Reviewerly/exHarmony huggingface dataset:
Decentralized Medical Healthcare Record Management System Using Blockchain

The possibilities and benefit of using distributed ledger technology for storing and protecting patient medical information is greatest in the health care industry, which is one of the primary industries in this regard. Blockchain technology is being used by several companies. Inorder to protect the security and privacy of its people’ personally identifiable information and to promote the use of blockchain technology, the Government of India (GoI) is eager to digitize. Using the Ethereum blockchain, we provide a solution in this study for the safe keeping of patient medical records (PMR).Our solution provides secure and hasslefree access, storage, and patient medical record sharing. We have developed the front end as well as the back end of our project using TypeScript and Next UI Users need to register themselves if they are visiting for the first time. The information shared by the user will be stored entirely in our backend applications. We have used the IPFS file system for storing and accessing files on the internet. IPFS is a file system that helps to create a permanent and decentralized method for storing and accessing files. First, a user will upload his or her document, and the document will be uploaded to the Inter Planetary File System (IPFS). Once the document is uploaded, a unique hash is returned, which is also known as a CID (content identifier). This is used to uniquely identify the uploaded file on IPFS. Then, that CID is stored in the blockchain as a private variable, so not everyone can access it. When someone can access it, we will specify the conditions.
Solar H<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e264" altimg="si9.svg"><mml:mi>α</mml:mi></mml:math> image interference fringes removal based on stationary wavelet deep neural network

The solar Hα images acquired by the Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO) are superimposed by interference fringes. These fringes degrade the image quality and seriously affect later research that uses these data. To remove the interference fringes in Hα images, this paper proposed a method combining stationary wavelet deep convolutional neural network (SWT-CNN) and image pyramid. First, the image pyramid was used to downsample the fringe image, which sped up the neural network training speed. Then, SWT-CNN used the difference between fringe image and fringe-free image to remove the interference fringe. Finally, the image size was recovered by the Laplacian pyramid. The experimental results show that our method can effectively remove the interference fringes in Hα images and preserve the solar structure.
A<sub>2</sub>M: An Amplification-Arbitrary Module for Remote Sensing Image Super-Resolution

Remote-sensing (RS) image super-resolution (SR) aims to recover high-resolution (HR) images from the corresponding low-resolution (LR) images. In recent years, the SR methods based on convolutional neural networks (CNNs) have achieved incredible performance in case of fixed scale factors (e.g., ×2, ×3, and ×4). However, these methods need to train a single model for each scale factor, and fail to directly reconstruct the HR image of decimal factors. To solve the lack of research on arbitrary scale of RS image SR, we propose a novel amplification module called amplification-arbitrary module (A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> M). A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> M can be easily embedded in the tail of the previous SR networks, so that the previous networks can also achieve end-to-end arbitrary scale SR. Specifically, we first utilize the combination of convolutional and pixelshuffle layers to zoom in the deep feature matrix 2×, 3×, and 4× along spatial dimension. Information cross transmission (ICT) is then utilized to gather information of multiple spatial sizes. ICT is not only beneficial to enrich the diversity of information, but also can avoid training only a single branch in the training stage. To make better use of multi-scale features, we designed an efficient signal weighting unit (SWU) to generate a correlation matrix at a small cost, and then the signals of multi-scale features at the same position are fused according to the correlation matrix. Experimental results on RS and generic datasets demonstrate that our method with single pre-training model can perform well at any scale factors.
Rigid Motion Gaussian Processes With SE(3) Kernel and Application to Visual Pursuit Control

We address in this letter the learning of unknown rigid body motions in the Special Euclidian Group SE(3) based on Gaussian Processes. A new covariance kernel for SE(3) is presented and proven to be a valid kernel for Gaussian Process Regression. The learning error of the proposed Gaussian Process model is extended to a highprobability statement on SE(3). We employ it in a visual pursuit scenario of a moving target with unknown velocity in 3D space. Our approach is validated in a simulated 3D environment in Unity, and shows significant better prediction accuracy than the most commonly used Gaussian kernel. When compared to other covariance kernels proposed on SE(3), its advantages are a natural extension of covering numbers to SE(3), that it is computationally more efficient, and that stability of target pursuit can be guaranteed without limiting the target rotational space to SO(2).
A First-Arrival Picking Technique Based on Texture Segmentation Exploring Seismic Data

In this study, we propose an innovative first arrival picking technique based on texture segmentation of seismic shot records for exploring seismic data. The seismic shot records are divided into clusters (depending on the seismic event types) based on texture-extracted features and fuzzy c-means. This technique utilizes the industrial energy ratio procedure to be conducted prior to clustering and applied to the cluster containing the first arrivals to recognize seismic shot points corresponding to the direct arrival picks. The suggested procedure was tested on one synthetic and two real seismic shot records. Using the proposed technique, a pick accuracy of more than 99% was achieved for the synthetic data set with a noise level of 10%, and more than 80% accuracy was achieved for the real data shot records, and all tests were within an absolute error tolerance of ±20 ms. Additionally, the proposed technique picks were more accurate than the picks of the standard industrial Coppen’s method as well as the projection onto convex sets segmentation technique by an overall average accuracy of approximately 28.98%.
A Provably Secure Lattice-Based Fuzzy Signature Scheme Using Linear Sketch

Fuzzy signatures (FS) are a kind of signature scheme that employs a noisy string (e.g., biometric data) as the secret key without requiring the user-specific auxiliary data. As the quantum computing era approaches, some research has been dedicated to developing quantum-resistant FS schemes, which can be classified into fuzzy extractor (FE) approach and linear sketch (LS) approach. However, the existing schemes utilizing FEs to obtain (variants of) fuzzy signatures require to produce the user-specific auxiliary information known as helper data to retrieve secret keys, leading to an additional computational cost. In light of the circumstance, we seek to construct a fuzzy signature scheme by employing a linear sketch, since this approach does not require the user-specific auxiliary data to derive secret keys. We modify the linear sketch which is an essential ingredient of the most practical fuzzy signature proposed by Katsumata et al. (CCS’ 21). Then we combine it with Lyubashevsky’s lattice-based signature scheme (EUROCRYPT’ 12) to construct our lattice-based fuzzy signature scheme. Moreover, to further demonstrate the security of our proposed scheme, we provide a rigorous security proof in the random oracle model. Finally, the comparison indicates that our proposed FS scheme not only avoids the use of FE but also shows a promising tendency in efficiency among the existing quantum-resistant FS schemes.
Using Architecture Decision Records in Open Source Projects—An MSR Study on GitHub

Architecture decision records (ADRs) have been proposed as a resource-efficient means for capturing architectural design decisions (ADDs), and have received attention not only from researchers but also from practitioners. We conducted a mining software repositories (MSR) study, in which we analyzed the use of ADRs in open source repositories at GitHub. Our results show that the adoption of ADRs is still low, although the number of repositories using ADRs is increasing every year. About 50% of all repositories with ADRs contain just one to five ADRs suggesting that the concept has been tried but not yet definitively adopted. In repositories that use ADRs more systematically, we observed that recording decisions is a team activity conducted by two or more users over a longer period of time. In most repositories the template proposed by Michael Nygrad is used. We, finally, provide an interpretation of the obtained results and discuss open future research challenges by elaborating on implications of the study’s findings as well as on recommendations on how to further increase the adoption of ADRs.
DroneChase: A Mobile and Automated Cross-Modality System for Continuous Drone Tracking

This paper presents DroneChase, an automated sensing system that monitors acoustic and visual signals captured from a nearby flying drone to track its trajectory in both line-of-sight and non-line-of-sight conditions under mobility settings. Although drone monitoring has been an active research topic, most of the existing monitoring systems focus only on line-of-sight conditions and do not perform well under blockage conditions. Inspired by the human ability to localize objects in the environment using both visual and auditory signals, we develop a mobile system that integrates the information from multiple modalities into a reference scenario and performs real-time drone detection and trajectory monitoring. Our developed system, controlled by the Raspberry Pi platform, collects acoustic signals from 6 hexagonal channels placed 5 cm away from each other and video signals from an HD RGB camera. The monitoring system is placed in a moving vehicle and is able to track the drone even when it is flying/hovering behind the bush or trees. Furthermore, the portability of the system enables continuous chasing of the drone, allowing for uninterrupted monitoring and tracking even while on the move. In addition, the proposed system performs reliably in both day and night conditions.
Auditable Blockchain Rewriting in Permissioned Setting With Mandatory Revocability for IoT

The Internet of Things (IoT) connects everyday devices and generates real-time data that have greatly prompted business and life efficiency. The integration of IoT and blockchain has made IoT data management and storage more trustworthy. However, despite the immutability property contributes a lot to the trustable reputation of blockchain-based IoT systems, from a data processing perspective, it is desired to achieve skillful and secure blockchain rewriting for scenarios such as device data sharing. Existing blockchain rewriting solutions usually rely on centralized modifiers where the rewriting power is difficult to control or withdraw. In this article, we propose a new auditable redactable blockchain (RB) scheme named ACHR that supports self-management and mandatory revocation of the rewriting privilege. The scheme allows user devices to rewrite their blockchain transactions under strict auditing to ensure content security. To prevent centralization or rewriting power abuses, the revocation trapdoor can be computed compulsorily by an auditor when a redaction is published to the blockchain. We introduce a generic construction and an instantiation of the ACHR scheme for building the RB and prove its security. We provide a prototype implementation to demonstrate that our scheme is effective and efficient compared to the traditional blockchain-IoT system with immutability.
Spectral Representation Learning and Fusion for Autonomous Vehicles Trip Description Exploiting Recurrent Transformer

A thorough analysis and comprehension of the entire cue set in visual data are indispensable for an ideal video description model. As outlined in recent algorithm proposals, video descriptions have primarily been generated by learning RGB and optical flow representations rather than exploring and incorporating the media’s spectral components referring to the patterns or characteristics in the distribution of colors or intensities across different frequencies or wavelengths of light. These components may enhance the description quality and impact the generated text for accuracy, diversity, and coherence. We propose a novel Fourier-based algorithm for extracting spectral features in 3D visual volume by decomposing the video signal into its frequency components, to fill this research gap. Further, the captured spectral features are fused with learned spatial and temporal representations in recurrent transformer architecture for accurate content understanding and appropriate description generation in natural language. The transformer includes an external memory module that produces summarized memory states based on the history of previously observed video fragments and already-generated sentences. These memory states ensure the establishment of sound semantic and linguistic cues. As a result, our proposed algorithm integrates spatial, temporal, spectral, and semantic representations for precise and grammatically accurate descriptions. The effectiveness of our proposed algorithm for the coherent and diverse video description is demonstrated through qualitative and quantitative experimentation on the DeepRide driving trip description dataset. A comprehensive ablation study validates the efficacy of the spectral features fusion with spatial and temporal visual representations for the rich video-to-textual narration generation.
Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Accuracy Perspectives

The proposed tutorial aims to introduce the UMAP community to modern user profiling approaches leveraging graph neural networks (GNNs). We will begin by discussing the conceptual foundations of user profiling and GNNs and providing a literature review of the two topics. We will then present a systematic overview of the state-of-the-art GNN architectures designed for user profiling, including the types of data that are typically used for this purpose. We will also discuss ethical considerations and beyond-accuracy perspectives (i.e. fairness and explainability), which can arise within the potential applications of adopting GNNs for user profiling. In the practical session of the tutorial, attendees will have the opportunity to understand concretely how recent GNN models for user profiling are built and trained with open-source tools and publicly available datasets. The audience will also be engaged in investigating the impact of the presented models on case studies involving bias detection and mitigation, as well as user profiles explanations. The tutorial will end with an analysis of existing and emerging open challenges in the field and their future research directions.
Human-centered Information Visualization Adaptation Engine

Data Analytics is the art of turning data into insights for efficient and effective business decisions. Data visualization is among the most powerful tools in the data analyst’s arsenal, enabling the transformation of data into effective visualizations that can be easily comprehended. However, its effectiveness is often affected by the data analysts’ experience and their ability to quickly understand and interpret information. Even though business analytics tools have made a significant progress to deliver immersive data visualization environments for improving users’ efficiency and effectiveness, they still do not consider the individual differences in the core process that influences the visualization structure, encoding, and readability.
Interactive Personalization of Classifiers for Explainability using Multi-Objective Bayesian Optimization

Explainability is a crucial aspect of models which ensures their reliable use by both engineers and end-users. However, explainability depends on the user and the model’s usage context, making it an important dimension for user personalization. In this article, we explore the personalization of opaque-box image classifiers using an interactive hyperparameter tuning approach, in which the user iteratively rates the quality of explanations for a selected set of query images. Using a multi-objective Bayesian optimization (MOBO) algorithm, we optimize for both, the classifier’s accuracy and the perceived explainability ratings. In our user study, we found Pareto-optimal parameters for each participant, that could significantly improve explainability ratings of queried images while minimally impacting classifier accuracy. Furthermore, this improved explainability with tuned hyperparameters generalized to held-out validation images, with the extent of generalization being dependent on the variance within the queried images, and the similarity between the query and validation images. This MOBO-based method has the potential to be used in general to jointly optimize any machine learning objective along with any human-centric objective. The Pareto front produced after the interactive hyperparameter tuning can be useful during deployment, allowing for desired trade-offs between the objectives (if any) to be chosen by selecting the appropriate parameters. Additionally, user studies like ours can assess if commonly assumed trade-offs, such as accuracy versus explainability, exist in a given context.
Together Yet Apart: Multimodal Representation Learning for Personalised Visual Art Recommendation

With the advent of digital media, the availability of art content has greatly expanded, making it increasingly challenging for individuals to discover and curate works that align with their personal preferences and taste. The task of providing accurate and personalized Visual Art (VA) recommendations is thus a complex one, requiring a deep understanding of the intricate interplay of multiple modalities such as image, textual descriptions, or other metadata. In this paper, we study the nuances of modalities involved in the VA domain (image and text) and how they can be effectively harnessed to provide a truly personalized art experience to users. Particularly, we develop four fusion-based multimodal VA recommendation pipelines and conduct a large-scale user-centric evaluation. Our results indicate that early fusion (i.e, joint multimodal learning of visual and textual features) is preferred over a late fusion of ranked paintings from unimodal models (state-of-the-art baselines) but only if the latent representation space of the multimodal painting embeddings is entangled. Our findings open a new perspective for a better representation learning in the VA RecSys domain.
Testing innovative preparation tools for immersive virtual environments. A case study in the didactics of Art

In recent years, the usage of immersive media and Virtual Reality (VR) has become widespread in the education field. VR has been claimed to offer significant benefits in terms of motivation, engagement, and learning outcomes in various disciplines. In this research, we aim to assess the potential of VR in improving the way university students engage with VR art in the classroom. More specifically, we focus on how this technology can be used to provide preliminary knowledge and training for experiencing immersive art, and how this impacts on the students’ level of satisfaction, immersion, and engagement. Our results contribute to advancing the scientific debate on the usage of VR in the education field; additionally, they may be useful to artists and curators, by suggesting an innovative way of introducing the users to immersive artworks.
ANALYSIS OF THE CORRELATION BETWEEN AGILE TEAM MATURITY AND STANDARDISED KEY PERFORMANCE INDICATORS IN AUTOMOTIVE DEVELOPMENT

Abstract The application of agile development methods in response to increasing market dynamics and product complexity is a key lever in the automotive industry. Agile methods originally come from the software industry and enable fast, flexible and customer-oriented product development. These methods are also increasingly being used in hardware development. However, the evaluation of the benefits of agile methods in the context of automotive development has been primarily subjective. The publication aims to present a first data-based approach to objectify the benefits of agile methods in automotive development by highlighting the effects in the quality of collaboration within teams. A standardised procedure is therefore designed and presented. On the one hand, a model for measuring the agile maturity of teams is described. On the other hand, the quality of collaboration within a team is examined in different aspects using standardised key performance indicators. Based on the proposed procedure, a strong positive correlation was found between the considered key performance indicators of the quality of collaboration and the agile maturity of the development teams within the investigated organisation.
Image-based Approaches for Identifying Harmful Sites using OCR and Average Hash Methods

Recently, websites containing harmful information such as gambling, illegal drugs, pornography, and prostitution are exposed to the public. These harmful sites cause damage to copyright holders and related service industries, and cause various social problems. In this paper, we propose an image-based harmful site identification system using OCR and Average Hash techniques to identify and classify harmful sites. This system uses the characteristic that most gambling banner advertisements repeatedly use similar images, and analyzes the similarity with the average hash value of the banner advertisement image. And using Easy OCR, it determines whether the phrase written in the banner advertisement is harmful or not. To evaluate the performance of the proposed idea, a program was created to determine harmfulness by collecting and analyzing the site
A Deletion Algorithm for the Marginal Problem in Propositional Logic Based on Boolean Arrays

This paper proposes a deletion algorithm for the marginal problem in propositional logic. The algorithm is based on the general Davis and Putnam deletion algorithm DP, expressed as a bucket elimination algorithm, representing sets of clauses with the same set of variables employing a Boolean array. The main contribution is the development of alternative procedures when deleting a variable which allow more efficient computations. In particular, it takes advantage of the case in which the variable to delete is determined by a subset of the rest of the variables. It also provides a set of useful results and tools for reasoning with Boolean tables. The algorithms are implemented using Python and the NumPy library. Experiments show that this procedure is feasible for intermediate problems and for difficult problems from hard Bayesian networks cases