Reviewerly/exHarmony huggingface dataset:
Blockchain Security in Edge Applications with Novel Load Balancing Approach

The rapid growth of edge computing and the increasing adoption of block-chain technology have led to the emergence of block-chain-based applications at the edge. However, ensuring security in such edge applications poses unique challenges due to the distributed nature of edge devices and the inherent vulnerabilities of block-chain networks. This research addresses these challenges by proposing a novel load-balancing (NLB) approach for enhancing blockchain security in edge applications. Firstly, the study investigates the security vulnerabilities of blockchain networks deployed at the edge. It analyzes the potential threats such as Sybil attacks, double-spending attacks, and collusion attacks that can exploit the distributed nature of edge devices. The research also explores the limitations of existing load-balancing techniques in ensuring security in edge applications. To mitigate these vulnerabilities, a novel load-balancing approach is proposed. The system leverages machine learning algorithms and block-chain consensus mechanisms to distribute computational tasks across edge devices securely. By considering the trustworthiness and capabilities of individual edge devices, the load-balancing algorithm optimizes the allocation of functions, minimizing the risk of malicious attacks. Furthermore, the proposed approach incorporates a reputation system that evaluates the behavior and reliability of edge devices based on their past performance. The effectiveness of the proposed load-balancing approach is evaluated through extensive simulations and comparative analysis with existing load-balancing techniques. The results demonstrate that the novel approach significantly improves the security and performance of block-chain in edge applications, effectively mitigating the impact of various attacks and ensuring reliable and secure data processing at the edge.
Why Fuzzy Control Is Often More Robust (and Smoother): A Theoretical Explanation

In many practical situations, practitioners use easier-to-compute fuzzy control to approximate the more-difficult-to-compute optimal control. As expected, for many characteristics, this approximate control is slightly worse than the optimal control it approximates. However, with respect to robustness or smoothness, the approximating fuzzy control is often better than the original one. In this paper, we provide a theoretical explanation for this somewhat mysterious empirical phenomenon.
Neuron-level Transfer Learning for ANN-based QoT Estimation in Optical Networks

Quality of transmission (QoT) estimation of lightpaths is essential for their deployment in optical networks. Machine learning (ML) is a promising solution for QoT estimation, which needs a large set of training data to ensure the accuracy of ML models. However, due to the shortage of monitorable lightpaths and/or the absence of optical performance monitors, the acquisition of such a number of training data is unavailable. To solve this issue, we propose a neuron-level transfer learning (NL-TL) scheme for ANN-based QoT estimation, where a neuron importance-based transfer model search algorithm is proposed to determine the trainable neurons. Simulation results show that the NL-TL-based QoT estimation scheme improves estimation accuracy compared with conventional layer-level transfer learning-based QoT estimation schemes.
Q-Learning Based Network Access Policy Management for Zero Trust Security in IIoT

Network protection is a major concern with current technology development. Zero trust security is an emerging trend irrespective of the domain and application area. Zero trust security is a model that ensures authentic access to the network. In recent years, zero trust security has attained notable significance in research and real-world practices to accomplish the security desires of the technologies. To overcome the risk and consequences of cyber-attacks, the application of zero trust security is a novel approach. Various past pieces of research were made targeting on the application and advantages of zero trust security but are not aware of the major challenge in the efficient implementation of the technology. The time consumption of each node authentication and verification hinders the seamless working of zero trust. Integrating artificial intelligence with zero trust is an underlying approach to providing a solution to the time-delay issue. The paper focuses on implementing zero-trust security with artificial intelligence for reducing network access time. The artificial intelligence-enabled zero trust model reported that the network access time is reduced by 18% as compared to CRH-SRP and 38% as compared to Cyber Security via Determinisms
Quantum Gateway Security with Fault Tolerance

The quantum communications is a far distant theoretical concept that has been taken up in many developing nations, to practically actualize it in daily activities. This has its own caveat which cannot be left unhindered. Networking with optical communication and controlling entangled-photons are areas that needs to be bridged with the assistance of quantum mechanics and physics of information theory. A quantum network test bed with existing quantum devices in real life network environment is being studied at NIST since a decade. This work highlights a part of the network gateway, that can assist in communicating over long channels and geographies, considering the loss of information in terms of qubits on the go. This work presents a concept to enhance secure exchange of information, in a faulty channel, and attempts to make the communication resilient to loss of qubits, following existing architecture.
Blockchain-Enabled Pharmacy Supply Chain Management: Ensuring Transparency and Efficiency

The demand for medicines in households is growing exponentially. There are some medicines which are not easily available in every pharmacy and people must travel a long time in search of such medicine. These medicines can also be counterfeit drugs as the origin of the medicines cannot be identified by any means. These hardships can be avoided with the pharmacy supply management website. The pharmacies can be in contact with each other, and the availability of medicines can be stored by each pharmacy on the blockchain database. Users can access and analyze the available data to identify the necessary medicines. The user can also avail of door delivery for the medicine through the website to avoid traveling, this will be very much useful for the elderly who are unable to travel to get their medicines or for remote ordering where some users can order medicine from one city to another. The medicines purchased by the customer can be traced back to its origin through the blockchain technology. Individual pharmacies can sign up or log in to enter their data on the medicine they have in stock and this is all stored by using smart contracts. The users can log in to search for the medicine they are looking for and select the pharmacy. After selecting the quantity of medicine required the user can opt for pick-up from the pharmacy or door delivery where they will have to provide the address of the delivery location along with an added delivery fee set by the pharmacy based on the distance. The payment can be done by using blockchain wallets.
Tracing Food Products in Supply Chain Using DLT Enabled Blockchain Technology

The most emerging new technology of trust is the concept of blockchain technology, which is a combined ecosystem of entwined dependencies of agricultural supply chain and agricultural produce. A sufficiently high transparency is the most essential requirement where agricultural produce and supply chain are to be implemented in a combined manner. Thus, blockchain is emerging as the most reliable and secure way to ensure consumer benefits. The ledger of records, usually maintained in the form of blocks, are encapsulated and maintained in a network. The newer records are stored in the next block and each of the existing block is connected to the newer block so that the sequence of blocks in maintained. Each block leads to the next block in the chain and the distributed nature of the network ensures transparency and integrity of each block. Any alteration in any block by any agent can be easily detected and the integrity can be maintained. Cryptography is another major concept that plays a pivot role in ensuring security and integrity of the blocks formed. Various types of online transactions, documentations, or records can be managed using blockchain technology. In particular, agriculture is a sector which fulfils the demand supply chain of food and the demand for better quality food is having the highest demand.
Variance of ML-based software fault predictors: are we really improving fault prediction?

Assuring high software quality becomes increasingly difficult as software systems become more and more complex and continuously grow in size. Moreover, testing becomes even more expensive when dealing with large-scale systems. Thus, to effectively allocate quality assurance resources, researchers have proposed fault prediction (FP) which utilizes machine learning (ML) to predict fault-prone code areas. However, ML algorithms typically make use of stochastic elements to increase the prediction models’ generalizability and efficiency of the training process. These stochastic elements, also known as nondeterminism-introducing (NI) factors, lead to variance in the training process and as a result, lead to variance in prediction accuracy and training time. This variance poses a challenge for reproducing research results. More importantly, while fault prediction models may have shown good performance in the lab (e.g., often-times involving multiple runs and averaging outcomes), high variance of results can pose the risk that these models show low performance when applied in practice. In this work, we experimentally analyze the variance of a state-of-the-art fault prediction approach. Our experimental results indicate that NI factors can indeed cause considerable variance in the fault prediction models’ accuracy. We observed a maximum variance of 10.10% in terms of the per-class accuracy metric. We thus, also discuss how to deal with such variance.
FPGA Implementation of Systolic Array for 2D Convolution Using Residue Number System

This paper presents an FPGA implementation of a systolic architecture for 2D convolution using the residue number system (RNS) with a designed new residue adder and residue multiplier. The proposed architecture offers improved processing speed by leveraging the benefits of RNS. Additionally, the use of a systolic array enhances parallelism, further boosting the overall performance of the convolution operation. Experimental results demonstrate the effectiveness of the approach, highlighting its potential for accelerated convolutional neural networks and real-time signal processing applications.
Two-Dimensional Panoramic Image Stitching using PCA-SIFT Approach

Image stitching is a prominent field within computer vision and graphics, widely integrated into tools like cell phones and image processing APIs. This paper delves into image stitching using Python and OpenCV, aiming to merge images with shared regions into panoramic scenes. The technique merges photographs from different cameras to create seamless panoramas. The approach relies on invariant features, specifically the PCA-SIFT algorithm, which uses Scale Invariant Feature Transform (SIFT) for detecting and describing local image features. SIFT identifies significant points, extracting position, scale, and rotation invariants for feature descriptors. This method benefits both general image stitching and stitching based on local features. The Python-implemented PCA-SIFT outperforms standard SIFT in key point matching accuracy, leading to better retrieval results. Additionally, PCA-SIFT demonstrates faster matching performance. Here, the research objective is to enhance image stitching quality by using the PCA-SIFT algorithm, addressing the limitations of standard SIFT. The projected achievements include extracting key points using the SIFT algorithm, matching them with Euclidean distance, and comparing stitching quality between SIFT and PCA-SIFT.
A Comparative Analysis of Risk Factors for Appropriate Screening of Cervical Cancer

Cervical cancer is one of the leading causes of cancer as well as death from cancer in women worldwide. Effective screening programs especially in low-income countries are the need of the hour. Using Machine Learning methods, we can analyze the risk factors associated with cervical cancer. In this report, we present a comparative analysis of 4 Machine Learning Algorithms, Support Vector Machine(SVM), Random Forest Classifier(RFC), K Nearest Neighbour(KNN), and XG-Boost(XGB) in classifying various cervical cancer screening techniques, namely, Hinselmann, Schiller, Cytology and Biopsy based on the associated risk factors. Instead of using classification accuracy which is less relevant in imbalanced medical datasets, we evaluate the models based on ROC-AUC scores and Precision-Recall AUC scores. Finally, we extract the relevant features by calculating feature importance using permutation importance. The results obtained are consistent with the medical science research.
Modelling the Recovery of Malaria Patients in West Lombok District Using Cox Regression

Malaria is one of the health problems in West Lombok Regency. There are 413 positive malaria cases, so it is necessary to research the models and factors affecting malaria sufferers' recovery. The analysis used is survival analysis using the Cox Proportional Hazard Regression method. The data used in this study is in the form of secondary data obtained from medical record data from all patients with malaria disease in West Lombok Regency from 2019 to 2020, with dependent variables in the form of recovery time of malaria patients and nine independent variables that are suspected of affecting the recovery of malaria sufferers. This study aims to determine a recovery model for malaria sufferers based on Cox regression and determine the factors that influence the recovery of malaria sufferers in West Lombok Regency. Based on the survival analysis results with the Cox Proportional hazard Regression method, the best model was obtained with two significant variables affecting the recovery time of malaria patients: the parasite type variable and the incidence of pregnancy or not getting pregnant. The model can be interpreted based on hazard ratio values that the variable type of parasite category Plasmodium vivax has a probability of being able to recover within one month of treatment by 2,542 times faster than Plasmodium falciparum. In comparison, the type of parasite in the Plasmodium mix category has a probability of being able to recover within one month of treatment 1.108 times faster than Plasmodium vivax, and for the pregnant or non-pregnant variables for the category of pregnant patients had a 2,307 times faster probability of recovery within one month of treatment compared to non-pregnant patients.
Implementation of Graphical User Interface using Virtual Assistants based on LLMs

This paper describes the process of implementing a graphical user interface (GUI) using two currently popular virtual assistants: ChatGPT 3.5 (OpenAI) and Bard (Google). The implementation process was divided into three phases: in the first phase, we defined the features of our GUI and selected Python Tkinter as the framework for building the application; in the second phase the user interface was implemented, and in the third phase the generated code was refactored and evaluated using the Flake8 linter. We have shown that virtual assistants can be efficient and convenient in the process of implementing a user interface, but they occasionally make unpredictable errors, so it is necessary to critically assess the software code they generate. ChatGPT proved to be easier to use because it less frequently produces unpredictable errors, but linting analysis showed that Bard generates code that adheres better to the PEP8 standard.
Nonlinear Distortion Mitigation via Coherent All-Optical Reservoir Computing for Long-Haul IM-DD transmission Systems

Photonic reservoir computing (RC), as a promising paradigm of optical signal processing, has attracted a great deal of attention in fiber nonlinearity equalization recently. For real long-haul transmission scenarios, we propose an all-optical RC operated in a coherent scheme to mitigate the nonlinear distortion of high-speed signals. Compared with the direct decision architecture, the proposed RC can dramatically improve the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{Q}^{2}$</tex> factor by 2.6 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{dB}$</tex> for a single-wavelength system and 1.9 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{dB}$</tex> for a 7-channel wavelength-division multiplexing (WDM) system over 3960-km fiber transmission. Furthermore, the robustness of the coherent all-optical RC is also verified by conducting studies on different training sequences. The coherent all-optical RC shows great potential in real-time nonlinearity mitigation for long-haul fiber transmissions.
Non-Volatile Photonic Synapse with Ultra-Low Insertion Loss for Deep Neural Network

Neuromorphic computing underlies many computational tasks, from signal processing to classification, artificial intelligence, and deep learning applications. Compared with the traditional Von Neumann architecture, the non-volatile photonic neural network adopts the in-memory computing architecture to improve the computing speed and reduce the computing energy consumption. The non-volatile photonic neural network consists of photonic synapses with linear responses and nonlinear activation functions. However, existing non-volatile photonic neural synapses with large insertion loss (IL) and small output intensity modulation range hamper the construction of the deep neural network (DNN) with multiple hidden layers for high-accuracy complex signal recognition. Here, we show a non-volatile synapse with excellent performance to construct the non-volatile deep neural network (NVDNN). The obtained results show that the IL and output intensity modulation range of our photonic synapse is 0.01 dB and 30.03 dB with a 54 nm 3 dB bandwidth. For the complex picture classification task, the NVDNN based on our photonic synapse has a higher accuracy compared with the single-layer non-volatile photonic neural network. Our results demonstrate how to design non-volatile synapses and construct complex optical neural networks, and reveal their advantages in information processing capabilities.
Autonomously Adaptive Machine Learning Systems: Experimentation-Driven Open-Source Pipeline

Machine Learning Operations (MLOps), derived from DevOps, aims to unify the development, deployment, and maintenance of machine learning (ML) models. Continuous training (CT) automatically retrains ML models, and continuous deployment (CD) automatically deploys the retrained models to production. CT and CD are essential for maintaining ML model performance in dynamic production environments and, therefore, need to be considered when practicing MLOps. We present our CTCD-e MLOps pipeline, implemented mostly using existing open-source software, being able to autonomously adapt an ML system to changing production environments by enabling flexible model CT and CD. The pipeline can automatically trigger a model retraining round when the model performance degrades. Then it automatically conducts an A/B test for the retrained model and its predecessor in production to start serving the better one. The pipeline was evaluated by two experiments. In the pipeline, users can flexibly configure the model retraining, as well as the redeployment and production A/B test of the retrained models based on various requirements.
Underwater Diver Gesture Recognition

In recent years, usage and applications of Autonomous Underwater Vehicles has grown rapidly. Interaction of divers with the AUVs remains an integral part of the usage of AUVs for various applications and makes building robust and efficient underwater gesture recognition systems extremely important. In this paper we propose an Underwater Gesture Recognition system trained on the Cognitive Autonomous Diving Buddy Underwater gesture dataset using deep learning that achieves 98.01% accuracy on the dataset, which to the best of our knowledge is the best performance achieved on this dataset at the time of writing this paper. We also improve the Gesture Recognition's System Interpretability by using XAI techniques to visualize model's predictions.
Meningkatkan Hasil Belajar Ilmu Pengetahuan Sosial melalui Penerapan Keterampilan Proses pada Siswa Sekolah Dasar

This study aims to improve social studies learning outcomes by applying a process skills approach to fifth grade students at SD Negeri 30 Sumpang Bita. This study uses classroom action research (Classroom Action Research) consisting of 2 cycles. Each cycle has four stages, which consist of: planning, action, observation and reflection. Observation sheets and learning outcomes tests are used as research instruments. Observations, learning outcomes tests and documentation used in data collection techniques. Quantitative and qualitative analysis techniques were used in data analysis. The results showed that the first cycle got an average score of 64.7, the second cycle scored higher student learning outcomes, namely the average value reached 86.7. Only 7 students (41%) achieved completeness in cycle I. In contrast to cycle II, there were 16 (96%) students who achieved completeness. This means that classical learning mastery is achieved in cycle II, because the number of students is more than 80%. Thus it was concluded that the application of the process skills approach to social science subjects could improve student learning outcomes.
Multi-Agent Pathfinding with Obstacle Movement for Realistic Virtual Tactical Simulations on Topographic Terrains

Multi-Agent Pathfinding (MAPF) algorithms represent a powerful tool for modeling realistic tactical movements of troops in military simulation systems. Solving MAPF problems while dealing with topographic terrains involves computing the most cost-effective and safe relief routes for agents with movement constraints. To minimize the overall topographic cost of agents' movement and the need to deviate from other stationary agents, this work considers a MAPF approach that respects real-world agents' movement characteristics, such as the agents' orientation, the limits of the agents' turning angles, and the relief inclinations they can safely navigate. To solve conflicts between agents while navigating uneven terrains, the proposed approach explores the attribution of agents' movement priorities related to the need to execute given missions. Most importantly, other agents without planned movement at the current mission situation, as they are stationary on safe and low-cost routes according to the terrain relief, are minimally displaced to nearby locations to give passage to the mission-critical agents. The MAPF algorithm is evaluated on a comprehensive set of test scenarios, with results analyzed using Generalized Linear Regression models. This analysis provides valuable insights into the MAPF algorithm's effectiveness in virtually modeling organized agent movement behaviors for developing simulation-based training and instruction activities.