Reviewerly/exHarmony huggingface dataset:
Supply Chain Decision Model Based on Blockchain: A Case Study of Fresh Food E-Commerce Supply Chain Performance Improvement

Aiming at the problems in the performance improvement of the fresh food e-commerce supply chain, in this article, the author defines the constraints on the performance improvement of the fresh food e-commerce supply chain and determines the demand to improve performance in quality and safety, logistics traceability, resource integration, and benefit distribution. In addition, this article conducts coupling analysis on the characteristics of blockchain technology, such as intelligent contract, information sharing, traceability, and performance improvement requirements. The author embeds the alliance chain in the fresh food e-commerce supply chain and designs the blockchain information platform model from four levels: application layer, contract layer, network layer, and data layer, with the purpose of optimizing and improving the performance of the fresh food e-commerce supply chain. What is more, the Stackelberg game model between the supplier and the e-commerce platform was constructed to study and compare the profit changes of the fresh food e-commerce supply chain before and after the application of the blockchain information platform. Finally, the result of Stackelberg game is numerically simulated by Matlab software. The results show that blockchain technology is a booster for the development of the fresh food e-commerce supply chain to a higher stage of the whole industry chain management, coordination, and integration. Investing in the blockchain system within a certain budget range can not only improve the reliability of products, but also improve the performance of each main component of the fresh food e-commerce supply chain, as well as the overall performance. Results can offer a theoretical guidance for producer and retailer in investing in BIP, pricing decision, and supply chain coordination after applying BIP.
uTHCD: A New Benchmarking for Tamil Handwritten OCR

Handwritten character recognition is a challenging research in the field of document image analysis over many decades due to numerous reasons such as large writing styles variation, inherent noise in data, expansive applications it offers, non-availability of benchmark databases etc. There has been considerable work reported in literature about creation of the database for several Indic scripts but the Tamil script is still in its infancy as it has been reported only in one database [5]. In this paper, we present the work done in the creation of an exhaustive and large unconstrained Tamil Handwritten Character Database (uTHCD). Database consists of around 91000 samples with nearly 600 samples in each of 156 classes. The database is a unified collection of both online and offline samples. Offline samples were collected by asking volunteers to write samples on a form inside a specified grid. For online samples, we made the volunteers write in a similar grid using a digital writing pad. The samples collected encompass a vast variety of writing styles, inherent distortions arising from offline scanning process viz stroke discontinuity, variable thickness of stroke, distortion etc. Algorithms which are resilient to such data can be practically deployed for real time applications. The samples were generated from around 650 native Tamil volunteers including school going kids, homemakers, university students and faculty. The isolated character database will be made publicly available as raw images and Hierarchical Data File (HDF) compressed file. With this database, we expect to set a new benchmark in Tamil handwritten character recognition and serve as a launchpad for many avenues in document image analysis domain. Paper also presents an ideal experimental set-up using the database on convolutional neural networks (CNN) with a baseline accuracy of 88% on test data.
BCS : Blockchain Smart Curriculum System for Verification Student Accreditation

The main objective of Higher Education is to prepare students who have academic abilities in developing knowledge and intellectual development of students to prepare technology to enter the world of work. The linkage of higher education and employment is the reason why the services provided by educational institutions direct opportunities for students to find work, because the current job market places more emphasis on the educated workforce. Therefore, tertiary institutions must have a good curriculum structure, requiring students to be productive, creative and innovative individuals who aim to form character students with the level of expertise required by the world of work. This study presents a work funded by the European Union, namely the Qualichain research project which aims to test the quality of education with the labor market related to Blockchain technology. The aim of this research is to develop a Qualichain platform that will offer educational qualifications that focus on the potential of Blockchain technology and perform data analysis to support the curriculum optimization process. This Quanlichain platform will prove its implementation that there are four pilot cases that have their respective differences. Therefore the research is focused on optimizing the administration of higher education through data developed with algorithmic and semantic techniques to support decision making that will be applied in tertiary institutions.
Experimental Quantum Target Detection Approaching the Fundamental Helstrom Limit

Quantum target detection is an emerging application that utilizes entanglement to enhance the sensing of the presence of an object. Although several experimental demonstrations for certain situations have been reported recently, the single-shot detection limit imposed by the Helstrom limit has not been reached because of the unknown optimum measurements. Here we report an experimental demonstration of quantum target detection, also known as quantum illumination, in the single-photon limit. In our experiment, one photon of the maximally entangled photon pair is employed as the probe signal and the corresponding optimum measurement is implemented at the receiver. We explore the detection problem in different regions of the parameter space and verify that the quantum advantage exists even in a forbidden region of the conventional illumination, where all classical schemes become useless. Our results indicate that quantum illumination breaks the classical limit for up to 40%, while approaching the quantum limit imposed by the Helstrom limit. These results not only demonstrate the advantage of quantum illumination, but also manifest its valuable potential of target detection.
A Heterogeneous Ensemble Learning Model Based on Data Distribution for Credit Card Fraud Detection

Credit card fraud detection (CCFD) is important for protecting the cardholder’s property and the reputation of banks. Class imbalance in credit card transaction data is a primary factor affecting the classification performance of current detection models. However, prior approaches are aimed at improving the prediction accuracy of the minority class samples (fraudulent transactions), but this usually leads to a significant drop in the model’s predictive performance for the majority class samples (legal transactions), which greatly increases the investigation cost for banks. In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. We validate the effectiveness of HELMDD on two real credit card datasets. The experimental results demonstrate that compared with current state-of-the-art models, HELMDD has the best comprehensive performance. HELMDD not only achieves good recall rates for both the minority class and the majority class but also increases the savings rate for banks to 0.8623 and 0.6696, respectively.
Facial image denoising using AutoEncoder and UNET

Image denoising is a crucial topic in image processing. Noisy images are generated due to technical and environmental errors. Therefore, it is reasonable to consider image denoising an important topic to study, as it also helps to resolve other image processing issues. However, the challenge is that the classical techniques used are time-consuming and not flexible enough. This article compares the two major neural network architecture which looks promising to resolve this issues. The AutoEncoder and UNET is now the most researched subject in deep learning for image denoising. Multiple model architectures are designed, implement, and evaluated. The dataset is preprocessed and then it is used to train and test the model. It is clearly shown in this paper which model performs the best in this task by comparing both models using the most used parameters to evaluate image quality PSNR and SSIM.
Smartphone Use among Undergraduate STEM Students during COVID-19: An Opportunity for Higher Education?

Due to the COVID-19 pandemic, students worldwide have continued their education remotely. One of the challenges of this modality is that students need access to devices such as laptops and smartphones. Among these options, smartphones are the most accessible because of their lower price. This study analyzes the usage patterns of smartphone users of undergraduate Science, Technology, Engineering, and Math (STEM) students during the COVID-19 pandemic. This cross-sectional descriptive study included 365 students: 162 (44.4%) women and 203 (55.6%) men from a Chilean university. The results revealed that students often accessed the learning management system (LMS) with their computers rather than with their smartphones. Students were connected to the LMS for more hours on their computers than on their smartphones. However, they spent more hours simultaneously connected on their computers and smartphones than just on their computers. During the day, students accessed the LMS mainly from 13:00 to 1:00. The number of connections decreased from 1:00 to 8:00 and increased from 8:00 to 13:00. The LMS resource that students accessed the most using smartphones was discussion forums, while the one they accessed the least was wiki pages. We expect these results to motivate faculties to schedule their activities during the hours students tend to be online and promote discussion forums.
Clustering-Induced Adaptive Structure Enhancing Network for Incomplete Multi-View Data

Incomplete multi-view clustering aims to cluster samples with missing views, which has drawn more and more research interest. Although several methods have been developed for incomplete multi-view clustering, they fail to extract and exploit the comprehensive global and local structure of multi-view data, so their clustering performance is limited. This paper proposes a Clustering-induced Adaptive Structure Enhancing Network (CASEN) for incomplete multi-view clustering, which is an end-to-end trainable framework that jointly conducts multi-view structure enhancing and data clustering. Our method adopts multi-view autoencoder to infer the missing features of the incomplete samples. Then, we perform adaptive graph learning and graph convolution on the reconstructed complete multi-view data to effectively extract data structure. Moreover, we use multiple kernel clustering to integrate the global and local structure for clustering, and the clustering results in turn are used to enhance the data structure. Extensive experiments on several benchmark datasets demonstrate that our method can comprehensively obtain the structure of incomplete multi-view data and achieve superior performance compared to the other methods.
Anytime Multi-Agent Path Finding via Large Neighborhood Search

Multi-Agent Path Finding (MAPF) is the challenging problem of computing collision-free paths for multiple agents. Algorithms for solving MAPF can be categorized on a spectrum. At one end are (bounded-sub)optimal algorithms that can find high-quality solutions for small problems. At the other end are unbounded-suboptimal algorithms that can solve large problems but usually find low-quality solutions. In this paper, we consider a third approach that combines the best of both worlds: anytime algorithms that quickly find an initial solution using efficient MAPF algorithms from the literature, even for large problems, and that subsequently improve the solution quality to near-optimal as time progresses by replanning subgroups of agents using Large Neighborhood Search. We compare our algorithm MAPF-LNS against a range of existing work and report significant gains in scalability, runtime to the initial solution, and speed of improving the solution.
Motivation, Social Emotion, and the Acceptance of Artificial Intelligence Virtual Assistants—Trust-Based Mediating Effects

The complexity of the emotional presentation of users to Artificial Intelligence (AI) virtual assistants is mainly manifested in user motivation and social emotion, but the current research lacks an effective conversion path from emotion to acceptance. This paper innovatively cuts from the perspective of trust, establishes an AI virtual assistant acceptance model, conducts an empirical study based on the survey data from 240 questionnaires, and uses multilevel regression analysis and the bootstrap method to analyze the data. The results showed that functionality and social emotions had a significant effect on trust, where perceived humanity showed an inverted U relationship on trust, and trust mediated the relationship between both functionality and social emotions and acceptance. The findings explain the emotional complexity of users toward AI virtual assistants and extend the transformation path of technology acceptance from the trust perspective, which has implications for the development and design of AI applications.
Domain Adaptive Ship Detection in Optical Remote Sensing Images

With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between different domains. Specifically, the proposed method minimizes the domain discrepancies via both image-level adaption and instance-level adaption. In image-level adaption, we use multiple receptive field integration and channel domain attention to enhance the feature’s resistance to scale and environmental changes, respectively. Moreover, a novel boundary regression module is proposed in instance-level adaption to correct the localization deviation of the ship proposals caused by the domain shift. Compared with conventional regression approaches, the proposed boundary regression module is able to make more accurate predictions via the effective extreme point features. The two adaption components are implemented by learning the corresponding domain classifiers respectively in an adversarial training way, thereby obtaining a robust model suitable for both of the two domains. Experiments on both supervised and unsupervised domain adaption scenarios are conducted to verify the effectiveness of the proposed method.
Grover search revisited: Application to image pattern matching

The landmark Grover algorithm for amplitude amplification serves as an essential subroutine in various type of quantum algorithms, with guaranteed quantum speedup in query complexity. However, there have been no proposal to realize the original motivating application of the algorithm, i.e., the database search or more broadly the pattern matching in a practical setting, mainly due to the technical difficulty in efficiently implementing the data loading and amplitude amplification processes. In this paper, we propose a quantum algorithm that approximately executes the entire Grover database search or pattern matching algorithm. The key idea is to use the recently proposed approximate amplitude encoding method on a shallow quantum circuit, together with the easily implementable inversion-test operation for realizing the projected quantum state having similarity to the query data, followed by the amplitude amplification operation that is independent to the target data index. We provide a thorough demonstration of the algorithm in the problem of image pattern matching.
Philosophy of a Transdisciplinary Approach in Designing an Open Information and Educational Environment of Institutions of Higher Education

The proposed article unveils a philosophical understanding of the technology of designing an open information and educational environment of higher education institutions. The expediency of introducing a transdisciplinary approach into this process as the most generalized and universal, one that overcomes the limits of disciplinary fragmentation with its shortcomings, gives a more complete picture of the world and fundamental relationships is shown. In philosophical terms, the goal of the proposed research involves the actualization of the study of the problems of transdisciplinary paradigmatics, namely, aspects of transdisciplinary education; focusing the attention of the scientific and educational community on the necessity and importance of transdisciplinary research and the development of strategies related to this phenomenon; the obviousness of obtaining a synergistic effect as a result of the introduction of a transdisciplinary approach in the design and formation of the information and educational environment of higher education institutions, recognition of the importance of adaptive management of social and pedagogical systems in this aspect. The article proposes the author's definition of the educational environment as a set of systems, technologies, means, relations, conditions, phenomena that interact with the educational process, contributing to the effective learning of the student and his formation as a person, as a professional specialist, as well as if you look into the academic context then a scientist. The article also points out the prospects of using cloud technologies in combination with a transdisciplinary approach. The introduction of modern technological and methodological approaches to the construction of the information and educational environment of the university blurs its boundaries, making it part of the global open information and educational space.
Improving the potential of Enhanced Teager Energy Cepstral Coefficients (ETECC) for replay attack detection

In the scope of voice biometrics, the term replay attack, (RA) refers to the dishonest attempt made by an impostor to spoof someone else’s identity by replaying the subject’s previously recorded speech close to the Automatic Speaker Verification (ASV) system under attack. State-of-the-art strategies for RA detection, such as the Enhanced Teager Energy Cepstral Coefficients (ETECC), have shown promising results due to their precision in measuring energy from high frequency components of speech, as a function of two recently defined concepts: signal mass and Enhanced Teager Energy Operator (ETEO). Nevertheless, since the replay mechanism prominently deteriorates the speech signal spectrum just in those spectral zones, we propose the association of ETEO with different strategies to further improve the previous results in getting effective countermeasures against RAs. Specifically, comprehensive evaluations which include a detailed mathematical analysis, a simulation on amplitude and frequency modulated (AM–FM) signals, and a spectrographic inspection involving different filterbank structures, along with their experimental results, are provided in this paper. In addition, ETEO-derived features are contrasted to existing feature sets by using Paraconsistent Feature Engineering (PFE) for feature ranking, expanding our previously published results. Lastly, experiments are performed with ASVSpoof-2017 version 2.0 dataset, Realistic Replay Attack Microphone Array Speech Corpus (ReMASC), BTAS-2016, dataset, ASVSpoof-2019 challenge dataset, and ASVSpoof-2015 challenge dataset, considering Gaussian Mixture Models (GMMs), Convolutional Neural Networks (CNNs) and Light-CNN architectures as being the classifiers. The standalone ETECC-GMM system showed the best performance by producing equal error rates (EERs) of 5.55% and 10.75% on development and evaluation sets, respectively.
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees

Code summarization aims to generate concise natural language descriptions of source code, which can help improve program comprehension and maintenance.Recent studies show that syntactic and structural information extracted from abstract syntax trees (ASTs) is conducive to summary generation.However, existing approaches fail to fully capture the rich information in ASTs because of the large size/depth of ASTs.In this paper, we propose a novel model CAST that hierarchically splits and reconstructs ASTs.First, we hierarchically split a large AST into a set of subtrees and utilize a recursive neural network to encode the subtrees.Then, we aggregate the embeddings of subtrees by reconstructing the split ASTs to get the representation of the complete AST.Finally, AST representation, together with source code embedding obtained by a vanilla code token encoder, is used for code summarization.Extensive experiments, including the ablation study and the human evaluation, on benchmarks have demonstrated the power of CAST.To facilitate reproducibility, our code and data are available at https://github.com/DeepSoftwareAnalytics/CAST.
eXtended Artificial Intelligence: New Prospects of Human-AI Interaction Research

Artificial Intelligence (AI) covers a broad spectrum of computational problems and use cases. Many of those implicate profound and sometimes intricate questions of how humans interact or should interact with AIs. Moreover, many users or future users do have abstract ideas of what AI is, significantly depending on the specific embodiment of AI applications. Human-centered-design approaches would suggest evaluating the impact of different embodiments on human perception of and interaction with AI. An approach that is difficult to realize due to the sheer complexity of application fields and embodiments in reality. However, here XR opens new possibilities to research human-AI interactions. The article’s contribution is twofold: First, it provides a theoretical treatment and model of human-AI interaction based on an XR-AI continuum as a framework for and a perspective of different approaches of XR-AI combinations. It motivates XR-AI combinations as a method to learn about the effects of prospective human-AI interfaces and shows why the combination of XR and AI fruitfully contributes to a valid and systematic investigation of human-AI interactions and interfaces. Second, the article provides two exemplary experiments investigating the aforementioned approach for two distinct AI-systems. The first experiment reveals an interesting gender effect in human-robot interaction, while the second experiment reveals an Eliza effect of a recommender system. Here the article introduces two paradigmatic implementations of the proposed XR testbed for human-AI interactions and interfaces and shows how a valid and systematic investigation can be conducted. In sum, the article opens new perspectives on how XR benefits human-centered AI design and development.