Reviewerly/exHarmony huggingface dataset:
Dual U-Net-Based Conditional Generative Adversarial Network for Blood Vessel Segmentation with Reduced Cerebral MR Training Volumes

Segmenting vessels in brain images is a critical step for many medical interventions and diagnoses of illnesses. Recent advances in artificial intelligence provide better models, achieving a human-like level of expertise in many tasks. In this paper, we present a new approach to segment Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) images, relying on fewer training samples than state-of-the-art methods. We propose a conditional generative adversarial network with an adapted generator based on a concatenated U-Net with a residual U-Net architecture (UUr-cGAN) to carry out blood vessel segmentation in TOF-MRA images, relying on data augmentation to diminish the drawback of having few volumes at disposal for training the model, while preventing overfitting by using regularization techniques. The proposed model achieves 89.52% precision and 87.23% in Dice score on average from the cross-validated experiment for brain blood vessel segmentation tasks, which is similar to other state-of-the-art methods while using considerably fewer training samples. UUr-cGAN extracts important features from small datasets while preventing overfitting compared to other CNN-based methods and still achieve a relatively good performance in image segmentation tasks such as brain blood vessels from TOF-MRA.
Feature-oriented clone and pull operations for distributed development and evolution

Abstract Software companies frequently customize and extend product lines in multiple projects concurrently to quickly deliver solutions to customers. Engineers use a distributed and feature-oriented development process, commonly supported by version control systems to track implementation-level changes. For instance, feature branches are widely used to add new or modify existing features. However, when merging back features to the product line, the information about feature-to-code mappings is usually lost. Furthermore, the granularity of merging is limited to branches, making it hard to extract and merge selected individual features from one product to another. This paper thus presents feature-oriented clone and pull operations for distributed development, which are implemented in the FORCE 2 platform. Our evaluation uses variants of the ArgoUML product line to investigate the correctness and performance of our approach. The results show that the feature-oriented operations work with high precision and recall for different cases of feature interactions, also when feature implementations are scattered across many locations in the source code. The performance measurements demonstrate that the operations can be integrated in the typical workflows of engineers.
A new approach for training a physics-based dehazing network using synthetic images

• We present a novel method of using synthetic images, captured from a 3D urban virtual environment, for a physics-based dehazing task. • A style transfer strategy, followed by unlit image prior extraction from the synthetic images, allows the dehazing network to perform effectively on real-world hazy images. • A three-stage dehazing network is presented, which is easier to train as compared to unsupervised approaches. • Our method achieves competitive performance with other state-of-the-art methods, without the network seeing any real hazy images during training. • We provide DLSU-SYNSIDE (SYNthetic Single Image Dehazing) dataset, which contains 100K synthetic images. In this study, we propose a new approach for training a physics-based dehazing network, using RGB images and depth maps gathered from a 3D urban virtual environment, with simulated global illumination and physically-based shaded materials. Since 3D scenes are rendered with depth buffers, full image depth can be extracted based on this information, using a custom shader, unlike the extraction of real-world depth maps, which tend to be sparse. Our proposed physics-based dehazing network uses generated transmission and atmospheric maps from RGB images and depth maps from the virtual environment. To make our network compatible with real-world images, we incorporate a novel strategy of using unlit image priors during training, which can also be extracted from the virtual environment. We formulate the training as a supervised image-to-image translation task, using our own DLSU-SYNSIDE (SYNthetic Single Image Dehazing Dataset), which consists of clear images, unlit image priors, transmission, and atmospheric maps. Our approach makes training stable and easier as compared to unsupervised approaches. Experimental results demonstrate the competitiveness of our approach against state-of-the-art dehazing works, using known benchmarking datasets such as I-Haze, O-Haze, and RESIDE, without our network seeing any real-world images during training. The DLSU-SYNSIDE dataset and source code can be accessed through this link: https://neildg.github.io/SynthDehazing/.
Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model

In this paper, we develop an optimal placement of solar-wind energy systems using restricted Boltzmann machine (RBM). The RBM considers various factors to scale the process of optimal placement and enables proper sizing and placement for attaining increased electricity production from both wind and solar systems. The multiobjective criterion from both solar and wind energy farms simulated on MATLAB simulator shows an increased number of accuracies with reduced mean average error and computation time during training and testing. The results show that the RBM achieves improved rate of finding the optimal placement with a lesser cost and computation time of lesser than 2 ms than other methods.
Seeking Patterns of Visual Pattern Discovery for Knowledge Building

Currently, the methodological and technical developments in visual analytics, as well as the existing theories, are not sufficiently grounded by empirical studies that can provide an understanding of the processes of visual data analysis, analytical reasoning and derivation of new knowledge by humans. We conducted an exploratory empirical study in which participants analysed complex and data-rich visualisations by detecting salient visual patterns, translating them into conceptual information structures and reasoning about those structures to construct an overall understanding of the analysis subject. Eye tracking and voice recording were used to capture this process. We analysed how the data we had collected match several existing theoretical models intended to describe visualisation-supported reasoning, knowledge building, decision making or use and development of mental models. We found that none of these theoretical models alone is sufficient for describing the processes of visual analysis and knowledge generation that we observed in our experiments, whereas a combination of three particular models could be apposite. We also pondered whether empirical studies like ours can be used to derive implications and recommendations for possible ways to support users of visual analytics systems. Our approaches to designing and conducting the experiments and analysing the empirical data were appropriate to the goals of the study and can be recommended for use in other empirical studies in visual analytics.
A New Blind IoT-Based MP3 Audio Watermarking Scheme for Content Integrity Checking and Copyright Protection

Rapid development in the area of information and embedded technology, mobile communication networks, IoT multimedia applications, compression, and distribution over Internet has led to a significant need to answer the question: “How to protect, secure, and authenticate multimedia documents?” One of the proposed responses to this challenge is digital watermarking, which hides an inaudible watermark in digital multimedia content. Despite the large number of proposed competent watermarking algorithms in the literature, few are suitable for the compression domain, mainly the authentication application. In this context, this paper depicts a new MP3 audio watermarking scheme for copyright protection and content integrity checking operating directly in the compression domain using Huffman data and side information features. This scheme overcomes the problem of computational time as it operates directly on the compressed bitstream. In addition, it provides enough embedding space by using Huffman data features. A strong advantage of our scheme is that it has been used successfully for both authentication and copyright protection applications. Experimental results have revealed that the proposed watermarking schema has achieved very competitive results compared to others from the literature in terms of inaudibility, robustness, and especially capacity ratio. Our approach offers also good values of ODG and NC even after double recompression-StirMark attacks.
Traditional Ceramic Sculpture Feature Recognition Based on the Machine Learning Algorithm

Aiming at the problems of low recognition accuracy, high line noise, and high time cost of feature recognition in traditional ceramic sculpture modeling feature recognition methods, this paper designed a traditional ceramic sculpture modeling feature recognition method based on the machine learning algorithm. By constructing the sparse representation model of a traditional ceramic sculpture image, the posterior probability of a traditional ceramic sculpture image was determined and Gaussian distribution of pixels in the image was carried out to determine the distribution law of pixels in the image and the super-resolution reconstruction of the traditional ceramic sculpture image was realized. The feature of the line state of a traditional ceramic sculpture image was determined and classified by the kernel classification method. Finally, the machine learning algorithm red BP neural network is introduced to construct the traditional ceramic sculpture modeling feature recognition algorithm and the error threshold is constantly iterated to realize the traditional ceramic sculpture modeling feature recognition. The experimental results show that the recognition algorithm designed in this paper has high accuracy in identifying the traditional ceramic sculpture features, can effectively suppress the line noise, and has a short recognition time overhead, which has a certain feasibility.
Structure of skew cyclic codes over

In this paper, for any integer k &gt; 1, we study skew cyclic codes of length n over , where uk = 0 and p is an odd prime. It is noted that the ring Rk is isomorphic to the quotient ring . We characterize skew cyclic codes of length n over Rk as free left Rk[x;θ] -submodules of Rk[x;θ]/〈xn - 1〉 and construct their generators and minimal generating sets. Also, an algorithm of encoding and decoding of these codes has been provided. Finally, some examples of optimal linear codes are given from Gray images of skew cyclic codes over Rk.
Development of a 2D Game using Artificial Intelligence in Unity

The game industry comes to life like never before thanks to ever-evolving technology, as new games are released every day and promising remakes are released every year. The gaming industry is getting more and more vibrant as an interactive and entertaining medium for a wide range of people of all ages and backgrounds, as its revenue rises. The main goal of our 2D video pixel art game named ‘Disappear’ is to create awareness of the ‘Mental Health’ issues. When the game requires a particular character, the Artificial Intelligence needs to mimic as that Non-Playable Character. It helps the player more engaged in the game space while exploring the game. While fighting against the mob, player will be facing difficulty based on the enemy AI. The AI will have their own set of rules and their own roles to play, which are already enforced in their decision making. By engaging the players in the game, the awareness of depression will be conveyed subtle manner.
Vectorizing Line Drawings of Arbitrary Thickness via Boundary‐based Topology Reconstruction

Abstract Vectorization is a commonly used technique for converting raster images to vector format and has long been a research focus in computer graphics and vision. While a number of attempts have been made to extract the topology of line drawings and further convert them to vector representations, the existing methods commonly focused on resolving junctions composed of thin lines. They usually fail for line drawings composed of thick lines, especially at junctions. In this paper, we propose an automatic line drawing vectorization method that can reconstruct the topology of line drawings of arbitrary thickness. Our key observation is that no matter the lines are thin or thick, the boundaries of the lines always provide reliable hints for reconstructing the topology. For example, the boundaries of two continuous line segments at a junction are usually smoothly connected. By analyzing the continuity of boundaries, we can better analyze the topology at junctions. In particular, we first extract the skeleton of the input line drawing via thinning. Then we analyze the reliability of the skeleton points based on boundaries. Reliable skeleton points are preserved while unreliable skeleton points are reconstructed based on boundaries again. Finally, the skeleton after reconstruction is vectorized as the output. We apply our method on line drawings of various contents and styles. Satisfying results are obtained. Our method significantly outperforms existing methods for line drawings composed of thick lines.
Morphological Approaches and Segmentation of Medical Images with Different Watershed Transformations

Image segmentation with prior information is most vital and important part of image processing. Various kind of image segmentation techniques are used to partition the images in small parts in form of region known as segments, it contains a set of pixels to analysis of every important information regarding segmentation from all the images. Information retrieval techniques become more simplified and easier by using segmentation where gradients and ridged lines of images took most important role. MRI images most relevant and crucial information can get by using this process. A comparative analysis of different watershed segmentation techniques on different medical MRI Images along with all the statistical approaches to represent image improved segmentation presented in this paper.
Feasibility Analysis of Quantum Key Distribution Technology in Unique Code Application

Unique code system (UQS) can solve the security problem of the key system well, but it is insufficient in dealing with human malicious behavior. Quantum key distribution can ensure that any attempt to steal the key in transit will be discovered by legitimate users. The combination of quantum key distribution technology and UQS technology can effectively deal with the occurrence of malicious initiation events. This paper analyzes the feasibility of quantum key distribution technology in the application of UQS and looks forward to the application prospect in the field of security control of critical systems.
Towards the sustainability of small and medium software enterprises through the implementation of software process improvement: Empirical investigation

Abstract To improve and sustain the quality of software products, software process improvement (SPI) is needed. Currently, small and medium software enterprises (SMSEs) represent a high proportion of companies around the world and become a cornerstone in the worldwide industry economy. These companies have realized that improving their process is crucial for success, but they are facing difficulties to implement it due to limited resources, limited knowledge, and time constraints. This study aimed to identify the sustainability success factors (SSFs) that have a positive impact on implementing SPI efforts in SMSEs. Data were collected through a systematic literature review (SLR) approach and quantitatively through a survey questionnaire. A list of 44 SSFs was identified during SLR and empirical study. Results illustrate that there is a positive correlation between the ranks obtained from both dataset ( rs (44) = .548, ρ = .001). Therefore, there would be significant differences between the SSFs identified in both datasets. In conclusion, the top‐ranked factors can then be used to guide the SPI coordinators on where they should focus their attention to reach the desired SPI goals, which is crucial to deliver the software products and also facilitates in development of model for SPIs in the future.
Grover algorithm circuit optimization and noise analysis on integer factorization

The classical computer is restricted by Moore's law and other factors into the development bottleneck, quantum computer by virtue of its natural acceleration advantage into the field of vision. Quantum computing can achieve the acceleration effect compared with classical computing in many fields. In the field of integer decomposition, Shor algorithm can be solved in polynomial time, which breaks the security of RSA public key cryptosystem. However, the quantum bits (logn + 2, where n is the integer to be decomposed) required by Shor algorithm are difficult to be realized on today's quantum computers. Therefore, the hybrid quantum algorithm model of "classical + quantum (Grover algorithm)" is tried to realize the decomposition of large integers. In this paper, based on the previous hybrid quantum algorithm model, considering that the continuous accumulation of errors in the circuit of the quantum algorithm will lead to the failure of the algorithm as the depth increases, part of the circuit of the quantum algorithm oracle is optimized to reduce the consumption of the depth, quantum gate and quantum bit in the circuit of the quantum algorithm, so as to make it easier to run on the actual quantum computer. By running the optimized quantum circuit on the back end of the simulator of IBM Q quantum cloud platform, and trying to add thermal relaxation and depolarization noises to the circuit, the influence of different noises on the circuit under different iterations was explored.
Evaluate Similarity of Requirements with Multilingual Natural Language Processing

Abstract Finding redundant requirements or semantically similar ones in previous projects is a very time-consuming task in engineering design, especially with multilingual data. Due to modern NLP it is possible to automate such tasks. In this paper we compared different multilingual embeddings models to see which of them is the most suitable to find similar requirements in English and German. The comparison was done for both in-domain data (requirements pairs) and out-of-domain data (general sentence pairs). The most suitable model were sentence embeddings learnt with knowledge distillation.
Technology for the smart and connected world – era of data explosion

Emerging AI and 5G applications such as connected devices, autonomous vehicles, virtual/augmented reality, and clouds are driving beyond traditional pitch scaling. Wired and wireless infrastructure devices are accelerating their migration towards leading-edge technologies in order to achieve higher performance, better power efficiency, and improved functionality, which leads unprecedented demand for compute power in datacenters and communication infrastructures. In addition, high-speed interfaces are supporting faster data throughput and improved connectivity. But the area scaling and performance improvement of semiconductors are getting harder every year and numerous technologies are suggested to overcome these limitations. In this presentation we aim to provide an overview of the requirements, outline some of the key design parameters and provide a few examples of contemporary soluions beyond traditional Moore’s-Law scaling for the insatiable compute demand.
Learning rate of distribution regression with dependent samples

In this paper, we study the learning rate of distribution regression for strong mixing sequences. The distribution regression containing two stages of sampling aims at regressing from distributions to real valued outputs. In order to form our regressor, we embed distributions to a reproducing kernel Hilbert space by utilizing the tool of mean embedding. By using the property of integral operator and the covariance inequality for strong mixing sequence, we show that under some priori conditions of regression function, the distribution regression still reaches the optimal learning rate with dependent samples. Hence, we extend the applicable range of distribution regression to the identically distributed but dependent samples.
Secure Communication in Factories - Benchmarking Elliptic Curve Diffie-Hellman Key Exchange Implementations on an Embedded System

Securing factory communication to protect corporate data is an important concern in the context of the Industrial Internet of Things (IIoT). Various cryptographic protocols can be used to establish secure communication channels. One of these protocols is the Transport Layer Security 1.3 (TLS 1.3) protocol. A key component of the TLS handshake protocol is the Elliptic Curve Diffie-Hellman Key Exchange (ECDHKE), a public key cryptosystem used to exchange keys over insecure channels which can be based on a number of standardized elliptic curves. A special form of elliptic curves are Montgomery curves which are advantageous compared to more traditional Weierstrass curves due to their fast arithmetic. This is especially important when the ECDHKE is performed on embedded devices and in time-critical situations. In this work, the performance of ECDHKE implementations using standardized Montgomery curves Curve25519 and Curve448 included in the wolfSSL library are evaluated on an embedded 32-bit STM32L476RG Nucleo development board designed by STMicroelectronics. The benchmark results show that using Curve25519 with around 220ms for the key pair generation and the key agreement respectively is approximately 75% faster than using Curve448 with around 900ms for each of the algorithms, which can be attributed to their differing security levels. These results suggest that the algorithms might not be fast enough for time critical situations.
Image feature selection based on orthogonal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mi>ℓ</mml:mi><mml:mrow><mml:mtext>2</mml:mtext><mml:mo>,</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math> norms

This study presents a feature selection method based on orthogonal ℓ2,0-norms to reduce dimensions, especially for images, where correlated and redundant information is frequently present by nature. Recent ℓ2,0-norm methods have shown a way of discovering sparsity, but redundant features could still be selected in the process. In light of such, this study considers imposing an orthogonal constraint on sparsity, further limiting ℓ2,0 norms. To such an end, projection onto Stiefel manifolds is computed to satisfy the orthogonal constraint while ℓ2,0-norm regularization is computed via ℓp-box zero-one programming. Experiments on open datasets were carried out to evaluate the proposed method and different models, including ℓ1-, ℓ2,1-, and ℓ2,0-norm approaches. The experimental results showed that the mean accuracy and F1 scores of the proposed method were higher than those of the ℓ2,0-norm method without orthogonal constraints and those of the other baselines, subsequently proving the effectiveness of the proposed idea.