In addition, this model attempts to determine the factors that favor HRC in the industry. This study also assessed the moderating role of strategic HR’s involvement in the behavioral intention of robot use, robot awareness and HRC.