Reviewerly/exHarmony huggingface dataset:
Corn Leaf Disease Classification Using Local Binary Patterns (LBP) Feature Extraction

Abstract Corn is a plant that is widely grown in developing countries such as Indonesia. To increase maize yields, researchers are always innovating on the current state of technology for classifying maize plant diseases. Three kinds of diseases attack corn leaves, namely Gray leaf Spot, Blight, and Common Rush. The amount of data that we use is 3500 data consisting of 500 Gray Leaf Spots, 1000 Blights, 1000 Common Rushes, and 1000 healthy leaves. This study aims to develop an artificial intelligence model. The artificial intelligence model that we developed uses LBP feature extraction combined with k-NN for the classifier. In addition to using the k-NN method, our tests were carried out using several classification methods such as Naïve Bayes and Adaboost. The result of our test is that the k-NN method has the highest value compared to the Naïve Bayes and Adaboost methods. The results of the performance using k-NN with k=5 resulted in a value of 81.1%, the AUC value of 94.1%, the F1-Score of 80.9%, Precision of 81.8%, and Recall of 81.1%.
HARMONIA: strategy of an integrated resilience assessment platform (IRAP) with available tools and geospatial services.

Abstract The huge amount of the available data nowadays has raised some major challenges which are related to the storage, fusion, structure, streaming and processing of these data. In this paper, we present the development of a holistic framework, entitled HARMONIA, that encompasses State-of-The-Art solutions for the emerging issues related to Climate Change, natural and/or man-made hazards and urban/peri-urban risks. The Horizon 2020 HARMONIA project is developing an Integrated Resilience Assessment Platform (IRAP) which plans to provide targeted services for different groups of end-users. In particular, it will actively support urban decision-makers in strategic decisions and planning and citizens in facing daily effects and risks of Climate Change. Additionally, the platform will be a place to interconnect cities which end up facing similar Climate Change effects. HARMONIA IRAP leverages cuttingedge technologies (i.e., explainable Artificial Intelligence, Data Mining, multi-criteria analysis, dynamic programming) and services (ie., Virtual Machines, Containers) in order to provide solutions considering the complexity and diversity of extreme earth and non-earth data. In addition, this platform includes a Decision Support System providing early-warning feedback and recommendations to the end-users. In this way the HARMONIA IRAP design tends to address these challenges by offering the corresponding dynamic, scalable and robust mechanisms with the aim to provide useful integrated tools for the related users. Datacubes architecture, which is a major part of the IRAP, offers the opportunity to investigate more sophisticated correlations among the data and provide a more tangible representation of the extracted information.
Complexity Analysis of Encoding in CKKS-Fully Homomorphic Encryption Algorithm

In this paper, we analyze the complexity of encoding in CKKS algorithm. The encoding process in CKKS requires some steps such as Inversion of PI $\pi^{-1}$, Scaling, Discretization, and Inverse of Sigma $\sigma^{-1}$, and Rounding. However, the decoding requires Sigma function to convert the encoded message back to the plaintext. In this paper, we evaluate each function and determine the complexities. Experimental results show that the complexity of the encoding is ${O}(n^{3})$ and the complexity of the decoding is ${O}(n^{2})$. The work is useful for optimization and speeding up the CKKS computation on custom computing platforms such as GPUs and FPGAs.
Human-Autonomy Teaming-Based Robot Informative Path Planning and Mapping Algorithms with Tree Search Mechanism

Human-autonomy teaming (HAT)-based informative path planning (IPP) approaches are proposed to enable an autonomous robot to explore hazardous environments more efficiently in this paper. The proposed informative path planning approaches based on multi-objective optimization enable the robot to explore the environments, while also visiting several high-interest sites more frequently. Furthermore, the robot can be tailored to human needs; for instance, it reaches the human-set destination point or the direction indicated. One proposed HAT-based IPP method utilizes Rapid-exploration Random Tree (RRT*) to select the most informative tree nodes while traveling to human-set target points within global path planning. The other utilizes the direction set by humans, converts it into direction information. Thus, HAT is integrated in the information map for multi-target Pareto Monte Carlo Tree Search (MCTS) in local path planning. Simulation and comparison studies validate the effectiveness and robustness of the proposed approaches. The results demonstrate that our proposed approaches significantly improve the object search time and trajectory length.
Transparent voting system using blockchain

The aim of this paper is to create a decentralized transparent voting and analysis system that can be implemented with blockchain to provide an efficient and highly secure justifiable method of election systems in countries where traditional physical voting with gameable securities is used, increasing the chances of rigged elections. This system is designed to focus on a secure voting system, lower costs, faster wait times, no disparities due to various erroneous proxies, high scalability, and geographic independence. Overall, an effective election mechanism to strengthen the democratic process. The proposed dApp allows voters to vote from the comfort of their own homes, saving time and reducing the number of false votes registered.
Unsupervised medical image feature learning by using de-melting reduction auto-encoder

Unsupervised feature learning is a fundamental and highly prioritized problem in medical image analysis. Although it has shown considerable improvements, it remains challenging because of its weak feature expression ability, low model-learning efficiency, and weak robustness. To address these limitations, a novel unsupervised feature learning method in the medical image classification task, named de-melting reduction auto-encoder (DMRAE), is proposed in this study. A joint fusion network structure is constructed; it not only improves the expression of target features but also reduces the loss of feature decoding and parameters. To obtain a robust solution, a newly designed decomposed-reconstructed loss function is used to strengthen the semantic context between adjacent feature extractor layers, successfully avoiding the insufficient model-learning ability from the single optimization objective and improving the quality of the extracted features. Finally, extensive experiments on datasets consisting of 400 breast ultrasonographic images and 6000 lung computed tomography images are conducted to demonstrate the effectiveness of the proposed method. Experimental results reveal that the DMRAE significantly reduces the annotation effort and outperforms existing methods by a significant margin.
Coarse-to-fine feature representation based on deformable partition attention for melanoma identification

In the histopathological melanoma image diagnosis system, manual identification of super-scale slides with dense cells is tedious, time-consuming, and subjective. To deal with this problem, we propose an automatic identification network based on the deformable partition attention to identify lots of dense slides as an assistant. A coarse-to-fine strategy is adopted in feature representation and qualitative identification to improve the identification accuracy of melanomas and nevi. First of all, because it is difficult to extract features in the lesion area with blurred boundaries and uneven distribution, we develop a deformable partition attention module, which integrates the advantage of the attention mechanism and deformable convolution. The module overcomes the limitation of rectangular convolution and gradually refines the channel and spatial features, which enriches feature representation by combining global and local features. Secondly, to address the problem of difficult convergence and poor recognition rate caused by the excessive non-aligned distance between benign-malignant and benign subcategories, we propose a progressive architecture via a coarse sub-network closely followed by a fine sub-network. Moreover, to further increase the inter-class differences and reduce the intra-class disparities, we propose a joint loss function to mine hard samples, which effectively improves the identification performance. Experimental results on the clinical dataset show that the proposed algorithm has higher sensitivity and specificity and outperforms state-of-the-art deep neural networks.
ExTrA: Explaining architectural design tradeoff spaces via dimensionality reduction

In software design, guaranteeing the correctness of run-time system behavior while achieving an acceptable balance among multiple quality attributes remains a challenging problem. Moreover, providing guarantees about the satisfaction of those requirements when systems are subject to uncertain environments is even more challenging. While recent developments in architectural analysis techniques can assist architects in exploring the satisfaction of quantitative guarantees across the design space, existing approaches are still limited because they do not explicitly link design decisions to satisfaction of quality requirements. Furthermore, the amount of information they yield can be overwhelming to a human designer, making it difficult to see the forest for the trees. In this paper we present ExTrA (Explaining Tradeoffs of software Architecture design spaces), an approach to analyzing architectural design spaces that addresses these limitations and provides a basis for explaining design tradeoffs. Our approach employs dimensionality reduction techniques employed in machine learning pipelines like Principal Component Analysis (PCA) and Decision Tree Learning (DTL) to enable architects to understand how design decisions contribute to the satisfaction of extra-functional properties across the design space. Our results show feasibility of the approach in two case studies and evidence that combining complementary techniques like PCA and DTL is a viable approach to facilitate comprehension of tradeoffs in poorly-understood design spaces.
Research on logistics service recommendation model and application under mobile cloud environment

Today's personal computer applications are mainly cloud computing services, but it is usually inevitable to apply cloud computing to mobile cloud environments. Computing in the mobile cloud environment has the following advantages: more resources, free access to the "cloud" through any device anytime and anywhere, and provides powerful functions for computer storage and computing, while meeting the needs of more convenient software services. It has become the focus of attention in the last two years. Logistics service quality is an important indicator to measure service quality and reflects customer satisfaction with service. This is very important for service users and logistics providers. However, at present, there is a lack of unified judgment standards for the measurement of logistics service quality, and different scoring systems have different points of emphasis, so users can't get effective recommendation when choosing products. Therefore, this paper applies the principle of mobile cloud environment, combines the evaluation of logistics service quality with the big data system, selects the appropriate logistics service according to the needs of users, and recommends it to users, so as to provide more powerful assistance for users to choose products and improve the efficiency of product selection.This article introduces the recommendation model design, analyzes the complete update problem of the traditional recommendation model, proposes the incremental update recommendation model design, and implements it in a distributed manner on the basis of the Hadoop cloud computing platform.
Leveraging hybrid machine learning and data fusion for accurate mapping of malaria cases using meteorological variables in western India

We propose a hybrid machine learning algorithm (i.e., P2CA−PSO−ANN) to model malaria outbreak in three districts (Barmer, Bikaner, and Jodhpur) of Rajasthan in the Western India. We have used different meteorological variables (i.e., relative humidity, temperature, and rainfall) as input features to predict malaria. We have also considered the combined impact of these variables through a linear data fusion. We then extract the uncorrelated information from the feature set by applying Probabilistic Principal Component Analysis (P2CA). We trained the fully connected feed-forward Artificial Neural Network (ANN) by optimising its hyperparameters iteratively through a bio-inspired optimisation algorithm (Particle Swarm Optimisation). We train and evaluate the performance of this algorithm using monthly meteorological variables from 2009 - 2012. This accurately predicts the malaria cases with the coefficient of correlation (R = 0.99), and Root Mean Square Error (RMSE = 1.76). Finally, we compare our model with different benchmark algorithms (Generalised Regression Neural Network (GRNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest, and Radial Basis Neural Networks (RBNN)) in terms of accuracy. We observed the performance of hybrid machine learning model relatively high. This study can be used as an early warning intelligent system to predict the malaria outbreaks solely from meteorological data.
Novel optimization-based bidimensional empirical mode decomposition

Despite its rapid advancement in the past two decades, bidimensional empirical mode decomposition (BEMD) still has several limitations in multi-scale feature description of input images. To ameliorate this issue, in this paper we present several optimization-based approaches to BEMD. First, we articulate an improved unconstrained optimization approach to BEMD (IUOA-BEMD). The essential idea is to formulate an optimization model to decompose an input image based on the Delaunay triangulation of its local maxima (minima). Second, we design a scale-guided optimization approach to BEMD (SGO-BEMD) so as to arrive at an improved modal image. SGO-BEMD uses the initial modal image (obtained from the aforementioned proposed IUOA-BEMD) as a necessary guide and can capture much clearer features at various spatial scales of the input image. In addition, an additional edge-preserving property can be obtained with the edge-aware decomposition if an edge-aware scale-guided optimization to BEMD (EASGO-BEMD) is used. The visualization and quantitative results for many artificial amplitude-modulated–frequency-modulated (AM-FM) images and real images have shown that the newly-proposed methods are very competitive with state-of-the-art BEMD methods. Moreover, we further evaluate the performance of BEMD methods according to their applications in image detail enhancement and image contrast & brightness enhancement. It may be noted that image contrast & brightness enhancement represents the first attempt to integrate BEMD with Retinex theory. Collectively, both types of enhancement validate the utility of the novel optimization-based approaches to BEMD proposed herein.
A curated dataset for hate speech detection on social media text

Social media platforms have become the most prominent medium for spreading hate speech, primarily through hateful textual content. An extensive dataset containing emoticons, emojis, hashtags, slang, and contractions is required to detect hate speech on social media based on current trends. Therefore, our dataset is curated from various sources like Kaggle, GitHub, and other websites. This dataset contains hate speech sentences in English and is confined into two classes, one representing hateful content and the other representing non-hateful content. It has 451,709 sentences in total. 371,452 of these are hate speech, and 80,250 are non-hate speech. An augmented balanced dataset with 726,120 samples is also generated to create a custom vocabulary of 145,046 words. The total number of contractions considered in the dataset is 6403. The total number of bad words usually used in hateful content is 377. The text in each sentence of the final dataset, which is utilized for training and cross-validation, is limited to 180 words. The generated contractions dataset can be used for any projects in the area of NLP for data preprocessing. The augmented dataset can help to reduce the number of out-of-vocabulary words, and the hate speech dataset can be used as a classifier to detect hate or no hate on social media platforms.
DGRN: Image super-resolution with dual gradient regression guidance

In single image super-resolution (SISR), deep neural networks learn mainly nonlinear functions to obtain promising high-resolution (HR) images. However, there are usually two undesired limitations to recovered images of existing SR methods. First, since this task is typically an ill-posed problem, recovering the structural information for the SR process is usually sharp edges but distorted. Second, since this task generally has an extremely large space for the mapping function, learning this mapping from low-resolution (LR) to HR images is typically difficult. For the most part, SR models usually suffer from the lack of structural information for the objects in images and poor performance. To address the above issues, we propose a dual gradient regression scheme in the first stage of the network to recover the structure information of objects in images and the corresponding loss function to learn the difference between structural maps. In the second stage of the network, we restore HR gradient maps by the first stage to provide additional structural information for the second stage, further constraining the structure of the SR image and reducing the space of the possible functions. Extensive experiments demonstrate our results outperform the state-of-the-art SR methods in the SSIM, and achieve better visual effects than the state-of-the-art SR methods.
AI-Blockchain Systems in Aerospace Engineering and Management: Review and Challenges

Artificial Intelligence (AI) and Blockchain technologies are increasingly used for various industrial applications. AI offers self-learning capabilities that can be used to automate certain industrial applications. On the other hand, blockchain provides decentralized system capabilities to ensure transaction security, transparency, and auditability. The aerospace industry is struggling with several challenges that the above-mentioned technologies can help resolve. This paper adopts an exploratory literature review to evaluate potential use cases for AI and Blockchain in aerospace engineering and management. This research shows that blockchain technologies can help with records management especially concerning aircraft maintenance, repair, and overhaul operations. In addition, AI provides significant capabilities in predictive fault detection, guidance systems, and optimization of flight paths. Nevertheless, most of these capabilities still require experimental validation and compliance with regulatory requirements.
Virtual and Augmented Reality Infused in AEC Ir 4.0

Industry executives in the Architecture, Engineering, and Construction (AEC) business have deliberately escalated the need for precise, unambiguous, and intuitive knowledge. Industry 4.0 is of paramount importance towards the formation of a virtual logistics system in AEC that facilitates automation in manufacturing operations and acts as a link between distinct stakeholders and construction initiatives. Immersive Virtual and Augmented Reality (VAR) technologies are rolling out in many entertainment and scientific areas as consumer-based Head-Mounted Displays (HMDs) become more ubiquitous. Furthermore, VAR innovations have great practical utility in the AEC industry due to improvements in Building Information Modeling (BIM). Although the AEC business has been reluctant to adopt VAR innovations in comparison to certain other sectors, mainly due to a paucity of reliable research contrasting the rise in benefits versus the unit price of deployment. This study aims to explore the dynamics of the industry in VAR technology adoption and addresses AEC strategic initiatives. Moreover, this paper discusses the VAR use cases in the AEC industry, along with its challenges and immersive VAR technology for BIM.
A Survey on Filter Pruning Techniques for Optimization of Deep Neural Networks

Deep Neural Networks (DNNs) have been an important and fast-developing tool used for computer vision, and artificial intelligence. Since these algorithms are widely used for image classification, they are bound to a few issues, creating a need for the DNN models to be optimized. The need for optimization is created due to computational complexity, the number of parameters and model size. Pruning techniques have been employed to mitigate this issue in DNNs, one of these techniques is Filter pruning. There are huge numbers of methods under Filter pruning that have been proposed and each one of them is based on specific sub-objectives. In this paper, we aim to represent different types of pruning methods in a summarized way and conclude on a method that is most efficient in delivering pruned model. The conclusion is stated after trying the methods in a common environment of data set and computational system.
A Comparative VGGNET and DENSENET Approaches to Recognize Malayalam Characters using Transfer Learning Techniques

Transfer Learning has become the pillar of the future when it comes to character recognition. It brought improvements in one of the south Indian languages as Malayalam. The Malayalam complex character pattern recognition from Malayalam document images is one of the recent trends in the area of recognition. Malayalam Handwritten Character Recognition, a domain under pattern recognition, has captured huge interest due to its relevance and complex type of characters. But when it comes to recognition of characters from ancient documents like manuscripts or palm leaves, the challenges get even higher. This research study focuses on the recognition of Malayalam, which is an Indian script commonly used in Southern India by using a transfer learning approach. The proposed research work aims to digitize ancient Malayalam manuscripts and other documents so that the information written in these documents can be preserved that will be used for training the system. The proposed work is a comparison recognition performance of various Deep Learning (DL) architectures in our own dataset created from various sources using the trained ancient script. In the transfer learning, the knowledge transferred from image net dataset weights to our dataset and train the system using ancient characters to recognize the new Malayalam character. The proposed models fine-tuned DENSENET and VGGNET to provide a recognition accuracy of 98.39%, 99.35% respectively for dataset containing126 classes.
Human Detection in Restricted Areas Using Deep Convolutional Neural Networks

With the help of state-of-the-art DCNNs precise detection of persons is possible in images and videos acquired from UAVs at low and medium altitudes. The current paper proposes for comparison of two DCNNs: Scaled-YOLOv4 and YOLOv7 trained on a custom dataset through transfer learning with data acquired from UAV. The aim is to take advantage of the capabilities of YOLOv7 to train a lightweight model with good accuracy that can be loaded on embedded systems present onboard UAVs capable of real-time person detection. Through transfer learning, the model achieves detection scores above 90% at altitudes of 30m using YOLOv7 architecture. Experiments were conducted to prove its ability to successfully multiple human detection frame-by-frame with over 70% confidence scores.