Reviewerly/exHarmony huggingface dataset:
An Implementation of Trust Chain Framework with Hierarchical Content Identifier Mechanism by Using Blockchain Technology

Advances in information technology (IT) and operation technology (OT) accelerate the development of manufacturing systems (MS) consisting of integrated circuits (ICs), modules, and systems, toward Industry 4.0. However, the existing MS does not support comprehensive identity forensics for the whole system, limiting its ability to adapt to equipment authentication difficulties. Furthermore, the development of trust imposed during their crosswise collaborations with suppliers and other manufacturers in the supply chain is poorly maintained. In this paper, a trust chain framework with a comprehensive identification mechanism is implemented for the designed MS system, which is based and created on the private blockchain in conjunction with decentralized database systems to boost the flexibility, traceability, and identification of the IC-module-system. Practical implementations are developed using a functional prototype. First, the decentralized application (DApp) and the smart contracts are proposed for constructing the new trust chain under the proposed comprehensive identification mechanism by using blockchain technology. In addition, the blockchain addresses of IC, module, and system are automatically registered to InterPlanetary File System (IPFS), individually. In addition, their corresponding hierarchical CID (content identifier) values are organized by using Merkle DAG (Directed Acyclic Graph), which is employed via the hierarchical content identifier mechanism (HCIDM) proposed in this paper. Based on insights obtained from this analysis, the trust chain based on HCIDM can be applied to any MS system, for example, this trust chain could be used to prevent the counterfeit modules and ICs employed in the monitoring system of a semiconductor factory environment. The evaluation results show that the proposed scheme could work in practice under the much lower costs, compared to the public blockchain, with a total cost of 0.002094 Ether. Finally, this research is developed an innovation trust chain mechanism that could be provided the system-level security for any MS toward Industrial 4.0 in order to meet the requirements of both manufacturing innovation and product innovation in Sustainable Development Goals (SDGs).
An efficient and privacy-preserving data aggregation scheme supporting arbitrary statistical functions in IoT

The Internet of Things (IoT) has profoundly impacted our lives and has greatly revolutionized our lifestyle. The terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent decisions. However, the high frequency of collecting user data will raise people concerns about personal privacy. In recent years, many privacy-preserving data aggregation schemes have been proposed. Unfortunately, most existing schemes cannot support either arbitrary aggregation functions, or dynamic user group management, or fault tolerance. In this paper, we propose an efficient and privacy-preserving data aggregation scheme. In the scheme, we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location sequence. On this basis, the proposed scheme supports not only arbitrary aggregation functions, but also flexible dynamic user management. Furthermore, the scheme achieves fault-tolerant capabilities by utilizing a future data buffering mechanism. Security analysis reveals that the scheme can achieve the desired security properties, and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.
Unmanned aerial vehicle formation obstacle avoidance control based on light transmission model and improved artificial potential field

To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.
Digital Inclusive Finance Data Mining and Model-Driven Analysis of the Impact of Urban-Rural Income Gap

Digital inclusive finance, rural human capital, and agricultural green total factor productivity are simultaneously incorporated into the framework of urban-rural income gap analysis. Based on digital inclusive finance indices from several provincial panel data in China, a systematic generalized moment estimation method is used to the moderating effect of rural human capital and the mediating role of agricultural green total factor productivity. Annual time series data of foreign trade indicators and urban-rural income gap indicators are estimated by building a state-space model and applying Kalman filter to investigate the effects of these factors on the urban-rural income gap. The results show that the impact of urban-rural fixed asset investment ratio and foreign trade indicator on the urban-rural income gap in China tends to increase gradually, while the impact of rural financial development indicator is decreasing but still positive. The study finds that the development of digital inclusive finance significantly contributes to the reduction of the income gap between urban and rural residents; the breadth of coverage of digital inclusive finance and the degree of digitalization help to suppress the widening of the income gap between urban and rural residents; and rural health-based human capital, education and training-based human capital, and migration-based human capital not only have a convergence effect on the income gap between urban and rural residents themselves.
MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network

Abstract Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.
M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation

End-to-end speech-to-text translation models are often initialized with pre-trained speech encoder and pre-trained text decoder.This leads to a significant training gap between pretraining and fine-tuning, largely due to the modality differences between speech outputs from the encoder and text inputs to the decoder.In this work, we aim to bridge the modality gap between speech and text to improve translation quality.We propose M-Adapter, a novel Transformer-based module, to adapt speech representations to text.While shrinking the speech sequence, M-Adapter produces features desired for speech-to-text translation via modelling global and local dependencies of a speech sequence.Our experimental results show that our model outperforms a strong baseline by up to 1 BLEU score on the Must-C En→DE dataset.
An exploratory study of productivity perceptions in software teams

Software development is a collaborative process requiring a careful balance of focused individual effort and team coordination. Though questions of individual productivity have been widely examined in past literature, less is known about the interplay between developers' perceptions of their own productivity as opposed to their team's. In this paper, we present an analysis of 624 daily surveys and 2899 self-reports from 25 individuals across five software teams in North America and Europe, collected over the course of three months. We found that developers tend to operate in fluid team constructs, which impacts team awareness and complicates gauging team productivity. We also found that perceived individual productivity most strongly predicted perceived team productivity, even more than the amount of team interactions, unplanned work, and time spent in meetings. Future research should explore how fluid team structures impact individual and organizational productivity.
Deep Knowledge Graph Representation Learning for Completion, Alignment, and Question Answering

A knowledge graph (KG) has nodes and edges representing entities and relations. KGs are central to search and question answering (QA), yet research on deep/neural representation of KGs, as well as deep QA, have moved largely to AI, ML and NLP communities. The goal of this tutorial is to give IR researchers a thorough update on the best practices of neural KG representation and inference from AI, ML and NLP communities, and then explore how KG representation research in the IR community can be better driven by the needs of search, passage retrieval, and QA. In this tutorial, we will study the most widely-used public KGs, important properties of their relations, types and entities, best-practice deep representations of KG elements and how they support or cannot support such properties, loss formulations and learning methods for KG completion and inference, the representation of time in temporal KGs, alignment across multiple KGs, possibly in different languages, and the use and benefits of deep KG representations in QA applications.
Incorporating Retrieval Information into the Truncation of Ranking Lists for Better Legal Search

The truncation of ranking lists predicted by retrieval models is vital to ensure users' search experience. Particularly, in specific vertical domains where documents are usually complicated and extensive (e.g., legal cases), the cost of browsing results is much higher than traditional IR tasks (e.g., Web search) and setting a reasonable cut-off position is quite necessary. While it is straightforward to apply existing result list truncation approaches to legal case retrieval, the effectiveness of these methods is limited because they only focus on simple document statistics and usually fail to capture the context information of documents in the ranking list. These existing efforts also treat result list truncation as an isolated task instead of a component in the entire ranking process, limiting the usage of truncation in practical systems. To tackle these limitations, we propose LeCut, a ranking list truncation model for legal case retrieval. LeCut utilizes contextual features of the retrieval task to capture the semantic-level similarity between documents and decides the best cut-off position with attention mechanisms. We further propose a Joint Optimization of Truncation and Reranking (JOTR) framework based on LeCut to improve the performance of truncation and retrieval tasks simultaneously. Comparison against competitive baselines on public benchmark datasets demonstrates the effectiveness of LeCut and JOTR. A case study is conducted to visualize the cut-off positions of LeCut and the process of how JOTR improves both retrieval and truncation tasks.
Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing

This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorithm as a benchmark, the shortcomings of the traditional Goodness of Pronunciation method are explored through statistical experiments, and the validity of the overall posterior probability output from the speech model for pronunciation quality assessment is verified. For the analysis of rhythm, there is no common algorithm framework, but in this paper, the F0 similarity algorithm based on dynamic time regularization and the stop similarity algorithm based on forced alignment is proposed for the two main factors of rhythm, intonation, and pause, respectively. After framing, the Hamming window processing is used to make the signal smoother, reduce the side lobe size after fast Fourier transform processing, and solve the problem of spectrum leakage. Compared with the ordinary rectangular window function, the Hamming window can obtain a higher quality spectrum. And combined with CTC for speech recognition modeling, the recognition rates are comparable in the case of using BLSTM and bidirectional threshold cyclic unit BGRU as the hidden layer unit, respectively, and the training time is 23% less than BLSTM using BGRU; in addition, the BGRU-CTC model is improved by using a 2-BGRU-CTC model with 256 hidden layer nodes, so that the error rate of phoneme recognition is reduced to 33%. The effectiveness of the algorithm framework is also verified through experiments, which further proves the effectiveness of our proposed phoneme segment feature and rhyme similarity algorithm.
Effect of the Volatility of the Crypto Currency and Its Effect on the Market Returns

In this study, cryptocurrency is a type of digital money that uses cryptography to protect transactions, limit the production of new units, and verify asset transfers. The focus of this research is to see how volatile Bitcoin exchange rates and returns. The standard deviation of logarithmic returns is calculated to gauge volatility. The finding of the results was based on the Shapiro-Wilk test that was employed to predict normality in this investigation. In addition, the box-whisker plot and statistical process control chart were used to find high volatility. Volatility is now regarded to be a high value. Eventually, Because of the current high level of volatility, investing in Bitcoin is seen as a high-risk endeavor. The purpose of this study is to assist investors in developing a strategy that maximizes returns while minimizing risk.
Unsupervised Multi-sensor Anomaly Localization with Explainable AI

Multivariate and Multi-sensor data acquisition for the purpose of device monitoring had a significant impact on recent research in Anomaly Detection. Despite the wide range of anomaly detection approaches, localization of detected anomalies in multivariate and Multi-sensor time-series data remains a challenge. Interpretation and anomaly attribution is critical and could improve the analysis and decision-making for many applications. With anomaly attribution, explanations can be leveraged to understand, on a per-anomaly basis, which sensors cause the root of anomaly and which features are the most important in causing an anomaly. To this end, we propose using saliency-based Explainable-AI approaches to localize the essential sensors responsible for anomalies in an unsupervised manner. While most Explainable AI methods are considered as interpreters of AI models, we show for the first time that Saliency Explainable AI can be utilized in Multi-sensor Anomaly localization applications. Our approach is demonstrated for localizing the detected anomalies in an unsupervised multi-sensor setup, and the experiments show promising results. We evaluate and compare different classes of saliency explainable AI approach on the Server Machine Data (SMD) Dataset and compared the results with the state-of-the-art OmniAnomaly Localization approach. The results of our empirical analysis demonstrate a promising performance.
A Systematic Review of Visualization Techniques and Analysis Tools for Eye-Tracking in 3D Environments

This systematic literature review presents an update on developments in 3D visualization techniques and analysis tools for eye movement data in 3D environments. With the introduction of affordable and non-intrusive eye-tracking solutions to the mass market, access to users' gaze is now increasingly possible. As a result, the adoption of eye-tracking in virtual environments using head-mounted displays is expected to increase since the trend is to incorporate gaze tracking as part of new technical solutions. The systematic literature review presented in this paper was conducted using the Scopus database (using the period 2017 to 17th of May 2022), which after analysis, resulted in the inclusion of 15 recent publications with relevance in eye-tracking visualization techniques for 3D virtual scenes. First, this paper briefly describes the foundations of eye-tracking and traditional 2D visualization techniques. As background, we also list earlier 3D eye-tracking visualization techniques identified in a previous review. Next, the systematic literature review presents the method used to acquire the included papers and a description of these in terms of eye-tracking technology, observed stimuli, application context, and type of 3D gaze visualization techniques. We then discuss the overall findings, including opportunities, challenges, trends, and present ideas for future directions. Overall the results show that eye-tracking in immersive virtual environments is on the rise and that more research and developments are needed to create novel and improved technical solutions for 3D gaze analysis.
Deep Learning-Based Football Player Detection in Videos

The main task of football video analysis is to detect and track players. In this work, we propose a deep convolutional neural network-based football video analysis algorithm. This algorithm aims to detect the football player in real time. First, five convolution blocks were used to extract a feature map of football players with different spatial resolution. Then, features from different levels are combined together with weighted parameters to improve detection accuracy and adapt the model to input images with various resolutions and qualities. Moreover, this algorithm can be extended to a framework for detecting players in any other sports. The experimental results assure the effectiveness of our algorithm.
Optical processor for a binarized neural network

We propose and experimentally demonstrate an optical processor for a binarized neural network (NN). Implementation of a binarized NN involves multiply-accumulate operations, in which positive and negative weights should be implemented. In the proposed processor, the positive and negative weights are realized by switching the operations of a dual-drive Mach-Zehnder modulator (DD-MZM) between two quadrature points corresponding to two binary weights of +1 and -1, and the multiplication is also performed at the DD-MZM. The accumulation operation is realized by dispersion-induced time delays and detection at a photodetector (PD). A proof-of-concept experiment is performed. A binarized convolutional neural network (CNN) accelerated by the optical processor at a speed of 32 giga floating point operations/s (GFLOPS) is tested on two benchmark image classification tasks. The large bandwidth and parallel processing capability of the processor has high potential for next generation data computing.
Science in Libraries

The discussion reflects the points of view of doctors of sciences from the Russian State Library, the Russian National Library, the Saint-Peterburg State Institute of Culture and the State Public Scientific and Technical Library of the Siberian Branch of the Russian Academy of Sciences (SPSTL SB RAS), voiced on March 16, 2022 at the round table “Science in Libraries”. The round table was held within the framework of the international conference “Libway” held by the SPSTL SB RAS. The participants of the discussion answered the following questions: general assessment of the state of domestic library science, book science, bibliography; key areas of library science, book science and bibliography in the second decade of the 21st Century; problems of scientific personnel training; the place of library science and the prospects for its development within the new Nomenclature of scientific specialties. The discussion made it possible to single out the following problems: the importance of solving practice-oriented problems of library science and bibliography, and only then their theoretical analysis and generalization; work in the library field of specialists from other industries with a poor understanding of librarianship and bibliography issues; lack of educational and methodological materials for graduate students and their supervisors; a very small number of state-funded places in graduate school and the lack of material incentives to attract specialists to scientific research. In a general assessment of the state of the disciplines of the documentary and communication cycle, opinions differed – from an optimistic view of the future of these disciplines to a pessimistic one due to the presence of numerous internal problems. The necessity of collaboration is stressed. All participants expressed their deepest satisfaction with the inclusion of library, bibliography and book sciences in the new Nomenclature of scientific specialties and the emergence of a new specialty passport.
Visual Smart Contracts for DAML

The Digital Asset Modelling Language (DAML) enables low-code development of smart contract applications. Starting from a high-level but textual notation, DAML thus implements the lower end of a model-driven development process, from a platform-specific level to implementations on a range of blockchain platforms. Existing approaches for modelling smart contracts support a domain-oriented, conceptual view but do not link to the same technology-specific level. We develop a notation based on class diagrams and visual contracts that map directly to DAML smart contracts. The approach is grounded in an operational semantics in terms of graph transformation that accounts for the more complex behavioural features of DAML, such as its role-based access control and the order of contract execution and archival. The models, with their mappings to DAML and their operational semantics, are introduced via the Doodle case study from a DAML tutorial and validated through testing the graph transformation system against the DAML code using the Groove model checker.