Reviewerly/exHarmony huggingface dataset:
Watch and Practice: Effectiveness of Using WhatsApp as a Multimedia Tool in Boosting Speaking Competence during the COVID-19 Pandemic in Indonesia

With their audio-visual capacity, linguistic and non-linguistic content, WhatsApp group video calls appear to have great potential for encouraging EFL learners' speaking development. However, the literature has yet to establish the degree to which WhatsApp group video calls promote English learning motivation and self-efficacy, reduce English speaking anxiety, authentic self-assessment, or urge teachers and students to improve their digital literacy. Therefore, this study aims to analyze the effect of using WhatsApp group video calls to improve students' speaking skills and reveal the extent of this media's impact on student learning. The pre-/post-test, the two-group quantitative explanatory research design was undertaken, featuring one hundred and nineteen students from four classes. A test and questionnaires were used for data collection, and then data were analyzed by using SPSS 24.0. The findings reveal that WhatsApp group video calls have been helpful for increasing learners’ speaking competence during the COVID-19 pandemic and promoting students' English speaking motivation. With all these advantages in mind, this study suggests that ESL/EFL speaking instructors use WhatsApp group video calls in their distance or online English speaking classes.
A Financial Management Platform Based on the Integration of Blockchain and Supply Chain

Internet of Things (IoT) finance extends financial services to the whole physical commodity society with the help of IoT technology to realize financial automation and intelligence. However, the security of IoT finance still needs to be improved. Blockchain has the characteristics of decentralization, immutability, faster settlement, etc., and has been gradually applied to the field of IoT finance. Blockchain is also considered to be an effective way to resolve the problems of the traditional supply chain finance industry, such as the inability to transmit core enterprise credit, the failure of full-chain business information connections and the difficulty of clearing and settlement. Supply chain finance allows the strongest enterprise in the supply chain to apply for credit guarantee from the bank to obtain bank loans, and use the funds for circulation in the supply chain to ensure that each enterprise in the whole supply chain can obtain working capital to realize profits, so as to maximize common interests. In this paper, a financial management platform based on the integration of blockchain and supply chain has been designed and implemented. Blockchain is used to integrate supply chain finance to synchronize the bank account payment system, realize the automatic flow of funds, process supervision and automatically settle account periods based on smart contracts. The four functional modules of the system are designed using unified modeling language (UML), and the model view controller (MVC) architecture is selected as the main architecture of the system. The results of the system test show that the proposed platform can effectively improve the system security, and can use the information in the blockchain to provide multi-level financing services for enterprises in supply chain finance.
A novel ultrasound image diagnostic method for thyroid nodules

Abstract The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules.
Supporting Remote Social Robot Design Collaboration with Online Canvases: Lessons Learned from Facilitators’ and Participants’ Experiences

Social robot design projects typically involve multidisciplinary teamwork and collaboration, adopt a Human-Centred Design (HCD) approach, and deal with physical (tangible) objects, i.e., robots. HCD takes a human to the centre point of the design process. A typical activity in HCD are design workshops where a facilitator is needed to guide and moderate the task-related and interactional activities throughout the session. Facilitation is also usually needed in longer-term design projects or courses to guide participants through the different phases of design during several sessions. Recently, due to the COVID-19 pandemic, most design activities including social robot design were rapidly transferred to online mode. Designing for tangible objects is challenging in online settings because the interaction experience with a physical object is hard to demonstrate online. In this article, we report how we harnessed online canvases to support both short-term social robot design workshops and a long-term design course. Based on participants' feedback and facilitators' experiences, we report lessons learned from utilizing collaborative design canvases for creative social robot design projects that specifically focus on early stages and concept ideation. We propose practical guidelines for canvas-based online facilitation focusing on creative design workshops and projects. In addition, we discuss the lessons learned concerning social robot design activities taking place in online mode. To respond to the challenges of designing tangible robots in a fully online mode, we suggest a Hybrid Robotic Design Model (HRDM), where the participants work in contact with facilitators, other participants and robots at specific points, while the other phases are conducted online.
A New Era of Automated Market-Makers (AMM) powered by Non-Fungible Tokens- A Review

The most well-known cryptocurrency, Bitcoin, is one of many that are sweeping the globe. Along with them, fungible tokens traded on numerous centralised or decentralised exchanges are different from non-fungible tokens traded on NFT marketplaces. The majority of NFTs are currently digital, and in the future, producers may innovate in this area to allow for more innovative user experiences. The blockchain and its NFTs can provide fantastic chances for artists and content producers to profit from their work. The NFT Marketplace intends to be at the centre of all these fantastic use cases for NFTs by giving users a platform to produce and exchange non-fungible tokens. NFTs offer a wide range of application cases. Automated Market Makers, which are essentially decentralised markets for crypto-tokens and offer users three core operations—depositing crypto tokens in exchange for AMM shares, performing a dual operation in which shares are obtained in exchange for base tokens, and exchanging two tokens for one another—are some of the main applications of DeFi. This conceptual research discusses AMMs that are already in existence on the Ethereum blockchain and their developments, including the AMM that is now being created on the Tezos blockchain. The goal of this study is to present a thorough understanding of blockchain technology and all of its practical uses, including voting, trading NFTs, etc. It then focuses on how NFTs are traded on various platforms before aiming for improved NFT trading marketplaces, namely Automated Market Makers on various blockchains like Ethereum and Tezos.&#x0D;
A unified logical framework for explanations in classifier systems

Abstract Recent years have witnessed a renewed interest in the explanation of classifier systems in the field of explainable AI (XAI). The standard approach is based on propositional logic. We present a modal language which supports reasoning about binary input classifiers and their properties. We study a family of classifier models, axiomatize it as two proof systems regarding the cardinality of the language and show completeness of our axiomatics. Moreover, we show that the satisfiability checking problem for our modal language is NEXPTIME-complete in the infinite-variable case, while it becomes polynomial in the finite-variable case. We moreover identify an interesting NP fragment of our language in the infinite-variable case. We leverage the language to formalize counterfactual conditional as well as a variety of notions of explanation including abductive, contrastive and counterfactual explanations and biases. Finally, we present two extensions of our language: a dynamic extension by the notion of assignment enabling classifier change and an epistemic extension in which the classifier’s uncertainty about the actual input can be represented.
DPACS: Hardware Accelerated Dynamic Neural Network Pruning through Algorithm-Architecture Co-design

By eliminating compute operations intelligently based on the run time input, dynamic pruning (DP) promises to improve deep neural network inference speed substantially without incurring a major impact on their accuracy. Although many DP algorithms with good pruning performance have been proposed, it remains a challenge to translate these theoretical reductions in compute operations into satisfactory end-to-end speedups in practical real-world implementations. The overhead of identifying operations to be pruned during run time, the need to efficiently process the resulting dynamic dataflow, and the non-trivial memory I/O bottleneck that emerges as the number of compute operations reduces, have all contributed to the challenge of implementing practical DP systems.
Ancient Epic Manuscript Binarization and Classification Using False Color Spectralization and VGG-16 Model

Ancient Malayalam manuscripts include a vast quantity of materials, much of which pertains to ancient custom and culture. These manuscripts are the foundation of the rich and diverse culture that we possess today. The information extraction from such scripts written in “Thaliyola” or palm leaves would be an extensive and tedious task due to various challenges such as understanding the ancient script, damages generated due to miss-handling, stains and fungus and other environmental factors caused to the palm leaves. Preserving and segregating these documents through binarization and classification is an important task. Classification of these documents will lead to understanding unique ancient documents and preserving them for our current and future generations. In proposed work primarily an RGB image of the palm leaf document is given as input to the Spectral Angle Mapper(SAM) algorithm which takes the input and converts into RGB-A image with an additional Alpha channel, after FCC and TCC is calculated and a spectral image is generated which possess good text readability which is then followed by classification. The spectral model is passed to using VGG-16 model along with other binarized images, after training and testing achieved an accuracy of 90% on Jadakam and an accuracy of 85% with Bhagavatham.
Deep Adversarial Cascaded Hashing for Cross-Modal Vessel Image Retrieval

In recent years, cross-modal remote sensing image retrieval(CMRSIR) has attracted a lot of attention in remote sensing(RS) information processing. It's worth mentioning that land cover scenes, whether unimodal or cross-modal, are the primary research contents of remote sensing image retrieval (RSIR), and there are few studies on vessel images captured by RS satellites, let alone cross-modal retrieval tasks. Vessel images have smaller scale, lower resolution, and less detailed information than land cover images, so it's difficult to retrieve the exact images we want. In this paper, a hashing method called deep adversarial cascaded hashing (DACH) is proposed to address these problems. To extract the subtle and discriminative features contained in RS vessel images accurately, we build a deep cascaded network that fuses multilevel features boosted both in depth and width, and the self-attention mechanism can further enhance the fused features. Combined with hash learning, we also design a weighted quintuplet loss to supervise the transition of discrimination and similarity between different metric spaces, and reduce cross-modal discrepancy at the same time. In addition, we apply the deep adversarial constraint to both feature learning and hash learning, trying to bridge the modality gap and achieve a cross-modal retrieval as precise as unimodal retrieval. Comprehensive experiments on two public bimodal vessel image datasets compared with several excellent cross-modal retrieval methods are conducted to demonstrate the effectiveness of our DACH, and the results show that the proposed method is effective and competitive on cross-modal vessel image retrieval tasks, outperforming state-of-the-art methods.
Deep Learning with Transformer or Convolutional Neural Network in the Assessment of Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Based on US Images: A Dual-Center Retrospective Study

This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. Of these, 396 patients from hospital 1 were divided into the training cohort (n = 298) and internal validation (IV) cohort (n = 98). Patients from hospital 2 (n = 98) were in the external validation (EV) cohort. TIL levels were confirmed by pathological results. Five different DL models were trained for predicting TIL levels in BC using US images from the training cohort and validated on the IV and EV cohorts. The overall best-performing DL model, the attention-based DenseNet121, achieved an AUC of 0.873, an accuracy of 79.5%, a sensitivity of 90.7%, a specificity of 65.9%, and an F1 score of 0.830 in the EV cohort. In addition, the stratified analysis showed that the DL models had good discrimination performance of TIL levels in each of the molecular subgroups. The DL models based on US images of BC patients hold promise for non-invasively predicting TIL levels and helping with individualized treatment decision-making.
Decoherence-suppressed nonadiabatic holonomic quantum computation

Nonadiabatic holonomic quantum computation~(NHQC) provides an essential way to construct robust and high-fidelity quantum gates due to its geometric features. However, NHQC is more sensitive to the decay and dephasing errors than conventional dynamical gate since it requires an ancillary intermediate state. Here, we utilize the Hamiltonian reverse engineering technique to study the influence of the intermediate state-decoherence on the NHQC gate fidelity, and propose the novel schemes to construct the arbitrary single-qubit holonomic gate and nontrivial two-qubit holonomic gate with high fidelity and robustness to the decoherence. Although the proposed method is generic and can be applied to many experimental platforms, such as superconducting qubits, trapped ions, quantum dots, here we take nitrogen-vacancy (NV) center as an example to show that the gate fidelity can be significantly enhanced from 89\% to 99.6\% in contrast to the recent experimental NHQC schemes [Phys. Rev. Lett. 119, 140503 (2017); Nat. Photonics 11, 309 (2017); Opt. Lett. 43, 2380 (2018)], and the robustness against the decoherence can also be significantly improved. All in all, our scheme provides a promising way for fault-tolerant geometric quantum computation.
Semiotic Multimodality Communication in The Age of New Media

The age of new media is changing the social order of communication. The availability of the widest access for internet users to communicate without time and region limitations is a feature of this digital media era. The second distinctive feature is the communication of digital media by using semiotic multimodality (a combination of verbal and nonverbal signs, for example, emojis, images, videos, sounds, and music). The purpose of this paper is to identify how cultures communicate with the New Media Age's semiotic multimodality. The approach used is semiotics, that is, an approach that views the media as a medium of exchange for signs of multimodality (complex verbal and nonverbal signs used in real-time). This paper concludes that the new media produce three models of communication between social media users, namely verbal and verbal language (sign) communication, verbal and nonverbal communication, and nonverbal-nonverbal communication. This study adds to the importance of internet users in communicating by utilizing the three models to remain critical in responding to various information received and disseminated. Because the use of semiotic multimodality that is compatible with communication models can cause problems in the meaning of the message conveyed.
Cloud Computing for Supply Chain Management and Warehouse Automation: A Case Study of Azure Cloud

In recent times, organizations are examining the art training situation to improve the operation efficiency and the cost of warehouse retail distribution and supply chain management. Microsoft Azure emerges as an expressive technology that leads optimization by giving infrastructure, software, and platform resolutions for the whole warehouse retail distribution and supply chain management. Using Microsoft Azure as a cloud computing tool in retail warehouse distribution and supply manacle management contributes to active and monetary benefits. At the same time, potential limitations and risks should be considered by the retail warehouse distribution and the supply chain administration investors. In this research summary of the cloud figuring tool, both public and hybrid in supply chain administration and retail, warehouse distribution is addressed. A brief introduction to the use of Microsoft Azure technology is provided. This is followed by the application of cloud computing to warehouse retail distribution and supply chain management activities. At the same time, the negative and positive aspects of familiarizing this Microsoft Azure technology in the modern supply chain and retail distribution are debated. Also, the circumstance for the third-party logistics services suppliers has indicated respect for automation and cybersecurity solutions in a cloud environment. Lastly, the upcoming research practices and following technological trends are offered as the conclusion.
Road-Map Aided Gaussian Mixture Labeled Multi-Bernoulli Filter for Ground Multi- Target Tracking

Ground multi-target tracking (MTT) is one of the core tasks of airborne ground moving target indicator (GMTI) radar and automotive radar. However, ground MTT still remains a challenging issue especially in complex traffic scenarios, since it often suffers from high clutter, dense targets, low visibility, etc. To enhance the tracking performance for ground multiple targets, in this paper, we present a comprehensive solution named road-map aided Gaussian mixture labeled multi-Bernoulli filter (RA-GMLMB) filter, which incorporates road-map information into the labeled multi-Bernoulli (LMB) filter. Specifically, we first propose a hybrid circular arc and line segments approximation approach to extract road-map information, which alleviates approximation errors in the procedure of road-map approximation. Then, we deduce the RA-GMLMB filter by integrating the extracted road-map information into the LMB filter with Gaussian mixture implementation. Simulation experiments are conducted and experimental results show that the proposed RA-GMLMB filter outperforms the state-of-the-art methods.
Using an augmented reality application for teaching plant parts: A case study in 1ˢᵗ-grade primary school students

The rapid development of augmented reality (AR) applications has led to wide adoption in education, acting as a supporting tool to increase the transmitted information. However, children need to improve their knowledge of plants, their categories, their conceptual framework as well as the human relationship with them. This research studies the learning outcomes of a plant-themed augmented reality mobile application and its key features designed for 1st graders of primary school. The results of the research showed that there was no statistically significant improvement between the experimental group and the control group.
The dark sıde of cryptocurrency markets: an integrated bibliometric analysis

Purpose- The aim of this study is to reveal the publications that shed light on the dark side of the cryptocurrency markets with a systematic approach. Methodology- For this purpose, 369 publications in the Scopus database between 2014-2022 were determined as samples. In the publications provided by the database, the keywords "cryptocurrency" and "fraud", "scam", "phishing", "ponzi", "crime" were scanned over the publication title, abstract and keywords, and an integrated bibliometric analysis was employed. The R program was used in the analysis, and the "Biblioshiny" application in the RStudio program was employed to visualize the findings. Findings- The analysis reveals that the number of publications, the number of citations and the interest in the field have increased especially in recent years. The rate of increase in the number of publications in the field and the fact that most of these publications are at the stage of notification have shown that the field is an important developing field. The most intense interest in the field has been shown from universities in China. On the other hand, it was seen that the most interest in the field was from computer sciences and the interest of journals in the field of finance remained weak. It has been determined that the topics that may attract attention in the future are digital forensics, digital assets, fraudulent cryptocurrencies, corruption prevention, mining and cyber attacks. Conclusion- The study reveals the evolution of the dark side of cryptocurrency markets in academic research. The findings provide researchers interested in the field with the opportunity to explore themes and issues that may be on the agenda in the future. Keywords: Cryptocurrency, fraud, ponzi, crime, bibliometric analysis JEL Codes: G11, G19