Reviewerly/exHarmony huggingface dataset:
Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

Recognition of human activity is one of the most exciting aspects of time-series classification, with substantial practical and theoretical implications. Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments. Consequently, researchers have demonstrated considerable passion for developing cutting-edge deep learning systems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts. This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called SenPyramidNet and motion information from wearable sensors (accelerometer and gyroscope). The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection capability. The proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset, which gathered motion signals from various indoor and outdoor activities, including practicing various body parts. The experimental findings demonstrate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37% for indoor and 97.25% for outdoor activity. Moreover, comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and F1-score. Furthermore, this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.
Decision-making within geochemical exploration data based on spatial uncertainty – A new insight and a futuristic review

In mineral prospectivity mapping, limitations in the density of geochemical sampling that can be collected across a region may generate the need for interpolation of data between sampling locations, with associated errors in the estimates. This study investigates quantification and modeling of this spatial uncertainty based on multivariate statistical quantile models of element concentrations in low-density till sampling across Sweden and the high-density soil geochemical atlas sampling of Cyprus. Sequential Gaussian simulation was applied to both datasets, with the dissimilarity between a large number of realizations quantified using a Euclidean distance-based model and the relevant spatial uncertainty. Using a P50-spatial uncertainty efficiency frontier plot, the classes with the highest P50 and lowest spatial uncertainty are compared with the distribution of known base metal mineral deposits.
Overview of trials on artificial intelligence algorithms in breast cancer screening – A roadmap for international evaluation and implementation

Accumulating evidence from retrospective studies demonstrate at least non-inferior performance when using AI algorithms with different strategies versus double-reading in mammography screening. In addition, AI algorithms for mammography screening can reduce work load by moving to single human reading. Prospective trials are essential to avoid unintended adverse consequences before incorporation of AI algorithms into UK's National Health Service (NHS) Breast Screening Programme (BSP). A stakeholders' meeting was organized in Newnham College, Cambridge, UK to undertake a review of the current evidence to enable consensus discussion on next steps required before implementation into a screening programme. It was concluded that a multicentre multivendor testing platform study with opt-out consent is preferred. AI thresholds from different vendors should be determined while maintaining non-inferior screening performance results, particularly ensuring recall rates are not increased. Automatic recall of cases using an agreed high sensitivity AI score versus automatic rule out with a low AI score set at a high sensitivity could be used. A human reader should still be involved in decision making with AI-only recalls requiring human arbitration. Standalone AI algorithms used without prompting maintain unbiased screening reading performance, but reading with prompts should be tested prospectively and ideally provided for arbitration.
Byzantine Fault-Tolerant Consensus Algorithms: A Survey

The emergence of numerous consensus algorithms for distributed systems has resulted from the swift advancement of blockchain and its related technologies. Consensus algorithms play a key role in decentralized distributed systems, because all nodes in the system need to reach a consensus on requests or commands through consensus algorithms. In a distributed system where nodes work together to reach consensus, there may be Byzantine nodes present. The emergence of Byzantine nodes will affect the consensus of nodes in the distributed system. Therefore, tolerating Byzantine nodes in a distributed system and then reaching a consensus is an essential function of a consensus algorithm. So far, many Byzantine fault-tolerant (BFT) consensus algorithms have emerged, and there are correspondingly many methods to improve the performance of these algorithms. In order to allow researchers to have a clearer understanding of the existing methods, this paper systematically investigated and studied the research progress of the current Byzantine fault-tolerant consensus algorithm. The scope of the research ranged from the classic Byzantine consensus algorithm to some of the latest Byzantine consensus algorithms. The articles were classified according to the methods used to improve the Byzantine consensus algorithm. Through classification and centralized analysis and discussion, we achieved a clearer understanding of the development of Byzantine consensus algorithms and, at the same time, clarified the advantages and disadvantages of this type of method and the latest research progress using this method. At the end of this article, an in-depth discussion and analysis is also presented. By analyzing the impact of the use of these methods on the performance of the BFT consensus algorithm, it is proposed that future research can be improved.
Deep Learning and Neural Networks: Decision-Making Implications

Deep learning techniques have found applications across diverse fields, enhancing the efficiency and effectiveness of decision-making processes. The integration of these techniques underscores the significance of interdisciplinary research. In particular, decisions often rely on the output’s projected value or probability from neural networks, considering different values of the relevant output factor. This interdisciplinary review examines the impact of deep learning on decision-making systems, analyzing 25 relevant papers published between 2017 and 2022. The review highlights improved accuracy but emphasizes the need for addressing issues like interpretability, generalizability, and integration to build reliable decision support systems. Future research directions include transparency, explainability, and real-world validation, underscoring the importance of interdisciplinary collaboration for successful implementation.
A Survey of Deep Learning-Based Low-Light Image Enhancement

Images captured under poor lighting conditions often suffer from low brightness, low contrast, color distortion, and noise. The function of low-light image enhancement is to improve the visual effect of such images for subsequent processing. Recently, deep learning has been used more and more widely in image processing with the development of artificial intelligence technology, and we provide a comprehensive review of the field of low-light image enhancement in terms of network structure, training data, and evaluation metrics. In this paper, we systematically introduce low-light image enhancement based on deep learning in four aspects. First, we introduce the related methods of low-light image enhancement based on deep learning. We then describe the low-light image quality evaluation methods, organize the low-light image dataset, and finally compare and analyze the advantages and disadvantages of the related methods and give an outlook on the future development direction.
Boosting adversarial robustness via self-paced adversarial training

Adversarial training is considered one of the most effective methods to improve the adversarial robustness of deep neural networks. Despite the success, it still suffers from unsatisfactory performance and overfitting. Considering the intrinsic mechanism of adversarial training, recent studies adopt the idea of curriculum learning to alleviate overfitting. However, this also introduces new issues, that is, lacking the quantitative criterion for attacks' strength and catastrophic forgetting. To mitigate such issues, we propose the self-paced adversarial training (SPAT), which explicitly builds the learning process of adversarial training based on adversarial examples of the whole dataset. Specifically, our model is first trained with "easy" adversarial examples, and then is continuously enhanced by gradually adding "complex" adversarial examples. This way strengthens the ability to fit "complex" adversarial examples while holding in mind "easy" adversarial samples. To balance adversarial examples between classes, we determine the difficulty of the adversarial examples locally in each class. Notably, this learning paradigm can also be incorporated into other advanced methods for further boosting adversarial robustness. Experimental results show the effectiveness of our proposed model against various attacks on widely-used benchmarks. Especially, on CIFAR100, SPAT provides a boost of 1.7% (relatively 5.4%) in robust accuracy on the PGD10 attack and 3.9% (relatively 7.2%) in natural accuracy for AWP.
Blockchain-assisted multi-keyword fuzzy search encryption for secure data sharing

Cloud-based data sharing has become a significant technology trend thanks for its convenience and economy. Data owners are suggested to encrypt data before outsourcing for data security and users' privacy. This approach makes it a challenging problem to implement the retrieval of encrypted data while enforcing fine-grained access control over the ciphertext. In this paper, we propose a blockchain-assisted multi-keyword fuzzy search encryption (BCFSE) scheme for secure data sharing which supports fine-grained access control and attribute revocation. The BCFSE scheme maps the attributes in the ciphertext policy attribute-based encryption (CP-ABE) to attribute tokens on the blockchain, which provides lightweight, secure and trusted attribute management. Moreover, we construct a keyword search scheme based on CP-ABE by using searchable symmetric encryption algorithm in the keyword encryption and retrieval phases, effectively reducing computational cost. BCFSE combines the advantages of CP-ABE and searchable encryption based on blockchain, which implements authorization and search with the assistance of smart contracts to ensure the reliability of the search process. In addition, the BCFSE scheme supports multi-keyword fuzzy search with both AND and OR semantics. Extensive experiments have been performed on real datasets to validate the effectiveness and efficiency of our BCFSE scheme.
A study of adhesive bonding in metal–metal, metal–CFRP, and CFRP–CFRP material combinations under shear deformation: Fracture morphologies and damage mechanisms

Safe design of adhesive joining in multi-materials in engineered structures requires the accumulation of numerous experimental data on the failure behavior of various adhesively-bonded material combinations under different loading conditions. The deep understanding of mechanical performance, fracturing morphologies, and main damage mechanisms is also quintessential for accelerating the development of proper physics-based and multi-scale models for assisting the design. Towards this goal, this work presents a comprehensive characterization of the failure behavior of adhesively-bonded metal–metal, metal–CFRP, and CFRP–CFRP material combinations under global shear deformation via single lap shear testing. Thanks to a synergistic combination of measurement methods by using Digital Imaging Correlation (DIC) and 3D optical profilometry, adhesive features on the adherend after failure were quantified and the main progressive damage mechanisms were identified. The characterization performed in this work provides quantitative data that contributes to a better understanding of shear failure in adhesive bonding across different bi-material combinations. The obtained results have practical implications, including the potential to enhance adhesive bonding design, identify failure causes in adhesive joints, and develop or validate computational models capable of capturing the observed behavior in various adhesively-bonded materials under global shear deformation.
Sparse discriminant PCA based on contrastive learning and class-specificity distribution

Much mathematical effort has been devoted to developing Principal Component Analysis (PCA), which is the most popular feature extraction method. To suppress the negative effect of noise on PCA performance, there have been extensive studies and applications of a large number of robust PCAs achieving outstanding results. However, existing methods suffer from at least two shortcomings: (1) They expressed PCA as a reconstruction model measured by Euclidean distance, which only considers the relationship between the data and its reconstruction and ignores the differences between different data points; (2) They did not consider the class-specificity distribution information contained in the data itself, thus lacking discriminative properties. To overcome the above problems, we propose a Sparse Discriminant Principal Components Analysis (SDPCA) model based on contrastive learning and class-specificity distribution. Specifically, we use contrastive learning to measure the relationship between samples and their reconstructions, which fully takes the discriminative information between data into account in PCA. In order to make the extracted low-dimensional features profoundly reflect the class-specificity distribution of the data, we minimize the squared ℓ1,2-norm of the low-dimensional embedding. In addition, to reduce the effects of redundant features and noise and to improve the interpretability of PCA at the same time, we impose sparsity constraints on the projection matrix using the squared ℓ1,2-norm. Our experimental results on different types of benchmark databases demonstrate that our model has state-of-the-art performance.
A hybrid model for post-treatment mortality rate classification of patients with breast cancer

Terminal cancer is not curable and eventually results in death. Breast cancer (BC) is a prevalent malignancy affecting women. Although there are prognostic indicators, BC prognosis is still challenging because of the intricate connections between various survival factors and influencing factors. This study proposes an ensemble classifier for predicting BC survivability using a new BC post-treatment dataset for the number of survivals. However, the classes survival cases dataset for BC is skewed, which caused a sub-optimal classification performance. Hence, a hybrid sampling scheme of Synthetic Minority Over-Sampling TEchnique (SMOTE) and Wilson's Edited Nearest Neighbor (ENN) is employed to treat the class imbalance in the dataset. Random Forest (RF) ensemble classifier is for classifying the dataset. The proposed framework performs well in terms of accuracy, recall of the two classes, Receiver Operating Characteristics (ROC) and Kappa Statistic (KS) metric on the dataset. The results demonstrated that the RF, with 97.0% accuracy on the holdout sample, is the best predictor. This prediction accuracy is superior to any noted in the literature, compared with Logistic Regression (LR) and Bagging classifiers.
Improving student retention in higher education institutions – Exploring the factors influencing employees extra-role behavior

This study explores the impact of internal service quality on employee job satisfaction (JOS) and the extra role behaviors (ERB) of frontline employees (FLE) in higher education institutions (HEIs). With the hope of improving student retention, the study specifically examined how Human Resources Management Practices (HRMP), Empathetic Leadership (EML), Colleague Support (CS), and Service Technology (ST) indirectly influence FLE ERB, as well as the contextual factor of employee Job satisfaction (JOS) that affects the strength of this relationship. Drawing on social exchange theory and equity theory, the authors propose that HRMP, EML, CS, and ST have positive effects on JOS, and JOS has positive impacts on FLE ERB. The research was conducted drawing on a sample of 251 frontline employees and 251 students using the dyadic research design and self-design questionnaires as the main instrument within one HEI in the United Arab Emirates (UAE). Structural Equation Modeling (SEM) was employed utilizing SmartPLS software to perform bootstrapping routines and generate the SEM to explore the relationships between different variables. The results were analyzed using measures of internal consistency, validity, and goodness of fit. The study's findings support the proposed hypotheses except for the positive influence of job satisfaction on FLE ERB. The study highlights the mechanisms through which internal service quality can impact FLE ERB and identifies the factors that enhance this process. The results revealed that HRMP, CS, and EML have a significant positive impact on JOS, and ST has a significant positive effect on ERB. However, JOS has a significant negative impact on ERB. The study has practical implications for understanding how to promote ERB among FLE in HEIs to improve the quality of service provided to students, which can help to sustain student loyalty and retention.
A selection hyper-heuristic algorithm with Q-learning mechanism

The selection of an algorithm in the real world of the application domain is a challenging problem as no specific algorithm exists capable of solving all issues to a satisfactory requirement. Selecting a suitable algorithm presents major challenges such as solving problems requiring expert knowledge or trial-and-error algorithms, which have hindered advancements in this field. In this work, we introduce a novel method that uniquely addresses these challenges by integrating hyper-heuristic and Q-learning mechanism techniques. A selection hyper-heuristic algorithm with Q-learning (QLSHH) is proposed to select appropriate low-level heuristic (LLH) for the computation stages of the optimization process. The Q-learning mechanism guided by the feedback of the solution state was designed according to the environment. Four low-level heuristics (LLHs) were proposed according to the optimization mechanism for continuous optimization problems. The QLSHH learns the successful experience in the optimization process through Q-learning to select the appropriate LLH at each decision point. The results tested on the CEC 2017 and CEC 2020 benchmark suite show that the QLSHH outperforms the other nine comparison algorithms on 50% of the functions and the experimental results of algorithm complexity show that the proposed algorithm is the fastest compared with other algorithms.
Secure blockchain assisted Internet of Medical Things architecture for data fusion enabled cancer workflow

In today's digital healthcare landscape, numerous clinical institutions collaborate to enhance healthcare quality in a ubiquitous fog and cloud environment. Data fusion plays a vital role in healthcare collaboration, enabling the integration of diverse healthcare sources. The primary advantage is the improvement of healthcare treatments and the availability of services throughout the network. However, despite these benefits, there is room for improvement in addressing various security issues regarding collaboration among clinical healthcare institutions to meet data fusion requirements. The primary challenge lies in processing lung cancer workflow data fusion on heterogeneous nodes while ensuring security in fog cloud networks. As a result, security emerges as a critical issue in the digital healthcare system operating within this ubiquitous environment. We present the secure Blockchain Internet of Medical Things (BIoMT) architecture for lung cancer workflow data fusion processing in fog cloud networks. The BIoMT architecture introduces the Blockchain Data Fusion Secure (BDFS) algorithm framework, which consists of task scheduling and blockchain validation schemes. The study aims to minimize the makespan of the lung workflow tasks based on security and deadline constraints in fog and cloud networks. We consider security at an advanced level, where runtime ransomware attacks are also identified in fog and cloud networks. Simulation results demonstrate that BDFS outperforms all existing BIoMT architectures regarding workflow processing while adhering to the specified constraints. Overall, the BDFS algorithm presented in the BIoMT architecture provides an efficient and secure solution for lung cancer workflow data fusion in fog cloud networks, contributing to the advancement of digital healthcare systems in a ubiquitous environment.
COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models

A characteristic feature of human semantic cognition is its ability to not only store and retrieve the properties of concepts observed through experience, but to also facilitate the inheritance of properties (can breathe) from superordinate concepts (animal) to their subordinates (dog)—i.e. demonstrate property inheritance. In this paper, we present COMPS, a collection of minimal pair sentences that jointly tests pre-trained language models (PLMs) on their ability to attribute properties to concepts and their ability to demonstrate property inheritance behavior. Analyses of 22 different PLMs on COMPS reveal that they can easily distinguish between concepts on the basis of a property when they are trivially different, but find it relatively difficult when concepts are related on the basis of nuanced knowledge representations. Furthermore, we find that PLMs can show behaviors suggesting successful property inheritance in simple contexts, but fail in the presence of distracting information, which decreases the performance of many models sometimes even below chance. This lack of robustness in demonstrating simple reasoning raises important questions about PLMs’ capacity to make correct inferences even when they appear to possess the prerequisite knowledge.