Reviewerly/exHarmony huggingface dataset:
Research resources of Japanese Mokkan: Turning information on ancient wooden tablets into research data

This paper focuses on ancient Japanese Mokkan (woodcuts) to discuss the background, current status and issues in the digitisation of cultural heritage, including matters of research, protection, and legal rights. As valuable primary sources, Mokkan are important in a wide range of academic fields, including history and linguistics. On the other hand, they are extremely fragile and fragmented. Digital technologies can support an improvement in access and study of this material. In this paper, we illustrate the current efforts to improve digitisation workflows and strengthen research capacity: we discuss open data and open access, equal and transparent inter-institutional collaboration, the application of the IIIF standard, the construction of a character database search system, and the application of Deep Learning technology. The issue of open data and open access is particularly challenging in Japan, where there is still a gap between the legal principles and the actual situation of cultural heritage institutions. In addition, information needs to be standardized and aggregated, but at the same time the specificity and historical characteristics of the different materials need to be addressed. In this paper, we strongly advocate for the establishment of methods for sharing cultural heritage data. In humanities research, the linkage, open access and standardization of information has become essential, not only to foster data-driven research but also to ensure wide access and dissemination of cultural heritage in Japan. For this reason, we encourage equal and open collaboration not just across cultural heritage stakeholders, but across society as a whole.
An Efficient Global Trajectory Planner for Highly Dynamical Nonholonomic Autonomous Vehicles on 3D Terrains

A novel hierarchical global trajectory planner is presented to allow highly dynamical nonholonomic off-road autonomous vehicles to achieve high mobility on 3D terrains. On complex terrains with uneven topology, designing safe and feasible vehicle trajectories often demands an understanding of the vehicle's dynamical and nonholonomic constraints. Prior research, however, treats the global planning problem as a path planning problem without effectively accounting for topology or dynamical constraints. To address this gap, this paper presents a three-phase trajectory planning algorithm composed of an A*, a rapidly exploring random tree (RRT), and a local trajectory refining (LTR) phase to incorporate dynamical and nonholonomic constraints on uneven terrain. The algorithm is tested in scenarios with randomized terrain fields and obstacles to demonstrate the necessity for all three phases. The algorithm is shown to have lower cost, higher success rate, and higher computational efficiency compared to state-of-the-art methods. The algorithm is then tested by controlling a simulated MRZR vehicle on a 3D terrain along with a local controller, with comparisons to state-of-the-art algorithms. It is demonstrated that the new algorithm is capable of planning dynamically feasible trajectories with lower cost where the state-of-the-art algorithms fail to perform due to neglecting dynamical vehicle limitations.
Avoidance of Concave Obstacles through Rotation of Nonlinear Dynamics

Controlling complex tasks in robotic systems, such as circular motion for cleaning or following curvy lines, can be dealt with using nonlinear vector fields. This paper introduces a novel approach called the rotational obstacle avoidance method (ROAM) for adapting the initial dynamics when obstacles partially occlude the workspace. ROAM presents a closed-form solution that effectively avoids star-shaped obstacles in spaces of arbitrary dimensions by rotating the initial dynamics toward the tangent space. The algorithm enables navigation within obstacle hulls and can be customized to actively move away from surfaces while guaranteeing the presence of only a single saddle point on the boundary of each obstacle. We introduce a sequence of mappings to extend the approach for general nonlinear dynamics. Moreover, ROAM extends its capabilities to handle multi-obstacle environments and provides the ability to constrain dynamics within a safe tube. By utilizing weighted vector-tree summation, we successfully navigate around general concave obstacles represented as a tree-of-stars. Through experimental evaluation, ROAM demonstrates superior performance in minimizing occurrences of local minima and maintaining similarity to the initial dynamics, outperforming existing approaches in multi-obstacle simulations. Due to its simplicity, the proposed method is highly reactive and can be applied effectively in dynamic environments. This was demonstrated during the collision-free navigation of a 7-degree-of-freedom robot arm around dynamic obstacles.
Allowing Supervision in Unsupervised Deformable-Instances Image-to-Image Translation

Replacing objects in images is a practical functionality of Photoshop, e.g., clothes changing. This task is defined as Unsupervised Deformable-Instances Image-to-Image Translation (UDIT), which maps multiple foreground instances of a source domain to a target domain, involving significant changes in shape. Although previous works incorporate instance masks of source domain for instance shape indication, their translation still fails in shape because of inadequate utilization of shape information in masks. To mitigate this issue, we introduce an effective two-stage pipeline for UDIT called Mask-Guided Deformable-instances GAN++ (MGD-GAN++), which generates target masks in the first stage named Mask Morph and utilizes the masks to guide the synthesis of corresponding instances in the second stage named Mask-Guided Image Generation. To further provide sufficient supervision with existing unpaired datasets, an overall set of training schemes is proposed for the two stages of MGD-GAN++, coined as Aligned Supervision and Inpainting Supervision, respectively. Extensive experiments on four datasets demonstrate the significant advantages of our MGD-GAN++ over existing methods both quantitatively and qualitatively. Furthermore, our training time consumption is hugely reduced compared to the state-of-the-art.
Classification of Student Graduation Using Backpropagation Neural Network with Features Selection and Dimensions Reduction

Students' graduation is a very important thing in universities as a determinant of the quality of a university. To improve the number of student graduates, a university must know the factors that affect students and classify student graduation. The efforts that can be made to detect student graduation are by predicting student graduation early on using data mining. The data mining technique used is using feature selection techniques with Mutual Information and Chi-Square, then dimensional reduction with PCA which will later be classified using BPNN. The dataset in this study is data on graduates of the Information Systems Study Program for the 2013-2022 academic year which contains 7 attributes with 38 questions. The 7 attributes are GPA, Total Credits, Taking FP Course on 7th Semester, Number of Repetitive Courses, Procrastination, Confidence, and Disciplin. The best experimental results can be obtained in BPNN classification with Mutual Information feature selections with 100% accuracy and RMSE 0 at a learning rate of 0.005 with a maximum iteration of 1000. While the results of the attributes that greatly affect student graduation are procrastination, discipline, confidence, and total credits.
Enhancing Face Recognition in Masked Environments: A Deep Neural Network Investigation with Attention Modules

This research comprehensively investigates improving face recognition performance in masked environments. This study investigates the deep neural networks (DNN) models with Channel Attention Module (CBAM) on masked face recognition tasks using the VGGFace2 dataset. The study leverages the unique feature of residual connections in ResN et-50 to overcome vanishing gradients, thereby enhancing representation learning. The DNN with Attention aiming to improve face recognition performance in masked environments reveals significant accuracy improvements for Unmasked-Masked (UM) and All-Pair comparisons compared to less deep networks. Surprisingly, the results demonstrate that CBAM performs poorly, behaving similarly to a random classifier. The research demonstrates the efficacy of DNN s like ResN et-50 in challenging tasks. It underlines its potential to facilitate enhanced face recognition in masked scenarios. The research sheds light on the limitations of CBAM in handling masked face recognition scenarios. It highlights the need for guided attention explicitly targeting the region above masks and around the eyes and the nose bridge. Addressing these shortcomings paves the way for more effective and accurate masked face recognition models.
A Visual Analytics Approach to Exploring the Feature and Label Space Based on Semi-structured Electronic Medical Records

Electronic health records (EHRs), serving as patient-centered repositories for medical data, offer the opportunity for researchers to uncover concealed patterns using machine learning (ML). However, in real-world medical settings, clinicians often face the task of selecting pertinent feature dimensions from a range of potential medical metrics and then deducing potential labels from vague diagnostic descriptions, prior to the modeling phase. This complexity presents challenges in obtaining reliable training/testing data and conducting thorough analysis. Consequently, these hurdles hinder the practical application of ML for automated modeling and comprehensible interpretation of inﬂuencing factors. To tackle these challenges, we introduce a visual analytics approach designed to navigate the feature and label space within EHRs, while also streamlining the modeling process through automated ML algorithms and techniques for improved interpretability.
Blockchain for Identity Management: Ensuring Trust and Integrity in the Education Sector

Blockchain-based identity management has the potential to establish a robust system in the education sector. This study introduces a blockchain-based approach to verifying the identity of learners. Our research underscores the importance of blockchain in revolutionizing the education sector by fostering a learner identity-centric system. The present study presents a novel blockchain-based system that involves the utilization of cryptographic hashing techniques to generate a distinct digital identifier for a learner. The implementation of this proposed system can be used to issue, store, and verify a learner’s identifier using blockchain technology. This paper illustrates the workflow of our innovative blockchain-based user identity management system and outlines the implementation process for learner identity registration and verification. This unalterable digital proof of identity can be subsequently shared with higher educational institutions (HEIs) and recruitment companies, enabling the accurate and reliable verification of identity.
A Compact 1,257-Gbps/W Byte-Serial AES Accelerator for IoT Applications in 22 nm

Security is becoming even more critical for the digital society such as loT. Especially quantum computers pose serious threats to the currently deployed crypto-systems [1], which requires double key size for symmetric cryptographic algorithms to preserve security. As the de-facto standard block cipher, Advanced Encryption Standard (AES) acts as the mainstay to secure versatile applications like data communication and storage. Therefore, an even more compact and energy-efficient AES accelerator is in urgent demand for billions of miniaturized and battery-supplied devices in loT field considering quantum security. In contrast to the traditional 128-bit datapath designs, several byte-serial architectures have been proposed obtaining smaller area [2]–[7]. On one hand, single SBox is exploited for both data encryption and key expander for further area reduction [2]–[5]. However, significant redundant data movements result in large energy overhead, and additional cycles for ShiftRow lower the throughput [2]–[3]. The additional registers for the intermediate storage add to control complexity as well as power consumption [4]–[5]. The long latency (336/337 cycles per encryption) negatively impacts the hardware utilization and energy efficiency. On the other hand, double SBoxes boost the throughput with a corresponding area penalty [6]–[7]. Compared to the separate SBox for data encryption and key expander in [7], one of the two Sboxes is shared between data encryption and key expander in [6], which reduces the latency from 160 cycles/encryption to 113 but is still above the algorithmic lower bound of 100 cycles for 2 SBoxes. In this paper, the proposed AES accelerator reaches a throughput equivalent to the corresponding lower bound of 200 cycles/encryption using a single SBox at 100% utilization and achieves the lowest number of registers (32 bytes) for data storage. The redundant data movement is significantly reduced with efficient data structure while preserving high clock frequency. Through the multi-objective optimization, the proposed AES-128 encryption accelerator is well suited for embedded systems with tight area, throughput and energy constraints.
Measuring the Effects of Document Size on RAM Artifacts: A Memory Forensics Approach

Cybercrimes have significantly increased due to the rapid adoption of software and technology in almost every aspect of our lives. The main memory or RAM of a computing machine is often used to provide critical information about the recent system activities, including the running processes, network connections, used passwords, and encryption keys. Moreover, RAM may contain information about the contents of recently used documents and digital files. Therefore, this paper is focused on studying the RAM-based digital artifacts of different sizes of computer documents. It evaluates the amount of document-based digital artifacts that are recovered from the RAM of a confiscated machine. Our methodology analyzes various memory dumps and learns about the digital artifacts in relation to the contents of a document that might be related to a criminal investigation. Two different usage scenarios are assumed: The first assumes that the RAM dump is created while the document is being opened and viewed whereas the second one assumes the RAM dump is created shortly after the document is being closed. Experiments show that the recovered contents are significantly affected by the used document size; the amount of recovered volatile artifacts of a used document is impacted by the original document size. Results show that the ratio of the recovered contents is very close for various document sizes during the same usage scenario. Additionally, closing the document will reduce the amount of recovered content, but still allow for a significant ratio to be considered as evidence of the actual use of the document on the confiscated machine.
Improving Customer Experience on Instagram: Online Food Shop Ordering

Instagram is one of the most used social media applications, and creating a business profile for an online business allows the owner to reach a wider audience and advertise their shop without spending money. Due to the COVID-19 pandemic, people are advised to stay at home, which leads to ordering all necessities online, especially food. This research improved Instagram's current search engine system using the Define, Measure, Analyze, Improve, and Control Methodology. To apply the proposed system, “Filled” acts as a 3rd party application that has the essential features used in searching for food to order online, wherein the accounts from Instagram of online food shops are linked to sending the orders to shop owners directly.
E-Commerce Engineering Navigating Web Development Based Voice for Local

Our Research in Rural areas of India shows that Different Communities running Different Small Scalable Enterprises and businesses need Social and Online Reach for which Technology and Financial Assistance are needed. It is observed that small businesses have low customer reach due to low financial assistance as a result they lack reach and individual business websites are also not possible in some Rural areas of India. The paper outlines the systematically designed platform for the change and development of a small Local market that aims to bring authentic products. The paper will discuss how it reduces the monetary burden on the shopkeeper's side. In the recent survey conducted on and outside campus with different age groups, we found that most users would like to choose online platforms like V-vocal. This paper concludes the V- vocal platform and algorithm contribute to small business and their growth. Social Media Influencers' impact has been discussed briefly.
A Two-phase Approach to Determine User-Preference and Feature Importance in Pricing of Cryptocurrencies using Twitter Data

Twitter is one of the best places to learn how people feel about current affairs related to the forecast of cryptocurrencies. There have been unavailability of any robust models for determining the user-preference of the major cryptocurrencies in the market on the basis of the tweets made by the major investors. Morever, the statistical dependency and the feature importance of the various contributing features in determining the prices of these cryptocurrencies has been a major concern too. This study proposes a novel two-phase robust hybrid approach for determining both user preference and feature importance of various contributing features on pricing of top cryptocurrencies in the market. It determines the user-preference on the basis of the subjectivity and polarity score obtained from the sentiment polarity classification of the tweets. It also determines both statistical dependency and feature importance of the various contributing features involved in pricing of the top cryptocurrencies in the market with the help of SHapley Additive exPlanations (SHAP) score. We have used two types of datasets for our detailed study. Additionally, knowledge graphs have been used to describe the capacity to recognise semantic data. With accuracy, precision, recall, F-1 score, and AUC-ROC values of 92%, 88%, 85%, 95%, and 94%, respectively our proposed approach outperformed conventional machine learning techniques.
The Blockchain Technology's Adoption by Research and Manuscript Publication in Higher Education : Conceptual Model

The adoption of blockchain technology has penetrated almost all sectors, including the education sector, especially higher education. Regardless of the pros and cons of adopting blockchain technology, the application of blockchain has the potential to provide benefits to higher education. Some previous research describes the adoption of blockchain technology in higher education to maintain the authenticity of diplomas, build symbiotic relationships with industry, and others; this research proposes the adoption of blockchain technology for research and publication in higher education. The high level of research and publication plagiarism is one of the main problems that initiated this research. Thus, the aim of this research is to help higher education improve research and publication supervision using blockchain technology. The literature review method is used to support this research. The proposed model is built from the conventional mechanism for distributing research results and publications in higher education and then combined with the basic principles of blockchain technology. Research also uses literature reviews to study articles about previous research and publications to strengthen the novelty of models developed because of this research. The results of this research are in qualitative form, namely a conceptual model regarding blockchain adoption in higher education publications. At the end of the results and discussion, a table is presented with examples of data using blockchain for the publication of manuscripts.
Multi-Criteria Decision-Making Methods in Healthcare Using Blockchain

A powerful decision making is provided by Multi Criteria Decision Making (MCDM) in areas where choosing the optimal choice is difficult. This paper provides a comprehensive overview of the most prominent schools of thought about multi criterion decision making, both theoretically and practically. The primary goal is to catalog the available applications and methods,then recommend those that are the most reliable and efficient in finding the optimal solution. Decisions based on multiple criteria have found widespread use. When there are multiple criteria to consider, the optimal option can be selected utilizing a multi- criteria decision-making (MCDM) process, which takes into account each criterion's weight and range of applicability. In this review, we look at how MCDM has evolved and the several ways ithas been put to use.
Scalable and Lightweight Distributed Local Routing for Quantum Network-Based Microgrids

Quantum networks present a potent solution to secure microgrid communication in the quantum era. Efficient quantum network routing protocols play an important role in developing practical and resilient quantum network-based microgrids. In this paper, we make the following contributions: 1) establish lightweight-yet-holistic routing tables for scalable and low-latency quantum routing in microgrids; 2) devise efficient distributed local routing protocols for quantum network-enabled microgrids; and 3) integrate the devised protocols in a real-time QNetGrid testbed and evaluate their performance under different scenarios. Experiments validate the efficacy of the devised methods and provide insights for developing efficient quantum network routing for cyber-resilient microgrids,
Super-Resolution in Sparse Representation Fusion for Medical Images

The application of super-resolution techniques enhances the quality of low-resolution images. The present study investigates the application of the super-resolution technique in the context of sparse fusion systems. This research integrates low-resolution functional anatomy positron emission tomography (PET) images with high-resolution structural anatomy magnetic resonance (MR) images in order to enhance the overall resolution. The fused image exhibits enhanced resolution and encompasses a greater amount of both structural and functional anatomical details. The simulations demonstrate that our proposed methods yield significant improvements. The mutual information feature increases from 0.85 to 0.92, the objective image fusion performance increases from 0.60 to 0.62, the edge strength for edge preservation also increases from 0.87 to 0.95, the mean squared error (MSE) decreases from 2.74E-04 to 1.85E-05, and the Pearson absolute correlation coefficient also decreases from 5.3E-03 to 2.6E-03.