Reviewerly/exHarmony huggingface dataset:
Underwater Image Enhancement Based on a Spiral Generative Adversarial Framework

Underwater image enhancement has drawn much attention due to the significance of underwater vision. Although considerable progress has been made in this field, a key problem remains unsolved: how can we extract and enhance minutiae while trying to remove the noise caused by scattering and attenuation? To address this limitation, we propose a new underwater image enhancement technique with a novel spiral generative adversarial framework, named Spiral-GAN, which can effectivelly recover real-world underwater images with more details, vivid colors and better contrast. For steady training and color correction, we include the pixelwise losses that consist of a mean squared error and an angle error in our objective function. In addition, we design our generator with several deconv-conv blocks to preserve the details from the original distorted images. Furthermore, we present a spiral learning strategy for generalizing the enhancing model to effectively recover the real-world underwater images. Finally, we perform a number of qualitative and quantitative evaluations that suggest that our proposed approach can efficiently enhance the quality of underwater images, which can be further used for underwater object detection.
Radar Super Resolution Using a Deep Convolutional Neural Network

Abstract Super resolution involves synthetically increasing the resolution of gridded data beyond their native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid-scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single-image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large-scale precipitation features and the associated sub-pixel-scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6 months of reflectivity observations from the Langley Hill, Washington, radar (KLGX), and we find that it substantially outperforms common interpolation schemes for 4× and 8× resolution increases based on several objective error and perceptual quality metrics.
ARAZ: A software modules clustering method using the combination of particle swarm optimization and genetic algorithms

A considerable percentage of software costs are usually related to its maintenance. Program comprehension is a prerequisite of the software maintenance and a considerable time of maintainers is spent to comprehend the structure and behavior of the software when the source code is the only product available. Program comprehension is one of difficult and challenging task especially in the absence of design documents of the software system. Clustering of software modules is an effective reverse-engineering method for extracting the software architecture and structural model from the source code. Finding the best clustering is considered to be a multi-objective NP hard optimization-problem and different meta-heuristic algorithms have been used for solving this problem. Local optimum, insufficient quality, insufficient performance and insufficient stability are the main shortcomings of the previous methods. Attaining higher values for software clustering quality, attaining higher success rate in clustering of software modules, attaining higher stability of the obtained results and attaining the higher convergence (speed) to generate optimal clusters are the main goals of this study. In this study, a hybrid meta heuristic method (ARAZ) includes particle swarm optimization algorithm and genetic algorithm (PSO-GA) is proposed to find the best clustering of software modules. An extensive series of experiments on 10 standard benchmark programs have been conducted. Regarding the results of experiments, the proposed method outperforms the other methods in terms of clustering quality, stability, success rate and convergence speed.
Multi‐image encryption algorithm based on image hash, bit‐plane decomposition and dynamic DNA coding

Problems such as large data volumes, slow transmission rates and low security often occur when transmitting multiple digital images over the internet. In order to protect the content security of multiple images and improve the speed of internet transmission, a multi-image encryption algorithm based on image hash, bit-plane decomposition and dynamic DNA coding is proposed in this study. First, the proposed algorithm merges several grey images and image hashing algorithm is used to generate the initial key for chaotic mapping. Second, the random sequence generated by the improved three-dimensional chaotic map is used to decompose the bit plane of the merged image and the bit-plane matrix is substituted numerically. Finally, the random sequence generated by the four-dimensional hyperchaotic system is used to perform dynamic DNA coding and calculation on the image and the final multiple ciphertext images are generated by decomposition of the pixel matrix. The experimental results show that the proposed algorithm has large key space, strong key sensitivity, strong security and robustness, and can resist various conventional attacks such as statistical analysis, differential attack, exhaustive attack, cropping and noise attack.
Health Monitoring of Air Compressors Using Reconstruction-Based Deep Learning for Anomaly Detection with Increased Transparency

Anomaly detection refers to detecting data points, events, or behaviour that do not comply with expected or normal behaviour. For example, a typical problem related to anomaly detection on an industrial level is having little labelled data and a few run-to-failure examples, making it challenging to develop reliable and accurate prognostics and health management systems for fault detection and identification. Certain machine learning approaches for anomaly detection require normal data to train, which reduces the need for historical data with fault labels, where the main task is to differentiate between normal and anomalous behaviour. Several reconstruction-based deep learning approaches are explored in this work and compared towards detecting anomalies in air compressors. Anomalies in such systems are not point-anomalies, but instead, an increasing deviation from the normal condition as the system components start to degrade. In this paper, a descriptive range of the deviation based on the reconstruction-based techniques is proposed. Most anomaly detection approaches are considered black box models, predicting whether an event should be considered an anomaly or not. This paper proposes a method for increasing the transparency and explainability of reconstruction-based anomaly detection to indicate which parts of a system contribute to the deviation from expected behaviour. The results show that the proposed methods detect abnormal behaviour in air compressors accurately and reliably and indicate why it deviates. The proposed approach is capable of detecting faults without the need for historical examples of similar faults. The proposed method for explainable anomaly detection is crucial to any prognostics and health management (PHM) system due to its purpose of detecting deviations and identifying causes.
GroupRegNet: a groupwise one-shot deep learning-based 4D image registration method

Accurate deformable four-dimensional (4D) (three-dimensional in space and time) medical images registration is essential in a variety of medical applications. Deep learning-based methods have recently gained popularity in this area for the significantly lower inference time. However, they suffer from drawbacks of non-optimal accuracy and the requirement of a large amount of training data. A new method named GroupRegNet is proposed to address both limitations. The deformation fields to warp all images in the group into a common template is obtained through one-shot learning. The use of the implicit template reduces bias and accumulated error associated with the specified reference image. The one-shot learning strategy is similar to the conventional iterative optimization method but the motion model and parameters are replaced with a convolutional neural network and the weights of the network. GroupRegNet also features a simpler network design and a more straightforward registration process, which eliminates the need to break up the input image into patches. The proposed method was quantitatively evaluated on two public respiratory-binned 4D-computed tomography datasets. The results suggest that GroupRegNet outperforms the latest published deep learning-based methods and is comparable to the top conventional method pTVreg. To facilitate future research, the source code is available at https://github.com/vincentme/GroupRegNet.
Video Forensics: Identifying Colorized Images Using Deep Learning

In recent years there has been a significant increase in images and videos circulating in social networks and media, edited with different techniques, including colorization. This has a negative impact on the forensic field because it is increasingly difficult to discern what is original content and what is fake. To address this problem, we propose two models (a custom architecture and a transfer-learning-based model) based on CNNs that allows a fast recognition of the colorized images (or videos). In the experimental test, the effect of three hyperparameters on the performance of the classifier were analyzed in terms of HTER (Half Total Error Rate). The best result was found for the Adam optimizer, with a dropout of 0.25 and an input image size of 400 × 400 pixels. Additionally, the proposed models are compared with each other in terms of performance and inference times and with some state-of-the-art approaches. In terms of inference times per image, the proposed custom model is 12x faster than the transfer-learning-based model; however, in terms of precision (P), recall and F1-score, the transfer-learning-based model is better than the custom model. Both models generalize better than other models reported in the literature.
Defining quantum divergences via convex optimization

We introduce a new quantum Rényi divergence<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mi>D</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>α</mml:mi></mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">#</mml:mi></mml:mrow></mml:msubsup></mml:math>for<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi><mml:mo>∈</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi mathvariant="normal">∞</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>defined in terms of a convex optimization program. This divergence has several desirable computational and operational properties such as an efficient semidefinite programming representation for states and channels, and a chain rule property. An important property of this new divergence is that its regularization is equal to the sandwiched (also known as the minimal) quantum Rényi divergence. This allows us to prove several results. First, we use it to get a converging hierarchy of upper bounds on the regularized sandwiched<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math>-Rényi divergence between quantum channels for<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>1</mml:mn></mml:math>. Second it allows us to prove a chain rule property for the sandwiched<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math>-Rényi divergence for<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>1</mml:mn></mml:math>which we use to characterize the strong converse exponent for channel discrimination. Finally it allows us to get improved bounds on quantum channel capacities.
Entanglement detection in quantum many-body systems using entropic uncertainty relations

The authors study experimentally accessible lower bounds on entanglement measures for quantum many-body systems based on entropic uncertainty relations. Rather than requiring the full reconstruction of the quantum state, the bound derived here requires measuring joint probability distributions in only two different measurement settings per subsystem.
Sistem Informasi UMKM Bengkel Berbasis Web Menggunakan Metode SCRUM

Strengthening on Micro, Small and Medium Enterprises (MSMEs) in Indonesia needs to be supported by the use and good information systems management. Therefore, business actors are expected to be able to use a operational strategy supported by the use of information systems. Workshop is one of the complex MSMEs with integrated warehousing and financial reporting. The problem faced at this workshop UMKM is that there is no synchronization between the existing stock of goods and the sales stock, as well as reporting both warehouse reporting and financial reporting. For this reason, this study aims to build a web-based MSME information system for tire &amp; rims workshops. In building this system, researchers used the agile software development method, namely SCRUM. This method is used because the system can adjust to the needs of the product owner, which is always changing and fast in processing. The results of this scrum stage, namely the product log, sprint backlog, sprint and working increment of the software, can resolve all problems that occur with regard to time, scope and cost issues so that in the implementation of making this system application can reduce the system requirements gap during the sprint process. So that the system can be completed in accordance with the requirements needed by the user. By using this workshop's UMKM information system, all sales operational activities can be monitored properly and sales and financial reports can be well structured.
SEOpinion: Summarization and Exploration of Opinion from E-Commerce Websites

Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research authors have designed opinion summarization systems based on customer reviews. They ignored the template information provided by manufacturers, although its descriptive information has the most useful product characteristics and texts are linguistically correct, unlike reviews. Therefore, this paper proposes a methodology coined as SEOpinion (summarization and exploration of opinions) to summarize aspects and spot opinion(s) regarding them using a combination of template information with customer reviews in two main phases. First, the hierarchical aspect extraction (HAE) phase creates a hierarchy of aspects from the template. Subsequently, the hierarchical aspect-based opinion summarization (HAOS) phase enriches this hierarchy with customers’ opinions to be shown to other potential buyers. To test the feasibility of using deep learning-based BERT techniques with our approach, we created a corpus by gathering information from the top five EC websites for laptops. The experimental results showed that recurrent neural network (RNN) achieved better results (77.4% and 82.6% in terms of F1-measure for the first and second phases, respectively) than the convolutional neural network (CNN) and the support vector machine (SVM) technique.
Malicious URL Detection Based on Associative Classification

Cybercriminals use malicious URLs as distribution channels to propagate malware over the web. Attackers exploit vulnerabilities in browsers to install malware to have access to the victim’s computer remotely. The purpose of most malware is to gain access to a network, ex-filtrate sensitive information, and secretly monitor targeted computer systems. In this paper, a data mining approach known as classification based on association (CBA) to detect malicious URLs using URL and webpage content features is presented. The CBA algorithm uses a training dataset of URLs as historical data to discover association rules to build an accurate classifier. The experimental results show that CBA gives comparable performance against benchmark classification algorithms, achieving 95.8% accuracy with low false positive and negative rates.
A New Method of Coding for Steganography Based on LSB Matching Revisited

LSB matching revisited is an LSB-based approach for image steganography. This method is a type of coding to increase the capacity of steganography. In this method, two bits of the secret message are hidden in two pixels with only one change. But this method provides no idea for hiding a message with a large number of bits. In other words, this method works only for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> <mo>=</mo> <mn>2</mn> </math> , where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> </math> is the number of bits in a block of the secret message. In this paper, we propose an improved version of the LSB matching revisited approach, which works for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>n</mi> <mo>&gt;</mo> <mn>2</mn> </math> . The proposed scheme contains two phases including embedding and extracting the message. In the embedding phase, we first convert the secret message into a bit-stream, and then the bit-stream is divided into a set of blocks including <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>n</mi> </math> bits in each block. Then we choose <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msup> </math> pixels for hiding such <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>n</mi> </math> bits of the secret message. In the next step, we choose the operations needed to generate such a message. Finally, we perform the obtained operations over the coefficients to hide the secret message. The proposed approach needs fewer changes than LSB MR when <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>n</mi> <mo>&gt;</mo> <mn>2</mn> </math> . The capacity of the proposed approach is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mfenced open="(" close=")" separators="|"> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msup> <mo>−</mo> <mn>1</mn> </mrow> </mfenced> <mo>/</mo> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </mrow> </mfenced> <mo>−</mo> <mn>1</mn> </mrow> </mfenced> <mo>×</mo> <mn>100</mn> <mo>%</mo> </math> higher than the F5 method where this value for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>n</mi> <mo>&gt;</mo> <mn>2</mn> </math> is bigger than 75%. For example, the capacity of our scheme is 75% higher than the capacity of F5 for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mi>n</mi> <mo>=</mo> <mn>3</mn> </math> . The proposed method can be used in the first step of every steganography method to reduce the change in the stego image. Therefore, this method is a new coding method for steganography. Our experimental results using steganalysis show that using our method provides around 10% higher detection error for SRNet over two steganography schemes.
Federated Learning on Non-IID Data Silos: An Experimental Study

Due to the increasing privacy concerns and data regulations, training data have been increasingly fragmented, forming distributed databases of multiple "data silos" (e.g., within different organizations and countries). To develop effective machine learning services, there is a must to exploit data from such distributed databases without exchanging the raw data. Recently, federated learning (FL) has been a solution with growing interests, which enables multiple parties to collaboratively train a machine learning model without exchanging their local data. A key and common challenge on distributed databases is the heterogeneity of the data distribution among the parties. The data of different parties are usually non-independently and identically distributed (i.e., non-IID). There have been many FL algorithms to address the learning effectiveness under non-IID data settings. However, there lacks an experimental study on systematically understanding their advantages and disadvantages, as previous studies have very rigid data partitioning strategies among parties, which are hardly representative and thorough. In this paper, to help researchers better understand and study the non-IID data setting in federated learning, we propose comprehensive data partitioning strategies to cover the typical non-IID data cases. Moreover, we conduct extensive experiments to evaluate state-of-the-art FL algorithms. We find that non-IID does bring significant challenges in learning accuracy of FL algorithms, and none of the existing state-of-the-art FL algorithms outperforms others in all cases. Our experiments provide insights for future studies of addressing the challenges in "data silos".
Arrow R-CNN for handwritten diagram recognition

Abstract We address the problem of offline handwritten diagram recognition. Recently, it has been shown that diagram symbols can be directly recognized with deep learning object detectors. However, object detectors are not able to recognize the diagram structure. We propose Arrow R-CNN, the first deep learning system for joint symbol and structure recognition in handwritten diagrams. Arrow R-CNN extends the Faster R-CNN object detector with an arrow head and tail keypoint predictor and a diagram-aware postprocessing method. We propose a network architecture and data augmentation methods targeted at small diagram datasets. Our diagram-aware postprocessing method addresses the insufficiencies of standard Faster R-CNN postprocessing. It reconstructs a diagram from a set of symbol detections and arrow keypoints. Arrow R-CNN improves state-of-the-art substantially: on a scanned flowchart dataset, we increase the rate of recognized diagrams from 37.7 to 78.6%.
An Active Sense and Avoid System for Flying Robots in Dynamic Environments

This article investigates a novel active-sensing-based obstacle avoidance paradigm for flying robots in dynamic environments. Instead of fusing multiple sensors to enlarge the field of view (FOV), we introduce an alternative approach that utilizes a stereo camera with an independent rotational degree of freedom to sense the obstacles actively. In particular, the sensing direction is planned heuristically by multiple objectives, including tracking dynamic obstacles, observing the heading direction, and exploring the previously unseen area. With the sensing result, a flight path is then planned based on real-time sampling and uncertainty-aware collision checking in the state space, which constitutes an active sense and avoid system. Experiments in both simulation and the real world demonstrate that this system can well cope with dynamic obstacles and abrupt goal direction changes. Since only one stereo camera is utilized, this system provides a low-cost and effective approach to overcome the FOV limitation in visual navigation.