Reviewerly/exHarmony huggingface dataset:
Boosting Object Detectors via Strong-Classification Weak-Localization Pretraining in Remote Sensing Imagery

Deep learning-based object detectors in remote sensing (RS) scenarios typically follow the paradigm of pretraining and fine-tuning, to alleviate the limitation of insufficient downstream data. Despite the improved performance, existing pretraining paradigms are sub-optimal due to three deficiencies: 1) inconsistent domains, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., pretraining on natural scenes and fine-tuning for RS scenes, 2) mismatched task objectives, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., classification-oriented pretraining while detection-oriented fine-tuning, and 3) misaligned architectures, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., pretraining only one bare backbone yet neglecting other vital detection components. Against these issues, this paper proposes a novel pretraining paradigm specifically for the task of RS object detection, namely RS strong-classification weak-localization (SCWL) pretraining. Unlike conventional classification pretraining, such as the widely used ImageNet pretraining, our pretraining strategy can adaptively perform bounding box generation on a reconstructed large-scale RS classification-style dataset. These pseudo bounding boxes are integrated with the original accurate class labels as location- and category-related supervisions, respectively, to pretrain the entire RS detectors. The proposed RS SCWL pretraining paradigm is able to significantly improve downstream detection performance and outperforms classification pretraining methods, including ImageNet pretraining. Extensive experiments on different object detection datasets demonstrate its effectiveness and superiority in boosting various RS detectors.
An Ensemble-Based Framework to Estimate Software Project Effort

Effort estimation is essential for successful software project planning, budgeting, and risk identification. However, the techniques used to estimate effort are often inaccurate, outdated, and only consider technical factors while neglecting project management or stakeholder engagement. Expert estimation remains an important technique for leveraging human expertise in software estimation, but solely relying on this technique causes biased and subjective predictions. Machine learning (ML) techniques have shifted the direction of software project effort estimation towards computational intelligence. Nonetheless, there is a lack of deployment due to ambiguous outcomes and ineffective model-building approaches. This study presents an ensemble-based framework that can estimate software project effort more accurately with the incorporation of domain knowledge and experiences. To achieve this, six homogeneous classifier ensembles will be constructed using six distinct classifiers on the proposed USP05-FT dataset. The collected expert estimations will be integrated into the proposed dataset as an additional feature in the form of numerical values such as expert-provided software project effort estimations (in person hours) that provide additional insight and knowledge. Subsequently, the predictions of all six homogeneous classifier ensembles will be combined through majority voting to obtain a more accurate and reliable prediction with increased robustness against errors and uncertainties. The performance of the proposed framework will be evaluated using Recall, F-measure, Precision, and Accuracy. It is expected that the proposed ensemble-based framework for software project effort estimation will lead to more efficient and effective software project management, an improvement in resource allocation, empowering informed decision-making, and timely project delivery.
Designing an E-Voting Framework Using Blockchain: A Secure and Transparent Attendance Approach

With the increasing demand for secure, trustworthy, and transparent voting systems, electronic voting (e-voting) has emerged as a promising solution to address the shortcomings of traditional methods. However, traditional e-voting systems face numerous challenges in ensuring integrity, privacy, and auditability such as paper-based ballots and electronic voting machines. These traditional systems are susceptible to issues such as voter fraud, coercion, and a lack of transparency, undermining the democratic process. This paper proposes a novel approach to designing an e-voting framework using blockchain technology. By leveraging the distributed ledger and consensus mechanisms of blockchain, our framework aims to provide a secure, transparent, and tamper-resistant voting system. These features enable the creation of a secure and transparent voting system that ensures the integrity of votes, protects voter privacy, and enables verifiability and auditability of the entire voting process. The paper presents the key components, design considerations, and benefits of the proposed framework, along with an analysis of its potential challenges and future directions for research and development. The system architecture of the proposed framework establishes communication channels, data flows, and interfaces that facilitate voters' attendance, secure interactions and information exchange within the voting system. The use of smart contracts helps enforce the rules and conditions of the voting process on the blockchain, ensuring the accuracy and fairness of the electoral outcome. In conclusion, the proposed e-voting framework using blockchain technology has the potential to revolutionize the electoral process by providing a secure, transparent, and tamper-resistant voting system. By addressing the challenges of traditional e-voting systems and leveraging the inherent features of blockchain technology, we can enhance the integrity, privacy, and trustworthiness of the voting process.
Quality of service aware improved coati optimization algorithm for efficient task scheduling in cloud computing environment

The task scheduling has a potential impact on overall system performance and resource utilization in the domain of Cloud Computing. Cloud computing adopts a cloud service provider (CSP) for facilitating access and delivering services to the shared resources. Task scheduling in clouds can be attained some symmetry form which helps in achieving predominant resource optimization that includes energy efficiency and load balancing. This task scheduling process of cloud computing pertains to the problem of Non-deterministic Polynomial (NP) which can be significantly solved using the techniques of metaheuristic optimization for enhancing the job scheduling effectiveness. In this paper, an Improved Coati Optimization Algorithm-based Task Scheduling (ICOATS) is presented for addressing the issues of lengthier scheduling time, high consumptions of cost and maximized load on Virtual Machine (VM) in cloud computing environment. In this proposed ICOATS, a model for distribution and scheduling of tasks is constructed using the factors of VMs, cost and time. It further included a multi-objective fitness function which targets on minimizing makespan, and at the same time maximizing the rate of resource utilization. It established possible plan for every coati with respect to task scheduling process that aids in determining the best solution (optimal assignment of incoming tasks to VMs). It is proposed with the capability of handing the problem of premature convergence by incorporating an exploitation strategy which improving the local search potential with well-balanced trade-off amid exploration and exploitation. The simulation results of this ICOATS approach under its evaluation with the existing metaheuristic task scheduling approaches confirmed better improvement in reducing makespan, and simultaneously enhances the turnaround efficiency, success rate and availability.
Unsupervised anomaly detection by densely contrastive learning for time series data

Time series data continuously collected by different sensors play an essential role in monitoring and predicting events in many real-world applications, and anomaly detection for time series has received increasing attention during the past decades. In this paper, we propose an anomaly detection method by densely contrasting the whole time series with its sub-sequences at different timestamps in a latent space. Our approach leverages the locality property of convolutional neural networks (CNN) and integrates position embedding to effectively capture local features for sub-sequences. Simultaneously, we employ an attention mechanism to extract global features from the entire time series. By combining these local and global features, our model is trained using both instance-level contrastive learning loss and distribution-level alignment loss. Furthermore, we introduce a reconstruction loss applied to the extracted global features to prevent the potential loss of information. To validate the efficacy of our proposed technique, we conduct experiments on publicly available time-series datasets for anomaly detection. Additionally, we evaluate our method on an in-house mobile phone dataset aimed at monitoring the status of Parkinson's disease, all within an unsupervised learning framework. Our results demonstrate the effectiveness and potential of the proposed approach in tackling anomaly detection in time series data, offering promising applications in real-world scenarios.
Correcting non-independent and non-identically distributed errors with surface codes

A common approach to studying the performance of quantum error correcting codes is to assume independent and identically distributed single-qubit errors. However, the available experimental data shows that realistic errors in modern multi-qubit devices are typically neither independent nor identical across qubits. In this work, we develop and investigate the properties of topological surface codes adapted to a known noise structure by Clifford conjugations. We show that the surface code locally tailored to non-uniform single-qubit noise in conjunction with a scalable matching decoder yields an increase in error thresholds and exponential suppression of sub-threshold failure rates when compared to the standard surface code. Furthermore, we study the behaviour of the tailored surface code under local two-qubit noise and show the role that code degeneracy plays in correcting such noise. The proposed methods do not require additional overhead in terms of the number of qubits or gates and use a standard matching decoder, hence come at no extra cost compared to the standard surface-code error correction.
A Simulation-Based Approach to Evaluate the Performance of Automated Surveillance Camera Systems for Smart Cities

A surveillance camera is the typical device that makes up a surveillance camera system in a modern city. It will still be a representative surveillance unit in future scenarios such as in smart cities. Furthermore, as the demand for public safety increases, a massive number of surveillance cameras will be in use in the future, and an automated system that controls surveillance cameras intelligently will also be required. Meanwhile, installing a surveillance system without any verification system might not be cost-effective, so a simulation that evaluates the system’s performance is required in advance. For this reason, we introduce how to simulate a surveillance area and evaluate surveillance performance in this paper to assess a surveillance system consisting of large amounts of surveillance cameras. Our simulator defined the surveillance area as a pair of two-dimensional planes, which depend on various camera-related configurations. Both surveillance areas are used to determine if the moving object belongs to the coverage of a surveillance camera. In addition, our simulator adopts several performance indices to evaluate a surveillance camera system in terms of target detection and quality. The simulation study provides comprehensive results on how various components of the surveillance system affect the performance of the surveillance system, leading to the conclusion that building a sophisticated scheme to control a large number of surveillance cameras can provide a cost-effective and reliable surveillance system for smart cities.
Pure dephasing induced single-photon parametric down-conversion in a strongly coupled plasmon-exciton system

We propose highly efficient photon pair generation via down-conversion in a polaritonic cyclic three-level system formed via coupling an exciton and a localized plasmon polariton. Pure dephasing of the exciton enables optical transitions from the upper to lower polariton state via symmetry breaking and thus opens a pathway for the cascaded emission of two photons. The strength of this down-conversion pathway is determined by the coupling strength between exciton and plasmon polariton and the exciton dephasing rate. In the case of strong up to ultrastrong coupling and strong dephasing, this results in a nanoscopic ${\ensuremath{\chi}}^{(2)}$-system capable of few-photon down-conversion. A semiconductor quantum dot embedded in a gap plasmon, which supports strong coupling between plasmon and exciton, in combination with fast pure dephasing in the quantum dot exciton, allows one to reach conversion efficiencies down to the single-photon limit under experimentally achievable parameters at ambient conditions.
Beyond Staircasing Effect: Robust Image Smoothing via ℓ0 Gradient Minimization and Novel Gradient Constraints

In this paper, we propose robust image-smoothing methods based on ℓ0 gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the ℓ0 gradient, i.e., the number of nonzero gradients in an image, and the ℓ2 data fidelity results in a smooth image. However, this optimization often leads to undesirable artifacts, such as pseudo-edges, known as the “staircasing effect”, and halos, which become more visible in image enhancement tasks, like detail enhancement and tone mapping. To address these issues, we introduce two types of gradient constraints: box and ball. These constraints are applied using a reference image (e.g., the input image is used as a reference for image smoothing) to suppress pseudo-edges in homogeneous regions and the blurring effect around strong edges. We also present an ℓ0 gradient minimization problem based on the box-/ball-type gradient constraints using an alternating direction method of multipliers (ADMM). Experimental results on important applications of ℓ0 gradient minimization demonstrate the advantages of our proposed methods compared to existing ℓ0 gradient-based approaches.
Securing the Future Railway System: Technology Forecast, Security Measures, and Research Demands

The railway industry—traditionally a conservative industry with low adaption speed for innovation—is currently entering its digitization phase. The sector faces a challenge in integrating new technologies and approaches into the employed—often safety-critical—systems. Keeping the systems secure while conforming to the demanding safety norms creates previously unknown problems. In the last decades, the number of attacks on the railway system has increased. Furthermore, with standardized digital technologies, the attack surface will keep growing. Therefore, in this work, we look into the foreseeable future of the railway system and present 21 likely use cases. We analyze these use cases regarding possible threats, rate the severity of these threats, and deduce and rate necessary countermeasures. To this end, we model these use cases and the corresponding threats and countermeasures using Attack Graphs. We use a graphical solution for the risk and security analysis due to advantages over other methods, i.e., table-based solutions, like simplified presentation and an easier understanding of relationships, dependencies, and interactions between various elements. From these Attack Graphs, we extracted 14 commonly recurring attack strategies. After analyzing 49 countermeasures regarding their current maturity and further research and standardization demands, we identified 21 in need of further investigation. This implies that 21 necessary countermeasures to secure these future use cases require further research to apply to railway systems or require standardization. These results will help researchers focus on the necessary research and standardization and railway operators to ensure the security of their systems.
Dual Histogram Equalization Algorithm Based on Adaptive Image Correction

For the visual measurement of moving arm holes in complex working conditions, a histogram equalization algorithm can be used to improve image contrast. To lessen the problems of image brightness shift, image over-enhancement, and gray-level merging that occur with the traditional histogram equalization algorithm, a dual histogram equalization algorithm based on adaptive image correction (AICHE) is proposed. To prevent luminance shifts from occurring during image equalization, the AICHE algorithm protects the average luminance of the input image by improving upon the Otsu algorithm, enabling it to split the histogram. Then, the AICHE algorithm uses the local grayscale correction algorithm to correct the grayscale to prevent the image over-enhancement and gray-level merging problems that arise with the traditional algorithm. It is experimentally verified that the AICHE algorithm can significantly improve the histogram segmentation effect and enhance the contrast and detail information while protecting the average brightness of the input image, and thus the image quality is significantly increased.
Optimization of Median Modified Wiener Filter for Improving Lung Segmentation Performance in Low-Dose Computed Tomography Images

In low-dose computed tomography (LDCT), lung segmentation effectively improves the accuracy of lung cancer diagnosis. However, excessive noise is inevitable in LDCT, which can decrease lung segmentation accuracy. To address this problem, it is necessary to derive an optimized kernel size when using the median modified Wiener filter (MMWF) for noise reduction. Incorrect application of the kernel size can result in inadequate noise removal or blurring, degrading segmentation accuracy. Therefore, various kernel sizes of the MMWF were applied in this study, followed by region-growing-based segmentation and quantitative evaluation. In addition to evaluating the segmentation performance, we conducted a similarity assessment. Our results indicate that the greatest improvement in segmentation performance and similarity was at a kernel size 5 × 5. Compared with the noisy image, the accuracy, F1-score, intersection over union, root mean square error, and peak signal-to-noise ratio using the optimized MMWF were improved by factors of 1.38, 33.20, 64.86, 7.82, and 1.30 times, respectively. In conclusion, we have demonstrated that by applying the MMWF with an appropriate kernel size, the optimization of noise and blur reduction can enhance segmentation performance.
Advancing into Spaces of Possibility: How the Fridays for Future Movement Intertwines Future-Making Practices with the Creation of Educational Formats

Abstract The article explores the question of how political and pedagogical programs are intertwined in the process of designing a sustainable future, using the climate change protest movement Fridays for Future (FFF) as an empirical example. Particular attention is payed to the German Public Climate Schools, a collection of educational courses offered by FFF. The FFF movement emphasizes science and education and has created postdigital spaces for climate education. The article outlines the connection between education and the future, describes FFF and its approach to designing a sustainable future, explores the educational spaces developed by the movement via an analysis of some of the movement’s Twitter (now rebranding as ‘X’) feeds and web site, and provides a basis for further reflection and exploration. It is argued that the FFF movement represents a contemporary form of political future practice that is focused on creating a livable and shapeable future within a postdigital context.
Learning with Euler Collaborative Representation for Robust Pattern Analysis

The Collaborative Representation (CR) framework has provided various effective and efficient solutions to pattern analysis. By leveraging between discriminative coefficient coding (l 2 regularization) and the best reconstruction quality (collaboration), the CR framework can exploit discriminative patterns efficiently in high-dimensional space. Due to the limitations of its linear representation mechanism, the CR must sacrifice its superior efficiency for capturing the non-linear information with the kernel trick. Besides this, even if the coding is indispensable, there is no mechanism designed to keep the CR free from inevitable noise brought by real-world information systems. In addition, the CR only emphasizes exploiting discriminative patterns on coefficients rather than on the reconstruction. To tackle the problems of primitive CR with a unified framework, in this article we propose the Euler Collaborative Representation (E-CR) framework. Inferred from the Euler formula, in the proposed method, we map the samples to a complex space to capture discriminative and non-linear information without the high-dimensional hidden kernel space. Based on the proposed E-CR framework, we form two specific classifiers: the Euler Collaborative Representation based Classifier (E-CRC) and the Euler Probabilistic Collaborative Representation based Classifier (E-PROCRC). Furthermore, we specifically designed a robust algorithm for E-CR (termed as R-E-CR ) to deal with the inevitable noises in real-world systems. Robust iterative algorithms have been specially designed for solving E-CRC and E-PROCRC. We correspondingly present a series of theoretical proofs to ensure the completeness of the theory for the proposed robust algorithms. We evaluated E-CR and R-E-CR with various experiments to show its competitive performance and efficiency.
Research progress in quantum key distribution

Quantum key distribution (QKD) is a sophisticated method for securing information by leveraging the principles of quantum mechanics. Its objective is to establish a confidential key between authorized partners who are connected via both a quantum channel and a classical authentication channel. This paper presents a comprehensive overview of QKD protocols, chip-based QKD systems, quantum light sources, quantum detectors, fiber-based QKD networks, space-based QKD systems, as well as the applications and prospects of QKD technology.
AOGC: Anchor-Free Oriented Object Detection Based on Gaussian Centerness

Oriented object detection is a challenging task in scene text detection and remote sensing image analysis, and it has attracted extensive attention due to the development of deep learning in recent years. Currently, mainstream oriented object detectors are anchor-based methods. These methods increase the computational load of the network and cause a large amount of anchor box redundancy. In order to address this issue, we proposed an anchor-free oriented object detection method based on Gaussian centerness (AOGC), which is a single-stage anchor-free detection method. Our method uses contextual attention FPN (CAFPN) to obtain the contextual information of the target. Then, we designed a label assignment method for the oriented objects, which can select positive samples with higher quality and is suitable for large aspect ratio targets. Finally, we developed a Gaussian kernel-based centerness branch that can effectively determine the significance of different anchors. AOGC achieved a mAP of 74.30% on the DOTA-1.0 datasets and 89.80% on the HRSC2016 datasets, respectively. Our experimental results show that AOGC exhibits superior performance to other methods in single-stage oriented object detection and achieves similar performance to the two-stage methods.