Reviewerly/exHarmony huggingface dataset:
From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images

Accurate object detection is important in computer vision. However, detecting small objects in low-resolution images remains a challenging and elusive problem, primarily because these objects are constructed of less visual information and cannot be easily distinguished from similar background regions. To resolve this problem, we propose a Hierarchical Small Object Detection Network in low-resolution remote sensing images, named HSOD-Net. We develop a point-to-region detection paradigm by first performing a key-point prediction to obtain position hypotheses, then only later super-resolving the image and detecting the objects around those candidate positions. By postponing the object prediction to after increasing its resolution, the obtained key-points are more stable than their traditional counterparts based on early object detection with less visual information. This hierarchical approach, HSOD-Net, saves significant run-time, which makes it more suitable for practical applications such as search and rescue, and drone navigation. In comparison with the state-of-art models, HSOD-Net achieves remarkable precision in detecting small objects in low-resolution remote sensing images.
A Wiener-type attack on an RSA-like cryptosystem constructed from cubic Pell equations

This paper investigates a novel RSA-like cryptosystem proposed by Murru-Saettone. This cryptosystem is constructed from a cubic field connected to the cubic Pell equation and Redei rational functions. The scheme is claimed to be secure against the Wiener-type attack. However, in this paper, we show a Wiener-type attack that can recover the secret key from the continued fraction constructed from public information.
People of Determination (Disabilities) Recruitment Model Based on Blockchain and Smart Contract Technology

Blockchain technology is an innovative technology that has grown in prominence in recent years that will certainly regulate the development of our network society in the upcoming future.Blockchain technology has received increased care and interest from both academic and general practitioners across the world.Various research articles have been written on the approach, how blockchain technology works and its possible applications in different industries, governmental authorities, etc.Nevertheless, there are no conducted studies that have focused on the usage of blockchain technology in the recruitment process of people of determination (disabilities).This paper aims to establish a POD (People of Determination) platform model.The aim of the model is to support the recruitment process of people of determination (disabilities) by enhancing the chances of them who were hired in different types of United Arab Emirates organizations.To the best of our knowledge, no previous research has been conducted on the usage of blockchain technology in recruitment process of people of determination (disabilities).This research paper will therefore aim to contribute to the existing literature about blockchain technology and recruitment process by providing a proposed model on how to implement the process.
A Two-Dimensional Explanation Framework to Classify AI as Incomprehensible, Interpretable, or Understandable

Because of recent and rapid developments in Artificial Intelligence (AI), humans and AI-systems increasingly work together in human-agent teams. However, in order to effectively leverage the capabilities of both, AI-systems need to be understandable to their human teammates. The branch of eXplainable AI (XAI) aspires to make AI-systems more understandable to humans, potentially improving human-agent teamwork. Unfortunately, XAI literature suffers from a lack of agreement regarding the definitions of and relations between the four key XAI-concepts: transparency, interpretability, explainability, and understandability. Inspired by both XAI and social sciences literature, we present a two-dimensional framework that defines and relates these concepts in a concise and coherent way, yielding a classification of three types of AI-systems: incomprehensible, interpretable, and understandable. We also discuss how the established relationships can be used to guide future research into XAI, and how the framework could be used during the development of AI-systems as part of human-AI teams.
A combination of Super-resolution and Deep Learning Approaches applied to Image Forgery Detection

There are a number of techniques used to create forgery images and with the case of copy-move or splicing techniques normally compatible resolutions of original and faked images are assumed in the detection. This would make the process of forgery images becoming monotony and not much challenges. In this paper, we propose an appropriate method to detect tampered images with the change in resolutions of the splicing areas that have been inserted within the images then a combination method of the superresolution approach and the deep learning technique would make an efficient method for image forgery detection. Specifically, we implement the CNN model namely VGG16 trained by the VGG network. We use the VGG16 model with 16 classes. With the test model mentioned above, the results were given with an accuracy of up to 94.64%.
Child Face Age Progression and Regression using Self-Attention Multi-Scale Patch GAN

Face age progression and regression have accumulated significant dynamic research enthusiasm because of its gigantic effect on a wide scope of handy applications including finding lost/wanted persons, cross-age face recognition, amusement, and cosmetic studies. The two primary necessities of face age progression and regression, are identity preservation and aging exactitude. The existing state-of-the-art frameworks mostly focus on adult or long-span aging. In this work, we propose a child face age-progress and regress framework that generates photo-realistic face images with preserved identity.To facilitate child age synthesis, we apply a multi-scale patch discriminator learning strategy for training conditional generative adversarial nets (cGAN) which in-creases the stability of the discriminator, thereby making the learning task progressively more difficult for the generator. Moreover, we also introduce Self-Attention Block (SAB) to learn global and long-term dependencies within an internal representation of a child’s face. Thus, we present coarse-to-fine Self-Attention Multi-Scale Patch generative adversarial nets (SAMSP-GAN) model. Our new objective function, as well as multi-scale patch discrimination and, has shown both qualitative and quantitative improvements over the state-of-the-art approaches in terms of face verification, rank-1 identification, and age estimation on benchmarked children datasets.
Pushing open the door to reality: On facilitating the transitions from virtual to real environments

The recent rise of virtual reality technology has led researchers to investigate how to adapt transitions to virtual environments. Transitions play a key role in facilitating the return to reality, which is of particular importance when the virtual world is far more agreeable than the real world. In the present study, the efficacy of a door transition − an almost “transparent” door falling out the top of the virtual environment and controlled by the user − was evaluated and compared to two basic transitions: a direct transition and a fading transition. Participants reported a strong preference for the door transition that was evaluated as being smoother, more controllable, and greatly facilitated the return to reality. Moreover, the results showed that the door transition triggered no greater sickness in participants than the two other types of transition.
Joint state and dynamics estimation with high-gain observers and Gaussian process models

With the rising complexity of dynamical systems generating ever more data, learning dynamics models appears as a promising alternative to physics-based modeling. However, the data available from physical platforms may be noisy and not cover all state variables. Hence, it is necessary to jointly perform state and dynamics estimation. In this paper, we propose interconnecting a high-gain observer and a dynamics learning framework, specifically a Gaussian process state-space model. The observer provides state estimates, which serve as the data for training the dynamics model. The updated model, in turn, is used to improve the observer. Joint convergence of the observer and the dynamics model is proved for high enough gain, up to the measurement and process perturbations. Simultaneous dynamics learning and state estimation are demonstrated on simulations of a mass-spring-mass system.
Extended B-polynomial neural network for time-delayed system modeling using sampled data

The combination of machine learning and artificial intelligent has already proved its potential in achieving remarkable results for modeling unknown systems. These techniques commonly use enough data samples to train and optimize their architectures. In the present era, with the availability of enough storage and computation power, the machine learning based data-driven system modeling approaches are getting popular as they do not interrupt the normal system operations and work solely on collected data. This work proposes a data-driven parametric neural network technique for modeling time-delayed systems, which is demanding but challenging area of research and comes under nonlinear optimization problem. The key contribution of this work is the inclusion of an extended B-polynomial into the network structure for estimating time-delayed first and second order system models. These type of models extensively used for addressing simulations, predictions, controlling and monitoring related issues. Also, an adaptive learning based convergence of the proposed algorithm is proved with the help of the Lyapunov stability theory. The proposed algorithm compared with existing techniques on some well-known example problems. A real practical system plant is also included for validating the proposed concept.
Distributed Density Filtering for Large-Scale Systems Using Mean-Filed Models

This work studies distributed (probability) density estimation of large-scale systems. Such problems are motivated by many density-based distributed control tasks in which the real-time density of the swarm is used as feedback information, such as sensor deployment and city traffic scheduling. This work is built upon our previous work [1] which presented a (centralized) density filter to estimate the dynamic density of large-scale systems through a novel integration of mean-field models, kernel density estimation (KDE), and infinite-dimensional Kalman filters. In this work, we further study how to decentralize the density filter such that each agent can estimate the global density only based on its local observation and communication with neighbors. This is achieved by noting that the global observation constructed by KDE is an average of the local kernels. Hence, dynamic average consensus algorithms are used for each agent to track the global observation in a distributed way. We present a distributed density filter which requires very little information exchange, and study its stability and optimality using the notion of input-to-state stability. Simulation results suggest that the distributed filter is able to converge to the centralized filter and remain close to it.
Weakly-supervised contrastive learning in path manifold for Monte Carlo image reconstruction

Image-space auxiliary features such as surface normal have significantly contributed to the recent success of Monte Carlo (MC) reconstruction networks. However, path-space features, another essential piece of light propagation, have not yet been sufficiently explored. Due to the curse of dimensionality, information flow between a regression loss and high-dimensional path-space features is sparse, leading to difficult training and inefficient usage of path-space features in a typical reconstruction framework. This paper introduces a contrastive manifold learning framework to utilize path-space features effectively. The proposed framework employs weakly-supervised learning that converts reference pixel colors to dense pseudo labels for light paths. A convolutional path-embedding network then induces a low-dimensional manifold of paths by iteratively clustering intra-class embeddings, while discriminating inter-class embeddings using gradient descent. The proposed framework facilitates path-space exploration of reconstruction networks by extracting low-dimensional yet meaningful embeddings within the features. We apply our framework to the recent image- and sample-space models and demonstrate considerable improvements, especially on the sample space. The source code is available at https://github.com/Mephisto405/WCMC.
Model-Based Rate-Distortion Optimized Video-Based Point Cloud Compression with Differential Evolution

The Moving Picture Experts Group (MPEG) video-based point cloud compression (V-PCC) standard encodes a dynamic point cloud by first converting it into one geometry video and one color video and then using a video coder to compress the two video sequences. We first propose analytical models for the distortion and bitrate of the V-PCC reference software, where the models’ variables are the quantization step sizes used in the encoding of the geometry and color videos. Unlike previous work, our analytical models are functions of the quantization step sizes of all frames in a group of frames. Then, we use our models and an implementation of the differential evolution algorithm to efficiently minimize the distortion subject to a constraint on the bitrate. Experimental results on six dynamic point clouds show that, compared to the state-of-the-art, our method achieves an encoding with a smaller error to the target bitrate (4.65% vs. 11.94% on average) and a slightly lower rate-distortion performance (on average, the increase in Bjøntegaard delta (BD) distortion is 0.27, and the increase in BD rate is 8.40%).
Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation

Unsupervised Domain adaptation is an effective method in addressing the domain shift issue when transferring knowledge from an existing richly labeled domain to a new domain. Existing manifold-based methods either are based on traditional models or largely rely on Grassmannian manifold via minimizing differences of single covariance matrices of two domains. In addition, existing pseudo-labeling algorithms inadequately consider the quality of pseudo labels in aligning the conditional distribution between two domains. In this work, a deep spherical manifold Gaussian kernel (DSGK) framework is proposed to map the source and target subspaces into a spherical manifold and reduce the discrepancy between them by embedding both extracted features and a Gaussian kernel. To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss. Extensive experimental results show that DSGK outperforms state-of-the-art methods, especially on challenging cross-domain learning tasks.
A Summary of F-Transform Techniques in Data Analysis

Fuzzy transform is a technique applied to approximate a function of one or more variables applied by researchers in various image and data analysis. In this work we present a summary of a fuzzy transform method proposed in recent years in different data mining disciplines, such as the detection of relationships between features and the extraction of association rules, time series analysis, data classification. After having given the definition of the concept of Fuzzy Transform in one or more dimensions in which the constraint of sufficient data density with respect to fuzzy partitions is also explored, the data analysis approaches recently proposed in the literature based on the use of the Fuzzy Transform are analyzed. In particular, the strategies adopted in these approaches for managing the constraint of sufficient data density and the performance results obtained, compared with those measured by adopting other methods in the literature, are explored. The last section is dedicated to final considerations and future scenarios for using the Fuzzy Transform for the analysis of massive and high-dimensional data.
A Symmetrically Diminished Interconnected Database Segmentation Framework Using Data Mining

Abstract Clustering is a series of mathematical learning methods for the exploration of heterogeneous partition structures grouping homogeneous data known as clusters. Clustering has successfully been implemented in many areas, such as medicine, genetics, economics, industry, and so on. We propose the notion of clustering for problems of multifactorial data processing in this article. The aim of a case study is to examine trends in 813 individuals for an issue in occupational medicine. To minimise the dimensionality of the data set, we use the key component analysis as the most widely used statistical method in factor analysis. The natural problems, particularly in the field of medicine, are mostly focused on performance criteria of a stratified kind, while PCA processes only quantitative. In comparison, consistency data are typically binary-coded, initially unnoticeable, quantitative replies. We are therefore introducing a new approach that enables theoretical and practical data to be analysed simultaneously. The idea of this approach is to project important variables on the quantitative feature space. The corresponding Clustering algorithm subspaces are then given an ideal model.
Analyzing Cyber Crimes during COVID-19 Time in Indonesia

COVID-19 spreads fast and uncontrollably, causing panic and anxiety of the uncertain times worldwide. Remote working becomes a favorable option to keep activities running as usual. Cybercriminals use these conditions to commit cybercrime with either a specific target or not. The study is conducted by identifying cybercrime in Indonesia that utilizes COVID-19 issues with the help of timeline mapping of key events and cyberattacks to analyze targeted sectors and their cybersecurity. Most attacks are made by malware with the general target that can cause damage on a broader scope. Individual awareness of cybersecurity and support from an authority figure is needed to obtain a secure environment for remote working.
Distribution-aware Adaptive Multi-bit Quantization

In this paper, we explore the compression of deep neural networks by quantizing the weights and activations into multi-bit binary networks (MBNs). A distribution-aware multi-bit quantization (DMBQ) method that incorporates the distribution prior into the optimization of quantization is proposed. Instead of solving the optimization in each iteration, DMBQ search the optimal quantization scheme over the distribution space beforehand, and select the quantization scheme during training using a fast lookup table based strategy. Based upon DMBQ, we further propose loss-guided bit-width allocation (LBA) to adaptively quantize and even prune the neural network. The first-order Taylor expansion is applied to build a metric for evaluating the loss sensitivity of the quantization of each channel, and automatically adjust the bit-width of weights and activations channel-wisely. We extend our method to image classification tasks and experimental results show that our method not only outperforms state-of-the-art quantized networks in terms of accuracy but also is more efficient in terms of training time compared with state-of-the-art MBNs, even for the extremely low bit width (below 1-bit) quantization cases.
A study of the performance of embedding methods for Arabic short-text sentiment analysis using deep learning approaches

Sentiment analysis aims to classify a text according to sentimental polarities of people's opinions, such as positive, negative, or neutral. While most of the studies focus on eliciting features from English text, the research on Arabic is limited due to the morphological and grammatical complexity of Arabic language. In this paper, we proposed an optimized sentiment classification for dialectal Arabic short text at the document level using deep learning (DL). The contributions of this paper are in three areas. First, we extracted semantic features for Arabic short text at the word level and character level. Second, we used three DL topologies for classification models: a long short-term memory recurrent neural network (LSTM); a convolutional neural network (CNN); and an ensemble model combining both models' advantages to improve the prediction performance. Third, we used a hyperparameter tuning estimation method to optimize the neural network performance. We trained and tested our proposed models on a dataset that consists of Modern Standard Arabic and dialectal Arabic corpus collected from Twitter. The results showed significant improvement in Arabic text classification in term of classification accuracy that ranges between 88% and 69.7%. The ensemble model scored the highest accuracy of 96.7% on the test set.
Stealthy Logic Misuse for Power Analysis Attacks in Multi-Tenant FPGAs

FPGAs have been used in the cloud since several years, for workloads such as machine learning, database processes and security tasks. As for other cloud services, a highly desired feature is virtualization in which multiple tenants share a single FPGA to increase utilization and by that efficiency. By solely using standard FPGA logic in the untrusted tenant, on-chip logic sensors have recently been proposed, allowing remote power analysis side-channel and covert channel attacks on the victim tenant. However, such sensors are implemented by unusual circuit constructions, such as ring oscillators or delay lines, which might be easily detected by bitstream and/or netlist checking. In this paper we show that such structural checking methods are not universal solutions as the attacks can make use of “benign-looking” circuits. We demonstrate this by showing a successful Correlation Power Analysis attack on the Advanced Encryption Standard.
Towards Multi-Domain Face Synthesis Via Domain-Invariant Representations and Multi-Level Feature Parts

Cross-domain face synthesis plays a positive role in the real world. It is challenging to synthesize high-quality faces across multiple domains based on limited paired data because the multiple mappings between different domains may interfere with each other. Cognitive science investigates that the brain can recognize the same person with multiple different expressions by extracting invariant information on the face and we humans perceive instances by decomposing them into parts. Motivated by these cognition, we propose a unified semi-supervised framework for multi-domain face synthesis by extracting a domain-invariant representation and exploiting parts of multi-level features. Specifically, realized by adversarial training with additional ability to utilize domain-specific information, a encoder is trained to remove domain-specific information and extract the domain-invariant representation from multiple inputs. Then, we utilize the multi-level feature parts extracted from inputs and reconstructed faces via a pre-trained recognition model to ensure that the domain-invariant representation contains enough useful semantic information. we also utilize the feature parts extracted from inputs and limited paired data to compose pseudo features in target domain for supervising the synthesis, which makes our framework suitable for large amounts of unpaired training data. By exploiting this framework, we can achieve face synthesis between multiple domains using some paired data together with a large training database without ground truth target faces. Experimental results demonstrate our framework achieves great performances on qualitative and quantitative evaluations under both artificial and uncontrolled environments, and our framework has competitive performances in single translation compared with specialized methods for translation between two specific domains.