Reviewerly/exHarmony huggingface dataset:
Pear Deck: bridging the dilemma of Education and Online Learning in the COVID-19 Pandemic

Teachers are required to think creatively and innovatively in making learning innovations in schools that can be done with interactive and contextual learning concepts. One of these learning concepts to overcome learning loss is Pear Deck. This research aimed to determine the effectiveness of the application of pear deck learning media to overcome learning loss and know the perspective of learners and teachers to apply this learning media innovation. This is descriptive research with 40 students and ten lecturers as respondents. Data collection techniques used were observations, interviews, and questionnaires. The study revealed that the role of teachers is very important in providing quality education to the nation's students, as well as facilitating the use of Pear Deck learning media because it helps students undergo interactive classroom lectures and maintain enthusiasm on academic topics in an interesting and up to date learning way. The researcher found several educational dilemmas in the pandemic period, including students and parents of gaptek (stuttering technology) students, internet access, difficulty understanding the material, feeling lazy and difficulty concentrating due to monotonous learning. However, the said obstacles can be overcome by creating an online learning system media, communication with the student's guardian, creating a summary of learning, providing tasks that encourage students to be actively bridged using Pear Deck learning media. Based on the research results, it can be concluded that the application of Pear Deck media with interactive contextual concepts is declared effective and able to provide solutions to learning loss faced by learners and increase the learning motivation of learners.
An Interoperability Cross-Block Chain Framework for Secure Transactions in IoT

The purpose of this research is to deal with effective block chain framework for secure transactions. The rate of effective data transactions and the interoperability of the ledger are the two major obstacles involved in Blockchain and to tackle this issue, Cross-Chain based Transaction (CCT) is introduced. Traditional industries have been restructured by the introduction of Internet of Things (IoT) to become smart industries through the feature of data-driven decision-making. Still, there are a few limitations, like decentralization, security vulnerabilities, poor interoperability, as well as privacy concerns in IoTs. To overcome this limitation, Blockchain has been employed to assure a safer transaction process, especially in asset exchanges. In recent decades, scalable local ledgers implement Blockchains, simultaneously sustaining peer validations of transactions which can be at local or global levels. From the single Hyperledger-based blockchains system, the CCT takes the transaction amid various chains. In addition, the most significant factor for this registration processing strategy is the Signature to ensure security. The application of the Quantum cryptographic algorithm amplifies the proposed Hyperledger-based blockchains, to strengthen the safety of the process. The key has been determined by restricting the number of transactions that reach the global Blockchain using the quantum-based hash function and accomplished by scalable local ledgers, and peer validations of transactions at local and global levels without any issues. The rate of transaction processing for entire peers has enhanced with the ancillary aid of the proposed solution, as it includes the procedure of load distribution. Without any boosted enhancement, the recommended solution utilizes the current transaction strategy, and also, it’s aimed at scalability, resource conservation, and interoperability. The experimental results of the system have been evaluated using the metrics like block weight, ledger memory, the usage of the central processing unit, and the communication overhead.
A Lightweight Approach (BL-DAC) to Secure Storage Sharing in Cloud-IoT Environments

The growing advent of the Internet of Things (IoT) users is driving the adoption of cloud computing technologies. The integration of IoT in the cloud enables storage and computational capabilities for IoT users. However, security has been one of the main concerns of cloud-integrated IoT. Existing work attempts to address the security concerns of cloud-integrated IoT through authentication, access control, and blockchain-based methods. However, existing frameworks are somewhat limited by scalability, privacy, and centralized structures. To mitigate the existing problems, we propose a blockchain-based distributed access control method for secure storage in the IoT cloud (BL-DAC). Initially, the BL-DAC performs decentralized authentication using the Quantum Neural Network Cryptography (QNNC) algorithm. IoT users and edge nodes are authenticated in the blockchain deployed by distributed Trusted Authorities (TAs) using multiple credentials. The user data is classified into sensitive and non-sensitive categories using the Enhanced Seagull Optimization (ESO) algorithm. Also, the authentication to access this data is performed by a decentralized access control method using smart contract policy. Sensitive user data is encrypted using the QNNC algorithm and stored in the private cloud. In contrast, non-sensitive data is stored in the public cloud, and IPFS is used to store data in a decentralized manner with high reliability. In addition, data security is improved by using a hierarchical blockchain which improves scalability by managing the multiple blockchains hierarchically and is lightweight using Proof of Authentication Consensus (PoAH). The BL-DAC is simulated and validated using the Network Simulator-3.26 simulation tool and validated. This work shows better results than the compared ones in terms of validation metrics such as throughput (26%), encryption time (19%), decryption time (16%), response time (15%), block validation time (31%), attack detection rate (16%), access control precision (13%), and scalability (28%).
An Improved Fully Automated Breast Cancer Detection and Classification System

More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier on different datasets. This method specifically uses ResNet50 and AlexNet, convolutional neural networks (CNNs), for deep learning and a K-Nearest-Neighbor (KNN) algorithm to classify data. Various experiments have been conducted on five datasets: the Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD), King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD), Breast Histopathology Images (BHI), and Breast Cancer Histopathological Image Classification (BreakHis). These datasets were used to train, validate, and test the presented method. The obtained results achieved an average of 99.38% accuracy, surpassing other models. Essential performance quantities, including precision, recall, specificity, and F-score, reached 99.71%, 99.46%, 98.08%, and 99.67%, respectively. These outcomes indicate that the presented method offers essential aid to pathologists diagnosing breast cancer. This study suggests using the implemented algorithm to support physicians in analyzing breast cancer correctly.
ECGAN: Translate Real World to Cartoon Style Using Enhanced Cartoon Generative Adversarial Network

Visual illustration transformation from real-world to cartoon images is one of the famous and challenging tasks in computer vision. Image-to-image translation from real-world to cartoon domains poses issues such as a lack of paired training samples, lack of good image translation, low feature extraction from the previous domain images, and lack of high-quality image translation from the traditional generator algorithms. To solve the above-mentioned issues, paired independent model, high-quality dataset, Bayesian-based feature extractor, and an improved generator must be proposed. In this study, we propose a high-quality dataset to reduce the effect of paired training samples on the model’s performance. We use a Bayesian Very Deep Convolutional Network (VGG)-based feature extractor to improve the performance of the standard feature extractor because Bayesian inference regularizes weights well. The generator from the Cartoon Generative Adversarial Network (GAN) is modified by introducing a depthwise convolution layer and channel attention mechanism to improve the performance of the original generator. We have used the Fréchet inception distance (FID) score and user preference score to evaluate the performance of the model. The FID scores obtained for the generated cartoon and real-world images are 107 and 76 for the TCC style, and 137 and 57 for the Hayao style, respectively. User preference score is also calculated to evaluate the quality of generated images and our proposed model acquired a high preference score compared to other models. We achieved stunning results in producing high-quality cartoon images, demonstrating the proposed model’s effectiveness in transferring style between authentic images and cartoon images.
Peran Perpustakaan Daerah Kabupaten Karo Dalam Pemanfaatan Program Literasi Inklusi Sosial

The existence of social inclusion-based regional libraries is a regional library that helps develop community potential by improving the quality of library services in order to foster a successful and enlightened society. Regional library services must now be more proactive in inviting the public to use libraries, so that famous libraries are more than just lending and returning books. As a result, library services can be transformed by social inclusion. The purpose of this paper is to optimize libraries in order to provide individuals with the means to improve their quality of life. This study used qualitative methodology for data collection, including interviews, observations, and documentation in the regional library of Karo Regency. After collecting the data, it is evaluated by reducing the data, presenting the data, and coming up with conclusions. The findings of this study show that the Karo regional library has been able to transform its library services through the use of literacy programs aimed at encouraging social inclusion. It seems that the Karo regional library service has been transformed into a library with flexible services based on people's information needs, as evidenced by the implementation of a number of social inclusion services.
The Impact of Transcendentalism on Henry David Thoreau’s Perspectives on Consumerist Principles

The principal objective of this investigation was to explore the impact of the philosophy of transcendentalism on Henry David Thoreau’s perspectives on consumerist principles as described in a Life without Principle. In this study, the impact of transcendentalism was examined through the lenses of industrialization, consumerism, and capitalism. The transcendentalist elements of individualism, denouncing large institutions, refuge in nature, rejection of conformity, and free religious practice were analyzed to determine the impact the philosophy had on Henry David Thoreau. By analyzing industrialization through the transcendentalist lens, it became clear Thoreau rejected environmental degradation and strict religious rules. The negative impacts of the materialistic mindset, historically heightened by the Gold Rush, and social conformity became evident through the study of consumerism. Additionally, instrumental reasoning and political atomism espoused in the economic ideology of capitalism were rejected by Thoreau. This study provides evidence that transcendentalist elements led Thoreau to value nature, reject large societal institutions and collectivization, and conclude that consumerism promoted meaningless purchasing cycles.
Confident federated learning to tackle label flipped data poisoning attacks

Federated Learning (FL) enables collaborative model building among a large number of participants without revealing the sensitive data to the central server. However, because of its distributed nature, FL has limited control over the local data and corresponding training process. Therefore, it is susceptible to data poisoning attacks where malicious workers use malicious training data to train the model. Furthermore, attackers on the worker side can easily manipulate local data by swapping the labels of training instances to initiate data poisoning attacks. And local workers under such attacks carry incorrect information to the server, poison the global model, and cause misclassifications. So, detecting and preventing poisonous training samples from local training is crucial in federated training. To address it, we propose a federated learning framework, namely Confident Federated Learning to prevent data poisoning attacks on local workers. Here, we first validate the label quality of training samples by characterizing and identifying label errors in the training data and then exclude the detected mislabeled samples from the local training. To this aim, we experiment with our proposed approach on MNIST, Fashion-MNIST, and CIFAR-10 dataset and experimental results validated the robustness of the proposed framework against the data poisoning attacks by successfully detecting the mislabeled samples with above 85% accuracy.
A Critical Review on Increasing Student’s Motivation in Science Subject Using Augmented Reality

A critical review of literature was conducted to examine the student’s motivation level in science subject using Augmented Reality (AR). Augmented Reality technology is used widely because it allows enhancing the understanding of complex concept at most educational levels. The two main primary approaches used to review of literature are the topical and critical review approaches. This article mainly discusses about the critical review that was carried out using 5 steps method. The methods are (1) exhibiting a research objective and research question, (2) evaluating sample of sources in the literature observed, (3) analyzing the data that was collected, (4) interpreting the findings from critical analysis using descriptive statistics and finally, (5) discussing effects and implications from these results that were identified. The author collected total number of 45 articles from index journal and 14 articles are chosen after sorting. The critical review made the author to determine 4 main themes which were constructed into a conceptual framework based on continuity. This critical review gives benefit to learners to improve the level of motivation in learning using AR. Overall, the result shows Augmented Reality in teacher education is considered very valuable and should be implemented in pedagogical problem-solving skills, such as mathematics, physics, chemistry and biology in future studies.
Cyberspace exercises: defending against malicious cyber actors

Malicious cyber actors are well-prepared and trained to negatively impact organizations, assets, or individuals. Training exercises help to mitigate these adverse effects by enabling cyber defense analysts to demonstrate their abilities to respond to cyber incidents in real-time. When a security incident occurs, the response team must quickly contain the situation, diagnose the problem, and return to a previous good state so the system down time is minimized, and the issue does not spread to other systems. Training exercises allow both red (attack) and blue (defense) teams to practice in a secure, isolated environment ensuring all team members have the necessary experience if and when incidents occur. This paper describes simulated cybersecurity research scenarios based on open-source data repositories of adversary tactics and techniques including published frameworks by MITRE and NIST. Artificial Intelligence/Machine Learning (AI/ML) techniques and methods supporting cybersecurity research will also be discussed with an emphasis towards cloud environment frameworks. Results from using AL/ML techniques, cloud-based tools and methods, cyber tabletop exercises for cybersecurity research scenarios and use cases will be included.
Issue 112: A Note from the Editor-in-Chief

High-resolution (HR) medical images can provide rich details, which are important for discovering subtle lesions to make diagnoses. Convolutional neural networks (CNNs) are widely used in this field, but struggle to model long-range dependencies. Although transformer-based methods have improved in this respect, this method requires large quantities of data. Unfortunately, large quantities of low-resolution (LR) and HR medical image pairs may not always be available. In addition, most medical image superresolution (SR) methods are deterministic, while the degradation in real scenarios is stochastic. To address these problems, we introduce a probabilistic degradation model that combines natural and medical images for training. This design alleviates the problem of insufficient medical image pairs and learns the degradation process of the natural scene. In addition, we propose a new medical image SR model that consists of CNNs and the Swin Transformer structure to excavate both local and global semantic features. Moreover, to reduce computational stress, the spherical locality-sensitive hashing (SLSH) module is employed in the nonlocal attention (NLA) mechanism to form the ENLA module. This design enables the proposed Sparse Swin Transformer (SSFormer) model to generate HR medical images without extensive training images. Experiments on diverse datasets (natural images and medical images) demonstrate that the proposed method is robust and effective, qualitatively and quantitatively outperforming other medical image SR methods. Code is available at https://github.com/codehxj/SSFormer.
State machine execution traces for verifying and validating robot behaviors

This work demonstrates that a novice user of the Monterey Phoenix (MP) tool can encode their expertise of robot mission tasks in the form of a finite state machine. State machine diagrams are among several approaches available for describing behavior. They are appropriate for capturing and understanding behavior rules of systems that can be described in terms of possible states and state transitions. Tools that offer state machine automation enable modelers to test the activation of states and trace the transitions between states, providing assistance with verification and validation of the behavior logic. The United States Military Academy (USMA) at West Point’s Robotics Research Center (RRC) in collaboration with the Naval Postgraduate School (NPS), developed a minimal executable state machine model to test its implementation of behavior logic for a single robot agent in Project Aquaticus, a human-robot teaming capture the flag competition. Monterey Phoenix, a formal behavior modeling language, approach, and tool was used to express the behavior logic of the robots participating in this game as a finite state machine model. MP was used to generate every possible trace through the state machine behavior logic up to the specified scope limit. This model informed the team’s understanding of the desired states for the single robot competitor in Aquaticus, assisted in identifying behavior improvements, and identifying dependencies among behaviors and where the process could possibly go wrong. In essence, this work demonstrates that encoding the finite state machine of the single robot tasks in MP allows experts to verify and validate expected robot behaviors and adjust as needed. Future work will look to expand from the single agent modeling to multi-agent modeling in MP to generate and verify possible sequences of events for the Aquaticus competition between competing teams. By using this modeling method to understand the state of each actor and their response to inputs from other actors, the team expects to improve the communication within the human-robot team and ultimately achieve a better understanding of command intent in complex, contested environments. Today’s mission-critical systems are actually, “systems of systems” with complexity that will surpass the designer’s cognitive ability to anticipate all interactions. Without tools, languages, and methodologies to analyze system-level behavior early in the design, mission systems can be at risk of emergent behaviors that may negatively impact safety and security. Our team believes that the MP environment can assist commanders in anticipating potentially unsafe or insecure emergent behaviors of human-robot teams in complex, contested environments and adjust human intent/command intent accordingly for allowable combat crew drill behaviors required to achieve assigned tasks and missions.
Graph-based autonomy behavior classification

Graph neural networks (GNNs) are becoming increasingly popular for multi-agent autonomy applications. For mission planning, when numerous autonomous agents are involved, even simple behaviors can overwhelm the human commander. This is because the number of possible interactions between agents and possible outcomes for coordinating effective teaming grows exponentially. Artificial intelligence can be used to learn effective agent behaviors to create what we define as a Virtual Commander (VC). The VC can be used to learn friendly and/or adversary behaviors in a complex mission planning scenario. A VC may be comprised of multiple machine learning components, such as behavior classification and behavior prediction. Based on classifying a particular behavior mode at any instant in time, future behaviors can be learned and therefore predicted to aid a commander in effective mission planning. In this work we compare 2 methods of behavior mode classification for a virtual commander using graphs. The first method uses an image-based digit (IBD) classifier for mode classification, while the second method uses adjacency matrix-based (AMB) classifier to perform mode classification. Though both classifiers predict the VC mode classification correctly &lt;90% of the time, we show that the AMB performs better than IBD, while also reducing the data generation complexity for training and testing by multiple orders of magnitude.
Hierarchical Bayesian neural network

The Hierarchical Bayesian Neural Network (HNN) is a machine learning algorithm that attempts to use the natural hierarchical structure of data. HNN has demonstrated gains in robustness, accuracy, and reporting capabilities by addressing the technical challenge of classifying data at different levels of a hierarchical structure. There is a significant operational benefit in classifying at different levels of an ontology where the extracted knowledge is used for future decision-making, especially when classification at the finest level is unfeasible.
The formulation of the sequential sliding innovation filter and its application to complex road maneuvering

This study presents the development of a new filter, the sequential sliding innovation filter (SSIF), designed for estimating quantities of interest from noisy measurements. The SIF is formulated in a sequential manner, allowing for multiple updates of estimates, making it well-suited for systems with multiple measured states. The filter is applied to an unmanned ground vehicle (UGV) maneuvering in 2-D path in this study, and the results demonstrate that the SSIF outperforms conventional filter and Kalman Filter (KF) in terms of accuracy and efficiency. The SSIF has the potential for use in signal processing, tracking, and surveillance, making it a valuable tool in various fields.