Reviewerly/exHarmony huggingface dataset:
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity

Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the devices, FL frameworks struggle to tackle the problems of straggler effects and outdated models. In addition, the data heterogeneity incurs severe accuracy degradation of the global model in the FL training process. To address aforementioned issues, we propose a hierarchical synchronous FL framework, i.e., FedHiSyn. FedHiSyn first clusters all available devices into a small number of categories based on their computing capacity. After a certain interval of local training, the models trained in different categories are simultaneously uploaded to a central server. Within a single category, the devices communicate the local updated model weights to each other based on a ring topology. As the efficiency of training in the ring topology prefers devices with homogeneous resources, the classification based on the computing capacity mitigates the impact of straggler effects. Besides, the combination of the synchronous update of multiple categories and the device communication within a single category help address the data heterogeneity issue while achieving high accuracy. We evaluate the proposed framework based on MNIST, EMNIST, CIFAR10 and CIFAR100 datasets and diverse heterogeneous settings of devices. Experimental results show that FedHiSyn outperforms six baseline methods, e.g., FedAvg, SCAFFOLD, and FedAT, in terms of training accuracy and efficiency.
An asset-backed decentralised finance instrument for food supply chains – A case study from the livestock export industry

Decentralised finance (DeFi) has emerged as a blockchain-enabled innovation in supply chains that makes use of supporting infrastructure to transform financial services by reducing or removing the need for intermediaries. While an increasing number of pilot projects have been exploring DeFi applications for use by small and medium enterprises (SMEs) in various finance scenarios, there is not yet a real-world adoption of DeFi beyond these projects to unlock their potential for progressing the digitisation of supply chain finance (SCF). This paper reports on a university-industry collaborative study to leverage the use of blockchain-based DeFi technology in two ways: (i) to benefit SMEs by reducing their cost of finance and increasing ease of access to funding, and; (ii) to address the prevalent asymmetric information risks and costs in a supply chain finance context. The research informed the design and development of a DeFi architecture for asset tracking in agricultural supply chains implemented and evaluated in a real-world pilot adoption with a producer in the Australian livestock industry. The use case shows that there are numerous risks pertaining to the adoption of DeFi for SCF. In response, the paper suggests new ways for supply chain SMEs to secure benefits from their investment in DeFi based on the key findings derived from evaluating our use cases.
Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research

Artificial intelligence (AI) and machine learning (ML) are emergent tools for fake review detection. This study provides a comprehensive overview of AI applications in fake review detection, using a hybrid integrated review and by combining bibliometric analysis with a framework-based review: the 4Ws (What, Where, Why, and How). We draw on the thematic structure of AI and ML research in fake review detection for the 2012–2021 period by conducting bibliometric coupling, keyword co-occurrence, and conceptual thematic, social network, and cluster-based content analyses of scientific articles. The findings indicate that field research has thus far concentrated on three overarching groups of fake review detection: (a) word of mouth, quality, reputation, and price; (b) classification, moderating role, intention, and analytics; and (c) impact and participation. This article provides researchers, companies and policymakers with insights into how AI can detect fraudulent reviews and drives further research on the adequate use of advanced AI techniques in fake review detection.
Model poisoning attack in differential privacy-based federated learning

Although federated learning can provide privacy protection for individual raw data, some studies have shown that the shared parameters or gradients under federated learning may still reveal user privacy. Differential privacy is a promising solution to the above problem due to its small computational overhead. At present, differential privacy-based federated learning generally focuses on the trade-off between privacy and model convergence. Even though differential privacy obscures sensitive information by adding a controlled amount of noise to the confidential data, it opens a new door for model poisoning attacks: attackers can use noise to escape anomaly detection. In this paper, we propose a novel model poisoning attack called Model Shuffle Attack (MSA), which designs a unique way to shuffle and scale the model parameters. If we treat the model as a black box, it behaves like a benign model on test set. Unlike other model poisoning attacks, the malicious model after MSA has high accuracy on test set while reducing the global model convergence speed and even causing the model to diverge. Extensive experiments show that under FedAvg and robust aggregation rules, MSA is able to significantly degrade performance of the global model while guaranteeing stealthiness.
End-to-End Document Recognition and Understanding with Dessurt

We introduce Dessurt, a relatively simple document understanding transformer capable of being fine-tuned on a greater variety of document tasks than prior methods. It receives a document image and task string as input and generates arbitrary text autoregressively as output. Because Dessurt is an end-to-end architecture that performs text recognition in addition to document understanding, it does not require an external recognition model as prior methods do. Dessurt is a more flexible model than prior methods and is able to handle a variety of document domains and tasks. We show that this model is effective at 9 different dataset-task combinations.
An IoT-Based GeoData Production System Deployed in a Hospital

Navigation in large hospitals remains a challenge, especially for patients, visitors and, in some cases, for staff, but in particular it is notable in the case of tracking ambulatory equipment. Current techniques generally seek to reproduce what outdoor navigation systems provide, i.e., "good" accuracy. In many cases, especially in hospitals, reliability is much more important than accuracy. We show that it is possible to realize a simple, reliable system with a low accuracy, but which perfectly fulfills the task assigned in the particular case of tracking stretchers. Optimizing the use of hospital equipment requires the knowledge of its movement. The possibility to access equipment location in real time as well as on the knowledge of the time necessary to move it between two locations allows to predict or to estimate the load and possibly to scale the necessary number of stretchers, and thus the availability of the stretcher bearers. In this paper, an approach of the real-time location of these devices is proposed, and it is called "symbolic". The principle is described, as well as the practical implementation and the data that can be retrieved. In the second part, an analysis of the results obtained is provided in two directions: the location of stretchers and the determination of travel times. The methodology followed is described, and it is shown that a correct positioning rate of 90% is reached, which is slightly lower than expected, explained by the chosen practical implementation. Moreover, the average error on the determination of travel times is approximately ten seconds on 2 to 7 min trips. The "reliability" (the terminology of which is discussed at the end of the paper) of the results is related to the simplicity of the approach.
SCOBY BREASTPLATE: SLOWLY GROWING A MICROBIAL INTERFACE

We present an interactive breastplate grown and fabricated from SCOBY (Symbiotic Culture Of Bacteria and Yeast) biofilm over the course of 13 weeks. Challenging the fail-fast and rapid prototyping trends that inhabit HCI research, we instead explore what it means to design at the pace of another living organism. To create our wearable, we combined DIY-Bio knowledge with digital fabrication methods and traditional crafting techniques in order to tune aspects of the SCOBY such as strength, flexibility, shape, color, and electrical conductivity. We then embedded sensors and LEDs within the SCOBY to create a wearable that visually signals based on touch interactions. We demonstrate the interactivity of the breastplate in an everyday context, where differing light responses result from the wearer being hugged, tapped, or brushed. Lastly, we analyze the biodegradability of the SCOBY breastplate and observe the limitations and opportunities of SCOBY as a grown microbial interface.
Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

Most heating, ventilation, and air-conditioning (HVAC) systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time.Today, most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure occurs.This is mainly because the building owners do not previously have good tools to detect and diagnose these faults, determine their impact, and act on findings.Commercially available fault detection and diagnostics (FDD) tools have been developed to address this issue and have the potential to reduce equipment downtime, energy costs, maintenance costs, and improve occupant comfort and system reliability.However, many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the results.In this paper, supervised and semi-supervised machine learning (ML) approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive maintenance.The study data was collected from one packaged rooftop unit (RTU) HVAC system running under normal operating conditions at an industrial facility in Connecticut.This paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning, achieving accuracies as high as 95.7% using few-shot learning.
TSVFN: Two-Stage Visual Fusion Network for multimodal relation extraction

Multimodal relation extraction is a critical task in information extraction, aiming to predict the class of relations between head and tail entities from linguistic sequences and related images. However, the current works are vulnerable to less relevant visual objects detected from images and are not able to sufficiently fuse visual information into text pre-trained models. To overcome these problems, we propose a Two-Stage Visual Fusion Network (TSVFN) that employs the multimodal fusion approach in vision-enhanced entity relation extraction. In the first stage, we design multimodal graphs, whose novelty lies mainly in transforming the sequence learning into the graph learning. In the second stage, we merge the transformer-based visual representation into the text pre-trained model by a multi-scale cross-model projector. Specifically, two multimodal fusion operations are implemented inside the pre-trained model respectively. We finally accomplish deep interaction of multimodal multi-structured data in two fusion stages. Extensive experiments are conducted on a dataset (MNRE), our model outperforms the current state-of-the-art method by 1.76%, 1.52%, 1.29%, and 1.17% in terms of accuracy, precision, recall, and F1 score, respectively. Moreover, our model also achieves excellent results under the condition of fewer samples.
Modality Specific CBAM-VGGNet Model for the Classification of Breast Histopathology Images via Transfer Learning

Histopathology images are very distinctive, and one image may contain thousands of objects.Transferring features from natural images to histopathology images may not provide impressive outcomes.In this study, we have proposed a novel modality-specific CBAM-VGGNet model for classifying H and E stained breast histopathology images.Instead of using pre-trained models on ImageNet, we have trained VGG16 and VGG19 models on the same domain cancerous histopathology datasets which are then used as fixed feature extractors.We have added GAP layer and Convolutional block attention module (CBAM) after the first convolutional layer of convolutional blocks.CBAM is an effective module for neural networks to focus on relevant features.We have implemented the VGG16 and VGG19 in a novel way following the configuration of state-of-the-art models with our own concatenated layers.The addition of the GAP layer in VGGNet has reduced the number of parameters and hence requiring less computational power.Both models are ensembled using the averaging ensemble technique.Features are extracted from the final ensembled model then passed to the feed-forward neural network.A hybrid pre-processing technique is proposed that first use median filter and afterwards contrast limited adaptive histogram equalization (CLAHE).Median filter is utilized to remove the highly significant noise and is directly related to image quality.CLAHE improve the local contrast present in an image and boost the weak boundary edges in each image pixel.The proposed CBAM ensemble model has outperformed state-of-the-art models with an accuracy of 98.96% and 97.95% F1-score on 400X data of BreakHis dataset.
How can applications of blockchain and artificial intelligence improve performance of Internet of Things? – A survey

In the era of the Internet of Things (IoT), massive computing devices surrounding us operate and interact with each other to provide several significant services in industries, medical as well as in daily life activities at home, office, education sectors, and so on. The participating devices in an IoT network usually have resource constraints and the devices are prone to different cyber attacks, leading to the loopholes in the security and authentication. As a revolutionized and innovated technology, blockchain, that is applied in cryptocurrency, market prediction, etc., uses a distributed ledger that records transactions securely and efficiently. To utilize the great potential of blockchain, both industries and academia have paid a significant attention to integrate it with the IoT, as reported by several existing literature. On the other hand, Artificial Intelligence (AI) is able to embed intelligence in a system, and thus the AI can be integrated with IoT devices in order to automatically cope with different environments according to the demands. Furthermore, both blockchain and AI can be integrated with the IoT to design an automated secure and robust IoT model, as mentioned by numerous existing works. In this survey, we present a discussion on the IoT, blockchain, and AI, along with the descriptions of several research works that apply blockchain and AI in the IoT. In this direction, we point out strengths and limitations of the related existing researches. We also discuss different open challenges to exploit the full capacities of blockchain and AI in designing an IoT-based model. Therefore, the highlighted challenging issues can open the door for the development of future IoT models which will be intelligent and secure based on the integration of blockchain and AI with the IoT.
ANNA: advanced neural network algorithm for optimisation of structures

The purpose of this study is to develop an advanced neural network algorithm as a new optimisation for the optimal design of truss structures. The central concept of the algorithm is based on biological nerve structures and artificial neural networks. The performance of the proposed method is explored in engineering design problems. Two efficient methods for improving the standard neural network algorithm are considered here. The first is an enhanced initialisation mechanism based on opposite-based learning. The second relies on using a few tunable parameters to provide proper exploration and exploitation abilities for the algorithm, enabling better solutions to be found while the required structural analyses are reduced. The new algorithm's performance is investigated by using five well-known restricted benchmarks to assess its efficiency in relation to the latest optimisation techniques. The outcome of the examples demonstrates that the upgraded version of the algorithm has increased efficacy and robustness in comparison with the original version of the algorithm and some other methods.
Foundation Models for Transportation Intelligence: ITS Convergence in TransVerse

Smart cities are our aspiration for a better life where transportation intelligence is indispensable. Recent technological advances in intelligent transportation systems have opened up new possibilities for smart mobility in smart cities. Here we present TengYun, a transportation foundation model designed and developed with parallel learning and federated intelligence for our transportation metaverse called TransVerse. TengYun enables decentralized/distributed autonomous organizations with decentralized/ distributed operations, as well as various federated technologies, from federated security, federated control, federated management, federated services, to federated ecology for transportation intelligence in smart cities. An example for a federation of transportation transformers is discussed for illustrating the operating procedure of TengYun.
SecureRC: A system for privacy-preserving relation classification using secure multi-party computation

Natural Language Processing (NLP) transforms human language into machine language that can be understood by machines through computer technology. Relation classification is an important semantic extraction task in NLP, which can accurately obtain the semantic relationship between two entities in a text. It has been used extensively in many NLP applications, such as information extraction and question answering. Relation classification can extract accurately relationships between two entities from large amounts of linguistic data. However, collecting such vast amounts of data in relation classification poses serious privacy issues. As far as we know, there is no existing work that implements privacy-preserving relation classification task. In this paper, we consider how to implement privacy-preserving relation classification using attention-based gated recurrent unit (GRU) network. Specifically, we first design three basic privacy-preserving protocols for the non-linear functions (sigmoid and tanh) using secure multi-party computation (MPC). Then, we propose a secure computation protocol SecureGRU for the GRU network based on these three basic protocols. Finally, based on the SecureGRU and the attention mechanism, we obtain the privacy-preserving relation classification system SecureRC. In the semi-honest adversary model, we prove the security of these protocols. Any honest-but-curious adversary is not able to obtain anything beyond what he is allowed to learn. The proposed system is implemented in Python. Experimental results demonstrate the performance of our proposed protocols.
Efficient breast cancer detection via cascade deep learning network

Breast calcifications or irregular tissue growth are major health concerns that can lead to breast cancer. To enable early management, which significantly lowers death rates, it is crucial to perform screening and determine if a tumor is benign or malignant. Building a cascade network model that bases predictions on the shape, pattern, and spread of the tumor is how this research approaches the challenge. Pre-processing of images, followed by segmentation and classification, are common methods to accomplish this. The strategy in this research employs a cascade network with UNet architecture for segmentation with a ResNet backbone for classification. To enable classification to make predictions, segmentation process involves separating tumor from the image in the form of a mask. The segmentation model's F1-score measurement came out to be 97.30%. The final decision-making layer's neural network is a straightforward 8-layer network, which follows the ResNet50 model. The proposed model's classification accuracy was 98.61%, with F1 score of 98.41%. Comparative evaluations are conducted together with the comprehensive experimental results.
Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.
When Do We Accept Mistakes from Chatbots? The Impact of Human-Like Communication on User Experience in Chatbots That Make Mistakes

Chatbots are becoming omnipresent in our daily lives. Despite rapid improvements in natural language processing in the last years, the technology behind chatbots is still not completely mature, and chatbots still make a lot of mistakes during their interactions with users. Since it is not possible to completely prevent mistakes due to technological constraints, this article aims to investigate whether a human-like communication style can reduce the negative impact of chatbots' mistakes on users. Taking a combination of the Technology Acceptance Model and the concepts of Perceived Enjoyment and Social Presence as a theoretical basis, we conducted an online experiment in which participants interacted with a chatbot and completed a survey afterwards. We found that chatbot mistakes have a negative effect on users' perceptions of Ease of Use, Usefulness, Enjoyment, and Social Presence. Human-like communication was found to be effective in reducing the negative impact of mistakes on Perceived Enjoyment. Theoretical and practical implications are discussed.