Reviewerly/exHarmony huggingface dataset:
Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut

In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation.
Impact of color augmentation and tissue type in deep learning for hematoxylin and eosin image super resolution

Single image super-resolution is an important computer vision task with applications including remote sensing, medical imaging, and surveillance. Modern work on super-resolution utilizes deep learning to synthesize high resolution (HR) images from low resolution images (LR). With the increased utilization of digitized whole slide images (WSI) in pathology workflows, digital pathology has emerged as a promising domain for super-resolution. Despite extensive existing research into super-resolution, there remain challenges specific to digital pathology. Here, we investigated image augmentation techniques for hematoxylin and eosin (H&E) WSI super-resolution and model generalizability across diverse tissue types. In addition, we investigated shortcomings with common quality metrics (peak signal-to-noise ratio (PSNR), structure similarity index (SSIM)) by conducting a perceptual quality survey for super-resolved pathology images. High performing deep super-resolution models were used to generate 20X HR images from LR images (5X or 10X equivalent) for 11 different tissues and 30 human evaluators were asked to score the quality of the generated versus the ground truth 20X HR images. The scores given by a human rater and the PSNR or the SSIM were compared to investigate the correlation between model training parameters. We found that models trained on multiple tissues generalized better than those trained on a single tissue type. We also found that PSNR correlated with perceptual quality (R = 0.26) less accurately than did SSIM (R = 0.64), suggesting that the SSIM quality metric is insufficient. The methods proposed in this study can be used to virtually magnify H&E images with better perceptual quality than interpolation methods (i.e., bicubic interpolation) commonly implemented in digital pathology software. The impact of deep SISR methods is more notable when scaling to 4X is needed, such as in the case of super-resolving a low magnification WSI from 10X to 40X.
Physical layer security monitoring of optical communication using a quantum pilot tone signal

We demonstrate a low-complexity, quantum-level-sensitivity security monitoring system using a quantum pilot tone which is detected by an independent local oscillator, with compact data conversion acquisition units over a 10dB loss channel.
The shady economy: Understanding the difference in trading activity from underground forums in different layers of the Web

Underground forums are discussion outlets where criminal communities exchange knowledge about online malicious activities and trade illegal goods and services that promote an underground economy based on malicious software, stolen personal information, tools for financial fraud, drugs and more. Prior work has investigated the interactions between criminals and the type of assets traded in Surface Web forums. At the same time, research evidence suggests cybercriminals are moving their operations to the Dark Web to avoid getting caught and similar research has been carried out in Dark Web forums from different perspectives. However, there is no empirical evidence on how forum criminal activity related to the underground economy takes place in both Web environments. To address this problem, we conduct a quantitative exploratory analysis about the trading activity taking place in four prominent forums in the Surface Web and four in the Dark Web based on the type of posts found in the forums. Then, we compare the data to find differences in the malicious activity observed. Our results show that trading activity is higher in Dark Web forums compared to the Surface Web. We also find that different types of transactions, products and prices vary according to the Web environment.
An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification

The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications.The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells.After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%.Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.
Detection and Classification of Human-Carrying Baggage Using DenseNet-161 and Fit One Cycle

In recent decades, the crime rate has significantly increased. As a result, the automatic video monitoring system has become increasingly important for researchers in computer vision. A person’s baggage classification is essential in knowing who has abandoned baggage. This paper proposes a model for classifying humans carrying baggage. Two approaches are used for comparison using a deep learning technique. The first approach is based on categorizing human-containing image regions as either with or without baggage. The second approach classifies human-containing image regions based on the human position direction attribute. The proposed model is based on the pretrained DenseNet-161 architecture. It uses a "fit-one-cycle policy" strategy to reduce the training time and achieve better accuracy. The Fastai framework is used for implementation due to its super computational ability, simple workflow, and unique data cleansing functionalities. Our proposed model was experimentally validated, and the results show that the process is sufficiently precise, faster, and outperforms the existing methods. We achieved an accuracy of between 96% and 98.75% for the binary classification and 96.67% and 98.33% for the multi-class classification. For multi-class classification, the datasets, such as PETA, INRIA, ILIDS, and MSMT17, are re-annotated with one’s direction information about one’s stance to test the suggested approach’s efficacy.
Collaborative Administration of Role-Based Access Control in Smart Contracts

The emergence of blockchain technologies, including smart contracts, enables the decentralization of applications that were previously controlled by a single entity. In many cases, however, smart contracts still need to distinguish between different user roles according to their assigned duties. To ensure security, smart contracts often apply a simple form of role-based access control (RBAC), where a dedicated administrator assigns users to roles that are authorized to invoke specific smart contract functions. As this solution contradicts the blockchain principles of decentralization and democratic decision making, smart contracts may also form decentralized autonomous organizations where governance decisions are based on voting by all participants. Many applications would benefit from a hybrid approach that involves roles with different permissions while still allowing for collaborative management. Therefore, we propose a novel mechanism for RBAC administration in smart contracts based on hierarchical roles and configurable administration rules for each role. Any change in the user-role relation requires joint approval by members of selected roles according to the associated rule. This enables decentralized organizational charts with flexible administration constraints, where each role corresponds to specific permissions in the decentralized application and any change is transparently and securely recorded on the blockchain. The practical feasibility of the approach is demonstrated by means of a prototypical implementation for the Ethereum blockchain. Several benchmarks are performed to analyze the potential overhead of different solution variants.
Pseudo Decoder Guided Light-Weight Architecture for Image Inpainting

Image inpainting is one of the most important and widely used approaches where input image is synthesized at the missing regions. This has various applications like undesired object removal, virtual garment shopping, etc. The methods used for image inpainting may use the knowledge of hole locations to effectively regenerate contents in an image. Existing image inpainting methods give astonishing results with coarse-to-fine architectures or with use of guided information like edges, structures, etc. The coarse-to-fine architectures require umpteen resources leading to high computation cost of the architecture. Other methods with edge or structural information depend on the available models to generate guiding information for inpainting. In this context, we have proposed computationally efficient, light-weight network for image inpainting with very less number of parameters (0.97M) and without any guided information. The proposed architecture consists of the multi-encoder level feature fusion module, pseudo decoder and regeneration decoder. The encoder multi level feature fusion module extracts relevant information from each of the encoder levels to merge structural and textural information from various receptive fields. This information is then processed with pseudo decoder followed by space depth correlation module to assist regeneration decoder for inpainting task. The experiments are performed with different types of masks and compared with the state-of-the-art methods on three benchmark datasets i.e., Paris Street View (PARIS_SV), Places2 and CelebA_HQ. Along with this, the proposed network is tested on high resolution images ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times1024$ </tex-math></inline-formula> and 2048 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times2048$ </tex-math></inline-formula> ) and compared with the existing methods. The extensive comparison with state-of-the-art methods, computational complexity analysis, and ablation study prove the effectiveness of the proposed framework for image inpainting.
Reflexive pedagogy at the heart of educational digital transformation in Latin American higher education institutions

This paper makes a critical review of educational and operational issues related with pandemic and postpandemic lessons in Latin American higher education institutions (LATAM HEI), as background for uncovering key elements to innovate educational practices in technology-mediated higher education. The authors adapted the reflexive pedagogy framework to conduct in depth analysis of innovation experiences mediated with educational technologies and draw conclusions for curricular and digital transformation of LATAM HEI.
Analog Computing

Analog computing is one of the main pillars of Unconventional Computing. Almost forgotten for decades, we now see an ever-increasing interest in electronic analog computing because it offers a path to high-performance and highly energy-efficient computing. These characteristics are of great importance in a world where vast amounts of electric energy are consumed by today’s computer systems. Analog computing can deliver efficient solutions to many computing problems, ranging from general purpose analog computation to specialised systems like analog artificial neural networks. The book “Analog Computing” has established itself over the past decade as the standard textbook on the subject and has been substantially extended in this second edition, which includes more than 300 additional bibliographical entries, and has been expanded in many areas to include much greater detail. These enhancements will confirm this book’s status as the leading work in the field. It covers the history of analog computing from the Antikythera Mechanism to recent electronic analog computers and uses a wide variety of worked examples to provide a comprehensive introduction to programming analog computers. It also describes hybrid computers, digital differential analysers, the simulation of analog computers, stochastic computers, and provides a comprehensive treatment of classic and current analog computer applications. The last chapter looks into the promising future of analog computing.
Developing a knowledge management system for supporting flood decision-making

Flood has been a critical issue for many cities, especially Jakarta, due to geographic structure, weather, and citizen behaviors. The United Nations have assigned disaster management as one of the Sustainable Development Goals. However, many cities in developing countries still face challenges in implementing comprehensive flood management services. In this study, we elaborate on efforts that have been made to achieve a one-stop flood management service, using Jakarta as a case study. The proposed methodologies have three values: sensing, understanding, and acting. First, we build sensors to measure various flood parameters in the sensing approach. Second, we collect all data output from the previous system to provide robust analysis in the understanding part. Finally, in the acting module, we provide a dashboard for the decision support system in the flood management system. Although the system has been established, several challenges to achieving a comprehensive flood management system are clarified in this study, especially data management and governance issues. We conclude this study with the future applications of the flood management system that can be expected to minimize the risk of flood disasters in Jakarta.
Interaction-Aware Game-Theoretic Motion Planning for Automated Vehicles using Bi-level Optimization

Planning an interactive and cooperative behavior in the vicinity of multiple decision-makers is a challenging task. The game-theoretic perspective provides a suitable framework to describe such interactive scenarios. In this paper, we introduce a motion planning algorithm to generate interaction-aware behavior for highly interactive scenarios. Our algorithm is based upon a reformulation of a bi-level optimization problem which frames interactions among two decision makers as a Stackelberg game. In contrast to existing works in this field, we solve the prediction and planning problem simultaneously, which enables the generation of efficient behaviors even in highly interactive situations. The main novelty of our algorithm evolves around its ability to consider general nonlinear constraints. Further, we present mechanisms to introduce courtesy and cooperation into behavior planning which prevents overly aggressive driving, as issue that has been identified in existing interaction-aware planning approaches. Finally, we evaluate our approach in the context of automated driving. Our evaluation first investigates the algorithm's ability to purposefully influence and exploit the response of surrounding vehicles. We then illustrate how the approach can be used for cooperative and courteous planning.
MISCNN: A Novel Learning Scheme for CNN-Based Network Traffic Classification

By the rapid development of the Internet and online applications, traffic classification has changed to an important topic in the field of network management. Although many studies have been conducted in recent years, designing a robust classification model remains a major challenge. Even though previous researches have focused on changing the layer structure within the deep learning model, they do not consider the input shape that best represents the traffic. To this end, a new traffic classification method is presented in this paper that aims to utilize various input shape that can be derived from fixed-length packet bytes. The proposed method utilized MISCNN (Multi Input Shape Convolution Neural Network) to generate robust traffic classification model that can be used in many domains. Various experiments were carried out to verify superiority of proposed method for the tasks of traffic classification and application identification. According to the obtained results, MISCNN achieved higher score compared to previous researches that utilized only 2D square input shape and 1D linear input shape on the ISCX VPN-nonVPN dataset.
Deep Learning Application for Handwritten Arabic Word Recognition

Automatic handwriting recognition is the process of converting online and offline letters or words as a graphical form into its text format. Automatic Arabic handwriting words recognition using deep learning neural networks is still in the early stages in terms of research. There are no general, complete, and reliable Arabic Handwritten words database (lexicon) that can be used as a reference or a benchmark for all researchers who want to extend the work on automatic Arabic handwriting word recognition. Also, many historic Arabic manuscripts have deteriorated because of inappropriate storage and most of them have not been digitized due to the lack of reliable database that can be used to recognize the words of Arabic manuscripts. Deep Convolutional Neural Networks (DCNNs) can be used to solve the problems of automatic Arabic handwriting words recognition. In this work, a new DCNN algorithm applied to a new dataset of Handwritten Arabic words representing the seven days of the week named Arabic Handwritten Weekdays Dataset (AHWD) has been programmed, tested, and analyzed. Our dataset contains 21357 words equally distributed between the seven classes and prepared by 1000. So, it can be used for training and testing on a reliable DCNN model that will be able, after training to generalize to new datasets. The model works by training a (DCNN) model on a balanced-randomly-selected dataset using different structures. The results are improved by adding drop-out, image regularization, proper learning rate to avoid overfitting of the data. Finally, a blind test has been performed on the hidden test set and the performance was reported using a confusion matrix and learning curves as a validation tool for the model. Results show that our model’s performance is promising, achieving accuracy rate of 0.9939 with error rate of 0.0461 using AHWD dataset, and accuracy rate of 0.9971 with error rate of 0.0171 using IFN/ENIT dataset.
Spoofing Detection of Fake Speech Using Deep Neural Network Algorithm

Spoofing is a challenging research topic in Speaker Recognition. Spoofing, among others, can use fake speech, especially in the form of voice identity falsification or fraud, which is a problem that must be resolved. Various classification methods in Data Mining have been used in research to detect spoofing. However, The low level of accuracy, especially in managing large data, is an obstacle to using this approach. Deep Neural Network (DNN) is one of the methods in Deep Learning that is often used in research that processes extensive data. The DNN approach is proven to have good performance. This study uses the DNN method in detecting the authenticity of the speaker's voice. The results show that DNN is a method that has good performance in detecting fake speech spoofing with a model accuracy rate of 96.5%, 97.3% precision, 96.5% recall, and 96.7% F1 Measure.
Enhancing Image Resolution with Generative Adversarial Networks

Super-resolution is the process of generating high-resolution images from low-resolution images. There are a variety of practical applications used in real-world problems such as high-definition content creation, surveillance imaging, gaming, and medical imaging. Super-resolution has been the subject of many researches over the past few decades, as improving image resolution offers many advantages. Going beyond the previously presented methods, Generative Adversarial Networks offers a very promising solution. In this work, we will use the Generative Adversarial Networks-based approach to obtain 4x resolution images that are perceptually better than previous solutions. Our extensive experiments, including perceptual comparison, Peak Signal-to-Noise Ratio, and classification success metrics, show that our approach is quite promising for image super-resolution.
3D Visualization of Symbolic Execution Traces

Symbolic execution is a powerful software testing technique for finding bugs in complex software. Unfortunately, following the symbolic execution and understanding its results is challenging. However, since symbolic execution is commonly not complete (i.e. due to path explosion) it is important to understand the limitations of the performed analysis. Otherwise, insufficiently tested code parts may not be identified and bugs remain unnoticed. Prior work attempts to address this problem via 2D visualizations which communicate properties of the performed analysis to the verification engineer. Since symbolic execution requires a visualization of several properties, such 2D visualizations often lack important information or end up being dense and difficult to understand.In order to overcome this limitation, we propose a novel 3D visualization of symbolic execution which allows visualizing additional properties via the third dimension. For this purpose, we have implemented a 3D visualization for the symbolic execution of RISC-V machine code and evaluate this implementation by comparing it to an existing 2D visualization. Our results demonstrate that the third dimension allows us to include additional information which is not captured by the existing 2D visualization. In order to stimulate further research on 3D visualization of symbolic execution, we have released our implementation as open source software.