Reviewerly/exHarmony huggingface dataset:
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines

Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years. Increasingly more people are interested in exploring this promising field. However, it is challenging for new researchers and developers to find the right components to construct their own machine learning pipelines, given the diverse PLP tasks to be solved, the large number of datasets and models being released, and the set of complex compilers or tools involved. To improve the findability, accessibility, interoperability and reusability (FAIRness) of machine learning components, we collect and analyze a set of representative papers in the domain of machine learning-based PLP. We then identify and characterize key concepts including PLP tasks, model architectures and supportive tools. Finally, we show some example use cases of leveraging the reusable components to construct machine learning pipelines to solve a set of PLP tasks.
Identifying small decentralized solar systems in aerial images using deep learning

Statistics on installed solar energy systems (SES) play a crucial role in the solar energy industry, providing valuable information for a wide range of stakeholders, such as policy makers, authorities, and financial evaluators. For example, grid operators rely on accurate data on photovoltaic penetration levels to ensure the quality and stability of the power supply. In this research, we present an automatic approach helping generate these statistics using deep learning and image processing techniques. Our proposed model is a machine learning approach that utilizes a specific architecture of convolutional neural networks (CNN) called the "U-net" to detect SES from aerial images. We experimented different network settings to enhance the SES identification performance. In this study, the model was evaluated using two datasets from different locations, one from Sweden and one from Germany. Additionally, the model was trained and tested on a combination of both datasets. The impact of image resolution was also examined. The experimental results show that this architecture performs better than many recent CNN models that have been proposed in the literature for the task of SES identification from aerial images. To make it easy for others to replicate our findings, We have shared all the scripts, software, and dependencies required for running the model in this paper, along with instructions on how to use it in Appendix A.
Cloud computing enabled business model innovation

Cloud computing's primary layer of security is its widespread adoption by businesses due to its ability to provide easy, on-demand network access to a large pool of configurable processing resources. Pressure from developed-world governments to keep up with the latest IT trends makes it challenging for colleges around the world to adopt cloud computing in engineering education. Although cloud computing as a concept has become more popular in recent times and quite a few studies have been conducted, the adoption and application level is still very low, particularly in developing countries. This study provides a realistic framework for gauging the sustainability of a company model. While digitalization is widely acknowledged to be crucial, the study's results reveal that the perceived available options for business model innovation depend not only on the value proposition itself but also on the position in the value network. The primary goal of this research is to create a cloud computing service provider business model that is built on circular economy principles and can guarantee the sustainable usage of cloud computing resources.
Practical multi-party quantum homomorphic encryption

Quantum homomorphic encryption (QHE) is allowed to perform operations on encrypted data which is very useful for delegated quantum computing in quantum cloud environments. But most of current QHE schemes only consider the single-user and single-server model. To be more practical and suitable for quantum networks, multi-party QHE based on quantum voting is considered in this paper. Firstly, an improved quantum voting scheme is proposed to facilitate anonymous broadcast. Secondly, a multi-party QHE scheme is proposed based on the improved quantum voting method. The proposed QHE scheme becomes more practical and flexible due to the reduction of the cost of delegated quantum computation, which significantly contributes to broadening the applicability of QHE in quantum networks.
Multi-scale-based joint super-resolution and inverse tone-mapping with data synthesis for UHD HDR video

The performance of current joint super-resolution (SR) and inverse tone-mapping (ITM) frameworks is limited since they only account for information in small local receptive fields. Moreover, the SDR images in the existing dataset for joint SR-ITM are oversaturated compared to the HDR ground truths. The models trained on this dataset produce HDR images with color shift. This paper proposes a multi-scale-based joint SR-ITM model to reconstruct HR HDR videos. Image features are downsampled to different resolutions to increase the local receptive fields. Thus the proposed model can react to more complex patterns of input images. And we design a novel multi-path residual dense block (MRDB) as the model’s fundamental component to extract features. The proposed MRDBs can improve performance by combining dense feature learning and novel multi-path residual learning. Experiments show that the proposed model outperforms the state-of-the-art methods in terms of quantitative and qualitative results. Furthermore, we propose a data synthesis pipeline to generate perceptually identical SDR images from their HDR counterparts. These SDR-HDR pairs are used to create a new dataset. Experiments demonstrate that models trained with our new dataset prevent color shift and preserve creative intent.
FGCR: Fused graph context-aware recommender system

Context-aware recommender systems (CARS), which capture user preferences by incorporating user interactions and side information, have attracted widespread attention in today’s information age. With the emergence of graph convolution networks (GCN) in recent years, graph methods have been shown effective in establishing global connections between users, items, and contexts. In this paper, we first introduce a unified graph recommendation framework to incorporate both GCN and traditional models, showing the similarities and differences between them in terms of parameter configurations and message passing styles. Then, based on the empirical analysis of existing models, we propose a novel Fused Graph Context-aware Recommender system (FGCR) model to address the identified limitations. FGCR fuses the parameter configuration of both item-based models and GCN, modeling robust node relationships in user-item-context interaction graph through a sparse item correlation matrix and dense node embeddings. In addition, FGCR applies a novel masked graph convolution strategy to refine the information aggregation process with various types of nodes. Experiments conducted on five real-world datasets show that FGCR significantly improves performance compared to the seven baseline models. Ablation study and user group analysis validate the effectiveness of each component in FGCR, especially in modeling active users and sparse contextual information.
Guessing Winning Policies in LTL Synthesis by Semantic Learning

Abstract We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several applications. Not only can the guessed strategy be applied as best-effort in cases where the game’s huge size prohibits rigorous approaches, but it can also increase the scalability of rigorous LTL synthesis in several ways. Firstly, checking whether a guessed strategy is winning is easier than constructing one. Secondly, even if the guess is wrong in some places, it can be fixed by strategy iteration faster than constructing one from scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize exploration in the most fruitful directions. In contrast to previous works, we (i) reflect the highly structured logical information in game’s states, the so-called semantic labelling, coming from the recent LTL-to-automata translations, and (ii) learn to reflect it properly by learning from previously solved games, bringing the solving process closer to human-like reasoning.
Synthesizing Trajectory Queries from Examples

Abstract Data scientists often need to write programs to process predictions of machine learning models, such as object detections and trajectories in video data. However, writing such queries can be challenging due to the fuzzy nature of real-world data; in particular, they often include real-valued parameters that must be tuned by hand. We propose a novel framework called Quivr that synthesizes trajectory queries matching a given set of examples. To efficiently synthesize parameters, we introduce a novel technique for pruning the parameter space and a novel quantitative semantics that makes this more efficient. We evaluate Quivr on a benchmark of 17 tasks, including several from prior work, and show both that it can synthesize accurate queries for each task and that our optimizations substantially reduce synthesis time.
TS2Anet: Ship detection network based on transformer

Ship positioning is critical to the reduction of marine casualties and the minimisation of losses at sea. However, most existing object detection methods use horizontal box detection, which fails to detect the direction of the ship. In addition, since ships have a specific orientation, the target box identified by the horizontal box often contains impurities. This makes it difficult to accurately detect the ship's coordinates. To address these limitations, this paper proposes the TS2Anet model, which uses the S2Anet rotating box object detection network and the PVTv2 structure as its backbone. In addition, to reduce the attention to outlier samples and improve the experimental performance, the image pre-processing method “cutout” was employed to simulate occlusion due to clouds, and the GHM function was used. With an increase in accuracy of 11.98% on the HRSC2016 dataset, the proposed model showed excellent detection performance. Furthermore, the model performed well in the coastal region of the HRSID dataset. This demonstrates the accuracy of ship detection by the proposed model.
A Multivariate Anomaly Detector for Satellite Telemetry Data Using Temporal Attention-Based LSTM Autoencoder

Telemetry anomaly detection is a prominent health condition monitoring task that plays an increasingly crucial role in identifying unexpected events and improving satellite’s overall reliability. Since a component or subsystem of a satellite contains multiple telemetry parameters, it is essential and practical to develop a multivariate telemetry anomaly detection framework. However, the complex nonlinear correlations among different telemetry parameters and the temporal dependency hidden in each telemetry parameter pose significant challenges. In this study, a temporal attention-based long short-term memory autoencoder (TA-LSTM-AE) anomaly detector is proposed for detecting multivariate telemetry anomalies. Initially, an TA-LSTM-AE model is established to learn the latent representation of the correlations among the monitored telemetry parameters and the temporal dependency within each parameter. A temporal attention mechanism is applied to enhance the long-term temporal dependency modeling ability of the model. Subsequently, the Mahalanobis distance of the reconstruction error is defined as the anomaly score. An adaptive thresholding approach is specifically designed considering the variation of the learnt latent representation by balancing the tradeoff between the detection reliability and accuracy. A critical parameters identification strategy is also presented for recognizing the telemetry parameters that contribute significantly to the triggered multivariate anomalies. Finally, the proposed detector is applied and detect anomalies in multivariate telemetry data collected from a real-world satellite. The experimental results verify the capability of the proposed detector in multivariate telemetry anomaly detection.
Trends in Research Data Management and Academic Health Sciences Libraries

Spurred by the National Institute of Health mandating a data management and sharing plan as a requirement of grant funding, research data management has exploded in importance for librarians supporting researchers and research institutions. This editorial examines the role and direction of libraries in this process from several viewpoints. Key markers of success include collaboration, establishing new relationships, leveraging existing relationships, accessing multiple avenues of communication, and building niche expertise and cachè as a valued and trustworthy partner.
Construction of Piecewise Chaotic Maps With Tunable Statistical Mean

In numerous chaos based applications, it is often important to use chaotic maps that showcase desirable statistical characteristics, that can improve the performance of the designed architecture. Yet, it is hard to design maps that can achieve the desired statistical properties. Motivated by this, this work considers a family of piecewise chaotic maps, and studies how tuning their parameters can affect the mean value of the generated chaotic trajectories. Such a family of maps can be highly practical in future chaos based applications.
A FAIR Approach to Real-World Health Data Management and Analysis

The increasing of health data sources to support clinical practice is opening the path for its secondary use in biomedical research. This changes the research paradigm, from data generation to data management and analysis. Although the potential for secondary use of this data is vast, including the improvement of healthcare systems and the advancement of clinical research, data discovery is challenging. In order to maximize data reusability, the FAIR principles have been developed as a guiding framework for system development. Nevertheless, the discovery and reuse of biomedical data present two main challenges: i) data partners grappling with ethical and social concerns related to data discoverability; ii) clinical researchers struggling to find the best data sources for their research studies. In this paper, we present a platform that provides a set of tools, compliant with the FAIR principles, to help data custodians when sharing data about biomedical databases, while allowing researchers to search for and select databases that meet their specific research needs.
Effect of incorporating metadata to the generation of synthetic time series in a healthcare context

Synthetic data is becoming the way forward to manage legal and regulatory aspects of biomedical research involving personal and clinical data. As no matches are expected between artificial instances and real samples and/or subjects, external researchers performing secondary analyses could benefit significantly by having unlimited access to uncompromised information. In this context, one of the main objectives of the H2020 VITALISE project is to develop a platform for providing synthetic data generated from real data collected in Living Labs to those external researchers. In addition, while some time series specific synthetic data generation models exist, only a few of them consider metadata (e.g., patient demographics) as part of the time series generation process itself. Therefore, the objective of this research is to perform a comparative assessment of two synthetic data generation models that use and process the metadata of subjects differently: The Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). To achieve this goal making sure the analyses were data-independent, we selected two healthcare-related longitudinal datasets: (1) Treadmill Maximal Effort Test (TMET) measurements from the University of Málaga; and (2) a hypotension subset derived from the MIMIC-III v1.4 database. After synthetic data was generated, we assessed three pivotal aspects: resemblance to the original data, utility, and level of privacy. As a main conclusion, the importance of using metadata as context variables and the methodology to take them into account was proved to be significant and valuable, the DGAN model offering better results overall. A more extensive time series specific evaluation is left as the main avenue for future research.
Blockchain in Agriculture: A PESTELS Analysis

Blockchain (BC) represents a disruptive technology that has been extensively used to ensure immutability of digital transactions. Starting as an underlying mechanism in the digital currency sector, it has been applicable in a wide range of sectors and application domains. Agriculture represents a sector of significance for overall sustainability challenges that is benefiting from digitalisation and technological evolution and the enforcement of Industry 4.0 paradigm shift towards precision agriculture. Introduction of Internet of Things, and Cyber-Physical Systems increase overall complexity, with Big Data analysis and Machine Learning technologies providing innovative applications. BC appears to be a promising technology for agriculture providing mechanisms for tracing of products and overall agricultural SC (SC) management from the farm to the fork. Authors investigate the challenges and open issues for the application of BC in agriculture performing a state of the art analysis along the PESTELS framework. A large number of challenges including technological ones, create big research potential for the evolution of the area.
HGR-Net: Hierarchical Graph Reasoning Network for Arbitrary Shape Scene Text Detection

As a prerequisite step of scene text reading, scene text detection is known as a challenging task due to natural scene text diversity and variability. Most existing methods either adopt bottom-up sub-text component extraction or focus on top-down text contour regression. From a hybrid perspective, we explore hierarchical text instance-level and component-level representation for arbitrarily-shaped scene text detection. In this work, we propose a novel Hierarchical Graph Reasoning Network (HGR-Net), which consists of a Text Feature Extraction Network (TFEN) and a Text Relation Learner Network (TRLN). TFEN adaptively learns multi-grained text candidates based on shared convolutional feature maps, including instance-level text contours and component-level quadrangles. In TRLN, an inter-text graph is constructed to explore global contextual information with position-awareness between text instances, and an intra-text graph is designed to estimate geometric attributes for establishing component-level linkages. Next, we bridge the cross-feed interaction between instance-level and component-level, and it further achieves hierarchical relational reasoning by learning complementary graph embeddings across levels. Experiments conducted on three publicly available benchmarks SCUT-CTW1500, Total-Text, and ICDAR15 have demonstrated that HGR-Net achieves state-of-the-art performance on arbitrary orientation and arbitrary shape scene text detection.
Radio Frequency Fingerprint Identification for Device Authentication in the Internet of Things

Device authentication of wireless devices at the physical layer could augment security enforcement before fully decoding packets. At the upper layers of the stack, this is conventionally handled by cryptographic schemes. However, the associated computing overhead may make such regular approaches unsuitable for the emerging class of Internet of Things devices, which are typically resource-constrained and embedded in areas that make them difficult to retrieve and re-program. In contrast, radio frequency fingerprint identification (RFFI) exploits the unique hardware features as device identifiers at the physical layer. This article reviews both the state-of-the-art in engineered feature-based RFFI protocol design and advances in recent deep learning-based protocols, as well as a hybrid protocol that combines their advantages. Specifically, the hybrid approach leverages two methods: a more versatile distance-based classifier and an automatic feature extractor. This article also summarizes the goals of identification, verification and classification as applicable to RFFI, and how they can be achieved by the above protocols.
Multi-Similarity Enhancement Network for Few-Shot Segmentation

Few-Shot Segmentation (FSS) is challenging for intra-class diversity and support sample scarcity. Many works focus on the class-wise or pixel-wise similarity between the support foreground and query sample while neglecting the support background, which is vital for FSS to suppress the related query background. In this paper, we propose a Multi-Similarity Enhancement Network (MSENet) to remedy this issue by extracting the pixel-wise support-query similarity of the foreground and background. To remedy the shift issue, caused by the huge difference between support and query target objects, we extract and fuse multiple support-query similarity, and keep enhancing them with convolutional operations. Experiments on three benchmarks demonstrate that our approach reaches SOTA performances and outperforms the best competitors with many desirable advantages.
Postdigital Time

The “postdigital” is an inherently temporal concept, yet, along with the postdigital itself, the temporality of the postdigital remains open. With few exceptions, by rejecting the idea that the “postdigital” comes right after the “digital” in a successive manner, most scholars interpret the temporality of the concept in a way that is admittedly blurry and indeterminate. This opens various lines of inquiry. How should we understand and relate to postdigital time, including the role digital technologies play in it? What are the constraints it imposes on our imagination of the future and present? How does it naturalize the contemporary order?
Tilbot: A Visual Design Platform to Facilitate Open Science Research into Conversational User Interfaces

This paper introduces Tilbot, an open source, easy to use platform for the design of conversational user interfaces. Although many platforms for the creation of conversational agents already exist, they often are closed source and focus mainly on commercial purposes. Tilbot is created as an open source solution that can be hosted on-premises. By focusing primarily on students and researchers, Tilbot stimulates open science research, collaborations, and building a scientific community to create and share best practices and experiences. At the CUI conference, visitors will be able to try out Tilbot, and engage with its original creators to highlight their wishes and brainstorm directions for future development.
Comparative analysis of binary and one-class classification techniques for credit card fraud data

Abstract The yearly increase in incidents of credit card fraud can be attributed to the rapid growth of e-commerce. To address this issue, effective fraud detection methods are essential. Our research focuses on the Credit Card Fraud Detection Dataset, which is a widely used dataset that contains real-world transaction data and is characterized by high class imbalance. This dataset has the potential to serve as a benchmark for credit card fraud detection. Our work evaluates the effectiveness of two supervised learning classification techniques, binary classification and one-class classification, for credit card fraud detection. The performance of five binary-class classification (BCC) learners and three one-class classification (OCC) learners is evaluated. The metrics used are area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUC). Our results indicate that binary classification is a better approach for detecting credit card fraud than one-class classification, with the top binary classifier being CatBoost.