Reviewerly/exHarmony huggingface dataset:
Application of cascaded GAN based on CT scan in the diagnosis of aortic dissection

Currently, Computed Tomography Angiography (CTA) is the most commonly used clinical method for the diagnosis of aortic dissection, which is much better than plain CT. However, CTA examination has some disadvantages such as time-consuming image processing, complicated procedure and injection of developer. CT plain scanning is widely used in the early diagnosis of arterial dissection because of its convenience, speed and popularity. In order not to delay the optimal diagnosis and treatment time of patients, we use deep learning technology and network model to synthesize plain CT images into CTA images. Patients can be timely professional related departments of clinical diagnosis and treatment, and reduce the rate of missed diagnosis. In this paper, we propose a CTA image synthesis technique for cardiac aortic dissection based on the cascaded generative adjunctive network model.Firstly, we registered CT images, and then used nnU-Net segmentation network model to obtain CT and CTA paired images containing only the aorta. Then we proposed a CTA image synthesis method for aortic dissection based on cascaded generative adversarial. The core idea is to build a cascade generator and double discriminator network based on DCT channel attention mechanism to further enhance the synthesis effect of CTA.The model is trained and tested on CT plain scan and CTA image data set of aortic dissection. The results show that the proposed model achieves good results in CTA image synthesis. In the CT data set, the nnU-Net model improves 8.63% and reduces 10.87mm errors in the key index DSC and HD, respectively, compared with the benchmark model U-Net. In CTA data set, nnU-Net model improves 10.27% and reduces 6.56mm error in key index DSC and HD, respectively, compared with benchmark model U-Net. In the synthesis task, the cascaded generative adm network is superior to Pix2pix and Pix2pixHD network models in both PSNR and SSIM, which proves that our proposed model has significant advantages.This study provides new possibilities for CTA image synthesis of aortic dissection, and improves the accuracy and efficiency of diagnosis, and hopes to provide substantial help for the diagnosis of aortic dissection.
HEA-Net: Attention and MLP Hybrid Encoder Architecture for Medical Image Segmentation

The model, Transformer, is known to rely on a self-attention mechanism to model distant dependencies, which focuses on modeling the dependencies of the global elements. However, its sensitivity to the local details of the foreground information is not significant. Local detail features help to identify the blurred boundaries in medical images more accurately. In order to make up for the defects of Transformer and capture more abundant local information, this paper proposes an attention and MLP hybrid-encoder architecture combining the Efficient Attention Module (EAM) with a Dual-channel Shift MLP module (DS-MLP), called HEA-Net. Specifically, we effectively connect the convolution block with Transformer through EAM to enhance the foreground and suppress the invalid background information in medical images. Meanwhile, DS-MLP further enhances the foreground information via channel and spatial shift operations. Extensive experiments on public datasets confirm the excellent performance of our proposed HEA-Net. In particular, on the GlaS and MoNuSeg datasets, the Dice reached 90.56% and 80.80%, respectively, and the IoU reached 83.62% and 68.26%, respectively.
ANALYSIS OF SINGLE PHOTON SOURCES WITH QUANTUM KEY DISTRIBUTION SYSTEMS DEVELOPMENT PROSPECTS

Introduction: The analysis of the main directions of research in the field of creating sources of single photons for communication systems with quantum key distribution is carried out. Scientometric analysis based on Scopus database is used to identify the most promising areas and predict the trends in the field of single photon sources. Methods of single photon sources development and their applications in commercial communication systems with quantum key distribution is shown. At the time single photons sources based on quantum dots are the most developed and presented on the market. However, color centers in nanocrystals and carbon nanotubes are intensively studied. The prospects of creating compact and easy-to-use sources of single photons on telecommunication wavelengths is analyzed. There are many ways of realization of single photon sources design. Significant funding in China and Germany leads to notable growth of the technology. It was detected the main journals, author groups and organizations working in direction of single photon sources. Methods and Results: The research field of communication systems using sources of single photons is growing. It is available single photon sources on quantum dots and crystals. Applying of carbon nanotubes is promising for this problem according to some famous authors. It is revealed significant overlap of the research areas: single photon sources, quantum random number generators and quantum key distribution. At the time fundamental studies are implemented in technical devices with high potential for commercial applications.
Unsupervised outlier detection using neural network-based mixtures of probabilistic principal component analyzers for building chiller fault diagnosis

The application of fault detection and diagnosis (FDD) for chillers is very crucial for ensuring the smooth operation, stability, and durability of HVAC systems. Recent FDD models with outstanding diagnostic performance have been proposed for chiller systems. However, the high performance of FDD models is only guaranteed in condition observed data for FDD model construction are not contaminated, i.e., do not contain outliers, which is inevitable during data recording process. To resolve this problem, this paper proposes a novel outlier detection (OD) method named NN-MPPCA. The proposed method uses a neural network (NN) to model a mixture of probabilistic principal component analyzers (MPPCA) framework which parameters are updated via back-propagation. The combining mechanism of MPPCA guarantees NN-MPPCA to fit well complex data distributions, and unseen instances with low reconstruction probability under MPPCA framework are detected as outliers. Comprehensive experimental results of exhaustive comparisons have demonstrated the superiority of NN-MPPCA over other state-of-the-art OD algorithms as well as its effectiveness for chiller FDD application. Under the scenario that datasets contain 20% outliers (i.e., both local outliers and global outliers), the outlier detection performance of NN-MPPCA is highest with F1-scores achieving from 95.4% to 99.7%. Also, with NN-MPPCA applied in the data purification step, FDD models can achieve a diagnostic accuracy up to 99.5%; while they can only reach a diagnostic accuracy of 82.6% at most if contaminated data are directly used.
Quantum simulation with just-in-time compilation

Quantum technologies are moving towards the development of novel hardware devices based on quantum bits (qubits). In parallel to the development of quantum devices, efficient simulation tools are needed in order to design and benchmark quantum algorithms and applications before deployment on quantum hardware. In this context, we present a first attempt to perform circuit-based quantum simulation using the just-in-time (JIT) compilation technique on multiple hardware architectures and configurations based on single-node central processing units (CPUs) and graphics processing units (GPUs). One of the major challenges in scientific code development is to balance the level of complexity between algorithms and programming techniques without losing performance or degrading code readability. In this context, we have developed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext mathvariant="monospace">qibojit</mml:mtext></mml:mrow></mml:math>: a new module for the Qibo quantum computing framework, which uses a just-in-time compilation approach through Python. We perform systematic performance benchmarks between our JIT approach and a subset of relevant publicly available libraries for quantum computing. We show that our novel approach simplifies the complex aspects of the implementation without deteriorating performance.
Bridging Traditional and Machine Learning-Based Algorithms for Solving PDEs: The Random Feature Method

Stochastic gradient Langevin dynamics (SGLD) is a standard sampling technique for uncertainty estimation in Bayesian neural networks.Past methods have shown improved convergence by including a preconditioning of SGLD based on RMSprop.This preconditioning serves to adapt to the local geometry of the parameter space and improve the performance of deep neural networks.In this paper, we develop another preconditioning technique to accelerate training and improve convergence by incorporating a recently developed batch normalization preconditioning (BNP), into our methods.BNP uses mini-batch statistics to improve the conditioning of the Hessian of the loss function in traditional neural networks and thus improve convergence.We will show that applying BNP to SGLD will improve the conditioning of the Fisher information matrix, which improves the convergence.We present the results of this method on three experiments including a simulation example, a contextual bandit example, and a residual network which show the improved initial convergence provided by BNP, in addition to an improved condition number from this method.
Personalized recommender systems based on social relationships and historical behaviors

Recommender systems have a wide range of applications in the age suffering information overload. A promising way to design better recommender systems in the presence of ubiquitous social media is to utilize social relationships in recommendation algorithms, named social recommendation. One critical challenge in social recommendation is how to mine valuable information intrinsic to social relationships and integrate such information into the algorithm design. In this paper, we argue that both social relationships and historical behaviors are affected by the same implicit factors. For example, due to the existence of implicit factors such as peer influence or common interests in social networks, users with similar implicit factors will have a high probability to become friends and collect similar objects. Accordingly, we propose a recommendation algorithm that jointly utilizes social relationships and historical behaviors, based on the extended linear optimization technique. We test the performance of our algorithm for four groups of users on real networks, including all users, active users, inactive users and cold-start users. Results show that, in all the above four scenarios, the proposed algorithm performs overall best subject to accuracy and diversity metrics compared with the benchmarks. In particular, the algorithm remarkably improves the recommendation performance for cold-start users. Further analysis shows that the contribution of social relationships depends on the coupling strength between social relationships and historical behaviors.
Clouded Leopard Optimization: A New Nature-Inspired Optimization Algorithm

This paper proposes a new nature-inspired metaheuristic algorithm called Clouded Leopard Optimization (CLO), which mimics the natural behavior of clouded leopards in the wild.The fundamental inspiration of CLO is derived from two ways of natural behaviors of the clouded leopard, including hunting strategy and daily resting on trees.CLO is mathematically modeled in two phases of exploration and exploitation, based on the simulation of these two natural behaviors.CLO performance is evaluated in solving sixty-eight benchmark functions, including unimodal, multimodal, CEC 2015, and CEC 2017 types.The performance of CLO in solving optimization problems is compared with the performance of ten famous metaheuristic algorithms.The simulation results show that the proposed CLO approach with high ability in exploration, exploitation, and balancing between them has a high capability in optimization applications.Simulation results show that CLO performs better in most test functions than competitor algorithms.In addition, the implementation of CLO on four engineering design issues demonstrates the capability of the proposed approach in real-world applications.
Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

While visualizations are an effective way to represent insights about information, they rarely stand alone. When designing a visualization, text is often added to provide additional context and guidance for the reader. However, there is little experimental evidence to guide designers as to what is the right amount of text to show within a chart, what its qualitative properties should be, and where it should be placed. Prior work also shows variation in personal preferences for charts versus textual representations. In this paper, we explore several research questions about the relative value of textual components of visualizations. 302 participants ranked univariate line charts containing varying amounts of text, ranging from no text (except for the axes) to a written paragraph with no visuals. Participants also described what information they could take away from line charts containing text with varying semantic content. We find that heavily annotated charts were not penalized. In fact, participants preferred the charts with the largest number of textual annotations over charts with fewer annotations or text alone. We also find effects of semantic content. For instance, the text that describes statistical or relational components of a chart leads to more takeaways referring to statistics or relational comparisons than text describing elemental or encoded components. Finally, we find different effects for the semantic levels based on the placement of the text on the chart; some kinds of information are best placed in the title, while others should be placed closer to the data. We compile these results into four chart design guidelines and discuss future implications for the combination of text and charts.
Program merge conflict resolution via neural transformers

Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge conflict may occur. Such conflicts stall pull requests and continuous integration pipelines for hours to several days, seriously hurting developer productivity. To address this problem, we introduce MergeBERT, a novel neural program merge framework based on token-level three-way differencing and a transformer encoder model. By exploiting the restricted nature of merge conflict resolutions, we reformulate the task of generating the resolution sequence as a classification task over a set of primitive merge patterns extracted from real-world merge commit data. Our model achieves 63–68% accuracy for merge resolution synthesis, yielding nearly a 3× performance improvement over existing semi-structured, and 2× improvement over neural program merge tools. Finally, we demonstrate that MergeBERT is sufficiently flexible to work with source code files in Java, JavaScript, TypeScript, and C# programming languages. To measure the practical use of MergeBERT, we conduct a user study to evaluate MergeBERT suggestions with 25 developers from large OSS projects on 122 real-world conflicts they encountered. Results suggest that in practice, MergeBERT resolutions would be accepted at a higher rate than estimated by automatic metrics for precision and accuracy. Additionally, we use participant feedback to identify future avenues for improvement of MergeBERT.
Occlusion and Deformation Handling Visual Tracking for UAV via Attention-Based Mask Generative Network

Although the performance of unmanned aerial vehicle (UAV) tracking has benefited from the successful application of discriminative correlation filters (DCF) and convolutional neural networks (CNNs), UAV tracking under occlusion and deformation remains a challenge. The main dilemma is that challenging scenes, such as occlusion or deformation, are very complex and changeable, making it difficult to obtain training data covering all situations, resulting in trained networks that may be confused by new contexts that differ from historical information. Data-driven strategies are the main direction of current solutions, but gathering large-scale datasets with object instances under various occlusion and deformation conditions is difficult and lacks diversity. This paper proposes an attention-based mask generation network (AMGN) for UAV-specific tracking, which combines the attention mechanism and adversarial learning to improve the tracker’s ability to handle occlusion and deformation. After the base CNN extracts the deep features of the candidate region, a series of masks are determined by the spatial attention module and sent to the generator, and the generator discards some features according to these masks to simulate the occlusion and deformation of the object, producing more hard positive samples. The discriminator seeks to distinguish these hard positive samples while guiding mask generation. Such adversarial learning can effectively complement occluded and deformable positive samples in the feature space, allowing to capture more robust features to distinguish objects from backgrounds. Comparative experiments show that our AMGN-based tracker achieves the highest area under curve (AUC) of 0.490 and 0.349, and the highest precision scores of 0.742 and 0.662, on the UAV123 tracking benchmark with partial and full occlusion attributes, respectively. It also achieves the highest AUC of 0.555 and the highest precision score of 0.797 on the DTB70 tracking benchmark with the deformation attribute. On the UAVDT tracking benchmark with the large occlusion attribute, it achieves the highest AUC of 0.407 and the highest precision score of 0.582.
Automated GDPR Contract Compliance Verification Using Knowledge Graphs

In the past few years, the main research efforts regarding General Data Protection Regulation (GDPR)-compliant data sharing have been focused primarily on informed consent (one of the six GDPR lawful bases for data processing). In cases such as Business-to-Business (B2B) and Business-to-Consumer (B2C) data sharing, when consent might not be enough, many small and medium enterprises (SMEs) still depend on contracts—a GDPR basis that is often overlooked due to its complexity. The contract’s lifecycle comprises many stages (e.g., drafting, negotiation, and signing) that must be executed in compliance with GDPR. Despite the active research efforts on digital contracts, contract-based GDPR compliance and challenges such as contract interoperability have not been sufficiently elaborated on yet. Since knowledge graphs and ontologies provide interoperability and support knowledge discovery, we propose and develop a knowledge graph-based tool for GDPR contract compliance verification (CCV). It binds GDPR’s legal basis to data sharing contracts. In addition, we conducted a performance evaluation in terms of execution time and test cases to validate CCV’s correctness in determining the overhead and applicability of the proposed tool in smart city and insurance application scenarios. The evaluation results and the correctness of the CCV tool demonstrate the tool’s practicability for deployment in the real world with minimum overhead.
Disrupting the “empathy machine”: The power and perils of virtual reality in addressing social issues

This article looks through a critical media lens at mediated effects and ethical concerns of virtual reality (VR) applications that explore personal and social issues through embodiment and storytelling. In recent years, the press, immersive media practitioners and researchers have promoted the potential of virtual reality storytelling to foster empathy. This research offers an interdisciplinary narrative review, with an evidence-based approach to challenge the assumptions that VR films elicit empathy in the participant-what I refer to as the VR-empathy model. A review of literature from the fields of psychology, computer science, embodiment, medicine, and virtual reality was carried out to question and counter these claims through case studies of both fiction and non-fiction VR experiences. The results reveal that there is little empirical evidence of a correlation between VR exposure and an increase in empathy that motivates pro-social behavior, and a lack of research covering VR films exposure eliciting empathy. Furthermore, the results show an alarming lack of research into the long-term effects of VR films and other VR immersive experiences. This contribution aims to understand and demystify the current "empathy machine" rhetoric and calls for more rigorous, scientific research that can authenticate future claims and systemize ethical best practices.
Known-plaintext attack-based analysis of double random phase encoding using multiple known plaintext–ciphertext pairs

Double random phase encoding (DRPE) is vulnerable to known-plaintext attacks (KPAs) based on phase retrieval algorithms. We previously analyzed DRPE resistance to KPA cryptanalysis with multiple known plaintext–ciphertext image pairs and obtained secret keys at a higher probability rate than when performing KPA cryptanalysis using one known image pair. However, the robustness of KPA in the presence of noise or occlusion in DRPE is unclear. We analyzed KPA properties in relation to DRPE when white Gaussian noise was gradually added to the Fourier amplitude or phase of a known ciphertext complex amplitude image. Additionally, we analyzed KPA properties when the Fourier phase of the known ciphertext image was gradually occluded by zero-valued pixels. The results showed that KPAs performed using multiple known plaintext–ciphertext image pairs were largely affected by noise added to the Fourier phase and thus are not always a strong tool for DRPE cryptanalysis.