Reviewerly/exHarmony huggingface dataset:
Confidence Estimation for Object Detection in Document Images

Deep neural networks are becoming increasingly powerful and large and always require more labelled data to be trained. However, since annotating data is time-consuming, it is now necessary to develop systems that show good performance while learning on a limited amount of data. These data must be correctly chosen to obtain models that are still efficient. For this, the systems must be able to determine which data should be annotated to achieve the best results. In this paper, we propose four estimators to estimate the confidence of object detection predictions. The first two are based on Monte Carlo dropout, the third one on descriptive statistics and the last one on the detector posterior probabilities. In the active learning framework, the three first estimators show a significant improvement in performance for the detection of document physical pages and text lines compared to a random selection of images. We also show that the proposed estimator based on descriptive statistics can replace MC dropout, reducing the computational cost without compromising the performances.
Novel transfer learning schemes based on Siamese networks and synthetic data

Transfer learning schemes based on deep networks which have been trained on huge image corpora offer state-of-the-art technologies in computer vision. Here, supervised and semi-supervised approaches constitute efficient technologies which work well with comparably small data sets. Yet, such applications are currently restricted to application domains where suitable deep network models are readily available. In this contribution, we address an important application area in the domain of biotechnology, the automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation, where data characteristics are very dissimilar to existing domains and trained deep networks cannot easily be adapted by classical transfer learning. We propose a novel transfer learning scheme which expands a recently introduced Twin-VAE architecture, which is trained on realistic and synthetic data, and we modify its specialized training procedure to the transfer learning domain. In the specific domain, often only few to no labels exist and annotations are costly. We investigate a novel transfer learning strategy, which incorporates a simultaneous retraining on natural and synthetic data using an invariant shared representation as well as suitable target variables, while it learns to handle unseen data from a different microscopy technology. We show the superiority of the variation of our Twin-VAE architecture over the state-of-the-art transfer learning methodology in image processing as well as classical image processing technologies, which persists, even with strongly shortened training times and leads to satisfactory results in this domain. The source code is available at https://github.com/dstallmann/transfer_learning_twinvae, works cross-platform, is open-source and free (MIT licensed) software. We make the data sets available at https://pub.uni-bielefeld.de/record/2960030.
Registration on DCE-MRI images via multi-domain image-to-image translation

Registration of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is challenging as rapid intensity changes caused by a contrast agent lead to large registration errors. To address this problem, we propose a novel multi-domain image-to-image translation (MDIT) network based on image disentangling for separating motion from contrast changes before registration. In particular, the DCE images are disentangled into a domain-invariant content space (motion) and a domain-specific attribute space (contrast changes). The disentangled representations are then used to generate images, where the contrast changes have been removed from the motion. After that the resulting deformations can be directly derived from the generated images using an FFD registration. The method is tested on 10 lung DCE-MRI cases. The proposed method reaches an average root mean squared error of 0.3 ± 0.41 and the separation time is about 2.4 s for each case. Results show that the proposed method improves the registration efficiency without losing the registration accuracy compared with several state-of-the-art registration methods.
Solar Power Prediction using Regression Models

Solar power prediction is an important problem that has gained significant attention in recent years due to the increasing demand for renewable energy sources. In this paper, we present the results of using four different regression models for solar power prediction: linear regression, logistic regression, Lasso regression, and elastic regression. Our results show that all four models are able to accurately predict solar power, but Lasso regression and elastic regression outperform linear and logistic regression in terms of predicting the maximum solar power output. We also discuss the advantages and disadvantages of each model in the context of solar power prediction.
rigr: Regression, Inference, and General Data Analysis Tools in R

Regression models are an essential analytical tool in modern biomedical research, with applications ranging from disease outcomes prediction to vaccine efficacy estimation (Bland & Altman, 1986;Rothman et al., 2008).While R provides a set of comprehensive tools for fitting regression models (e.g., lm, glm in base R (R Core Team, 2021); coxph in the survival package (Therneau, 2022)), existing routines require users to navigate multiple R packages with different conventions and syntaxes, creating barriers for both practitioners and learners.To alleviate these barriers, we developed an R package called rigr that facilitates common data analyses in R, with an emphasis on straightforward, modern regression modeling.On a high level, rigr compiles output from existing routines together in an intuitive format, and adds functionality to existing functions.For example, users can fit linear models, generalized linear models, and proportional hazards models using a single function regress.They can also easily perform complex inference and obtain robust standard errors in this single package.We also provide functions for descriptive statistics and one-and two-sample inference with improved clarity of output.
Quantum phase recognition using quantum tensor networks

Machine learning (ML) has recently facilitated many advances in solving problems related to many-body physical systems. Given the intrinsic quantum nature of these problems, it is natural to speculate that quantum-enhanced machine learning will enable us to unveil even greater details than we currently have. With this motivation, this paper examines a quantum machine learning approach based on shallow variational ansatz inspired by tensor networks for supervised learning tasks. In particular, we first look at the standard image classification tasks using the Fashion-MNIST dataset and study the effect of repeating tensor network layers on ansatz's expressibility and performance. Finally, we use this strategy to tackle the problem of quantum phase recognition for the transverse-field Ising and Heisenberg spin models in one and two dimensions, where we were able to reach $$\ge 98\%$$ test-set accuracies with both multi-scale entanglement renormalization ansatz (MERA) and tree tensor network (TTN) inspired parametrized quantum circuits.
Fundamentals of Quantum Fourier Optics

All-quantum signal processing techniques are at the core of the successful advancement of most information-based quantum technologies. This article develops coherent and comprehensive methodologies and mathematical models to describe Fourier optical signal processing in full quantum terms for any input quantum state of light. We begin this article by introducing a spatially 2-D quantum state of a photon, associated with its wavefront and expressible as a 2-D creation operator. Then, by breaking down the Fourier optical processing apparatus into its key components, we strive to acquire the quantum unitary transformation or the input/output quantum relation of the 2-D creation operators. Subsequently, we take advantage of the above results to develop and obtain the quantum analogous of a few essential Fourier optical apparatuses, such as quantum convolution via a 4f-processing system and a quantum 4f-processing system with periodic pupils. Moreover, due to the importance and widespread use of optical pulse shaping in various optical communications and optical sciences fields, we also present an analogous system in full quantum terms, namely quantum pulse shaping with an 8f-processing system. Finally, we apply our results to two extreme examples of the quantum state of light. One is based on a coherent (Glauber) state and the other on a single-photon number (Fock) state for each of the above optical systems. We believe the schemes and mathematical models developed in this article can impact many areas of quantum optical signal processing, quantum holography, quantum communications, quantum radars and multiple-input/multiple-output antennas, and many more applications in quantum imaging, quantum computations, and quantum machine learning algorithms.
A Python library to check the level of anonymity of a dataset

Openly sharing data with sensitive attributes and privacy restrictions is a challenging task. In this document we present the implementation of pyCANON, a Python library and command line interface (CLI) to check and assess the level of anonymity of a dataset through some of the most common anonymization techniques: k-anonymity, (α,k)-anonymity, ℓ-diversity, entropy ℓ-diversity, recursive (c,ℓ)-diversity, t-closeness, basic β-likeness, enhanced β-likeness and δ-disclosure privacy. For the case of more than one sensitive attribute, two approaches are proposed for evaluating these techniques. The main strength of this library is to obtain a full report of the parameters that are fulfilled for each of the techniques mentioned above, with the unique requirement of the set of quasi-identifiers and sensitive attributes. The methods implemented are presented together with the attacks they prevent, the description of the library, examples of the different functions' usage, as well as the impact and the possible applications that can be developed. Finally, some possible aspects to be incorporated in future updates are proposed.
Spatial distribution of porphyry copper deposits in Kerman Belt, Iran

The Kerman Cenozoic Magmatic Arc (KCMA, or Kerman Belt) in the southeastern part of the Urumieh–Dokhtar Magmatic Belt (UDMB) is considered as the most prospective geological province for porphyry copper deposits in Iran. Distance-based point pattern analysis methods, fractal analysis and Fry analysis are applied to porphyry copper deposits in order to gain insights into the geological controls over mineralization in the belt and suggest strategies for future exploration targeting. The results confirmed a significant structural control of strike-slip faults over the spatial distribution of deposits at the regional scale as previous structural studies defined it. The emplacement of porphyries in the Kerman Belt is likely controlled by extensional fault duplexes. The analyses further confirmed that there are two distinctly different sectors of porphyry copper mineralization in the Kerman Belt, namely, Sarduieh–Dahaj sector in the northwest and Jebal-e-Barez sector in the southeast as previous geological studies had demonstrated it. It is found that intrusive rock units, which form the permissive tracts for porphyry copper deposits in the Kerman Belt, are clustered, while the copper deposits within the permissive tracts generally show random distribution. It is recommended that predictive prospectivity modeling can be used to target mineralized intrusive bodies in the Kerman Belt; however direct detection techniques should be used to identify mineral deposits within the intrusive bodies.
The uphill journey of FaaS in the open-source community

Since its introduction in 2014 by Amazon, the Function as a Service (FaaS) model of serverless computing has set the expectation to fulfill the promise of on-demand, pay-as-you-go, infrastructure-independent processing, originally formulated by cloud computing. Yet, serverless applications are fundamentally different than traditional service-oriented software in that they pose specific performance (e.g., cold start), design (e.g., stateless), and development challenges (e.g., debugging). A growing number of cloud solutions have been continuously attempting to address each of these challenges as a result of the increasing popularity of FaaS. Yet, the characteristics of this model have been poorly understood; therefore, the challenges are poorly tackled. In this paper, we assess the state of FaaS in open-source community with a study on almost 2K real-world serverless applications. Our results show a jeopardized ecosystem, where, despite the hype of serverless solutions in the last years, a number of challenges remain untackled, especially concerning component reuse, support for software development, and flexibility among different platforms — resulting in arguably slow adoption of the FaaS model. We believe that addressing the issues discussed in this paper may help researchers shaping the next generation of cloud computing models.
Online offensive behaviour in socialmedia: Detection approaches, comprehensive review and future directions

The enormous growth of social media provides a platform for displaying harmful, offensive online behaviour, which keeps increasing with time. The popularity of smartphones and the anonymity of the internet have made online offensive behaviour very common. Therefore, research on social media offensive behaviour has increased in recent years. In this paper, we have endeavoured to depict the variety of abusive behaviour one can encounter online and the significance of detecting them by classifying them into four categories: Content-Based, Sentiment and Emotion Based, User or Profile Based, and Network or Graph-Based approach. We review the state-of-the-art methods to detect bullies and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the future research directions that serve as a reference to overcome offensive content in social media.
A novel approach for Credit-Based Resource Aware Load Balancing algorithm (CB-RALB-SA) for scheduling jobs in cloud computing

In recent years, cloud computing has gained popularity, mainly because of its utility and relevance to current technological trends. It is an arrangement that is highly customizable and encapsulated for providing better computational services to its clients worldwide. In the cloud, computing scheduling plays a pivotal role in optimizing resources. A better scheduling algorithm should be efficient and impartial, reducing the makespan time with proper resource utilization. However, most scheduling algorithms customarily lead to less resource utilization, termed load imbalance. The analysis of the existing papers exhibits better Makespan time but cannot guarantee the load-balanced mapping of jobs with proper resource utilization. Therefore, to eliminate the shortcomings of the prevalent/existing algorithms and enhance the performance, CB-RALB-SA, Credit-based Resource Aware Load Balancing scheduling algorithm has been rendered. The proposed work ensures a balanced distribution of tasks based on the capabilities of the resources, which eventually proves sustainable improvement against the existing scheduling algorithms. Therefore, a novel Credit Based Resource Aware Load Balancing Scheduling algorithm (CB-RALB-SA) is proposed. The tasks weighted by the credit-based scheduling algorithm are then mapped to the resources considering each resource’s load and computing capability using FILL and SPILL functions of Resource Aware and Load using Honey bee optimization heuristic algorithm. With the experimental evaluations and results, it has been proved that the proposed approach provides 48.5% better in Processing Time and 16.90 % better results in makespan time than the Existing CBSA-LB algorithm. Thus, it improves the processor’s efficiency while uplifting the whole system’s performance and has saved memory allocated to tasks and RAM.
Diverse features discovery transformer for pedestrian attribute recognition

Recently, Swin Transformer has been widely explored as a general backbone for computer vision, which helps to improve the performance of vision tasks due to the ability to establish associations for long-range dependencies of different spatial locations. By implementing the pedestrian attribute recognition with Swin Transformer, we observe that Swin Transformer tends to focus on a relatively small number of local regions within which attributes may be correlated with other attributes, which leads Swin Transformer to predict attributes in those neglected regions based on such correlation. In fact, discriminative information may exist within these neglected regions, which is crucial for attribute identification. To address this problem, we propose a novel diverse features discovery transformer (DFDT) which can find more attribute relationship regions for robust pedestrian attribute recognition. First, Swin Transformer is used as a feature extraction network to acquire attribute features with the long-distance association, which predicts the corresponding attribute information. Second, we propose a diverse features suppression module (DFSM) to obtain semantic features directly associated with attributes by suppressing the peak locations of the most discriminative features and randomly selected feature regions to spread the feature regions that Swin Transformer is interested in. Third, we plug the diverse features suppression module into different stages of Swin Transformer to learn detailed texture features to help recognition. In addition, we have divided the attribute features into multiple vertical feature regions to improve the focus on local attribute features. Experiments on three benchmark datasets validate the effectiveness of the proposed algorithm.
When Sustainable Development Embraces Blockchain: A Systematic Literature Review

The non-renewable resources are limited on this earth and will get exhausted if not utilized carefully. Ensuring the availability of these resources for future generations is the key philosophy of sustainable development (SD). To protect and preserve the environment, United Nations has proposed 17 sustainability development goals, which can be attained by coupling nature, human and technology. Among several technological disruptions, blockchain technology (BCT) has the huge potential to verify SD. The current study aims at analysis and synthesis of available studies on implication of BCT for SD, using systematic literature review methodology. Out of 17 SDGs only two goals, zero hunger (goal no. 2) and clean water and sanitation (goal no. 6), were considered for this study. The literature search criteria comprised of automatic and manual search, resulted into 484 studies. After examining inclusion and exclusion criteria, 52 usable studies were obtained. Descriptive analysis of 52 studies was done on the basis of publication year, source of publication, research type and sustainability dimension reported. Synthesis and analysis of these 52 studies addressed the following: implication of BCT for SD, identification of research themes and gaps in available studies and future research direction. It was observed that none of the study has been done on the theme and methodology used in current study.
Luxury and the Metaverse

The Metaverse serves as a new emerging field in the field of marketing, the concept of the Metaverse is being used by different companies to increase their sale revenue. Most luxury brands see this as a new opportunity to rebrand themselves to attract the young generation of customers from online communities. In this article, two brands Gucci and Givenchy will be discussed and compared in terms of their perspective field: Feedback, Target, and Profit. After examination, it is believed that the Metaverse can gain some discussion and topics from the online communities and serves its purpose to attract online customers. However, the Metaverse is developed too fast in its initial stage in which the basic setting and virtual communities are not perfect which results in its decline in 2022.
Attention-Based Matching Approach for Heterogeneous Remote Sensing Images

Heterogeneous images acquired from various platforms and sensors provide complementary information. However, to use that information in applications such as image fusion and change detection, accurate image matching is essential to further process and analyze these heterogeneous images, especially if they have significant differences in radiation and geometric characteristics. Therefore, matching heterogeneous remote sensing images is challenging. To address this issue, we propose a feature point matching method named Cross and Self Attentional Matcher (CSAM) based on Attention mechanisms (algorithms) that have been extensively used in various computer vision-based applications. Specifically, CSAM alternatively uses self-Attention and cross-Attention on the two matching images to exploit feature point location and context information. Then, the feature descriptor is further aggregated to assist CSAM in creating matching point pairs while removing the false matching points. To further improve the training efficiency of CSAM, this paper establishes a new training dataset of heterogeneous images, including 1,000,000 generated image pairs. Extensive experiments indicate that CSAM outperforms the existing feature extraction and matching methods, including SIFT, RIFT, CFOG, NNDR, FSC, GMS, OA-Net, and Superglue, attaining an average precision and processing time of 81.29% and 0.13 s. In addition to higher matching performance and computational efficiency, CSAM has better generalization ability for multimodal image matching and registration tasks.
Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decom-poses joint action values into individual action values, has been an important work in MAS. However, many value decomposition methods ignore the coordination among different agents, leading to the notorious “lazy agents” problem. To enhance the coordination in MAS, this paper proposes HyperGraph CoNvo-lution MIX (HGCN-MIX), a method that incorporates hyper-graph convolution with value decomposition. HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nodes and hyperedges among nodes indicate that the corresponding agents can coordinate to achieve larger rewards. Then, it trains a hypergraph that can capture the collaborative relationships among agents. Leveraging the learned hypergraph to consider how other agents' observations and actions affect their decisions, the agents in a MAS can better coordinate. We evaluate HGCN-MIX in the StarCraft II multi-agent challenge benchmark. The experimental results demonstrate that HGCN-MIX can train joint policies that outperform or achieve a similar level of performance as the current state-of-the-art techniques. We also observe that HGCN-MIX has an even more significant improvement of performance in the scenarios with a large amount of agents. Besides, we conduct additional analysis to emphasize that when the hypergraph learns more relationships, HGCN-MIX can train stronger joint policies.