Reviewerly/exHarmony huggingface dataset:
Cost Prediction in Blockchain-Enabled Pharmaceutical Supply Chain under Uncertain Demand

Cost prediction can provide a pharma supply chain industry with completing their projects on schedule and within budget. This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its importance, and we applied the Feature Weighting approach to analyze their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a more substantial influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.
Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning

Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern-based approach. However, this type of learning model suffers from two fundamental problems: the computational complexity arising from relational queries and the lack of a robust and general framework to serve as the basis for relational learning methods. In this paper, we propose an efficient graph query framework that allows for cyclic queries in polynomial time and is ready to be used in pattern-based learning methods. This solution uses logical predicates instead of graph isomorphisms for query evaluation, reducing complexity and allowing for query refinement through atomic operations. The main differences between our method and other previous pattern-based graph query approaches are the ability to evaluate arbitrary subgraphs instead of nodes or complete graphs, the fact that it is based on mathematical formalization that allows the study of refinements and their complementarity, and the ability to detect cyclic patterns in polynomial time. Application examples show that the proposed framework allows learning relational classifiers to be efficient in generating data with high expressiveness capacities. Specifically, relational decision trees are learned from sets of tagged subnetworks that provide both classifiers and characteristic patterns for the identified classes.
Adversarial Attacks in Underwater Acoustic Target Recognition with Deep Learning Models

Deep learning models can produce unstable results by introducing imperceptible perturbations that are difficult for humans to recognize. This can have a significant impact on the accuracy and security of deep learning applications due to their poorly understood interpretability. As a field critical to security research, this problem clearly exists in underwater acoustic target recognition for ocean sensing. To address this issue, this article investigates the reliability of state-of-the-art deep learning models by exploring adversarial attack methods that add small, exquisite perturbations on acoustic Mel-spectrograms to generate adversarial spectrograms. Experimental results based on real-world datasets reveal that these models can be forced to learn unexpected features when subjected to adversarial spectrograms, resulting in significant accuracy drops. Specifically, when employing the iterative attack method, the overall accuracy of all models experiences a significant decrease of approximately 70% for two datasets under stronger perturbations.
Impact of real-life use of artificial intelligence as support for human reading in a population-based breast cancer screening program with mammography and tomosynthesis

To evaluate the impact of using an artificial intelligence (AI) system as support for human double reading in a real-life scenario of a breast cancer screening program with digital mammography (DM) or digital breast tomosynthesis (DBT).We analyzed the performance of double reading screening with mammography and tomosynthesis after implementarion of AI as decision support. The study group consisted of a consecutive cohort of 1 year screening between March 2021 and March 2022 where double reading was performed with concurrent AI support that automatically detects and highlights lesions suspicious of breast cancer in mammography and tomosynthesis. Screening performance was measured as cancer detection rate (CDR), recall rate (RR), and positive predictive value (PPV) of recalls. Performance in the study group was compared using a McNemar test to a control group that included a screening cohort of the same size, recorded just prior to the implementation of AI.A total of 11,998 women (mean age 57.59 years ± 5.8 [sd]) were included in the study group (5049 DM and 6949 DBT). Comparing global results (including DM and DBT) of double reading with vs. without AI support, we observed an increase in CDR, PPV, and RR by 3.2/‰ (5.8 vs. 9; p < 0.001), 4% (10.6 vs. 14.6; p < 0.001), and 0.7% (5.4 vs. 6.1; p < 0.001) respectively.AI used as support for human double reading in a real-life breast cancer screening program with DM and DBT increases CDR and PPV of the recalled women.Artificial intelligence as support for human double reading improves accuracy in a real-life breast cancer screening program both in digital mammography and digital breast tomosynthesis.• AI systems based on deep learning technology offer potential for improving breast cancer screening programs. • Using artificial intelligence as support for reading improves radiologists' performance in breast cancer screening programs with mammography or tomosynthesis. • Artificial intelligence used concurrently with human reading in clinical screening practice increases breast cancer detection rate and positive predictive value of the recalled women.
Multi-View Masked Autoencoder for General Image Representation

Self-supervised learning is a method that learns general representation from unlabeled data. Masked image modeling (MIM), one of the generative self-supervised learning methods, has drawn attention for showing state-of-the-art performance on various downstream tasks, though it has shown poor linear separability resulting from the token-level approach. In this paper, we propose a contrastive learning-based multi-view masked autoencoder for MIM, thus exploiting an image-level approach by learning common features from two different augmented views. We strengthen the MIM by learning long-range global patterns from contrastive loss. Our framework adopts a simple encoder–decoder architecture, thus learning rich and general representations by following a simple process: (1) Two different views are generated from an input image with random masking and by contrastive loss, we can learn the semantic distance of the representations generated by an encoder. By applying a high mask ratio, of 80%, it works as strong augmentation and alleviates the representation collapse problem. (2) With reconstruction loss, the decoder learns to reconstruct an original image from the masked image. We assessed our framework through several experiments on benchmark datasets of image classification, object detection, and semantic segmentation. We achieved 84.3% in fine-tuning accuracy on ImageNet-1K classification and 76.7% in linear probing, thus exceeding previous studies and showing promising results on other downstream tasks. The experimental results demonstrate that our work can learn rich and general image representation by applying contrastive loss to masked image modeling.
Automatic Recognition of Blood Cell Images with Dense Distributions Based on a Faster Region-Based Convolutional Neural Network

In modern clinical medicine, the important information of red blood cells, such as shape and number, is applied to detect blood diseases. However, the automatic recognition problem of single cells and adherent cells always exists in a densely distributed medical scene, which is difficult to solve for both the traditional detection algorithms with lower recognition rates and the conventional networks with weaker feature extraction capabilities. In this paper, an automatic recognition method of adherent blood cells with dense distribution is proposed. Based on the Faster R-CNN, the balanced feature pyramid structure, deformable convolution network, and efficient pyramid split attention mechanism are adopted to automatically recognize the blood cells under the conditions of dense distribution, extrusion deformation, adhesion and overlap. In addition, the Align algorithm for region of interest also contributes to improving the accuracy of recognition results. The experimental results show that the mean average precision of cell detection is 0.895, which is 24.5% higher than that of the original network model. Compared with the one-stage mainstream networks, the presented network has a stronger feature extraction capability. The proposed method is suitable for identifying single cells and adherent cells with dense distribution in the actual medical scene.
Service robots and hotel guests’ perceptions: anthropomorphism and stereotypes

Purpose Drawing on anthropomorphism and the stereotype content model, this study aims to explore the impacts of robot anthropomorphism on hotel guests’ word of mouth and continuous usage intention through perceived warmth and competence. This study also investigates the moderating effects of guests’ expertise. Design/methodology/approach A total of 524 valid data was collected with Chinese hotel guest through an online survey. Data were analyzed by using structural equation modeling. Findings The results indicate that robot anthropomorphism positively affects perceived warmth and competence. Perceived warmth positively affects guests’ word of mouth and continuous usage intention, while perceived competence only positively influences guests’ continuous usage intention. The findings further show that guest expertise weakens the positive relationship between robot anthropomorphism and perceived warmth and competence. Originality/value This study advances robot service literature by integrating the stereotype content model with robot anthropomorphism in exploring hotel guests’ perceptions on service robots. This study also discovers the vital boundary role of guest characteristics in human–robot interaction. This study provides valuable information for hoteliers to design and adopt better robots for optimal guest experiences.
Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction

Dialogue relation extraction identifies semantic relations between entity pairs in dialogues. This research explores a methodology harnessing the potential of prompt-based fine-tuning paired with a trigger-generation approach. Capitalizing on the intrinsic knowledge of pre-trained language models, this strategy employs triggers that underline the relation between entities decisively. In particular, diverging from the conventional extractive methods seen in earlier research, our study leans towards a generative manner for trigger generation. The dialogue-based relation extraction (DialogeRE) benchmark dataset features multi-utterance environments of colloquial speech by multiple speakers, making it critical to capture meaningful clues for inferring relational facts. In the benchmark, empirical results reveal significant performance boosts in few-shot scenarios, where the availability of examples is notably limited. Nevertheless, the scarcity of ground-truth triggers for training hints at potential further refinements in the trigger-generation module, especially when ample examples are present. When evaluating the challenges of dialogue relation extraction, combining prompt-based learning with trigger generation offers pronounced improvements in both full-shot and few-shot scenarios. Specifically, integrating a meticulously crafted manual initialization method with the prompt-based model—considering prior distributional insights and relation class semantics—substantially surpasses the baseline. However, further advancements in trigger generation are warranted, especially in data-abundant contexts, to maximize performance enhancements.
FLPP: A Federated-Learning-Based Scheme for Privacy Protection in Mobile Edge Computing

Data sharing and analyzing among different devices in mobile edge computing is valuable for social innovation and development. The limitation to the achievement of this goal is the data privacy risk. Therefore, existing studies mainly focus on enhancing the data privacy-protection capability. On the one hand, direct data leakage is avoided through federated learning by converting raw data into model parameters for transmission. On the other hand, the security of federated learning is further strengthened by privacy-protection techniques to defend against inference attack. However, privacy-protection techniques may reduce the training accuracy of the data while improving the security. Particularly, trading off data security and accuracy is a major challenge in dynamic mobile edge computing scenarios. To address this issue, we propose a federated-learning-based privacy-protection scheme, FLPP. Then, we build a layered adaptive differential privacy model to dynamically adjust the privacy-protection level in different situations. Finally, we design a differential evolutionary algorithm to derive the most suitable privacy-protection policy for achieving the optimal overall performance. The simulation results show that FLPP has an advantage of 8∼34% in overall performance. This demonstrates that our scheme can enable data to be shared securely and accurately.
Preservation of Sensitive Data Using Multi-Level Blockchain-based Secured Framework for Edge Network Devices

Abstract The proliferation of IoT devices has influenced end users in several aspects. Yottabytes (YB) of information are being produced in the IoT environs because of the ever-increasing utilization capacity of the Internet. Since sensitive information, as well as privacy problems, always seem to be an unsolved problem, even with best-in-class in-formation governance standards, it is difficult to bolster defensive security capabilities. Secure data sharing across disparate systems is made possible by blockchain technology, which operates on a decentralized computing paradigm. In the ever-changing IoT environments, blockchain technology provides irreversibility (immutability) usage across a wide range of services and use cases. Therefore, blockchain technology can be leveraged to securely hold private information, even in the dynamicity context of the IoT. However, as the rate of change in IoT networks accelerates, every potential weak point in the system is exposed, making it more challenging to keep sensitive data se-cure. In this study, we adopted a Multi-level Blockchain-based Secured Framework (M-BSF) to provide multi-level protection for sensitive data in the face of threats to IoT-based networking systems. The envisioned M-BSF framework incorporates edge-level, fog-level, and cloud-level security. At edge- and fog-level security, baby kyber and scaling kyber cryptosystems are applied to ensure data preservation. Kyber is a cryptosystem scheme that adopts public-key encryption and private-key decryption processes. Each block of the blockchain uses the cloud-based Argon-2di hashing method for cloud-level data storage, providing the highest level of confidentiality. Argon-2di is a stable hashing algorithm that uses a hybrid approach to access the memory that relied on dependent and independent memory features. Based on the attack-resistant rate (&gt; 96%), computational cost (in time), and other main metrics, the proposed M-BSF security architecture appears to be an acceptable alternative to the current methodologies.
Creating an information security policyin a bank

Information security policies are particularly important when it comes to maintaining information security within an organization. As a result, creating an information security policy is an invaluable process that cannot be undermined. This study aims at investigating how these information security policies are created, specifically in banks, from a linguistic perspective. The study employed the use of corpora and analyzed ten information security policies of banks randomly collected online. The analyses were categorized under five levels of linguistic analysis which included: the mode, lexis, grammar, speech acts, and discourse. Some statistical analyses which involved clustering were also performed at the level of discourse in order to find similar patterns in the information security policies. The results show that banks use the same linguistic features when writing their information security policies. The results also reveal how these linguistic features are used to develop a comprehensive and effective information security policy.
Contextual Adversarial Representation Learning for Multiword Expressions

Word composition is a promising method to learn the representations of long text. Unfortunately, word composition falls short when inferring representations for non-compositional multiword expressions, such as “go banana.” Presently, many methods treat multiword expressions as single words and learn their representations in a manner similar to individual word representations. However, numerous multiword expressions exhibit ambiguity, expressing distinct meanings, whether literal or idiomatic, depending on the context. In response to these challenges, our paper proposes an adversarial context-aware representation learning method for multiword expressions, which generates representations based on the contexts of their occurrences. An adversarial training framework is introduced to further enhance the representation learning method. Experimental results confirm the benefits of sense disambiguation for multiword expressions in representation learning. Moreover, our proposed method demonstrates competitive performance on idiom token classification and compositionality prediction tasks.
A Novel Lightweight Object Detection Network with Attention Modules and Hierarchical Feature Pyramid

Object detection methods based on deep learning typically require devices with ample computing capabilities, which limits their deployment in restricted environments such as those with embedded devices. To address this challenge, we propose Mini-YOLOv4, a lightweight real-time object detection network that achieves an excellent trade-off between speed and accuracy. Based on CSPDarknet-Tiny as the backbone network, we enhance the detection performance of the network in three ways. We use a multibranch structure embedded in an attention module for simultaneous spatial and channel attention calibration. We design a group self-attention block with a symmetric structure consisting of a pair of complementary self-attention modules to mine contextual information, thereby ensuring that the detection accuracy is improved without increasing the computational cost. Finally, we introduce a hierarchical feature pyramid network to fully exploit multiscale feature maps and promote the extraction of fine-grained features. The experimental results demonstrate that Mini-YOLOv4 requires only 4.7 M parameters and has a billion floating point operations (BFLOPs) value of 3.1. Compared with YOLOv4-Tiny, our approach achieves a 3.2% improvement in mean accuracy precision (mAP) for the PASCAL VOC dataset and obtains a significant improvement of 3.5% in overall detection accuracy for the MS COCO dataset. In testing with an embedded platform, Mini-YOLOv4 achieves a real-time detection speed of 25.6 FPS on the NVIDIA Jetson Nano, thus meeting the demand for real-time detection in computationally limited devices.
Information-theoretic approach to readout-error mitigation for quantum computers

We show that the method of iterative Bayesian unfolding for mitigating readout errors in quantum computers can be derived from an information theoretic analysis. This inspires more flexible applications of this error-mitigation scheme. In particular, we distinguish between structural mitigation and unstructural mitigation. Structural mitigation addresses nearly deterministic quantum computation, where the computer is expected to output a single- or few-outcome bit strings. It is shown that the readout errors alone can be corrected by few repetitions of the computation. In contrast, unstructural mitigation is designed for quantum simulation, where the computer outputs bit strings broadly distributed. In this case, one is interested in mitigating certain observables of interest. As most observables of interest are dependent on few bits and not the whole bit string, it is sufficient to mitigate the marginal distributions over these dependent bits. As long as the cross talk of readout errors can be ignored, it is shown that the iterative Bayesian unfolding applied locally for these marginal distributions gives similar results as mitigation using least squared errors. We illustrate our analysis using the data of the preparation of the Greenberger-Horne-Zeilinger state in a 127-qubit quantum computer.
Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization

Anomaly detection has a wide range of applications and is especially important in industrial quality inspection. Currently, many top-performing anomaly detection models rely on feature embedding-based methods. However, these methods do not perform well on datasets with large variations in object locations. Reconstruction-based methods use reconstruction errors to detect anomalies without considering positional differences between samples. In this study, a reconstruction-based method using the noise-to-norm paradigm is proposed, which avoids the invariant reconstruction of anomalous regions. Our reconstruction network is based on M-net and incorporates multiscale fusion and residual attention modules to enable end-to-end anomaly detection and localization. Experiments demonstrate that the method is effective in reconstructing anomalous regions into normal patterns and achieving accurate anomaly detection and localization. On the MPDD and VisA datasets, our proposed method achieved more competitive results than the latest methods, and it set a new state-of-the-art standard on the MPDD dataset.