Reviewerly/exHarmony huggingface dataset:
Let’s do it right the first time: Survey on security concerns in the way to quantum software engineering

Quantum computing is no longer a promise of the future but a rapidly evolving reality. Advances in quantum hardware are making it possible to make tangible a computational reality that until now was only theoretical. The proof of this is that development languages and platforms are appearing that bring physical principles closer to developers, making it feasible to begin to propose, in different areas of society, solutions to problems that until now were unsolvable. However, security vulnerabilities are also emerging that could hinder the progress of quantum computing, as well as its transition and development in industry. For this reason, this article proposes a review of some of the first artefacts that are emerging in the field of quantum computing. From this analysis, we begin to identify possible security issues that could become potential vulnerabilities in the quantum software of tomorrow. Likewise, and following the experience in classical software development, the testing technique is analysed as a possible candidate for improving security in quantum software development. Following the principles of Quantum Software Engineering, we are aware of the lack of tools, techniques and knowledge necessary to guarantee the development of quantum software in the immediate future. Therefore, this article aims to offer some first clues on what would be a roadmap to guarantee secure quantum software development.
Safe Session-Based Concurrency with Shared Linear State

Abstract We introduce $$\textsf{CLASS}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>CLASS</mml:mi> </mml:math> , a session-typed, higher-order, core language that supports concurrent computation with shared linear state.
An Efficient Privacy-Enhancing Cross-Silo Federated Learning and Applications for False Data Injection Attack Detection in Smart Grids

Federated Learning is a prominent machine learning paradigm which helps tackle data privacy issues by allowing clients to store their raw data locally and transfer only their local model parameters to an aggregator server to collaboratively train a shared global model. However, federated learning is vulnerable to inference attacks from dishonest aggregators who can infer information about clients’ training data from their model parameters. To deal with this issue, most of the proposed schemes in literature either require a non-colluded server setting, a trusted third-party to compute master secret keys or a secure multiparty computation protocol which is still inefficient over multiple iterations of computing an aggregation model. In this work, we propose an efficient cross-silo federated learning scheme with strong privacy preservation. By designing a double-layer encryption scheme which has no requirement to compute discrete logarithm, utilizing secret sharing only at the establishment phase and in the iterations when parties rejoin, and accelerating the computation performance via parallel computing, we achieve an efficient privacy-preserving federated learning protocol, which also allows clients to dropout and rejoin during the training process. The proposed scheme is demonstrated theoretically and empirically to provide provable privacy against an honest-but-curious aggregator server and simultaneously achieve desirable model utilities. The scheme is applied to false data injection attack detection (FDIA) in smart grids. This is a more secure cross-silo FDIA federated learning resilient to the local private data inference attacks than the existing works.
Creation of a Holistic Platform for Health Boosting Using a Blockchain-Based Approach: Development Study

Low adherence to healthy habits, which is associated with a higher risk of disease and death, among citizens of Organization for Economic Co-operation and Development countries is a serious concern. The World Health Organization (WHO) and the physical activity (PA) guidelines for Americans provide recommendations on PA and healthy diets. To promote these habits, we suggest using a blockchain-based platform, using the PA Messaging Framework to deliver messages and rewards to users. Blockchain is a decentralized secure platform for data management, which can be used for value-added controls and services such as smart contracts (SCs), oracles, and decentralized applications (dApps). Of note, there is a substantial penetration of blockchain technologies in the field of PA, but there is a need for more implementations of dApps to take advantage of features such as nonfungible tokens.This study aimed to create a comprehensive platform for promoting healthy habits, using scientific evidence and blockchain technology. The platform will use gamification to encourage healthy PA and eating habits; in addition, it will monitor the activities through noninvasive means, evaluate them using open-source software, and follow up through blockchain messages.A literature search was conducted on the use of blockchain technology in the field of PA and healthy eating. On the basis of the results of this search, it is possible to define an innovative platform for promoting and monitoring healthy habits through health-related challenges on a dApp. Contact with the user will be maintained through messages following a proposed model in the literature to improve adherence to the challenges.The proposed strategy is based on a dApp that relies on blockchain technology. The challenges include PA and healthy eating habits based on the recommendations of the WHO and the Food and Agriculture Organization. The system is constituted of a blockchain network where challenge-related achievements are stored and verified using SCs. The user interacts with the system through a dApp that runs on their local device, monitors the challenge, and self-authenticates by providing their public and private keys. The SC verifies challenge fulfillment and generates messages, and the information stored in the network can be used to encourage competition among participants. The ultimate goal is to create a habit of healthy activities through rewards and peer competition.The use of blockchain technology has the potential to improve people's quality of life through the development of relevant services. In this work, strategies using gamification and blockchain are proposed for monitoring healthy activities, with a focus on transparency and reward allocation. The results are promising, but compliance with the General Data Protection Regulation is still a concern. Personal data are stored on personal devices, whereas challenge data are recorded on the blockchain.
Bag of On-Phone ANNs to Secure IoT Objects Using Wearable and Smartphone Biometrics

The introduction of the Internet of Things (IoT) has made several emerging applications, from financial transactions to property access, possible through IoT-connected smart wearables (smartwatches). This creates an immediate need for an authentication system that can validate a user seamlessly, compared to knowledge-based approaches. In this work, we present an implicit authentication system that utilizes a bag of on-phone artificial neural network (ANN) models to validate a user based on the availability of three soft-biometrics (heart rate, gait, and breathing patterns) collected from smartphones and Fitbits. We find that using all three biometrics we can achieve an average accuracy of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$.973 \pm . 004$</tex-math></inline-formula> . Next, we implement the bag of models on smartphones using Google's TensorFlow Lite framework-supported <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TFL Auth</i> application, which requires around 56-65 KB memory and can verify a user in 5 seconds. Finally, we evaluate the system <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TFL Auth</i> using two cohorts of 25 subjects in total, and we find that the system has average understandability and importance scores of around 4.0 and 4.3 on a 1 – 5 scale.
Hybrid HP-BOA: An Optimized Framework for Reliable Storage of Cloud Data Using Hybrid Meta-Heuristic Algorithm

The prime objective of the cloud data storage process is to make the service, irrespective of being infinitely extensible, a more reliable storage and low-cost model that also encourages different data storage types. Owing to the storage process, it must satisfy the cloud users’ prerequisites. Nevertheless, storing massive amounts of data becomes critical as this affectsthe data quality or integrity. Hence, this poses various challenges for existing methodologies. An efficient, reliable cloud storage model is proposed using a hybrid heuristic approach to overcome the challenges. The prime intention of the proposed system is to store the data effectively in the cloud environment by resolving two constraints, which are general and specific (structural). The cloud data were initially gathered and used to analyze the storage performance. Since the data were extensive, different datasets and storage devices were considered. Every piece of data was specified by its corresponding features, whereas the devices were characterized by the hardware or software components. Subsequently, the objective function was formulated using the network’s structural and general constraints. The structural constraints were determined by the interactions between the devices and data instances in the cloud. Then, the general constraints regarding the data allocation rules and device capacity were defined. To mitigate the constraints, the components were optimized using the Hybrid Pelican–Billiards Optimization Algorithm (HP-BOA) to store the cloud data. Finally, the performance was validated, and the results were analyzed and compared against existing approaches. Thus, the proposed model exhibited the desired results for storing cloud data appropriately.
Sampling technique for noisy and borderline examples problem in imbalanced classification

Class imbalance Learning (CIL) is an important machine learning branch. Due to an imbalanced dataset, the efficiency of the classifiers is impacted. Various under/oversampling approaches are applied to the dataset to solve the class imbalance problem. The most successful among all the present solutions for data imbalance is the Synthetic Minority Oversampling Technique (SMOTE) which has a broad range of handling real-world applications. Noise and borderline examples are the two factors that degrade the performance of SMOTE and its variants. To address these issues, filtering-based methods have been developed. However, there are drawbacks associated with the filtering method. Firstly, error detection approaches in filtering methods are highly dependent on parameter settings. Secondly, samples identified during error detection are deleted or filtered from the sampling process which leads to abnormality of obtaining decision boundary and thus problem of again the class imbalance problem. To fix the problems associated with state-of-the-art filtering-based approaches, a novel oversampling filter-based method SMOTE-TLNN-DEPSO is proposed in this paper. In this hybrid variant SMOTE-TLNN-DEPSO, using SMOTE method the synthetic samples are generated to enhance the original class-imbalance data. Next, the two-layer natural neighbors' technique is used for error detection which identifies the noisy and borderline examples. Lastly, instead of deleting the identified noisy and borderline examples, the hybrid variant of the differential evolution (DE) algorithm based on particle swarm optimization (PSO) called DEPSO is applied to optimize and modify iteratively the position (attributes). SMOTE-TLNN-DEPSO technique shows the advantage over other state of art SMOTE-based filtering-based approaches by solving the noise problem; the error detection technique using the nearest neighbor is parameter-free; Utilizing DEPSO approach the identified noisy samples by error detection technique are optimized instead of removing them. This helps in maintaining the imbalance ratio and improving the boundary; this approach is very appropriate for data sets having more noisy attributes especially class attributes the efficiency and usefulness of the proposed SMOTE-TLNN-DEPSO are demonstrated by exhaustive comparison experiments on artificial and real data sets.
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?

The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based models (e.g., BERT) or their variants. However, training such models and exploring their hyperparameter space is computationally expensive. Prior work proposes several neural architecture search (NAS) methods that employ performance predictors (e.g., surrogate models) to address this issue; however, such works limit analysis to homogeneous models that use fixed dimensionality throughout the network. This leads to sub-optimal architectures. To address this limitation, we propose a suite of heterogeneous and flexible models, namely FlexiBERT, that have varied encoder layers with a diverse set of possible operations and different hidden dimensions. For better-posed surrogate modeling in this expanded design space, we propose a new graph-similarity-based embedding scheme. We also propose a novel NAS policy, called BOSHNAS, that leverages this new scheme, Bayesian modeling, and second-order optimization, to quickly train and use a neural surrogate model to converge to the optimal architecture. A comprehensive set of experiments shows that the proposed policy, when applied to the FlexiBERT design space, pushes the performance frontier upwards compared to traditional models. FlexiBERT-Mini, one of our proposed models, has 3% fewer parameters than BERT-Mini and achieves 8.9% higher GLUE score. A FlexiBERT model with equivalent performance as the best homogeneous model has 2.6× smaller size. FlexiBERT-Large, another proposed model, attains state-of-the-art results, outperforming the baseline models by at least 5.7% on the GLUE benchmark.
TC-Net: A joint learning framework based on CNN and vision transformer for multi-lesion medical images segmentation

With the rapid advancement of medical imaging technology, the demand for accurate segmentation of medical images is increasing. However, most existing methods are unable to capture locality and long-range dependency information in integrated ways for medical images. In this paper, we propose an elegant segmentation framework for medical images named TC-Net, which can utilize both the locality-aware and long-range dependencies in the medical images. As for the locality-aware perspective, we employ a CNN-based encoder and decoder structure. The CNN branch uses the locality of convolution operations to dig out local information in medical images. As for the long-range dependencies, we construct a Transformer branch to focus on the global context. Additionally, we proposed a locality-aware and long-range dependency concatenation strategy (LLCS) to aggregate the feature maps obtained from the two subbranches. Finally, we present a dynamic cyclical focal loss (DCFL) to address the class imbalance problem in multi-lesion segmentation. Comprehensive experiments were conducted on lesion segmentation tasks using two fundus image databases and a skin image database. The TC-Net achieves scores of 0.6985 and 0.5171 in the metric of mean pixel accuracy on the IDRiD and DDR databases, respectively. Moreover, on the skin image database, the TC-Net reached mean pixel accuracy of 0.8886. The experiment results demonstrate that the proposed method achieves better performance than other deep learning segmentation schemes. Furthermore, the proposed DCFL achieves higher performance than other loss functions in multi-lesion segmentation. The proposed TC-Net is a promising new framework for multi-lesion medical image segmentation and many other challenging image segmentation tasks. © 2001 Elsevier Science. All rights reserved.
Clothoid-Based Path Planning for a Formation of Fixed-Wing UAVs

Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many applications such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning.
Action Capsules: Human skeleton action recognition

Due to the compact and rich high-level representations offered, skeleton-based human action recognition has recently gained more attraction. Although joint relationships investigation in spatial and temporal dimensions provides effective information critical to action recognition, effectively encoding global dependencies of joints during spatio-temporal feature extraction is a prohibitive task. In this paper, we introduce Action Capsule which identifies action-related key joints by considering the latent correlation of joints in a skeleton sequence. We show that, during inference, our end-to-end network pays attention to a set of joints specific to each action, whose encoded spatio-temporal features are aggregated to recognize the action. Additionally, the use of multiple stages of action capsules enhances the ability of the network to classify similar actions. A comparative analysis of our capsule-based approach with other widely-used methods in skeleton action recognition is given, highlighting the advantages of the proposed approach in handling missing skeleton data by leveraging iterative processing. Consequently, our network outperforms the state-of-the-art approaches on the N-UCLA dataset and obtains competitive results on the NTURGBD dataset. This is while our approach has significantly lower computational requirements based on GFLOPs measurements.
Weakly-Supervised 3D Spatial Reasoning for Text-Based Visual Question Answering

Text-based Visual Question Answering (TextVQA) aims to produce correct answers for given questions about the images with multiple scene texts. In most cases, the texts naturally attach to the surface of the objects. Therefore, spatial reasoning between texts and objects is crucial in TextVQA. However, existing approaches are constrained within 2D spatial information learned from the input images and rely on transformer-based architectures to reason implicitly during the fusion process. Under this setting, these 2D spatial reasoning approaches cannot distinguish the fine-grained spatial relations between visual objects and scene texts on the same image plane, thereby impairing the interpretability and performance of TextVQA models. In this paper, we introduce 3D geometric information into the spatial reasoning process to capture the contextual knowledge of key objects step-by-step. Specifically, (i) we propose a relation prediction module for accurately locating the region of interest of critical objects; (ii) we design a depth-aware attention calibration module for calibrating the OCR tokens’ attention according to critical objects. Extensive experiments show that our method achieves state-of-the-art performance on TextVQA and ST-VQA datasets. More encouragingly, our model surpasses others by clear margins of 5.7% and 12.1% on questions that involve spatial reasoning in TextVQA and ST-VQA valid split. Besides, we also verify the generalizability of our model on the text-based image captioning task.
Improved GBS-YOLOv5 algorithm based on YOLOv5 applied to UAV intelligent traffic

As the road traffic situation becomes complex, the task of traffic management takes on an increasingly heavy load. The air-to-ground traffic administration network of drones has become an important tool to promote the high quality of traffic police work in many places. Drones can be used instead of a large number of human beings to perform daily tasks, as: traffic offense detection, daily crowd detection, etc. Drones are aerial operations and shoot small targets. So the detection accuracy of drones is less. To address the problem of low accuracy of Unmanned Aerial Vehicles (UAVs) in detecting small targets, we designed a more suitable algorithm for UAV detection and called GBS-YOLOv5. It was an improvement on the original YOLOv5 model. Firstly, in the default model, there was a problem of serious loss of small target information and insufficient utilization of shallow feature information as the depth of the feature extraction network deepened. We designed the efficient spatio-temporal interaction module to replace the residual network structure in the original network. The role of this module was to increase the network depth for feature extraction. Then, we added the spatial pyramid convolution module on top of YOLOv5. Its function was to mine small target information and act as a detection head for small size targets. Finally, to better preserve the detailed information of small targets in the shallow features, we proposed the shallow bottleneck. And the introduction of recursive gated convolution in the feature fusion section enabled better interaction of higher-order spatial semantic information. The GBS-YOLOv5 algorithm conducted experiments showing that the value of mAP@0.5 was 35.3[Formula: see text] and the mAP@0.5:0.95 was 20.0[Formula: see text]. Compared to the default YOLOv5 algorithm was boosted by 4.0[Formula: see text] and 3.5[Formula: see text], respectively.