Reviewerly/exHarmony huggingface dataset:
A New Approach to Predicting Cryptocurrency Returns Based on the Gold Prices with Support Vector Machines during the COVID-19 Pandemic Using Sensor-Related Data

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.
A Survey on Blockchain-Based IoMT Systems: Towards Scalability

Recently, blockchain-based Internet of Medical Things (IoMT) has started to receive more attention in the healthcare domain as it not only improves the care quality using real-time and continuous monitoring but also minimizes the cost of care. However, there is a clear trend to include many entities in IoMT systems, such as IoMT sensor nodes, IoT wearable medical devices, patients, healthcare centers, and insurance companies. This makes it challenging to design a blockchain framework for these systems where scalability is a most critical factor in blockchain technology. Motivated by this observation, in this survey we review the state-of-the-art in blockchain-IoMT systems. Comparison and analysis of such systems prove that there is a substantial gap, which is the negligence of scalability. In this survey, we discuss several approaches proposed in the literature to improve the scalability of blockchain technology, and thus overcoming the above mentioned research gap. These approaches include on-chain and off-chain techniques, based on which we give recommendations and directions to facilitate designing a scalable blockchain-based IoMT system. We also recommended that a designer considers the well-known trilemma along with the various dimensions of a scalable blockchain system to prevent sacrificing security and decentralization as well. Moreover, we raise several research questions regarding benchmarking; addressing these questions could help designers determining the existing bottlenecks, leading to a scalable blockchain.
Co-Designing AI Literacy Exhibits for Informal Learning Spaces

AI is becoming increasingly integrated in common technologies, which suggests that learning experiences for audiences seeking a "casual" understanding of AI-i.e. understanding how a search engine works, not necessarily understanding how to program one-is an increasingly important design space. Informal learning spaces like museums are particularly well-suited for such public science communication efforts, but there is little research investigating how to design AI learning experiences for these spaces. This paper explores how to design museum experiences that communicate key concepts about AI, using collaboration, creativity, and embodiment as inspirations for design. We present the design of five low-fidelity AI literacy exhibit prototypes and results from a thematic analysis of participant interactions during a co-design workshop in which family groups interacted with the prototypes and designed exhibits of their own. Our findings suggest new topics and design considerations for AI-related exhibits and directions for future research.
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

The common practice for training commonsense models has gone from-human-to-corpusto-machine: humans author commonsense knowledge graphs in order to train commonsense models.In this work, we investigate an alternative, from-machine-to-corpus-tomachine: general language models author these commonsense knowledge graphs to train commonsense models.Our study leads to a new framework, Symbolic Knowledge Distillation.As with prior art in Knowledge Distillation (Hinton et al., 2015), our approach uses larger models to teach smaller models.A key difference is that we distill knowledge symbolically-as text-in addition to the resulting neural model.We distill only one aspect-the commonsense of a general language model teacher, allowing the student to be a different type of model, a commonsense model.Altogether, we show that careful prompt engineering and a separately trained critic model allow us to selectively distill highquality causal commonsense from GPT-3, a general language model.Empirical results demonstrate that, for the first time, a human-authored commonsense knowledge graph is surpassed by our automatically distilled variant in all three criteria: quantity, quality, and diversity.In addition, it results in a neural commonsense model that surpasses the teacher model's commonsense capabilities despite its 100x smaller size.We apply this to the ATOMIC resource, and will share our new symbolic knowledge graph and commonsense models 1 .
A Survey of Graphical Page Object Detection with Deep Neural Networks

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that make the digitization of documents viable. Since the advent of deep learning, deep learning-based object detection performance has improved many folds. This work outlines and summarizes the deep learning approaches for detecting graphical page objects in document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.
Blockchain: Future of e-Governance in Smart Cities

In recent times, Blockchain has emerged as a transformational technology with the ability to disrupt and evolve multiple domains. As a decentralized, immutable distributed ledger, Blockchain technology is one of the most recent entrants to the comprehensive ideology of Smart Cities. The rise of urbanization and increased citizen participation have led to various technology integrations in our present-day cities. For cities to become smart, we need standard frameworks and procedures for integrating technology, citizens and governments. In this paper, we explore the potential of Blockchain technology as an enabler for e-governance in smart cities. We examine the daily challenges of citizens and compare them with the benefits being offered by Blockchain integration. On the basis of a comprehensive literature review, we identified four key areas of e-governance wherein Blockchain can provide monumental advantages. In the context of Blockchain integration for e-governance, the paper presents a survey of prominent published works discussing various urban applications.
A new parallel processing architecture for accelerating image encryption based on chaos

This study introduces a novel parallel processing architecture for accelerating image encryption based on chaos. In the proposed architecture, whole image data is split into partitions of particular size to create separate encryption threads. As the proposed cryptosystem employs several identical chaotic ciphers running concurrently and independently to process the partitions, it greatly leverages the degree of parallelism to some extent. A powerful output mixing logic based on an additional chaotic function, and simple exclusive-OR and shift operations is innovatively incorporated to ensure inter-partition diffusion. Since there is no dependency on previous data bytes in the introduced logic, blending operations applied on the outputs of independent encryption threads can be concurrently executed by exploiting loop-level parallelism to the extent allowed by data processing units available. The number of blending operations that should be carried out for an image is kept proportional to the partition size which also directly determines the number of separate encryption threads created. In order to measure encryption/decryption runtimes, the proposed architecture has been tested on two different multi-core CPUs, namely 4-core and 8-core. The obtained results show that the proposed cryptosystem parallelising sequential operations by introducing a multi-threaded encryption architecture is much faster than the base cipher and most of the other state-of-the-art algorithms. Having successfully passed various security tests, the proposed cryptosystem manifests its robustness against cryptographic attacks, and hence become evident that it is efficient for secure transmission.
Quantum Cryptography-as-a-Service for Secure UAV Communication: Applications, Challenges, and Case Study

The sudden demand rises in security made researchers come up with solutions that provide instantaneous safety better than the state of the art solutions.The quest for securing data began in the Spartan era.People are now looking to expand this field of research by attacking the existing paradigms and inventing new algorithms that prove to be better than their vulnerable counterparts.Unmanned aerial vehicles (UAVs) are very much prevailing due to their sleek design and flexible mobility in many sectors such as agriculture, army, healthcare, monitoring and surveillance, and many more.We discuss the growth and demand of drone technology along with its importance in this article.The paper also throws some light on the ongoing security issues in real-time scenarios and the role of quantum cryptography in securing the information over the traditional solutions.Motivated by this, we present a survey on quantum cryptography's importance, role, and benefits in securing UAV communications underlying beyond 5G networks.A novel quantum cryptography-based layered architectural solution is also proposed to achieve high data security and efficient transmission.This paper also present a case study on the battlefield application on the Internet of military things.The performance of the proposed case study system is evaluated by considering the latency, security, and reliability.
Study of Virtual Reality Immersive Technology Enhanced Mathematics Geometry Learning

Mathematics is an important foundation for the development of science education. In the past, when instructors taught mathematical concepts of geometry shapes, they usually used traditional textbooks and aids to conduct teaching activities, which resulted in students not being able to understand the principles completely. Nowadays, it has become a trend to integrate emerging technologies into mathematics courses and to use digital instructional aids. Emerging technologies can effectively enhance students' sensory experience while strengthening their impressions and understandings of subject concepts. In this paper, we apply virtual reality immersive technologies to develop a "virtual reality immersive learning mathematics geometry system," which is used to teach mathematical geometry concepts. Teachers use the system to develop three basic mathematical geometry learning materials: "Triangular pyramid volume = 1/3 prism volume," "Cone volume calculation," and "Triangle center of gravity derivation." In the experimental activity, the teacher uses virtual reality teaching aids to guide students to learn mathematical geometry concepts in a fun way so that they can achieve the effectiveness of immersive learning. This study explores the impact of using the virtual reality immersive learning mathematics geometry system on students' technology acceptance, learning motivations, and learning performance. The experimental result showed that using the virtual reality immersive learning mathematics geometry system can improve the learning motivation and learning performance of students. The findings indicated that the experimental group had better learning outcomes after completing the learning tasks of three geometric units. The experimental group used the virtual reality immersive learning mathematics geometry system which can lead to better learning outcomes. According to the ARCS questionnaire, students in the experimental group were confident to understand new subjects. At the same time, the mode of completing the game can effectively give students a sense of accomplishment. The use of emerging technologies in the classroom can be an attractive learning mode for students.
A Scale-Aware Pyramid Network for Multi-Scale Object Detection in SAR Images

Multi-scale object detection within Synthetic Aperture Radar (SAR) images has become a research hotspot in SAR image interpretation. Over the past few years, CNN-based detectors have advanced sharply in SAR object detection. However, the state-of-the-art detection methods are continuously limited in Feature Pyramid Network (FPN) designing and detection anchor setting aspects due to feature misalignment and targets’ appearance variation (i.e., scale change, aspect ratio change). To address the mentioned limitations, a scale-aware feature pyramid network (SARFNet) is proposed in this study, which comprises a scale-adaptive feature extraction module and a learnable anchor assignment strategy. To be specific, an enhanced feature pyramid sub-network is developed by introducing a feature alignment module to estimate the pixel offset and contextually align the high-level features. Moreover, a scale-equalizing pyramid convolution is built through 3-D convolution within the feature pyramid to improve inter-scale correlation at different feature levels. Furthermore, a self-learning anchor assignment is set to update hand-crafted anchor assignments to learnable anchor/feature configuration. By using the dynamic anchors, the detector of this study is capable of flexibly matching the target with different appearance changes. According to extensive experiments on public SAR image data sets (SSDD and HRSID), our algorithm is demonstrated to outperform existing boat detectors.
ShrimpChain: A blockchain-based transparent and traceable framework to enhance the export potentiality of Bangladeshi shrimp

Despite substantial progress achieved with shrimp production during the last three and a half decades, growth of shrimp export remains minimal in Bangladesh. In the absence of effective traceability and transparency practices, a wide range of malpractice occurs both at the production and post-harvest stages. Traditional paper-based record-keeping methods for the shrimp supply chain are disparate and, therefore, cannot provide efficient traceability capacity and holistic view of the supply chain. These limit the identification of any issues in earlier juncture to proactively ensure food safety, best practice, and good governance. Addressing this multiplexed challenge, we present here ShrimpChain, a public-private hybrid blockchain-based conceptual framework. Focusing on the export market and utilizing existing technologies, the conceptual framework addresses the traceability, transparency, and certification challenges associated with shrimp export. In this framework, from the post-larva purchasing to the final packaging stage, relevant data for every stage will be entered by the associated actors via mobile/web app or Internet of Things devices to the blockchain network. Data authentication will be achieved by a novel approach of incorporating community consensus in conjunction with the machine-derived data entry timestamping method. Instead of the traditional central and endpoint certification approach, we propose a distributed and accumulative score-based certification approach that will grade packaged shrimps according to the completeness and accuracy of the authenticated data entered during different stages. Such distributed approach of certification will enhance not only food safety but also the quality and compliance to best practices. Most importantly, engaging shrimp farmers in the safety and quality assurance as well as to the certification process will empower them to have better control over the market and incentive to produce high-quality shrimp for high-value market.
Focus-and-Detect: A small object detection framework for aerial images

Despite recent advances, object detection in aerial images is still a challenging task. Specific problems in aerial images makes the detection problem harder, such as small objects, densely packed objects, objects in different sizes and with different orientations. To address small object detection problem, we propose a two-stage object detection framework called "Focus-and-Detect". The first stage which consists of an object detector network supervised by a Gaussian Mixture Model, generates clusters of objects constituting the focused regions. The second stage, which is also an object detector network, predicts objects within the focal regions. Incomplete Box Suppression (IBS) method is also proposed to overcome the truncation effect of region search approach. Results indicate that the proposed two-stage framework achieves an AP score of 42.06 on VisDrone validation dataset, surpassing all other state-of-the-art small object detection methods reported in the literature, to the best of authors' knowledge.
Dynamic Prefix-Tuning for Generative Template-based Event Extraction

We consider event extraction in a generative manner with template-based conditional generation.Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information.In this paper, we propose a generative templatebased event extraction method with dynamic prefix (GTEE-DYNPREF) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context.Experimental results show that our model achieves competitive results with the state-ofthe-art classification-based model ONEIE on ACE 2005 and achieves the best performances on ERE.Additionally, our model is proven to be portable to new types of events effectively.
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise Perspective for Medical Image Segmentation

Recently, a variety of vision transformers have been developed as their capability of modeling long-range dependency. In current transformer-based backbones for medical image segmentation, convolutional layers were replaced with pure transformers, or transformers were added to the deepest encoder to learn global context. However, there are mainly two challenges in a scale-wise perspective: (1) intra-scale problem: the existing methods lacked in extracting local-global cues in each scale, which may impact the signal propagation of small objects; (2) inter-scale problem: the existing methods failed to explore distinctive information from multiple scales, which may hinder the representation learning from objects with widely variable size, shape and location. To address these limitations, we propose a novel backbone, namely ScaleFormer, with two appealing designs: (1) A scale-wise intra-scale transformer is designed to couple the CNN-based local features with the transformer-based global cues in each scale, where the row-wise and column-wise global dependencies can be extracted by a lightweight Dual-Axis MSA. (2) A simple and effective spatial-aware inter-scale transformer is designed to interact among consensual regions in multiple scales, which can highlight the cross-scale dependency and resolve the complex scale variations. Experimental results on different benchmarks demonstrate that our Scale-Former outperforms the current state-of-the-art methods. The code is publicly available at: https://github.com/ZJUGiveLab/ScaleFormer.
A Comprehensive Review of Path Planning for Agricultural Ground Robots

The population of the world is predicted to reach nine billion by 2050, implying that agricultural output must continue to rise. To deal with population expansion, agricultural chores must be mechanized and automated. Over the last decade, ground robots have been developed for a variety of agricultural applications, with autonomous and safe navigation being one of the most difficult hurdles in this development. When a mobile platform moves autonomously, it must perform a variety of tasks, including localization, route planning, motion control, and mapping, which is a critical stage in autonomous operations. This research examines several agricultural applications as well as the path planning approach used. The purpose of this study is to investigate the current literature on path/trajectory planning aspects of ground robots in agriculture using a systematic literature review technique, to contribute to the goal of contributing new information in the field. Coverage route planning appears to be less advanced in agriculture than point-to-point path routing, according to the finding, which is due to the fact that covering activities are usually required for agricultural applications, but precision agriculture necessitates point-to-point navigation. In the recent era, precision agriculture is getting more attention. The conclusion presented here demonstrates that both field coverage and point-to-point navigation have been applied successfully in path planning for agricultural robots.
Breast Cancer Detection and Classification using Deep Learning Xception Algorithm

Breast Cancer (BC) is one of the leading cause of deaths worldwide.Approximately 10 million people pass away internationally from breast cancer in the year 2020.Breast Cancer is a fatal disease and very popular among women globally.It is ranked fourth among the fatal diseases of different cancers, for example colorectal cancer, cervical cancer, and brain tumors.Furthermore, the number of new cases of breast cancer is anticipated to upsurge by 70% in the next twenty years.Consequently, early detection and precise diagnosis of breast cancer plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%.Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, Magnetic Resonance Imaging (MRI), computed tomography (CT) images.The aim of this study is to propose a deep learning model to detect and classify breast cancers.Breast cancers has eight classes of cancers: benign adenosis, benign fibroadenoma, benign phyllodes tumor, benign tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma.The dataset was collected from Kaggle depository for breast cancer detection and classification.The measurement that was used in the evaluation of the proposed model includes: F1-score, recall, precision, accuracy.The proposed model was trained, validated and tested using the preprocessed dataset.The results showed that Precision was (97.60%),Recall (97.60%) and F1-Score (97.58%).This indicates that deep learning models are suitable for detecting and classifying breast cancers precisely.