Reviewerly/exHarmony huggingface dataset:
Towards a World Wide Web without digital inequality

The World Wide Web (WWW) empowers people in developing regions by eradicating illiteracy, supporting women, and generating economic opportunities. However, their reliance on limited bandwidth and low-end phones leaves them with a poorer browsing experience compared to privileged users across the digital divide. To evaluate the extent of this phenomenon, we sent participants to 56 cities to measure the cost of mobile data and the average page load time. We found the cost to be orders of magnitude greater, and the average page load time to be four times slower, in some locations compared to others. Analyzing how popular webpages have changed over the past years suggests that they are increasingly designed with high processing power in mind, effectively leaving the less fortunate users behind. Addressing this digital inequality through new infrastructure takes years to complete and billions of dollars to finance. A more practical solution is to make the webpages more accessible by reducing their size and optimizing their load time. To this end, we developed a solution called Lite-Web and evaluated it in the Gilgit-Baltistan province of Pakistan, demonstrating that it transforms the browsing experience of a Pakistani villager using a low-end phone to almost that of a Dubai resident using a flagship phone. A user study in two high schools in Pakistan confirms that the performance gains come at no expense to the pages' look and functionality. These findings suggest that deploying Lite-Web at scale would constitute a major step toward a WWW without digital inequality.
Membership Mappings for Practical Secure Distributed Deep Learning

In this article, we consider the problem of privacy-preserving distributed deep learning where data privacy is protected by fully homomorphic encryption. The aim is to develop a method for practical and scalable distributed deep learning with fully homomorphic encrypted data. The method must address the issue arising from the large computational cost associated with fully homomorphic encrypted data to offer a practical and scalable solution. An approach that leverages fuzzy-based membership mappings for data representation learning is considered for distributed deep learning with fully homomorphic encrypted data. The method introduces globally convergent and robust variational membership mappings to build local deep models. The local models are combined in a robust and flexible manner by means of fuzzy attributes to build a global model such that the global model can be homomorphically evaluated in an efficient manner. The membership-mappings-based privacy-preserving distributed deep learning method is accurate, practical, and scalable. This is verified through numerous experiments that include demonstrations using MNIST and Freiburg Groceries datasets, and a biomedical application related to the detection of mental stress on individuals. This study develops globally convergent and robust variational membership mappings for their application to accurate, practical, and scalable privacy-preserving distributed deep learning.
How to Find Actionable Static Analysis Warnings: A Case Study With FindBugs

Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict which static code warnings should <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> ot be ignored, we suggest that analysts need to look deeper into their algorithms to find choices that better improve the particulars of their specific problem. Specifically, we show here that effective predictors of such warnings can be created by methods that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> ocally adjust the decision boundary (between actionable warnings and others). These methods yield a new high water-mark for recognizing actionable static code warnings. For eight open-source Java projects (cassandra, jmeter, commons, lucene-solr, maven, ant, tomcat, derby) we achieve perfect test results on 4/8 datasets and, overall, a median AUC (area under the true negatives, true positives curve) of 92%.
Relation-aware Graph Convolutional Networks for Multi-relational Network Alignment

The alignment of multiple multi-relational networks, such as knowledge graphs, is vital for many AI applications. In comparison with existing GCNs which cannot fully utilize relational information of multiple types, we propose a relation-aware graph convolutional network (ERGCN), which is equipped with both entity convolution and relation convolution to learn the entity embeddings and relation embeddings simultaneously. The role discrimination and translation property of knowledge graphs are adopted in the entity convolutional process to incorporate the relation information. To facilitate the relation convolution, we construct quadruples to model the connection between a pair of relations thus to determine their neighborhood, which also enables the relation convolution to be conducted in an efficient way. Thereafter, AERGCN, the alignment framework based on ERGCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function, which aims at minimizing the distances between anchors and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the competitive baselines in terms of link prediction, entity alignment, and relation alignment.
Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition

The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform.
Multi-Robot Task Scheduling with Ant Colony Optimization in Antarctic Environments

This paper addresses the problem of multi-robot task scheduling in Antarctic environments. There are various algorithms for multi-robot task scheduling, but there is a risk in robot operation when applied in Antarctic environments. This paper proposes a practical multi-robot scheduling method using ant colony optimization in Antarctic environments. The proposed method was tested in both simulated and real Antarctic environments, and it was analyzed and compared with other existing algorithms. The improved performance of the proposed method was verified by finding more efficiently scheduled multiple paths with lower costs than the other algorithms.
Secure Medical Data Collection in the Internet of Medical Things Based on Local Differential Privacy

As big data and data mining technology advance, research on the collection and analysis of medical data on the internet of medical things (IoMT) has gained increasing attention. Medical institutions often collect users’ signs and symptoms from their devices for analysis. However, the process of data collection may pose a risk of privacy leakage without a trusted third party. To address this issue, we propose a medical data collection based on local differential privacy and Count Sketch (MDLDP). The algorithm first uses a random sampling technique to select only one symptom for perturbation by a single user. The perturbed data is then uploaded using Count Sketch. The third-party aggregates the user-submitted data to estimate the frequencies of the symptoms and the mean extent of their occurrence. This paper theoretically demonstrates that the designed algorithm satisfies local differential privacy and unbiased estimation. We also evaluated the algorithm experimentally with existing algorithms on a real medical dataset. The results show that the MDLDP algorithm has good utility for key-value type medical data collection statistics in the IoMT.
SEDG-Yolov5: A Lightweight Traffic Sign Detection Model Based on Knowledge Distillation

Most existing traffic sign detection models suffer from high computational complexity and superior performance but cannot be deployed on edge devices with limited computational capacity, which cannot meet the direct needs of autonomous vehicles for detection model performance and efficiency. To address the above concerns, this paper proposes an improved SEDG-Yolov5 traffic sign detection method based on knowledge distillation. Firstly, the Slicing Aided Hyper Inference method is used as a local offline data augmentation method for the model training. Secondly, to solve the problems of high-dimensional feature information loss and high model complexity, the inverted residual structure ESGBlock with a fused attention mechanism is proposed, and a lightweight feature extraction backbone network is constructed based on it, while we introduce the GSConv in the feature fusion layer to reduce the computational complexity of the model further. Eventually, an improved response-based objectness scaled knowledge distillation method is proposed to retrain the traffic sign detection model to compensate for the degradation of detection accuracy due to light-weighting. Extensive experiments on two challenging traffic sign datasets show that our proposed method has a good balance on detection precision and detection speed with 2.77M parametric quantities. Furthermore, the inference speed of our method achieves 370 FPS with TensorRT and 35.6 FPS with ONNX at FP16-precision, which satisfies the requirements for real-time sign detection and edge deployment.
Augmented reality android based: Education of modern and traditional instruments

Musical instruments in Indonesia are divided into two types, namely traditional musical instruments and modern musical instruments, which in this study will be discussed with more than ten musical instruments. Today, there are still many Indonesian people who do not know traditional musical instruments and modern musical instruments due to lack of knowledge and the lack of means to provide visual access to both traditional and modern musical instruments. The purpose of this research is to help people recognize traditional musical instruments and modern musical instruments digitally easily. The method used in this research is the Marker-Based AR method, because it takes a marker on each object to display a clear object in the Augmented Reality application to find information from an image directly. The results of this study are in the form of an Android-based Augmented Reality application with a page display feature, displaying information on 3D objects of traditional musical instruments and modern musical instruments with writing and sound, as well as showing how to use the application.
Blockchain Empowered and Self-sovereign Access Control System

Lack of trustworthiness, access policy flexibility, and user privacy preservation in centralized access control systems raise numerous security issues and reduce the collaboration maturity of global data sharing systems. In this paper, we propose a Self-Sovereign Identity-based, Decentralized, and Dynamic (SSIDD) access control system. SSIDD utilizes blockchain technologies to build trust for untrusted data sharing networks and ensures user privacy. Our access control provides high access policy flexibility and security for global inter-enterprise collaborations from a diverse industrial environment. SSIDD authenticates its users based on their Decentralized Identifiers (DID), which are under control of users and can be resolved into a DID document stored on the blockchain. Our data management technology keeps the data sharing systems safe against issues such as data breaches, identity thefts, and privacy violations. Besides, the authorization process of SSIDD is dynamic by adopting several smart contracts. The transparency of rules and agreements in smart contracts and the traceability of records on blockchain ledger provide a high level of security and trust. For proof of concept, we have developed and evaluated a prototype of SSIDD. Our evaluations show that the throughput and latency of our method are within an acceptable range.
The application of hyperspectral core imaging for oil and gas

Abstract Hyperspectral core imaging as a commercial technology is fairly new to the oil and gas industry, providing a rapid and non-destructive method for mapping mineralogy of drill core and cuttings. The technology makes use of infrared spectrometers that collect imagery across the visible, near infrared, short-wave infrared and thermal (long-wave) infrared range that contains information related to a variety of mineral species, and in some cases including mineral chemistry and texture. In this paper we illustrate how the mineralogical information can be used directly for a variety of applications relevant to reservoir quality, including mineral mapping, sedimentological mapping and upscaling of micro information to the well scale. Statistical analysis allows the data to be used to segment cores into different rock types, and via calibration to quantitative mineralogical techniques, the data can be used to construct continuous curves of quantitative mineralogy, total organic content and production parameters.
Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models

Bayesian networks are a powerful tool for modelling multivariate random variables. However, when applied in practice, for example, for industrial projects, problems arise because the existing learning and inference algorithms are not adapted to real data. This article discusses two learning and inference problems on mixed data in Bayesian networks—learning and inference at nodes of a Bayesian network that have non-Gaussian distributions and learning and inference for networks that require edges from continuous nodes to discrete ones. First, an approach based on the use of mixtures of Gaussian distributions is proposed to solve a problem when the joint normality assumption is not confirmed. Second, classification models are proposed to solve a problem with edges from continuous nodes to discrete nodes. Experiments have been run on both synthetic datasets and real-world data and have shown gains in modelling quality.
Bet-GAT: An Efficient Centrality-Based Graph Attention Model for Semi-Supervised Node Classification

Graph Neural Networks (GNNs) have witnessed great advancement in the field of neural networks for processing graph datasets. Graph Convolutional Networks (GCNs) have outperformed current models/algorithms in accomplishing tasks such as semi-supervised node classification, link prediction, and graph classification. GCNs perform well even with a very small training dataset. The GCN framework has evolved to Graph Attention Model (GAT), GraphSAGE, and other hybrid frameworks. In this paper, we effectively usd the network centrality approach to select nodes from the training set (instead of a traditional random selection), which is fed into GCN (and GAT) to perform semi-supervised node classification tasks. This allows us to take advantage of the best positional nodes in the network. Based on empirical analysis, we choose the betweenness centrality measure for selecting the training nodes. We also mathematically justify why our proposed technique offers better training. This novel training technique is used to analyze the performance of GCN and GAT models on five benchmark networks—Cora, Citeseer, PubMed, Wiki-CS, and Amazon Computers. In GAT implementations, we obtain improved classification accuracy compared to the other state-of-the-art GCN-based methods. Moreover, to the best of our knowledge, the results obtained for Citeseer, Wiki- CS, and Amazon Computer datasets are the best compared to all the existing node classification methods.
Image Watermarking Based Data Hiding by Discrete Wavelet Transform Quantization Model with Convolutional Generative Adversarial Architectures

Traditional watermarking methods can remove a watermark from an image, making it possible to see the copyright information about the image owner or to estimate similarities using techniques such as bit error rate and normalized correlation. Deep learning is another examination field in AI, and is utilized to develop a deep network to extract objective elements and afterwards distinguish the general environment. To assure the robustness and security of computerized image watermarking, we propose a novel algorithm using convolutional generative adversarial neural networks. This research proposed a novel technique in digital watermarking, with data hiding based on segmentation and classification, using deep learning techniques. The used input images are medical images, including Magnetic Resonance Images (MRI) and Computed Tomography (CT) images, which have been processed for noise removal, smoothening and normalization. The processed image has been watermarked using the Singular Value Decomposition-based discrete wavelet transform quantization model, being segmented and classified using convolutional generative adversarial neural networks. The experimental analysis has been carried out in terms of bit error rate, Structural Similarity Index Measure (SSIM), Normalized Cross-Correlation (NCC), training accuracy, and validation accuracy. This achieved an attained bit error rate of 71%, an SSIM of 56%, a Normalized Cross-Correlation of 71%, a training accuracy of 98%, and a validation accuracy of 95%.
Singularity, Observability and Statistical Independence in the Context of Chaotic Systems

Pseudorandom number generators (PRNGs) have always been a central research topic in data science, and chaotic dynamical systems are one of the means to obtain scientifically proven data. Chaotic dynamical systems have the property that they have a seemingly unpredictable and random behavior obtained by making use of deterministic laws. The current paper will show how several notions used in the study of chaotic systems—statistical independence, singularity, and observability—can be used together as a suite of test methods for chaotic systems with high potential of being used in the PRNG or cryptography fields. In order to address these topics, we relied on the adaptation of the observability coefficient used in previous papers of the authors, we calculated the singularity areas for the chaotic systems considered, and we evaluated the selected chaotic maps from a statistical independence point of view. By making use of the three notions above, we managed to find strong correlations between the methods proposed, thus supporting the idea that the resulting test procedure is consistent. Future research directions consist of applying the proposed test procedure to other chaotic systems in order to gather more data and formalize the approach in a test suite that can be used by the data scientist when selecting the best chaotic system for a specific use (PRNG, cryptography, etc.).
Improving Architectural Reusability for Resource Allocation Framework in Futuristic Cloud Computing Using Decision Tree Based Multi-objective Automated Approach

Cloud computing is a highly popular computing technique. Cloud combined with IoT, fog, edge, and mist computing in 5G networks gives us realtime and highly predictive responses leading to a better and smart life. It requires a highly robust and integrated cloud administration, especially cloud resource allocation. Artificial intelligence and machine learning can be easily implemented along cloud design patterns for efficient resource allocation. In this paper we discuss multi-tenant cloud resource allocation problem. We propose to use a rule-based analysis pattern to dynamically reconfigure resource allocation processes. The pattern uses various attributes of clouds, resources, subscribers and requests along with heuristic data like configurations, policies, strategies, and methods to efficiently identify and apply rule of allocation. We implemented a decision tree to assist pattern to have automated decisions, which rule to follow. The pattern caters for multi-objectivity, simplifies architecture, enables the extension of the cloud framework and makes it possible to interact easily with cloud. This paper describes the architectural framework pattern, which learns from itself. This paper presents CK’s object-oriented metrics comparisons of pattern-based object-oriented code. The comparison shows that object-oriented code improves code quality, making pattern-based code more maintainable, flexible, extendable and secure.
<scp>HPKNN</scp>: <scp>Hyper‐parameter</scp> optimized <scp>KNN</scp> classifier for classification of poikilocytosis

Abstract Red blood cells (RBCs) are the more crucial components of a healthy organism. Poikilocytosis is a disorder in which blood cells lose their standard shapes. The human bodies are affected by disorders like anemia and thalassemia because the structure of RBCs shape changes. Hematologists take longer time to detect abnormalities in RBCs. The proposed hyper‐parameter optimizable K nearest neighbor (HPKNN) algorithm detects and classifies numerous types of poikilocytosis quickly and accurately. On the Chula‐PIC‐Lab and Erythrocytes IDB datasets, the proposed HPKNN algorithm was tested. After the preprocessing stage, the Freeman chain code has been utilized to retrieve the exact boundary shape of RBC morphology. On datasets of Erythrocytes IDB 1, 2, 3, and Chula‐PIC‐Lab(Chula‐RBC‐12), the morphological tools and Freeman chain code based segmentation provide 97.44% and 94.45% accuracy, respectively, which is superior than other methods. The proposed HPKNN classifier has an overall sensitivity of 97.9%, precision of 97.9%, and an f 1 score of 97.9% on the Chula‐PIC‐Lab (Chula‐RBC‐12) dataset to classify 10 classes, namely acanthocyte, dacrocyte, echinocyte, elliptocyte, normal, overlapped, schistocyte, spherocyte, stomatocyte, and target cell, which is greater than any other current approach. The HPKNN classifier achieves 98.085% precision, 98.085% sensitivity or recall, and 98.085% f 1 ‐score values on the Erythrocytes IDB datasets to classify circular, elongated, and other RBC classes.