Reviewerly/exHarmony huggingface dataset:
Deep learning based domain adaptation for mitochondria segmentation on EM volumes

Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in that direction during the past years, they usually require large amounts of annotated data to be trained, and perform poorly on other data acquired under similar experimental and imaging conditions. This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain. In this work, we address the complex case of deep learning based domain adaptation for mitochondria segmentation across EM datasets from different tissues and species.We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain based on (1) state-of-the-art style transfer between images of both domains; (2) self-supervised learning to pre-train a model using unlabeled source and target images, and then fine-tune it only with the source labels; and (3) multi-task neural network architectures trained end-to-end with both labeled and unlabeled images. Additionally, to ensure good generalization in our models, we propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. The code and its documentation are publicly available at https://github.com/danifranco/EM_domain_adaptation.We carried out all possible cross-dataset experiments using three publicly available EM datasets. We evaluated our proposed strategies and those of others based on the mitochondria semantic labels predicted on the target datasets.The methods introduced here outperform the baseline methods and compare favorably to the state of the art. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.
GP-GCN: Global features of orthogonal projection and local dependency fused graph convolutional networks for aspect-level sentiment classification

Aspect-level sentiment classification, a significant task of fine-grained sentiment analysis, aims to identify the sentimental information expressed in each aspect of a given sentence The existing methods combine global features and local structures to obtain good classification results. However, the introduction of global features will bring noise and reduce the classification accuracy. To solve this problem, a new method is proposed, named GP-GCN. In our proposed method, the global feature is further simplified to reduce the noise . The local structures and global features obtained by orthogonal feature projection are introduced into aspect-level sentiment classification. First, the simplified global feature structures of text are built. Through orthogonal projection, GCN not only weakens the dependency of the graph node in updating process but also reduces the dependency between node and corpus. Next, syntactic dependency structure and sentence sequence information are utilised to mine the local dependency structure of sentences. A percentage-based multi-headed attention mechanism is proposed to measure the critical output of GCN, which can better represent sentences for given aspects. Finally, location coding is input to simulate aspect-specific representations between each aspect and its context such that the text becomes more discriminative in sentiment classification. The experimental results show that the proposed method effectively improves the accuracy of text sentiment classification.
A Brief History of Exposure Notification During the COVID-19 Pandemic in the United States, 2020-2021

The swift global spread of COVID-19 prompted public health authorities to explore digital technologies to aid in contact tracing for infection control. Exposure notification, a mobile device-based technology that notifies individuals of potential exposure to COVID-19 without requiring personally identifiable information, has been broadly favored because of its relative ease of use, scalability, and protection of personal privacy. Although several exposure notification protocols were developed, a partnership between Google and Apple led to the development of the most widely implemented exposure notification protocol in the world, including in the United States. In this article, we first describe the development of the Google Apple Exposure Notification (GAEN) protocol, noting the value of the discourse among software developers and public health authorities concerning the protocol's design and features. We track states' deployment of GAEN mobile applications (apps) and population-level adoption rates, finding the nationwide rollout of GAEN apps to be more fragmented than anticipated. We then discuss how the limited data collected from these apps make assessments of their effectiveness challenging. Finally, we consider the importance of the federal government playing a greater role in GAEN's early development, emphasize the power of public-private partnerships, and highlight the overriding importance of public messaging over technological details.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.svg"><mml:mrow><mml:msup><mml:mtext>C</mml:mtext><mml:mtext>2</mml:mtext></mml:msup><mml:mtext>S</mml:mtext></mml:mrow></mml:math>: Class-aware client selection for effective aggregation in federated learning

Federated learning is proposed to train distributed data in a safe manner by avoiding to send data to server. The server maintains a global model and sends it to clients in each communication round, and then aggregates the updated local models to derive a new global model. Traditionally, the clients are randomly selected in each round and aggregation is based on weighted averaging. Researches show that the performance on IID data is satisfactory while significant accuracy drop can be observed for Non-IID data. In this paper, we explore the reasons and propose a novel aggregation approach for Non-IID data in federated learning. Specifically, we propose to group the clients according to classes of data they have, and select one set in each communication round. Local models from the same set are averaged as usual and the updated global model is sent to next group of clients for further training. In this way, the parameters are only averaged on similar clients and passed among different groups. Evaluation shows that the proposed scheme has advantages in terms of model accuracy and convergence speed with highly unbalanced data distribution and complex models.
A Multi-Task Feature Fusion Model for Cervical Cell Classification

Cervical cell classification is a crucial technique for automatic screening of cervical cancer. Although deep learning has greatly improved the accuracy of cell classification, the performance still cannot meet the needs of practical applications. To solve this problem, we propose a multi-task feature fusion model that consists of one auxiliary task of manual feature fitting and two main classification tasks. The auxiliary task enhances the main tasks in a manner of low-layer feature fusion. The main tasks, i.e., a 2-class classification task and a 5-class classification task, are learned together to realize their mutual reinforcement and alleviate the influence of unreliable labels. In addition, a label smoothing method based on cell category similarity is designed to bring inter-cell class information into the label. Comparative experimental results with other state-of-the-art models on the HUSTC and SIPaKMeD datasets prove the effectiveness of the proposed method. With a high sensitivity of 99.82% and a specificity of 98.12% for the 2-class classification task on the HUSTC dataset, our method shows potential to reduce cytologist workload.
Deep learning in light–matter interactions

Abstract The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light–matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics.
Quantum simulation of the Lindblad equation using a unitary decomposition of operators

Accurate simulation of the time evolution of a quantum system under the influence of an environment is critical to making accurate predictions in chemistry, condensed-matter physics, and materials sciences. Whereas there has been a recent surge in interest in quantum algorithms for the prediction of nonunitary time evolution in quantum systems, few studies offer a direct quantum analog to the Lindblad equation. Here, we present a quantum algorithm---utilizing a decomposition of nonunitary operators approach---that models dynamic processes via the unraveled Lindblad equation. This algorithm is employed to probe both a two-level system in an amplitude damping channel as well as the transverse field Ising model in a variety of parameter regimes; the resulting population dynamics demonstrate excellent agreement with classical simulation, showing the promise of predicting population dynamics utilizing quantum devices for a variety of important systems in molecular energy transport, quantum optics, and other open quantum systems.
SQL Injection Attack Detection by Machine Learning Classifier

SQL Injection is one of the top 10 vulnerabilities in web-based systems. This attack essentially penetrates the logical section of the database. If the database has a logical flaw, the attackers send a new type of logical payload and get all of the user's credentials. Despite the fact that technology has advanced significantly in recent years, SQL injections can still be carried out by taking advantage of security flaws. On the Kaggle SQL Injection Dataset, authors used multiple machine learning methods to identify and detect SQL Injection assaults, including Logistic Regression, AdaBoost (Adaptive Boosting), Random Forest, Naive Bayes, and XGBoost (Extreme Gradient Boosting) Classifier. According to this research, the best strategy for detecting SQL inject is Naive Bayes, which has an accuracy of 98.33, which is 2% better than previous work.
Employees' intentions toward complying with information security controls in Saudi Arabia's public organisations

Ensuring employees' compliance with information security controls is a major challenge experienced by the management of information security in organisations. While the investment in information security has been increased recently, many organisations around the world have failed to avoid security threats and data breaches because of noncompliant employees. This study proposes a model to explore employees' differences and identifies the factors that can shape their perceptions and intentions toward compliance. The model was examined and validated in a Saudi Arabian government organisation using partial least square structural equation modelling. The results of this study support the validity of the research model and disclose the factors that have a significant influence on employees' intentions toward compliance, particularly in Saudi Arabia's public organisations. To the best of our knowledge, this is the first study to investigate employees' perceptions and intentions toward complying with information security controls in public organisations across all regions of Saudi Arabia. The study contributes to the information security management domain in terms of the model's validity, which can be used to explore the influence of the identified factors on employees' perceptions and intentions toward complying with information security controls in different cultural and environmental contexts.
The Impact of Digital Economy of Resource-Based City on Carbon Emissions Trading by Blockchain Technology

To reduce the carbon emission intensity of resource-based cities and strengthen the sustainable development of these cities, firstly, blockchain technology is analyzed. Secondly, the development of the digital economy is discussed in digital resource-based cities. Finally, according to blockchain technology, a model of carbon emissions trading in the digital economy is designed, and the specific impact of the digital economy on carbon emissions trading is studied according to the model. The research results show that the mean value of the development index of the digital economy (digital) is -0.0168, the maximum value is 4.2560, the minimum value is -1.3429, and the standard deviation is 0.9572, indicating that the quality of digital economy development varies greatly among different regions. And according to the results of the digital model, it is found that the regression coefficient of the variable digital is significantly negative at the 1% level, showing that the digital economy will obviously suppress the carbon emission intensity of cities. After replacing the explained variables, the coefficient of the digital economy is still significantly negative. It indicates that the development of the digital economy can effectively suppress the carbon emission intensity of urban. Therefore, the designed model of carbon emissions trading under the blockchain technology can not only provide a secure platform for carbon emissions trading but also provide more comprehensive trading reference information for carbon emissions trading. It provides technical support for reducing the carbon emission intensity of resource-based cities and also contributes to the development of resource-based cities.
SPCA-Net: a based on spatial position relationship co-attention network for visual question answering

Recently, the latest method of VQA (visual question answering) mainly relies on the co-attention to link each visual object with the text object, which can achieve a rough interaction between multiple models. However, VQA models tend to focus on the association between visual and language features without considering the spatial relationship between image region features extracted by Faster R-CNN. This paper proposes an effective deep co-attention network to solve this problem. As a first step, BERT was introduced in order to better capture the relationship between words and make the extracted text feature more robust; secondly, a multimodal co-attention based on spatial location relationship was proposed in order to realize fine-grained interactions between question and image. It consists of three basic components: the text self-attention unit, the image self-attention unit, and the question-guided-attention unit. The self-attention mechanism of image visual features integrates information about the spatial position and width/height of the image area after obtaining attention so that each image area is aware of the relative location and size of other areas. Our experiment results indicate that our model is significantly better than other existing models.
Improving the scheme of bidirectional controlled teleportation with a five-qubit composite GHZ-Bell state

Abstract A bidirectional controlled quantum state teleportation (BCQST) scheme was proposed by Li et al (2013 Int. J. Theor. Phys. 52 1630) using a five-qubit composite Greenberger–Horne–Zeilinger-Bell state as the quantum channel. However, it was found to be unsuccessful for bidirectional teleportation control due to its own security loopholes (Shukla et al 2013 Int. J. Theor. Phys. 52 3790). In this paper, we put forward an improved version of the BCQST scheme so that the loopholes are fixed and the bidirectional control can indeed be achieved. In addition, after the improvement, the revised scheme becomes more feasible in terms of present experimental techniques.
Towards an AI-driven talking avatar in virtual reality for investigative interviews of children

Artificial intelligence (AI) and gaming systems have advanced to the stage where the current models and technologies can be used to address real-world problems. The development of such systems comes with different challenges, e.g., most of them related to system performance, complexity and user testing. Using a virtual reality (VR) environment, we have designed and developed a game-like system aiming to mimic an abused child that can help to assist police and child protection service (CPS) personnel in interview training of maltreated children. Current research in this area points to the poor quality of conducted interviews, and emphasises the need for better training methods. Information obtained in these interviews is the core piece of evidence in the prosecution process. We utilised advanced dialogue models, talking visual avatars, and VR to build a virtual child avatar that can interact with users. We discuss our proposed architecture and the performance of the developed child avatar prototype, and we present the results from the user study conducted with CPS personnel. The user study investigates the users' perceived quality of experience (QoE) and their learning effects. Our study confirms that such a gaming system can increase the knowledge and skills of the users. We also benchmark and discuss the system performance aspects of the child avatar. Our results show that the proposed prototype works well in practice and is well received by the interview experts.
Item feature refinement using matrix factorization and boosted learning based user profile generation for content-based recommender systems

A content-based recommender system uses essential item features that play a crucial role in building quality user preference profiles. However, in most real-world datasets, the item features are highly inconsistent and sparse, making it challenging to develop efficient user profiles. Additionally, the user preference profiles created by individual learners fail to learn from the misclassification of user ratings and preferences. Thus, to resolve these problems, this paper suggests a two-fold approach to improve the performance of the content-based recommender systems. The first approach is the refinement of the existing sparsity and inconsistencies in item features using matrix factorization. The second approach is the generation of individual preference profiles using iterative boosting of multiple weak learners for penalizing the misclassification of ratings. The suggested method is tested via benchmark recommender system datasets such as ML-1M, Last.fm, and Netflix. The results obtained during experiments show a significant improvement in recommendation quality over the state-of-the-art content-based recommender system models. • The model applies item feature refinement before applying content-based filtering. • The use of matrix factorization avoids the sparsity in item feature information. • The refined item features are used for generating similarity information. • User profiles are built using the ensemble learning technique for recommendation.
Super-Resolution of Sentinel-2 Images Using a Spectral Attention Mechanism

Many visual applications require high-resolution images for an adequate interpretation of the data stored within them. In remote sensing, the appearance of satellites such as Sentinel or Landsat has facilitated the access to data thanks to their free offer of multispectral images. However, the spatial resolution of these satellites is insufficient for many tasks. Therefore, the objective of this work is to apply deep learning techniques to increase the resolution of the Sentinel-2 Read-Green-Blue-NIR (RGBN) bands from the original 10 m to 2.5 m. This means multiplying the number of pixels in the resulting image by 4, improving the perception and visual quality. In this work, we implement a state-of-the-art residual learning-based model called Super-Resolution Residual Network (SRResNet), which we train using PlanetScope-Sentinel pairs of images. Our model, named SARNet (Spectral Attention Residual Network), incorporates Residual Channel Attention Blocks (RCAB) to improve the performance of the network and the visual quality of the results. The experiments we have carried out show that SARNet offers better results than other state-of-the-art methods.
Experimental Quantum Key Distribution with Integrated Silicon Photonics and Electronics

In the burgeoning field of quantum secure communication, the ``chipization'' and integration of quantum key distribution (QKD) systems is a prime topic. Here the authors demonstrate a prototype of an integrated optoelectronic QKD transmitter: a photonics chip that integrates the essential encoding components for the decoy-state BB84 protocol, plus electronics chips that integrate the dedicated driving circuits. Experiments at a repetition rate of 312.5 MHz verify the system's performance. This work takes a key step toward system-in-package QKD applications.
Algorithm 1027: <tt>NOMAD</tt> Version 4: Nonlinear Optimization with the MADS Algorithm

NOMAD is a state-of-the-art software package for optimizing blackbox problems. In continuous development since 2001, it constantly evolved with the integration of new algorithmic features published in scientific publications. These features are motivated by real applications encountered by industrial partners. The latest major release of NOMAD , version 3, dates to 2008. Minor releases are produced as new features are incorporated. The present work describes NOMAD 4, a complete redesign of the previous version, with a new architecture providing more flexible code, added functionalities, and reusable code. We introduce algorithmic components, which are building blocks for more complex algorithms and can initiate other components, launch nested algorithms, or perform specialized tasks. They facilitate the implementation of new ideas, including the MegaSearchPoll component, warm and hot restarts, and a revised version of the PsdMads algorithm. Another main improvement of NOMAD 4 is the usage of parallelism, to simultaneously compute multiple blackbox evaluations and to maximize usage of available cores. Running different algorithms, tuning their parameters, and comparing their performance for optimization are simpler than before, while overall optimization performance is maintained between versions 3 and 4. NOMAD is freely available at www.gerad.ca/nomad and the whole project is visible at github.com/bbopt/nomad.