Reviewerly/exHarmony huggingface dataset:
C-HUIM

High-utility itemset mining is one of the highly researched area. Many research enthusiasts have discovered various techniques and algorithms to mine high-utility itemsets from transaction databases. One of the limitations of the existing high-utility itemset mining techniques is that there is no any generalized framework for applying the custom combinations of input parameters and any other constraints for mining high utility itemsets. This paper proposes a novel customizable framework to discover customized high utility itemsets (C-HUI). Users can customize the constraints and/or input parameters as per their requirements. A novel C-HUIM algorithm is used to discover customized high utility itemsets (C-HUI) from real-life datasets. The experimental results of the proposed framework and C-HUIM algorithm highlight the effectiveness of the approach.
Predicting Human Behavior Using 3D Loop ResNet

In this research, we would like to predict human behavior from video images to help humans in the future medical and nursing care fields. As a preliminary step, we will predict human actions. As a baseline, we used the 3D ResNet, which can handle both temporal and spatial features and can identify human actions with high accuracy. To improve the feature representation of conventional 3D ResNet for small action, we propose a method using loop feature extraction based on Convolutional LSTM in a residual block. We conducted experiments using the UCF101, Kinetics-400, and HMDB-51 datasets. As a result, we confirmed that our method with the introduction of the loop mechanism can obtain higher accuracy than the conventional 3D ResNet. Next, we created our own data set for predicting human behavior and used it for evaluation. We were able to achieve high accuracy, indicating that it is possible to predict behavior to some extent.
YOLO Algorithm Implementation for Real Time Object Detection and Tracking

Nowadays modern society is over flooded with humongous masses of visual data. There exist many image analysis methods to dive into this sea of visual information. The constituents of these images and videos can be analyzed and further processed to recognize the useful information. The detection, identification, and localization of different objects could prove to be of mammoth use and can play a significant part in modern devices and technologies. This paper represents a comparative study of several entity recognizing methods like YOLO, Faster R-CNN and R-CNN over different parameters such as mAP, FPS, etc. This paper also introduces an intelligent system (robot) that is capable of localizing an object and following it in real-time. The required input image is provided by the ESP32 cam module which can be mounted on the robot. Machine Learning Algorithms are used for object detection. Position coordinates received are then used to locate, track and follow the moving object. Furthermore, it is of interest as it can scale down human tasks and help mortals to be aware of minute details about certain objects.
Effective Diagnosis of Cervical Cancer Using Balanced Random Forest Classifier

Cervical cancer is one of the leading causes of cancer-related deaths in females around the globe. However, if detected at an early stage, it is one of the most successfully treatable forms of cancer. Consequently, rapid early detection can greatly impact the survival of patients. In this regard, we have developed a decision support system for the diagnosis of cervical cancer using the clinical features of patients. We utilized a dataset originally collected from the Hospital Universitario de Caracas in Venezuela. However, the data is quite imbalanced which is a frequently occurring scenario in medical datasets. Conventional classification algorithms fail to provide desirable performance if they are trained on such imbalanced data as they get biased towards the majority class. To properly handle the large class imbalance and to build an accurate diagnostic tool, we propose to incorporate the sampling strategy into the construction of the Random Forest classifier. This balanced random forest classifier can effectively reduce the bias induced by the imbalanced nature of data while also ensuring better generalizability. Our proposed approach provides a more balanced predictive performance as compared to other popular approaches in imbalanced classification like SMOTE, Random Undersampling, or RUSBoost algorithm. Thus, our suggested approach has the potential to be an effective tool for guiding clinicians in accurately diagnosing cervical cancer.
Enhanced Motion Consistency and Guided Diffusion Feature Matching for 3D Reconstruction

Feature matching is one of the key steps to restore a 3D model from an image. To effectively improve the quality of 3D reconstruction, an enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction is presented. Firstly, based on the grid-based motion statistics algorithm, an enhanced motion consistency concept is proposed by adding a threshold <italic>β</italic>, which enhances the judgment condition of true and false matching points, avoids the false matching of highly similar features, and improves the initial matching points accuracy. Then, the RANSAC algorithm is used for feature point matching optimization to filter out outliers and further improve the feature point matching accuracy. Finally, a guided diffusion concept that combines guided matching and motion consistency is proposed, which reduces the possibility of concentrated distribution in the part of the image, thereby improving the feature points matching number and the 3D model stability. Experiments on 618 pairs of images in the public 3D reconstruction datasets demonstrate that this algorithm can achieve better performance in feature matching and 3D reconstruction. For the success percentage of pose estimation less than 1° error threshold, the proposed algorithm is 22.58% and 12.90% higher than the SIFT-based ratio test algorithm and the GMS algorithm, respectively. In particular, it is 46.15% and 30.77% higher on repeated texture image pairs.
Two-Dimensional Embedded Fuzzy Data Clustering

Fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means (FKM) is a popular clustering method by assigning data points into respective clusters with uncertainty measured by the membership degree. Usually, FKM performs clustering according to the distance between data points in the original space, which might contain undesirable noises and redundant features; therefore, the underlying data semantic connections cannot be accurately captured. Moreover, the vectorized representation of the two-dimensional data such as image inevitably leads to the loss of structural information. In this paper, we propose a novel FKM method termed two-dimensional embedded fuzzy data clustering (2DEFC) which has two merits. First, 2DEFC directly takes 2D data as input without vectorizing them in order to retain more structural information of data. Second, the two subspace projection matrices are jointly optimized with the data membership degree for better collaborating with each other, which effectively avoids the sub-optimality limitation caused by the conventional mode of sequentially performing dimensionality reduction and clustering. An efficient algorithm is proposed to optimize the 2DEFC objective function. Besides, we provide comprehensive analysis on 2DEFC including its convergence behavior, computational complexity, and the fuzzy weighting exponent. Extensive comparative studies on benchmark 2D data sets demonstrate that the competitive performance of 2DEFC in 2D data clustering.
Flexible Queries over Knowledge Graphs

The increasing interest in Knowledge Graphs to represent real-world knowledge and the common need to manage imprecise knowledge in many real-world applications demand the study of approaches to solve flexible queries over Knowledge Graphs. In this paper, we propose a novel approach to solve that problem which reuses Semantic Web standards (RDF and SPARQL) and builds a fuzzy layer on top of them.
An experimental comparison between view factor and ray tracing models for energy estimation of bifacial modules

Albedo, or the ground reflectance irradiance, is one of the crucial variables in the energy estimation for bifacial photovoltaic modules due to the modules' ability to collect electricity from both front and rear sides. There are several modeling tools for the energy estimation of these modules. Our paper reports on the comparison of on two available energy estimation models with view factor and ray tracing and compares the estimated data with actual measured values. For a one-year data gathered at the Anhalt Photovoltaic Performance and Lifetime Laboratory (APOLLO) in Bernburg, Germany, the view factor model-based algorithm for a bifacial module is more accurate when predicting PV energy yield. Furthermore, an albedo sensitivity analysis concludes that the tested Ray-tracing algorithm overestimates bifacial PV energy for large albedo values. However, the tested modeling methods follow the measured data trend with a deviation between −8 % to 6 %.
An Information Geometric Perspective to Adversarial Attacks and Defenses

Deep learning models have achieved state-of-the-art accuracy in complex tasks, sometimes outperforming human-level accuracy. Yet, they suffer from vulnerabilities known as adversarial attacks, which are imperceptible input perturbations that fool the models on inputs that were originally classified correctly. The adversarial problem remains poorly understood and commonly thought to be an inherent weakness of deep learning models. We argue that understanding and alleviating the adversarial phenomenon may require us to go beyond the Euclidean view and consider the relationship between the input and output spaces as a statistical manifold with the Fisher Information as its Riemannian metric. Under this information geometric view, the optimal attack is constructed as the direction corresponding to the highest eigenvalue of the Fisher Information Matrix - called the Fisher spectral attack. We show that an orthogonal transformation of the data cleverly alters its manifold by keeping the highest eigenvalue but changing the optimal direction of attack; thus deceiving the attacker into adopting the wrong direction. We demonstrate the defensive capabilities of the proposed orthogonal scheme - against the Fisher spectral attack and the popular fast gradient sign method - on standard networks, e.g., LeNet and MobileNetV2 for benchmark data sets, MNIST and CIFAR-10.
Privacy and Data Control on Social Networks using Deep Learning

Online social networks are huge data exchange platforms that help to promote and share a lot of good information about products, news, education, tourism, health care, etc., also there is a great risk involved to individual’s privacy and security. Online posted photos can be shared, tagged, and reposted again without having any consent from the people present in the images. In this project, we are proposing a facial recognition-based system that detects every single individual and checks their online account relationship between the individual. The post or image that is reposted, tagged, or copied by known or unknown individuals will be checked. If it is an unknown individual the images convolved through a low-pass filter kernel which removes the high-frequency content like noise and edges to smooth the images that make the image blur. For this mechanism, we proposed to use deep learning based VGG19 architecture for image classification and Haar cascade viola jones algorithm for detection, and computer vision-based blurring techniques used. algorithm performance as the area under the ROC curve (AUC) accuracy of 88% obtained. This proposed model achieved 91.53% classification accuracy and further we evaluated the model using Accuracy, Precision, Recall and F1-Score metrices and achieved 70.83%, 82%, 71% and 0.68%.
Online Deep Clustering with Video Track Consistency

Several unsupervised and self-supervised approaches have been developed in recent years to learn visual features from large-scale unlabeled datasets. Their main drawback however is that these methods are hardly able to recognize visual features of the same object if it is simply rotated or the perspective of the camera changes. To overcome this limitation and at the same time exploit a useful source of supervision, we take into account video object tracks. Following the intuition that two patches in a track should have similar visual representations in a learned feature space, we adopt an unsupervised clustering-based approach and constrain such representations to be labeled as the same category since they likely belong to the same object or object part. Experimental results on two downstream tasks on different datasets demonstrate the effectiveness of our Online Deep Clustering with Video Track Consistency (ODCT) approach compared to prior work, which did not leverage temporal information. In addition we show that exploiting an unsupervised class-agnostic, yet noisy, track generator yields to better accuracy compared to relying on costly and precise track annotations.
Joint Space–Frequency for Saliency Detection in Optical Remote Sensing Images

Most of the existing saliency detection methods are affected by the complex background and weak contrast in remote sensing images (RSIs), which easily leads to confusion between salient object and background. To solve this problem, we present a general space–frequency joint saliency detection method based on spatial contrast analysis (SCA) and adaptive spectrum analysis (ASA). The proposed SCA constructs a spatial distribution function to give higher saliency to bright elements with similar appearance and compact distribution, which can avoid the interference of similar elements in the background. In addition, for the weak contrast-level salient object, an adaptive spectral energy function is proposed in ASA by combining regional dispersion and frequency spectrum characteristic, which can make full use of frequency spectrum and obtains the complete information of salient object. Finally, the SCA and ASA information are fused by a joint optimization module, which is proposed to integrate the saliency of different contrast levels objects; thus, the final saliency maps are obtained. Evaluated on the extended optical remote sensing saliency detection (EORSSD) dataset and our dataset, the proposed method is superior to other five classical methods in RSI saliency detection.
Pemodelan Debit dengan Data Curah Hujan dari Rain Gauges dan Data TRMM pada DAS Temef di Pulau Timor - NTT

Rainfall data collection based on the TRMM (Tropical Rainfall Measuring Mission) satellite provides a good alternative in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and increase rainfall accuracy for hydrological analysis. From the results of the statistical relationship analysis, the most accurate results obtained are 9 years of calibration and 1 year of validation, the selected equation is a polynomial with the following equation y = 0.0112 x2 - 0.2564x + 4,1293, so that validation analysis is carried out with the selected equation so that the validation results are obtained against 1 year rainfall data for the RMSE value of 7.022. The NSE value is 0.923, the Correlation Coefficient is 0.94 and the relative error value is 5.48 and the results of the equation for discharge are modeling return periods of discharge 2, 5, 10, 20 and 25 years for discharge observations of 2 years return period; Q = 84.44, m3/s at 5 years return period; Q = 105.95 m3/s, 10 year return period; Q = 120.86 m3/s, 20 years return period; Q = 135.16 m3/s, 25 years return period; Q = 139.69 m3/s. Meanwhile, for the simulation discharge, the return period is 2 years; Q =51,84 m3/s at 5 year return period; Q = 65,35 m3/s, 10 year return period; Q = 74,15 m3/s, 20 years return period; Q = 84,89 m3/s, return period of 25 years; Q = 94,65 m3/s.
Infrared Image Colorization Algorithm Based on DCGAN and Its Edge Device Acceleration

The image collected by the infrared night vision device is a grayscale image, but the human eye can distinguish only dozens of grayscale levels, which restricts the analysis and processing of the measurement image. Infrared image colorization can greatly improve the target tracking and recognition performance, speed and accuracy. This paper proposes an infrared coloring model based on DCGAN and the corresponding model compression algorithm. The generator of the DGCAN model adopts the Unet structure, and the discriminator adopts the PatchGAN structure to ensure better performance in the domain transfer from infrared images to color images. Hinge Loss and L1 loss are used to ensure the stability of model training, while taking into account the edge device transplantation of the model, the network is distilled during the training process, network pruning and neural network structure search and other operations to achieve model compression and deploy on edge devices superior. The experimental results of multiple datasets show that the colorization algorithm proposed in this paper can greatly improve the processing speed while maintaining the colorization performance, and realize the real-time processing of infrared image colorization.
Public-Key Watermarking Schemes for Pseudorandom Functions

A software watermarking scheme can embed a message into a program while preserving its functionality. The embedded message can be extracted later by an extraction algorithm, and no one could remove it without significantly changing the functionality of the program. A watermarking scheme is public key if neither the marking procedure nor the extraction procedure needs a watermarking secret key. Prior constructions of watermarking schemes mainly focus on watermarking pseudorandom functions (PRFs), and the major open problem in this direction is to construct a public-key watermarkable PRF. In this work, we solve the open problem via constructing public-key watermarkable PRFs with different trade-offs from various assumptions, ranging from standard lattice assumptions to the existence of indistinguishability obfuscation. To achieve the results, we first construct watermarking schemes in a weaker model, where the extraction algorithm is provided with a “hint” about the watermarked PRF key. Then we upgrade the constructions to standard watermarking schemes using a robust unobfuscatable PRF. We also provide the first construction of robust unobfuscatable PRF in this work, which is of independent interest.
Service-Aware Dynamic Sharding Approach for Scalable Blockchain

We propose a service-aware dynamic sharding approach for enhancing the scalability of blockchain platforms. The objective of our proposed approach is to decrease the service latency for user transactions and increase the transaction throughput of conventional blockchain platforms. Sharding is a well-known solution to the problem of blockchain scalability. In sharded blockchains, transactions can be processed in parallel on different shards. However, compared to intra-transactions, cross-shard transactions, which are inevitably generated in sharded blockchains, are confirmed after a relatively long time. This attribute may result in the overall degradation of performance. To mitigate the effects of cross-shard transactions on the performance of blockchain systems, our proposed approach dynamically relocates the data accessed through cross-shard transactions to the same shard. Further, two or more highly related shards are adaptively merged into a single shard, or a single shard is split into smaller disjointed shards to fully utilize shards in parallel. To verify the efficacy of our proposed approach, we implemented a sharded blockchain using the Hyperledger-Fabric platform based on the design concepts of existing sharded blockchains. The experimental results show that the proposed approach can improve the scalability of existing blockchains while maintaining the consistency of ledgers during dynamic state changes.
Blockchain-Based Secure and Efficient Federated Learning with Three-phase Consensus and Unknown Device Selection

Blockchain-based decentralized federated learning (BCFL) protects data privacy and avoids the single point of failure, which has become a key technology in the Intelligent Internet of Things application. However, the BCFL is still challenged by model attacks from malicious devices. In addition, although the BCFL has many schemes for selecting candidate devices, most of them either assume that devices’ qualities are known in advance or cannot ensure devices to report information honestly. This will lead to inefficient training in the face of unknown candidate device. To address these issues, we consider two types of model attacks and design “Proof of Model Quality” (PoMQ) to protect the security of BCFL. The PoMQ is a three-phase consensus algorithm, which combines the FedAvg and Multi-Krum algorithms to defend against model update attacks. The PoMQ is also based on the model verification process to resist model aggregation attacks. Moreover, we define the unknown device selection problem as a Combinatorial Multi-Armed Bandit problem (CMAB) to improve the training efficiency, and propose an online learning algorithm based on PoMQ (OLAC) to solve CMAB. Finally, through analysis and extensive experiments, we prove that PoMQ and OLAC can better improve the robustness and training efficiency of BCFL.
Learning Global Optimization by Deep Reinforcement Learning

Learning to Optimize (L2O) is a growing field that employs a variety of machine learning (ML) methods to learn optimization algorithms automatically from data instead of developing hand-engineered algorithms that usually require hyperparameter tuning and problem-specific design. However, there are some barriers to adopting those learned optimizers in practice. For instance, they exhibit instability during training, poor generalization to problems outside the distribution, and lack scalability. Current research efforts suggest either improving L2O models or improving training techniques to overcome such hardships. We focus on the latter and propose to train a Deep Reinforcement Learning (Deep RL) agent to learn an optimization algorithm from training in a diverse set of benchmark functions. To this end, we propose a general framework for learning to optimize by reinforcement learning, which adapts training strategies used in other L2O approaches, such as curriculum learning and input normalization. We investigate the importance of these strategies through an ablation study and show that even though Deep RL, to the best of our knowledge, is not a well-explored theme in L2O, it provides a direct framework to learn an optimizer able to deal with the exploration-exploitation dilemma and that the applied techniques improved stability and generalization.