Reviewerly/exHarmony huggingface dataset:
Satellite-based quantum communication and extended physical theory tests in space

We present a compact room temperature single photon source based on a color center in hexagonal boron nitride for future long-distance satellite-based quantum networks. The performance of this quantum light source is sufficient to outperform state-of-the-art laser-based decoy quantum key distribution protocols. The emitter is directly coupled to a photonic integrated circuit that routes the single photons to different experiments. This includes both a verification of the single photon source via measuring the photon statistics, as well as a fundamental test extended quantum theory in microgravity. The payload is currently being integrated on a 3U CubeSat and will be launched in 2024 as part of the QUICK<sup>3</sup> mission.
To what extent are color and texture effective in person re-identification

In this study, we analyze the effect of color and texture information on person re-identification models. Identifying a person among different cameras involves several problems such as camera viewpoint and illumination changes, background dissimilarity, color tone as well as human pose changes. Thus, the performance of person re-identification strongly depends on the scenarios and datasets that are published for research. No matter how much convolutional neural network models (CNNs) improve, the limitations are valid since they depend on the nature of the problem. We classify person re-identification as a matching problem and focus on the effects of color and texture on the similarity scores of actual and false matches. The detailed analyses indicate that color is the most dominant cue for person re-identification and color constancy is vital to perform robust re-identification among different cameras. Besides, texture has less effect compared to color. According to these observations, we advise using color augmentations along the training stage of CNNs for re-identification problems.
Aerial image segmentation with minimal annotation effort using clustering of pretrained embeddings

antic segmentation of aerial images is a critical task in various domains such as urban planning, and monitoring deforestation or critical infrastructure. However, the annotation process required for training accurate segmentation models is often time-consuming and labor-intensive. This paper presents a novel approach to address this challenge by leveraging the power of clustering techniques applied to the embeddings obtained from a SimCLRv2 model pretrained on the ImageNet dataset. By using this clustering approach, fewer training samples are needed, and the annotation only needs to be done for each cluster instead of each pixel in the image, significantly reducing the annotation time. Our proposed method uses SimCLRv2 to obtain rich feature representations (embeddings) from a dataset of unlabeled aerial images. These embeddings are then subjected to clustering, enabling the grouping of semantically similar image regions. In addition to directly using these clusters as class labels, we can treat these clusters as pseudo-classes, allowing us to construct a pseudo-label dataset for fine-tuning a segmentation network. Through experiments conducted on two benchmark aerial image datasets (Potsdam and Vaihingen), we demonstrate the effectiveness of our approach in achieving segmentation results in line with similar works on few-shot segmentation while significantly reducing the annotation effort required, thereby highlighting its practical applicability. Overall, the combination of SimCLRv2 embeddings and clustering techniques presents a promising avenue for achieving accurate image segmentation while minimizing the annotation burden, making it highly relevant for remote sensing applications and aerial imagery analysis.
Covert fault detection with imbalanced data using an improved autoencoder for optical networks

Covert faults are characterized by the performance parameters falling within the normal range, without any observable abnormalities. These types of faults pose a significant risk as they present no apparent warning signs of potential danger. Therefore, it is crucial to establish an efficient covert fault detection method to ensure the reliable and stable operation of optical networks. Data-driven technology, which reveals the internal relations and data patterns between the historical data by mining and analyzing the historical data, offers a new perspective for covert fault detection. However, equipment failures are extremely rare in real optical network systems, and the data imbalance of covert fault samples poses a challenge for standard machine learning classifiers in learning precise decision boundaries. To address this challenge, we propose a fault detection scheme based on an improved autoencoder for covert fault detection under data imbalance. The designed covert fault detection model exclusively utilizes normal samples during training and remains unaffected by data imbalance. Specifically, the model is specifically designed according to a number of encoder and decoder components to learn the normal sample data patterns in the latent space and detect covert faults based on the reconstruction errors in that space. To validate the proposed scheme, we conducted experiments using actual backbone data. According to the results, the detection accuracy and F1 score of the designed model on the test set were 0.9811 and 0.9527, and the false negative and false positive rates were 0.0026 and 0.0227, respectively. Furthermore, the visualization of the latent space reconstruction error principle for detecting covert faults was implemented using the principal component analysis dimension reduction and scatter plots.
Hybrid Cross-Feature Interaction Attention Module for Object Detection in Intelligent Mobile Scenes

Object detection is one of the fundamental tasks in computer vision, holding immense significance in the realm of intelligent mobile scenes. This paper proposes a hybrid cross-feature interaction (HCFI) attention module for object detection in intelligent mobile scenes. Firstly, the paper introduces multiple kernel (MK) spatial pyramid pooling (SPP) based on SPP and improves the channel attention using its structure. This results in a hybrid cross-channel interaction (HCCI) attention module with better cross-channel interaction performance. Additionally, we bolster spatial attention by incorporating dilated convolutions, leading to the creation of the cross-spatial interaction (CSI) attention module with superior cross-spatial interaction performance. By seamlessly combining the above two modules, we achieve an improved HCFI attention module without resorting to computationally expensive operations. Through a series of experiments involving various detectors and datasets, our proposed method consistently demonstrates superior performance. This results in a performance improvement of 1.53% for YOLOX on COCO and a performance boost of 2.05% for YOLOv5 on BDD100K. Furthermore, we propose a solution that combines HCCI and HCFI to address the challenge of extremely small output feature layers in detectors, such as SSD. The experimental results indicate that the proposed method significantly improves the attention capability of object detection in intelligent mobile scenes.
The impact of educational training on improving the vigilance of public officials against cyber-attacks

Cybersecurity today is a phenomenon of global and multidimensional proportions. Protecting against cyber-attacks is a challenge for governments and the private sector. Increasing the security and protection of data and critical infrastructure against cyber-attacks is one of the essential factors of an institution that manages confidential data. The authors first present the findings from the research conducted regarding the skills that public officials have on various risks from cyber-attacks. The authors then present the results from testing the skills of public officials through the simulation of two cyber-attacks and the method applied by one public institution in Kosovo (as case study for this research is used Ministry of Internal Affairs - MIA) to raise the awareness of its officials about the various risks of cyber-attacks. Also, this paper focuses on analyzing the results before and after increasing the skills of MIA officials against cyber-attacks, as well as giving concrete recommendations from the results and analyses made. The presented results show that the applied method for raising the awareness of public officials on the risks of cyber-attacks has given impressive results. The obtained results are supported by statistics obtained from the conducted tests.
CPS in block chain smart city application based on distributed ledger based decentralized technique

This article offers a brief overview about cyber-physical systems enabled by blockchain (CPS). Dissects various blockchain-enabled CPS reported on the operations and blockchain characteristics used in the literature. Base of its time sensitiveness and throughput requirements, we identify and categories key common CPS operations that can be activated by blockchain. We also develop blockchain features and categories in terms of diverse levels of benefits to CPS like security, privacy, immutability, tolerance of defects, interoperability's, data origin, atomicity, automation, information/service sharing and trust. This paper provides an overview of the concept of intelligent cities as well as emerging technologies and a quick overview of cyber-physical systems. It then discusses CPS' potential role in the development of intelligent city apps and some real-life examples of CPS adaptation for city smart projects. A decentralized database based on distributed ledgers has been introduced. Distributed ledgers are a distributed database with a network connection node. These nodes include ledgers that list transactions with timestamps.
IoUNet++: Spatial cross‐layer interaction‐based bounding box regression for visual tracking

Abstract Accurate target prediction, especially bounding box estimation, is a key problem in visual tracking. Many recently proposed trackers adopt the refinement module called IoU predictor by designing a high‐level modulation vector to achieve bounding box estimation. However, due to the lack of spatial information that is important for precise box estimation, this simple one‐dimensional modulation vector has limited refinement representation capability. In this study, a novel IoU predictor (IoUNet++) is designed to achieve more accurate bounding box estimation by investigating spatial matching with a spatial cross‐layer interaction model. Rather than using a one‐dimensional modulation vector to generate representations of the candidate bounding box for overlap prediction, this paper first extracts and fuses multi‐level features of the target to generate template kernel with spatial description capability. Then, when aggregating the features of the template and the search region, the depthwise separable convolution correlation is adopted to preserve the spatial matching between the target feature and candidate feature, which makes their IoUNet++ network have better template representation and better fusion than the original network. The proposed IoUNet++ method with a plug‐and‐play style is applied to a series of strengthened trackers including DiMP++, SuperDiMP++ and SuperDIMP_AR++, which achieve consistent performance gain. Finally, experiments conducted on six popular tracking benchmarks show that their trackers outperformed the state‐of‐the‐art trackers with significantly fewer training epochs.
Bicategorical type theory: semantics and syntax

Abstract We develop semantics and syntax for bicategorical type theory. Bicategorical type theory features contexts, types, terms, and directed reductions between terms. This type theory is naturally interpreted in a class of structured bicategories. We start by developing the semantics, in the form of comprehension bicategories . Examples of comprehension bicategories are plentiful; we study both specific examples as well as classes of examples constructed from other data. From the notion of comprehension bicategory, we extract the syntax of bicategorical type theory, that is, judgment forms and structural inference rules. We prove soundness of the rules by giving an interpretation in any comprehension bicategory. The semantic aspects of our work are fully checked in the Coq proof assistant, based on the UniMath library.
DeepFTSG: Multi-stream Asymmetric USE-Net Trellis Encoders with Shared Decoder Feature Fusion Architecture for Video Motion Segmentation

Abstract Discriminating salient moving objects against complex, cluttered backgrounds, with occlusions and challenging environmental conditions like weather and illumination, is essential for stateful scene perception in autonomous systems. We propose a novel deep architecture, named DeepFTSG, for robust moving object detection that incorporates single and multi-stream multi-channel USE-Net trellis asymmetric encoders extending U-Net with squeeze and excitation (SE) blocks and a single shared decoder network for fusing multiple motion and appearance cues. DeepFTSG is a deep learning based approach that builds upon our previous hand-engineered flux tensor split Gaussian (FTSG) change detection video analysis algorithm which won the CDNet CVPR Change Detection Workshop challenge competition. DeepFTSG generalizes much better than top-performing motion detection deep networks, such as the scene-dependent ensemble-based FgSegNet_v2, while using an order of magnitude fewer weights. Short-term motion and longer-term change cues are estimated using general-purpose unsupervised methods—flux tensor and multi-modal background subtraction, respectively. DeepFTSG was evaluated using the CDnet-2014 change detection challenge dataset, the largest change detection video sequence benchmark with 12.3 billion labeled pixels, and had an overall F-measure of 97%. We also evaluated the cross-dataset generalization capability of DeepFTSG trained solely on CDnet-2014 short video segments and then evaluated on unseen SBI-2015, LASIESTA and LaSOT benchmark videos. On the unseen SBI-2015 dataset, DeepFTSG had an F-measure accuracy of 87%, more than 30% higher compared to the top-performing deep network FgSegNet_v2 and outperforms the recently proposed KimHa method by 17%. On the unseen LASIESTA, DeepFTSG had an F-measure of 88% and outperformed the best recent deep learning method BSUV-Net2.0 by 3%. On the unseen LaSOT with axis-aligned bounding box ground-truth, network segmentation masks were converted to bounding boxes for evaluation, DeepFTSG had an F-Measure of 55%, outperforming KimHa method by 14% and FgSegNet_v2 by almost 1.5%. When a customized single DeepFTSG model is trained in a scene-dependent manner for comparison with state-of-the-art approaches, then DeepFTSG performs significantly better, reaching an F-Measure of 97% on SBI-2015 (+ 10%) and 99% on LASIESTA (+ 11%). The source code, pre-trained weights, and video demo for DeepFTSG are available at https://github.com/CIVA-Lab/DeepFTSG .
Data-Driven Agility: A Case Study on Practical Use of Agile Concepts in Engineering

Abstract In 2020, Petrobras established the EF100 Program with a mission to increase operational efficiency while preserving value. As part of this program, BIP Consulting conducted a diagnostic in 2021 using Petrobras’ operational data. The diagnostic revealed that Compression and Gas Treatment systems accounted for approximately 20% of the inefficiencies in the company’s offshore production process at that time. These inefficiencies posed significant challenges to operational efficiency and profitability, highlighting the importance of the EF100 Program’s focus. This case study presents a novel approach to addressing these inefficiencies, combining the principles of agile methodology with data-driven decision-making techniques in an engineering project, called "Tratamento e Compressão de Gás" (TCG project), and demonstrated the effectiveness of this combination in project management, whilst enabling the project team to quickly assess and respond to changing circumstances, thereby optimizing the outcomes. A key element of this project’s success was the approach to culture and change management. Guided by the widely recognized principle that ‘Culture Eats Strategy for Breakfast’, the project team navigated Petrobras’s strong culture by involving executive, tactical and operational team members in the process, fostering a sense of co-creation and ownership. As a result, the project achieved a significant reduction in system inefficiencies. Specifically, for the systems within the scope of this project, in the business unit used as a pilot for the project within a period of 16 months, the Mean Time Between Failures (MTBF) increased by approximately 25%, and the Mean Time to Repair (MTTR) decreased by approximately 11%. These results, combined with the successful cultural navigation and co-creation approach, demonstrate the potential of this methodology for improving operational efficiency in the oil and gas industry.
Workplace reflections of librarians in India during the COVID pandemic

Purpose Covid-19 pandemic created a series of challenges for all professions, and libraries and library professionals were not spared. This study aims to attempt to analyze how qualified library professionals in India responded to the pandemic period and seeks their opinion on their preference for the job sector, activities and the challenges they faced during the pandemic. Design/methodology/approach The study is based on a quantitative survey method using simple random sampling technique. A structured Google forms questionnaire was used to collect the data. In total, 169 qualified professionals, working in different LIS areas, and a few nonworking professionals participated in the survey. The data were collected and analyzed using MS Excel, and statistical tests were done. Findings The major challenges include lack of proper equipment and official digital records, comfortable work environment, poor internet connection and over work load. Majority wish to work in the government rather than the private sector. The participants believe that Covid has affected employment opportunities. Despite the challenges, library professionals are satisfied with the profession and were actively engaged in the learning process. Research limitations/implications The challenges faced by the LIS profession during the post Covid era should be examined by professional bodies and organizations and should ensure extensive human resources development in different LIS sectors and stress the need to implement better services to society with a better digital experience. Originality/value The study explores how qualified library professionals from India have coped with the Covid pandemic and seeks to identify challenges in post-Covid LIS employment. To the best of the authors’ knowledge, this study is the first of its kind concerning work reflections of library professionals in India since the pandemic.
Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

Lithological mapping in highly vegetated areas using remote sensing techniques poses a significant challenge. Inspired by the concept of “geobotany”, we attempted to distinguish lithologies indirectly using machine learning algorithms (MLAs) based on Sentinel-2 and SRTM DEM in Zhangzhou City, Fujian Province. The study area has high vegetation cover, with lithologies that are largely obscured. After preprocessing such as cloud masking, resampling, and median image synthesis, 17 spectral bands and features from Sentinel-2 and 9 terrain features from DEM were extracted. Five widely used MLAs, MD, CART, SVM, RF, and GBDT, were trained and validated for lithological mapping. The results indicate that advanced MLAs, such as GBDT and RF, are highly effective for nonlinear modeling and learning with relative increases reaching 8.18%∼11.82% for GBDT and 6.36%∼10% for RF. Compared with optical imagery or terrain data alone, combining Sentinel-2 and DEM significantly improves the accuracy of lithological mapping, as it provides more comprehensive and precise spectral characteristics and spatial information. GBDT_Sen+DEM utilizing integrated data achieved the highest classification accuracy, with an overall accuracy of 63.18%. This study provides a case study for lithological mapping of areas with high vegetation cover at the local level. This also reinforces the idea that merging remote sensing and terrain data significantly enhances the precision and reliability of the lithological mapping methods.
Religious-Political Discussion on Instagram and WhatsApp and Perception of Religion among Youths in Delhi

This research paper examines the impact of social media on the perception of religion among young people in Delhi. The study explores the role of religious feelings and religious-political discourse that occur on social media platforms such as Instagram and WhatsApp, an instant messaging platform. The research uses a qualitative method in which interviews with young people in Delhi played a pivotal role in highlighting major aspects of the study. The findings indicate that social media has a significant impact on young people’s perceptions of religion in Delhi. While social media has provided young people with an opportunity to express their religious beliefs and engage in interfaith dialogue, it has also exposed them to extreme religious views and made it easier for political parties to influence their perception of religion and sometimes dehumanise other coreligionists on the basis of their system of belief and cultural context.
Swiss Army VAE: comprehensive sensor data analysis via explainable AI

An integrated system for processing sensor data has been developed based on novel variational autoencoder (VAE) algorithms with explainability that significantly eases analysis of sensor data. By continuously updating a generative model of the data, the system assists users with minimal artificial intelligence (AI) training or experience to perform data analysis. The system performs an extensive range of integrated machine learning (ML) tasks: anomaly detection, active learning, model-drift detection, synthetic data generation, semi-supervised classification, and counterfactual explanation generation. When the system is provided a data schema (map of Booleans, integers, reals, categories, time series, etc.) and data set, it automatically forms a preliminary generative model of the data. The construction of the system is modular, so new data types can be added as necessary. Counterfactually explainable anomaly detection is immediately performed via sparse gradient search. This informs the user how to interactively remove or repair bad records and/or begin labeling records of interest. The addition of labels to the data allows multi-class, semi-supervised, counterfactually explainable classification via the support vector machine embedded hyperplane algorithm (SVM-EH). Once some labels are added, active learning is used to assist further labeling by suggesting data elements that are highly likely to improve classification accuracy, significantly accelerating the labeling process by trading human effort for computational cycles. In production, the system detects when its training is becoming stale and requests retraining.