Reviewerly/exHarmony huggingface dataset:
On Releasing Annotator-Level Labels and Information in Datasets

A common practice in building NLP datasets, especially using crowd-sourced annotations, involves obtaining multiple annotator judgements on the same data instances, which are then flattened to produce a single "ground truth" label or score, through majority voting, averaging, or adjudication.While these approaches may be appropriate in certain annotation tasks, such aggregations overlook the socially constructed nature of human perceptions that annotations for relatively more subjective tasks are meant to capture.In particular, systematic disagreements between annotators owing to their socio-cultural backgrounds and/or lived experiences are often obfuscated through such aggregations.In this paper, we empirically demonstrate that label aggregation may introduce representational biases of individual and group perspectives.Based on this finding, we propose a set of recommendations for increased utility and transparency of datasets for downstream use cases.
Optimal strategies for financing a three-level supply chain through blockchain platform finance

The application of blockchain technology solves the trust problem between core enterprises (CEs), small and medium-sized enterprises (SMEs), and commercial banks, facilitating CEs and commercial banks to provide guarantee for SMEs and finance them, respectively. This study considers a three-level supply chain composed of a manufacturer, a distributor, and a capital-constrained retailer, and explores the operational strategies of blockchain platform finance (BPF), a supply chain finance mode enabled by blockchain technology. First, optimal decision solutions are obtained through decision and parameter sensitivity analyses. Second, by comparing the BPF and SME independent finance (SIF) modes, we obtain the applicable BPF mode conditions; for instance, when the retailer’s initial capital is low and the production cost is high, BPF is the better option for the manufacturer, distributor, and retailer compared to SIF. Third, we find that risk sharing improves the financing efficiency of the BPF mode. This study provides a theoretical basis for decision makers to implement blockchain supply chain finance at three levels: joint financing and operation decision-making, financing mode selection, and financing efficiency improvement.
Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape

Machine Learning and Deep Learning methods are widely adopted across financial domains to support trading activities, mobile banking, payments, and making customer credit decisions. These methods also play a vital role in combating financial crime, fraud, and cyberattacks. Financial crime is increasingly being committed over cyberspace, and cybercriminals are using a combination of hacking and social engineering techniques which are bypassing current financial and corporate institution security. With this comes a new umbrella term to capture the evolving landscape which is financial cybercrime. It is a combination of financial crime, hacking, and social engineering committed over cyberspace for the sole purpose of illegal economic gain. Identifying financial cybercrime-related activities is a hard problem, for example, a highly restrictive algorithm may block all suspicious activity obstructing genuine customer business. Navigating and identifying legitimate illicit transactions is not the only issue faced by financial institutions, there is a growing demand of transparency, fairness, and privacy from customers and regulators, which imposes unique constraints on the application of artificial intelligence methods to detect fraud-related activities. Traditionally, rule based systems and shallow anomaly detection methods have been applied to detect financial crime and fraud, but recent developments have seen graph based techniques and neural network models being used to tackle financial cybercrime. There is still a lack of a holistic understanding of the financial cybercrime ecosystem, relevant methods, and their drawbacks and new emerging open problems in this domain in spite of their popularity. In this survey, we aim to bridge the gap by studying the financial cybercrime ecosystem based on four axes: (a) different fraud methods adopted by criminals; (b) relevant systems, algorithms, drawbacks, constraints, and metrics used to combat each fraud type; (c) the relevant personas and stakeholders involved; (d) open and emerging problems in the financial cybercrime domain.
A Security Framework for Secure Cloud Computing Environments

Cloud computing technology is a relatively new concept of providing scalable and virtualized resources, software and hardware on demand to consumers. It presents a new technology to deliver computing resources as a service. It offers a variety of benefits like services on demand and provisioning and suffers from several weaknesses. In fact, security presents a major obstacle in cloud computing adoption. In this paper, the authors will deal with security problems in cloud computing systems and show how to solve these problems using a quantitative security risk assessment model named Multi-dimensional Mean Failure Cost (M2FC). In fact, they summarize first security issues related to cloud computing environments and then propose a generic framework that analysis and evaluate cloud security problems and then propose appropriate countermeasures to solve these problems.
An Effective Secured Privacy-Protecting Data Aggregation Method in IoT

Because privacy concerns in IoT devices are the most sensitive of all the difficulties, such an extreme growth in IoT usage has an impact on the privacy and life spans of IoT devices, because until now, all devices communicated one to one, resulting in high traffic that may shorten the life of unit nodes. In addition, delivering data repeatedly increases the likelihood of an attacker attacking the system. Such traffic may exacerbate security concerns. The employment of an aggregator in the system as an intermediary between end nodes and the sink may overcome these problems. In any system with numerous sensors or nodes and a common controller or sink, we can use an intermediate device to combine all of the individual sensor data and deliver it to the sink in a single packet. Aggregator is the name given to such a device or component. Data aggregation is carried out to decrease traffic or communication overhead. In general, this strategy helps to extend the life of a node while also reducing network transmission.
Attitudes of hotel customers towards the use of service robots in hospitality service encounters

This study aims to identify whether hotel guests' attitudes towards robots serving at hotels show differences according to their gender, level of education, generation, and the continent they are from. The data for the study were collected from 1078 hotel guests who lived on four continents through an online survey. The analysis of the results shows that while the hotel guests are uncertain about their attitudes towards the presence of robots in daily life, in general, they tend to have more positive attitudes towards services delivered by service robots. The study shows that there is no significant difference in the attitudes of guests towards service robots in terms of the continent they are from. Generation X guests tended to be less interested in experiencing service encounters with service robots compared with Generation Y and Generation Z guests, as guests from Generation X believed that service robots offered more disadvantages. The study is original as previous research has not studied hotel guests’ attitudes towards services delivered by service robots according to their gender, level of education, generation, and the continent they are from.
A preliminary assessment of the performance of DeFi cryptocurrencies in relation to other financial assets, volatility, and user-generated content

After the so-called “crypto-winter”, decentralised finance (DeFi) is reviving interest in cryptocurrency amongst the scientific community, public and private institutions, and investors. DeFi is a novel disruptive process that promotes the use of blockchain technology for creating and issuing all kinds of financial products and services. This study aimed to measure the relationship amongst the returns of DeFi tokens, other traditional assets, and user-generated content. While the relationship between other crypto assets and traditional assets has been researched, this has not been done on DeFi assets. This study uses a logit-probit model over a database comprising the daily returns of 13 DeFi, VIX, S&P GSCI Crude Oil Index, and S&P GSCI Gold Index, and the daily variation in DeFi mentions in Telegram chats and Twitter. The results show that all variables except the S&P GSCI crude oil index returns and the daily variation in Twitter mentions were significant. This suggests that DeFi acts, similar to other crypto assets, as a safe haven. This study contributes to the literature on decentralised finance tokens as investment assets, which requires much more research.
Approaching metaverses: Mixed reality interfaces in youth media platforms

The general aim of this paper is to examine mixed reality settings present in the media practices of young people, exploring the future metaverses in which interactive narrative experiences will be carried out. First, the emergent design of metaverses is approached, going from their conception to their current state. Second, a selection of preferred platforms for young people is proposed and then the interfaces of these platforms are analyzed based on youth practices, with special emphasis on the narrative dimension. Third, emerging models of metaverses preferred by young people are discussed.
Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation.
Efficient Federated Learning With Spike Neural Networks for Traffic Sign Recognition

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
TSB-UAD

The detection of anomalies in time series has gained ample academic and industrial attention. However, no comprehensive benchmark exists to evaluate time-series anomaly detection methods. It is common to use (i) proprietary or synthetic data, often biased to support particular claims; or (ii) a limited collection of publicly available datasets. Consequently, we often observe methods performing exceptionally well in one dataset but surprisingly poorly in another, creating an illusion of progress. To address the issues above, we thoroughly studied over one hundred papers to identify, collect, process, and systematically format datasets proposed in the past decades. We summarize our effort in TSB-UAD, a new benchmark to ease the evaluation of univariate time-series anomaly detection methods. Overall, TSB-UAD contains 13766 time series with labeled anomalies spanning different domains with high variability of anomaly types, ratios, and sizes. TSB-UAD includes 18 previously proposed datasets containing 1980 time series and we contribute two collections of datasets. Specifically, we generate 958 time series using a principled methodology for transforming 126 time-series classification datasets into time series with labeled anomalies. In addition, we present data transformations with which we introduce new anomalies, resulting in 10828 time series with varying complexity for anomaly detection. Finally, we evaluate 12 representative methods demonstrating that TSB-UAD is a robust resource for assessing anomaly detection methods. We make our data and code available at www.timeseries.org/TSB-UAD. TSB-UAD provides a valuable, reproducible, and frequently updated resource to establish a leaderboard of univariate time-series anomaly detection methods.
A medical assistant segmentation method for MRI images of osteosarcoma based on DecoupleSegNet

Nowadays, the most common primary bone tumor is osteosarcoma, which mostly occurs in teenagers. A common diagnosis method is currently that doctors manually diagnose osteosarcoma in magnetic resonance imaging (MRI) images because it is nonradioactive and has no biological damage to brain tissue and more obvious performance in soft tissue components such as tumors, blood vessels, and muscles in MRI images. However, this method is labor-intensive and time-consuming work, and cannot guarantee the accuracy of the diagnostic results. Existing osteosarcoma MRI image segmentation methods either aim to model the global context to improve the inner consistency of objects, or multiscale feature fusion to refine the detail of objects along their boundaries, which all ignore the interaction between the body of the object and the object boundary. Therefore, this paper proposes a novel segmentation method for osteosarcoma MRI images based on DecoupleSegNet, which explores the relationship between body feature and edge feature. It can assist doctors in diagnosing osteosarcoma and improve their work efficiency. First, we warp the feature of MRI images through learning a flow field so we can make the object more consistent. We then make further work to optimize the resulting body feature and residual edge feature through explicitly sampling pixels from different parts under decoupled supervision. Through these steps, we finally obtain the final feature map with fine boundaries from the MRI image of osteosarcoma. We take a test by using more than 80,000 osteosarcoma MRI images obtained from three hospitals in China. We find that compared with existing osteosarcoma MRI image segmentation methods, our proposed method achieves 90.51 Intersection of Union % with few parameters on the test, outperforming other models. In the test, we prove that our proposed method has better accuracy and lower resource consumption.
Augmented Reality in Surgery: A Scoping Review

Augmented reality (AR) is an innovative system that enhances the real world by superimposing virtual objects on reality. The aim of this study was to analyze the application of AR in medicine and which of its technical solutions are the most used. We carried out a scoping review of the articles published between 2019 and February 2022. The initial search yielded a total of 2649 articles. After applying filters, removing duplicates and screening, we included 34 articles in our analysis. The analysis of the articles highlighted that AR has been traditionally and mainly used in orthopedics in addition to maxillofacial surgery and oncology. Regarding the display application in AR, the Microsoft HoloLens Optical Viewer is the most used method. Moreover, for the tracking and registration phases, the marker-based method with a rigid registration remains the most used system. Overall, the results of this study suggested that AR is an innovative technology with numerous advantages, finding applications in several new surgery domains. Considering the available data, it is not possible to clearly identify all the fields of application and the best technologies regarding AR.
Integrated blockchain and internet of things in the food supply chain: Adoption barriers

Blockchain (BLC) and the Internet of Things (IoT) are two emerging technologies that have become popular among practitioners for improving the transparency, adaptability, and safety of any industry. This is especially critical for food security, as COVID-19 highlighted the vulnerability of food supply chain (FSC). However, Indian organizations are experiencing problems in implementing the integrated form of BLC-IoT due to limited knowledge and insufficient research. The current study aims to propose a conceptual framework to reduce the impact of adoption barriers against BLC-IoT in FSC. Thirteen key barriers were identified after a thorough literature review and consultation with experts. The relationship among barriers was established using Interpretive structural modeling (ISM) and Decision-making trial and evaluation laboratory (DEMATEL) methods. The analysis shows that the lack of government regulation and workers' low competency significantly influence BLC-IoT adoption. The results also indicate the intricacy of decision-making by demonstrating that 9 of the 13 barriers were a part of the linkage cluster. The study outcome will help practitioners in developing and planning strategies for effective adoption of BLC-IoT in FSC.
Supervise the data security and performance in cloud using artificial intelligence

Safety is a measure that shifts efficiency levels as a result of risks that impact the usage of cloud components during service delivery. Safety and performance demonstrate the reliability of cloud computing that tests the size of cloud creation are safety and protection. The study of computer algorithms that enhance natural reality is Artificial Intelligence (AI). We provide an outline of the cloud service problems and concerns for one or more AI algorithms such as supervised, unregulated, semi-controlled, and enhanced cloud safety concerns. A new paradigm of cloud data protection using advanced algorithms is introduced in the future framework. This technology is characterized as a computer science field that emphasizes the development of smart devices that function and respond much like human beings and enhance privacy and protection in the cloud environments. AI is primarily interested in voice recognition technologies, machine learning systems, planning decision systems and solving problems systems. The key industries of advanced technology processes are AI.
BSAS: A Blockchain-Based Trustworthy and Privacy-Preserving Speed Advisory System

Consensus-based Speed Advisory System (CSAS) is used to recommend a consensus speed to a group of vehicles for specific application purposes, such as minimizing emissions or energy consumption. To remedy data privacy concerns for speed advisory services, the latest works have investigated how to get an optimal speed in a privacy-preserving manner. However, almost all the designs are based on a centralized architecture, which could still meet service trust issues, such as that a random speed could be recommended when the central server meets cyber incursion attacks. To address the problem, in this paper we propose BSAS, a trustworthy and privacy-preserving CSAS over the blockchain. Specifically, BSAS follows a fully decentralized architecture with cryptographic primitives to guarantee service trust and data privacy. Moreover, to encourage vehicles to participate in the service computing process, a value-driven incentive mechanism is also employed. We present the detailed design and implementation of BSAS, and our emulation results show that the proposed BSAS can achieve promising system performance in terms of real-time speed recommendation in a trustworthy and privacy-preserving way.
Spiral-Transform-Based Fractal Sorting Matrix for Chaotic Image Encryption

Chaotic image encryption is widely used in the field of information security. This paper proposes a novel chaotic image encryption method with spiral-transform-based fractal sorting matrix (STFSM). First of all, the theory of STFSM with good scrambling effect is introduced, which has good irregularity and iterative. Then, the iterative algorithm and calculation example of STFSM are introduced. STFSM can be used as the map of spatial location transformations to implement the design of image encryption. Based on the complete STFSM theory and iterative algorithm, a chaotic image cryptosystem based on STFSM is proposed to achieve a good image encryption process. To test the security of the proposed algorithm, security tests and analyses such as entropy analysis, correlation analysis, resistance to differential attacks analysis, and robustness analysis are used for the proposed algorithm. The experimental analysis illustrates that the algorithm has a better encryption effect, whether using conventional tests or the attacks simulations described in this paper and can also effectively resist the attacks.