Reviewerly/exHarmony huggingface dataset:
Avoiding embarrassment online: Response to and inferences about chatbots when purchases activate self‐presentation concerns

Abstract We explore how self‐presentation concerns and the desire to avoid embarrassment impact two distinct types of interactions consumers have with chatbots: interactions when a chatbot's identity is (1) not disclosed and therefore ambiguous or (2) disclosed. We propose that consumers feel less embarrassed with a chatbot than a human service agent in purchase contexts where self‐presentation concerns are active because consumers ascribe less mind to chatbots. Therefore, when a chat agent's identity is ambiguous, consumers with greater self‐presentation concerns are more likely to infer that an agent is human because this judgment allows consumers to proactively protect themselves from potential embarrassment in the event they are interacting with a human. We further show that when agent identity is clearly disclosed, consumers respond more positively to chatbots than human agents. However, this effect is contingent on the extent to which the chatbot is imbued with human characteristics: Anthropomorphizing chatbots leads consumers with higher self‐presentation concerns to ascribe more mind to even clearly identified chatbots, resulting in a more negative consumer response.
Adoption of distributed ledger technology for construction projects: a study of the challenges in a developing country

Purpose The construction industry in Nigeria, like many others worldwide, grapples with increasing demands for enhanced efficiency, transparency and security. While distributed ledger technology (DLT) offers substantial potential in tackling these issues, its implementation faces numerous obstacles. This study aims to investigate the challenges hindering the adoption of DLT within the Nigerian construction sector, capitalizing on a recognized research gap in this field. Design/methodology/approach A comprehensive literature review identified 20 challenges to DLT adoption. A structured questionnaire was developed and distributed to 98 construction professionals selected from a pool of 5,330 members using purposive and snowball sampling. Background information was assessed using frequency and percentage, data normality with the Shapiro–Wilk test and nonparametric data compared using the Kruskal–Wallis H -test. Factor analysis was then used to reduce the challenges into a more coherent set. Findings Findings revealed the top five challenges: power supply issues, data privacy and compliance, an uncertain legal framework, insufficient training and education and a lack of skilled workforce. Further analysis revealed four clusters of challenges, namely, technical and infrastructure barriers, organizational and cultural challenges, financial and economic challenges and regulatory and legal challenges. Practical implications Through the evaluation of challenges and potential strategies to overcome them, this study aims to lay a foundation that assists construction entities in effectively harnessing the benefits of DLT while navigating the obstacles inherent in its adoption. Originality/value This study contributes to the existing body of knowledge by offering insights into the challenges surrounding the adoption of DLT, specifically within the Nigerian construction sector. By juxtaposing the findings from this study with existing research conducted in other countries such as India, South Africa and the USA, a more comprehensive understanding of the challenges unique to the Nigerian context can be gained.
Is deep learning a useful tool for the pure mathematician?

A personal and informal account of what a pure mathematician might expect when using tools from deep learning in their research.
Investigation on storage level data integrity strategies in cloud computing: classification, security obstructions, challenges and vulnerability

Abstract Cloud computing provides outsourcing of computing services at a lower cost, making it a popular choice for many businesses. In recent years, cloud data storage has gained significant success, thanks to its advantages in maintenance, performance, support, cost, and reliability compared to traditional storage methods. However, despite the benefits of disaster recovery, scalability, and resource backup, some organizations still prefer traditional data storage over cloud storage due to concerns about data correctness and security. Data integrity is a critical issue in cloud computing, as data owners need to rely on third-party cloud storage providers to handle their data. To address this, researchers have been developing new algorithms for data integrity strategies in cloud storage to enhance security and ensure the accuracy of outsourced data. This article aims to highlight the security issues and possible attacks on cloud storage, as well as discussing the phases, characteristics, and classification of data integrity strategies. A comparative analysis of these strategies in the context of cloud storage is also presented. Furthermore, the overhead parameters of auditing system models in cloud computing are examined, considering the desired design goals. By understanding and addressing these factors, organizations can make informed decisions about their cloud storage solutions, taking into account both security and performance considerations.
Joint streaming model for backchannel prediction and automatic speech recognition

Abstract In human conversations, listeners often utilize brief backchannels such as “uh‐huh” or “yeah.” Timely backchannels are crucial to understanding and increasing trust among conversational partners. In human–machine conversation systems, users can engage in natural conversations when a conversational agent generates backchannels like a human listener. We propose a method that simultaneously predicts backchannels and recognizes speech in real time. We use a streaming transformer and adopt multitask learning for concurrent backchannel prediction and speech recognition. The experimental results demonstrate the superior performance of our method compared with previous works while maintaining a similar single‐task speech recognition performance. Owing to the extremely imbalanced training data distribution, the single‐task backchannel prediction model fails to predict any of the backchannel categories, and the proposed multitask approach substantially enhances the backchannel prediction performance. Notably, in the streaming prediction scenario, the performance of backchannel prediction improves by up to 18.7% compared with existing methods.
Framework for evaluating code generation ability of large language models

Abstract Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, , which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the metric.
Named entity recognition using transfer learning and small human‐ and meta‐pseudo‐labeled datasets

Abstract We introduce a high‐performance named entity recognition (NER) model for written and spoken language. To overcome challenges related to labeled data scarcity and domain shifts, we use transfer learning to leverage our previously developed KorBERT as the base model. We also adopt a meta‐pseudo‐label method using a teacher/student framework with labeled and unlabeled data. Our model presents two modifications. First, the student model is updated with an average loss from both human‐ and pseudo‐labeled data. Second, the influence of noisy pseudo‐labeled data is mitigated by considering feedback scores and updating the teacher model only when below a threshold (0.0005). We achieve the target NER performance in the spoken language domain and improve that in the written language domain by proposing a straightforward rollback method that reverts to the best model based on scarce human‐labeled data. Further improvement is achieved by adjusting the label vector weights in the named entity dictionary.
Real-time sign language recognition based on YOLO algorithm

Abstract This study focuses on real-time hand gesture recognition in the Turkish sign language detection system. YOLOv4-CSP based on convolutional neural network (CNN), a state-of-the-art object detection algorithm, is used to provide real-time and high-performance detection. The YOLOv4-CSP algorithm is created by adding CSPNet to the neck of the original YOLOv4 to improve network performance. A new object detection model has been proposed by optimizing the YOLOv4-CSP algorithm in order to provide more efficient detection in Turkish sign language. The model uses CSPNet throughout the network to increase the learning ability of the network. However, Proposed YOLOv4-CSP has a learning model with Mish activation function, complete intersection of union (CIoU) loss function and transformer block added. The Proposed YOLOv4-CSP algorithm has faster learning with transfer learning than previous versions. This allows the proposed YOLOv4-CSP algorithm to perform a faster restriction and recognition of static hand signals simultaneously. To evaluate the speed and detection performance of the proposed YOLOv4-CSP model, it is compared with previous YOLO series, which offers real-time detection, as well. YOLOv3, YOLOv3-SPP, YOLOv4-CSP and proposed YOLOv4-CSP models are trained with a labeled dataset consisting of numbers in Turkish Sign language, and their performances on the hand signals recognitions are compared. With the proposed method, 98.95% precision, 98.15% recall, 98.55 F1 score and 99.49% mAP results are obtained in 9.8 ms. The proposed method for detecting numbers in Turkish sign language outperforms other algorithms with both real-time performance and accurate hand sign prediction, regardless of background.
Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study

This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable risk factors.The dataset includes 1,009 cases and 1,009 controls, with comprehensive data on lifestyle, health-behavior, reproductive and sociodemographic factors. Different machine learning models, namely Random Forest (RF), Neural Networks (NN), Bootstrap Aggregating Classification and Regression Trees (Bagged CART), and Extreme Gradient Boosting Tree (XGBoost), were employed to analyze the data.The findings highlight the significance of a chest X-ray history, deliberate weight loss, abortion history, and post-menopausal status as predictors. Factors such as second-hand smoking, lower education, menarche age (>14), occupation (employed), first delivery age (18-23), and breastfeeding duration (>42 months) were also identified as important predictors in multiple models. The RF model exhibited the highest Area Under the Curve (AUC) value of 0.9, as indicated by the Receiver Operating Characteristic (ROC) curve. Following closely was the Bagged CART model with an AUC of 0.89, while the XGBoost model achieved a slightly lower AUC of 0.78. In contrast, the NN model demonstrated the lowest AUC of 0.74. On the other hand, the RF model achieved an accuracy of 83.9% and a Kappa coefficient of 67.8% and the XGBoost, achieved a lower accuracy of 82.5% and a lower Kappa coefficient of 0.6.This study could be beneficial for targeted preventive measures according to the main risk factors for BC among high-risk women.
Blockchain and public service delivery: a lifetime cross-referenced model for e-government

The article presents the results of field studies, analysing the perspectives of blockchain developers on decentralised service delivery and elaborating on unique algorithms for lifetime ledgers to reliably and safely record e-government transactions in an intrinsically cross-referenced manner. New interesting technological niches of service delivery and emerging models of related data management in the industry were proposed and further elaborated such as the generation of unique lifetime personal data profiles, blockchain-driven cross-referencing of e-government metadata, parallel maintenance of serviceable ledgers for data identifiers and phenomena of blockchain 'black holes' to ensure reliable protection of important public, corporate and civic information.
A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack

Abstract In this paper, we address the difficulty of solving large-scale multi-dimensional knapsack instances (MKP), presenting a novel deep reinforcement learning (DRL) framework. In this DRL framework, we train different agents compatible with a discrete action space for sequential decision-making while still satisfying any resource constraint of the MKP. This novel framework incorporates the decision variable values in the 2D DRL where the agent is responsible for assigning a value of 1 or 0 to each of the variables. To the best of our knowledge, this is the first DRL model of its kind in which a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our framework is configured to solve MKP instances of different dimensions and distributions. We propose a K-means approach to obtain an initial feasible solution that is used to train the DRL agent. We train four different agents in our framework and present the results comparing each of them with the CPLEX commercial solver. The results show that our agents can learn and generalize over instances with different sizes and distributions. Our DRL framework shows that it can solve medium-sized instances at least 45 times faster in CPU solution time and at least 10 times faster for large instances, with a maximum solution gap of 0.28% compared to the performance of CPLEX. Furthermore, at least 95% of the items are predicted in line with the CPLEX solution. Computations with DRL also provide a better optimality gap with respect to state-of-the-art approaches.
LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction

Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce the parameters and computations of the model while ensuring segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on this basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; and the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.
Integrating foreground-background feature distillation and contrastive feature learning for ultra-fine-grained visual classification

In pattern recognition, ultra-fine-grained visual classification (ultra-FGVC) has emerged as a paramount challenge, focusing on sub-category distinction within fine-grained objects. The near-indistinguishable similarities among such objects, combined with the dearth of sample data, intensify this challenge. In response, our FDCL-DA method is introduced, which integrates Foreground-background feature Distillation (FD) and Contrastive feature Learning (CL) with Dual Augmentation (DA). This method uses two different data augmentation techniques, standard and auxiliary augmentation, to enhance model performance and generalization ability. The FD module reduces superfluous features and augments the contrast between the principal entity and its backdrop, while the CL focuses on creating unique data imprints by reducing intra-class resemblances and enhancing inter-class disparities. Integrating this method with different architectures, such as ResNet-50, Vision Transformer, and Swin-Transformer (Swin-T), significantly improves these backbone networks, especially when used with Swin-T, leading to promising results on eight popular datasets for ultra-FGVC tasks.1
When I use a word . . . Medical blue plaques in London

In 1863 the MP William Ewart suggested that “it might be practicable... to have inscribed on those houses in London which have been inhabited by celebrated persons, the names of such persons.” Accordingly, in 1867 the first such inscriptions, which came to be known as blue plaques, were put up by the Society of Arts, commemorating Lord Byron and Napoleon III at places in London where they had lived. The society put up 35 such plaques over the next 35 years when the scheme was taken over by the London County Council, which gave way to the Greater London Council in 1965 and finally English Heritage, in 1986. Among London’s 1000 or so blue plaques several medical men and women, doctors, nurses, and other healthcare workers, are commemorated. They include Cecil Belfield-Clarke, Hannah Billig, Richard Bright, Edith Louisa Cavell, Henry Hallett Dale, Charles Darwin, Henry Havelock Ellis, Elizabeth Garrett Anderson, Thomas Hodgkin, William Hunter and his brother John, Joseph Lister, James Mackenzie, Rachel McMillan and her sister Margaret, William Marsden, Florence Nightingale, Ronald Ross, Mary Seacole, Hans Sloane, and George Frederick Still.
Dynamic S-Box Construction Using Mordell Elliptic Curves over Galois Field and Its Applications in Image Encryption

Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.
Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformer

Abstract In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no relationship among them. This assumption is unsuited, as the instances in the bags are rarely independent in diverse MIL applications. In contrast, the instance relationship assumption-based techniques incorporate the instance relationship information in the classification process. However, in MIL, the bag composition process is complicated, and it may be possible that instances in one bag are related and instances in another bag are not. In present MIL algorithms, this relationship assumption is not explicitly modeled. The learning algorithm is trained based on one of two relationship assumptions (whether instances in all bags have a relationship or not). Hence, it is essential to model the assumption of instance relationships in the bag classification process. This paper proposes a robust approach that generates vector representation for the bag for both assumptions and the representation selection process to determine whether to consider the instances related or unrelated in the bag classification process. This process helps to determine the essential bag representation vector for every individual bag. The proposed method utilizes attention pooling and vision transformer approaches to generate bag representation vectors. Later, the representation selection subnetwork determines the vector representation essential for bag classification in an end-to-end trainable manner. The generalization abilities of the proposed framework are demonstrated through extensive experiments on several benchmark datasets. The experiments demonstrate that the proposed approach outperforms other state-of-the-art MIL approaches in bag classification.
Link prediction using low-dimensional node embeddings: The measurement problem

Graph representation learning is a fundamental technique for machine learning (ML) on complex networks. Given an input network, these methods represent the vertices by low-dimensional real-valued vectors. These vectors can be used for a multitude of downstream ML tasks. We study one of the most important such task, link prediction. Much of the recent literature on graph representation learning has shown remarkable success in link prediction. On closer investigation, we observe that the performance is measured by the AUC (area under the curve), which suffers biases. Since the ground truth in link prediction is sparse, we design a vertex-centric measure of performance, called the VCMPR@k plots. Under this measure, we show that link predictors using graph representations show poor scores. Despite having extremely high AUC scores, the predictors miss much of the ground truth. We identify a mathematical connection between this performance, the sparsity of the ground truth, and the low-dimensional geometry of the node embeddings. Under a formal theoretical framework, we prove that low-dimensional vectors cannot capture sparse ground truth using dot product similarities (the standard practice in the literature). Our results call into question existing results on link prediction and pose a significant scientific challenge for graph representation learning. The VCMPR plots identify specific scientific challenges for link prediction using low-dimensional node embeddings.