Reviewerly/exHarmony huggingface dataset:
Label-Aware Ranked Loss for Robust People Counting Using Automotive In-Cabin Radar

In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage of the ranked ordering of the labels in regression problems. To this end, we first show that the loss minimises when datapoints of different labels are ranked and laid at uniform angles between each other in the embedding space. Then, to measure its performance, we apply the proposed loss on a regression task of people counting with a short-range radar in a challenging scenario, namely a vehicle cabin. The introduced approach improves the accuracy as well as the neighboring labels accuracy up to 83.0% and 99.9%: An increase of 6.7% and 2.1% on state-of-the-art methods, respectively.
Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica

People convey their intention and attitude through linguistic styles of the text that they write.In this study, we investigate lexicon usages across styles throughout two lenses: human perception and machine word importance, since words differ in the strength of the stylistic cues that they provide.To collect labels of human perception, we curate a new dataset, HUMMINGBIRD, on top of benchmarking style datasets.We have crowd workers highlight the representative words in the text that makes them think the text has the following styles: politeness, sentiment, offensiveness, and five emotion types.We then compare these human word labels with word importance derived from a popular fine-tuned style classifier like BERT.Our results show that the BERT often finds content words not relevant to the target style as important words used in style prediction, but humans do not perceive the same way even though for some styles (e.g., positive sentiment and joy) humanand machine-identified words share significant overlap for some styles.
Your Smart Contracts Are Not Secure

Smart contracts on Ethereum enable billions of dollars to be transacted in a decentralized, transparent and trustless environment. However, adversaries lie await in the Dark Forest, waiting to exploit any and all smart contract vulnerabilities in order to extract profits from unsuspecting victims in this new financial system. As the blockchain space moves at a breakneck pace, exploits on smart contract vulnerabilities rapidly evolve, and existing research quickly becomes obsolete. It is imperative that smart contract developers stay up to date on the current most damaging vulnerabilities and countermeasures to ensure the security of users' funds, and to collectively ensure the future of Ethereum as a financial settlement layer. This research work focuses on two smart contract vulnerabilities: transaction-ordering dependency and oracle manipulation. Combined, these two vulnerabilities have been exploited to extract hundreds of millions of dollars from smart contracts in the past year (2020-2021). For each of them, this paper presents: (1) a literary survey from recent (as of 2021) formal and informal sources; (2) a reproducible experiment as code demonstrating the vulnerability and, where applicable, countermeasures to mitigate the vulnerability; and (3) analysis and discussion on proposed countermeasures. To conclude, strengths, weaknesses and trade-offs of these countermeasures are summarised, inspiring directions for future research.
Federated Management: Toward Federated Services and Federated Security in Federated Ecology

Welcome to the last issue of IEEE Transactions on Computational Social Systems (IEEE TCSS) in 2021. For IEEE TCSS, 2021 is an exciting year. TCSS has been added to the ISI Web of Science Sources Citation Index Expanded (SCIE) database in 2021, and all articles published since 2018 have been indexed by SCIE. This is an important milestone in the development of TCSS. We would like to take this opportunity to thank and congratulate everyone for their great efforts and supports. We are looking forward to working together to further improve the publication quality and speed up the review process of TCSS in the upcoming 2022.
Major applications of data mining in medical

Now-a-days, technology is required in every field of our lives. Technology is increasing day-by-day. In this research, I’ll collect some useful data that demonstrates how data-mining is important in healthcare. As the technology is increasing day-by-day, better data handling tools are required to get an approach of resources. Proper diagnosis of diseases can be done by process mining and data mining. To evaluate a patient’s condition, data-mining has become the most important source. I’ll gather the data and analyze it in the laboratory. If we make a use of all data at the same time, it can pose a threat to the data security and insurance. A huge amount of data is collected from a specimen and is sent to laboratory for further analysis. Computerized and automatic machines are available in the laboratory to make the process quick and easy. In this research paper, we’ll discuss some techniques and applications of data mining to make a better healthcare system.
Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.
A new method of nonlinear causality detection: Reservoir computing Granger causality

Identifying causal and interaction relationships from observational time series is a key step toward understanding complex systems and is also a challenging problem. Many methods have been developed to detect and identify the possible causal link between two variables. However, most of them often suffer from the false detection problems, including false-positive (detected causal links do not exist) and false-negative (an existing causal link fails to be detected). Compared to false-negative problem, false-positive is a more serious problem found in nonlinear systems or processes for almost all causality detection methods. In this study, a new method combining the advantages of Reservoir Computing (RC, a machine learning method) and classical Granger causality detection was developed to fully solve false-positive problems or considerably lower the false-positive rate in detecting causal links of nonlinear systems. Three typical coupled systems with known causal links are applied to show the better performance of new Reservoir Computing Granger (RCG) method over traditional nonlinear Granger causality and its extension methods, which indicates that RCG can accurately detect causal relationships in complex systems and its great potential in exploring the causal and interaction relationships from observational time series.
Improved detection of small objects in road network sequences using <scp>CNN</scp> and super resolution

Abstract The detection of small objects is one of the problems present in deep learning due to the context of the scene or the low number of pixels of the objects to be detected. According to these problems, current pre‐trained models based on convolutional neural networks usually give a poor average precision, highlighting some as CenterNet HourGlass104 with a mean average precision of 25.6%, or SSD‐512 with 9%. This work focuses on the detection of small objects. In particular, our proposal aims to vehicle detection from images captured by video surveillance cameras with pre‐trained models without modifying their structures, so it does not require retraining the network to improve the detection rate of the elements. For better performance, a technique has been developed which, starting from certain initial regions, detects a higher number of objects and improves their class inference without modifying or retraining the network. The neural network is integrated with processes that are in charge of increasing the resolution of the images to improve the object detection performance. This solution has been tested for a set of traffic images containing elements of different scales to check the efficiency depending on the detections obtained by the model. Our proposal achieves good results in a wide range of situations, obtaining, for example, an average score of 45.1% with the EfficientDet‐D4 model for the first video sequence, compared to the 24.3% accuracy initially provided by the pre‐trained model.
Voting blockchain for High Security NFT

NFT is a unit of data stored on a blockchain digital ledger to represent items such as photos, videos, audio, and other intellectual property. NFT certifies a digital asset to be unique i.e. not interchangeable. Current blockchain store the transaction from each node but it is required more security to show the proof of ownership. This paper proposes NFT secure blockchain architecture confirming the ownership of transaction and implement secure payment method to avoid mistake of transfer the token by escrow account on transaction confirmation node called J Node.
Optimization Model of Cross-Border E-commerce Payment Security by Blockchain Finance

Cross-border trade is also changing and innovating. Electronic payment has become the core application of modern cross-border e-commerce. However, although electronic payment has brought convenience and efficiency to enterprises and social life, there are also many problems in transaction information security. These problems not only hinder the development of electronic payment but also bring hidden dangers to people’s property security. Therefore, an encryption algorithm of e-commerce was proposed, and its algorithm security and risk control mode was further studied by means of case analysis, empirical analysis, and comparative analysis. Experimental data show that blockchain technology is a breakthrough. The aim of the study is to explore its application in specific circumstances and strive to make the new e-payment mode in line with international standards.
Blockchain-enabled Privacy Preserving of IoT Data for Sustainable Smart Cities using Machine Learning

The development of sensor technologies and an explosion of the inexpensive electronic circuit, the Internet of Things (IoT) is emergent as an encouraging innovation to comprehend sustainable smart city. Smart cities can bid various intelligent applications like smart transportation, smart banking, and industry 4.0, among others, to boost citizens' life quality. However, security is one of the critical problems of a smart city. These emerging smart infrastructure and applications based on IoT can benefit users only if vital private and secure features are guaranteed. Hence, in this paper, Blockchain-enabled Privacy-Preserving Access Control System (BPACS) has been suggested for IoT data in a smart city environment. This study utilizes blockchain methods to construct a reliable and secure data-sharing policy between numerous data providers, where IoT information is encoded and then verified on disseminated ledgers. Furthermore, this study design protected construction blocks, like secure comparison and secure polynomial multiplications, by retaining cryptosystems and build a secure Support Vector Machine (SVM) and Principle Component Analysis (PCA) training algorithms. Hard security analysis proves that the suggested model guarantees the privacy of the sensitive information for every data provider and the SVM and PCA model variables for data analysts.
Accounting for crypto-assets: stakeholders’ perceptions

Purpose The purpose of this paper is to analyse stakeholders’ perceptions on the accounting of crypto-assets. They also look at the need to amend/clarify existing accounting standards or develop new accounting standards. Design/methodology/approach The authors use a qualitative approach featuring interviews with four stakeholder groups including academics, professional bodies, standard setters and accounting practitioners. Interview recordings are transcribed and then analysed through NVivo. Findings The interviewees identify various issues in the application of current accounting standards to crypto-assets. The interviewees perceive that the rapid development of crypto-assets and fluidity hinder the development of accounting guidance. Hence, continuous monitoring by standard-setters is required. The general consensus is that unless there are crypto-assets with economic characteristics and functionality that are pervasive enough to warrant a new accounting standard, principles of current accounting standards are robust to address gaps in accounting requirements for crypto-assets. Originality/value This study adds to the discussion on harmonising the current practices in accounting of crypto-assets. By examining perceptions of multiple stakeholder groups, this study provides insights into the applicability of current accounting standards to the classification, measurement and disclosure of crypto-assets. The findings will inform standard setters and aid their efforts towards providing formal guidance on the accounting of crypto-assets.
Mini-review: Robotic wheelchair taxonomy and readiness

Robotic wheelchair research and development is a growing sector. This article introduces a robotic wheelchair taxonomy, and a readiness model supported by a mini-review. The taxonomy is constructed by power wheelchair and, mobile robot standards, the ICF and, PHAATE models. The mini-review of 2797 articles spanning 7 databases produced 205 articles and 4 review articles that matched inclusion/exclusion criteria. The review and analysis illuminate how innovations in robotic wheelchair research progressed and have been slow to translate into the marketplace.
FSAD-Net: Feedback Spatial Attention Dehazing Network

Recent dehazing networks learn more discriminative high-level features by designing deeper networks or introducing complicated structures, while ignoring inherent feature correlations in intermediate layers. In this article, we establish a novel and effective end-to-end dehazing method, named feedback spatial attention dehazing network (FSAD-Net). FSAD-Net is based on the recurrent structure and consists of four modules: a shallow feature extraction block (SFEB), a feedback block (FB), multiple advanced residual blocks (ARBs), and a reconstruction block (RB). FB is designed to handle feedback connections, and it can improve the dehazing performance by exploiting the dependencies of deep features across stages. ARB implements a novel attention-based estimation on a residual block to adapt to pixels with different distributions. Finally, RB helps restore haze-free images. It can be seen from the experimental results that FSAD-Net almost outperforms the state-of-the-arts in terms of five quantitative metrics. Moreover, the qualitatively comparisons on real-world images also demonstrate the superiority of the proposed FSAD-Net. Considering the efficiency and effectiveness of FSAD-Net, it can be expected to serve as a suitable image dehazing baseline in the future.
Domestic Ubimus

INTRODUCTION:In this paper we discuss concepts and practices that point to a new field of ubiquitous music (ubimus) research centered on domestic settings.OBJECTIVES: The objective of this paper is to document and share a preliminary study of the use of taste as a trigger for creative decisions and a comparative study of creative music making done at home and in transitional settings.METHODS: Groundwork in this area encompasses the design, implementation and deployment of ubimus systems.Key aspects include sonic resources and metaphors for creative action based on multimodality.RESULTS: The results indicate that unpredictable sonic environments foster originality but may compromise the subjects' creative performance -reducing their level of engagement and fun and the possibilities for collaboration, while increasing the cognitive demands of the activity.Home seems to furnish a positive context as long as the objective is not to increase the originality of the outcomes.CONCLUSION: The effectiveness of taste as a scaffold for creative decisions was partially confirmed, demanding further studies.These results have implications for both the artistic aspects of sound making and the everyday usage of sound for distant socializing in domestic settings, posing renewed challenges to the ongoing STEAM initiatives.
Complementary relation between tripartite entanglement and the maximum steering inequality violation

We investigate the relation between tripartite entanglement of the three-qubit system and the maximum steering inequality violation of the reduced two-qubit states. Firstly, it is found that a single parameter family of entangled three-qubit pure states have the maximum steering inequality violation among all of the three-qubit pure states for a fixed amount of tripartite entanglement. The tripartite entanglement is quantified by the genuinely multipartite concurrence, generalized geometric measure, and tangle. Subsequently, the complementary relation between tripartite entanglement and the maximum steering inequality violation for an arbitrary three-qubit pure state can be established. Particularly, the result also holds for three-qubit mixed states if the entanglement measure is tangle. The complementary relations indicate that the maximum steering inequality violation of the reduced two-qubit system is at the expense of tripartite entanglement.
AI Based Drones for Security Concerns in Smart Cities

The concept of a smart city has attracted the attention of all corners of the world. It updated with new technologies like AI, Blockchain, IoT, Drones, and many other things. Security in big cities is one of the main concerns, and everyone wants to feel safe 24/7 in every activity they do. In this paper, our research aims to highlight the use and importance of drones for intelligent city management, especially in the context of security. The drone security management flow is explained using the methods and technical details. This is followed by discussion on security concern in cities and how it is dealt with AI based drones. In the conclusion section, it is discussed how emerging technology such as blockchain can help improve the management of smart cities. The use of drones supports the intelligent city concept with all its advantages in regional monitoring. Concept of smart cities is catching attention across the globe and it’s important as per context of emerging economies. In this paper, we discussed about the use of emerging technologies to make it possible. AI, Blockchain and Drone technologies are playing important role. Their significance and attention by researchers in exploring possibilities is shown in this paper. This paper will be very useful for researchers and engineers working in the same domain.
A Novel Algorithm Based on a Common Subspace Fusion for Visual Object Tracking

Recent methods for visual tracking exploit a multitude of information obtained from combinations of handcrafted and/or deep features. However, the response maps derived from these feature combinations are often fused using simple strategies such as winner-takes-all or weighted sum approaches. Although some efficient fusion methods have also been proposed, these methods still do not leverage the individual strengths of the different features being fused. In the current work, we propose a novel information fusion strategy comprising a common low-rank subspace for the fusion of different types of features and tracker responses. Firstly, we interpret the response maps as smoothly varying functions which can be efficiently represented using individual low-rank matrices, thus removing high frequency noise and sparse artifacts. Secondly, we estimate a common low-rank subspace which is constrained to remain close to each individual low-rank subspace resulting in an efficient fusion strategy. The proposed algorithm achieves good performance by integrating the information contained in heterogeneous feature types. We demonstrate the efficiency of our algorithm using several combinations of features as well as correlation filter and end-to-end deep trackers. The proposed common subspace fusion algorithm is generic and can be used to efficiently fuse the response maps of varying types of feature representations as well as trackers. Extensive experiments on several tracking benchmarks including OTB100, TC128, VOT-ST 2018, VOT-LT 2018, UAV123, GOT-10K and LaSoT have demonstrated significant performance improvements compared to many SOTA tracking methods.
Russian Schools during the COVID-19 Pandemic: Impact of the First Two Waves on the Quality of Education

International studies about the impact of the COVID-19 pandemic on the quality of general education have yielded contradictory results: educational outcomes have fallen markedly in some countries while remaining more or less constant in others. At the same time, over half of published studies attest to the growth of educational inequality during the pandemic. The present article assesses the impact of the pandemic using the data of a regional monitoring study of all schoolchildren in grades 4, 6 and 8 in the Krasnoyarsk Region in 2019 and 2021. Tests of reading literacy in grades 4 and 6 as well as of science literacy in grade 8 have shown the satisfactory psychometric quality. Multilevel regression analysis was used to show that the level of functional literacy of the “pandemic” cohort of schoolchildren, controlled for contextual characteristics, was significantly lower for students in all grades except grade 4. The biggest loss was found for scientific literacy. No correlation was found between the pandemic effect size and the socioeconomic composition of the class (the gap between children with different SES remains at the same level as it was before the pandemic). The teachers’ opportunity to conduct online classes did not serve to improve the educational outcomes of schoolchildren during the pandemic.