Reviewerly/exHarmony huggingface dataset:
Grow, prune or select data: which technique allows the most energy-efficient neural network training?

The training energy efficiency of deep neural networks became an extensively studied research topic in the last years. Some of the existing approaches seek to reduce the size of the architecture by either starting the training with a large network and pruning it, or by beginning with a seed architecture and then growing it. Instead of compressing the architecture, other approaches aim to reduce the number of training examples through data selection. While various approaches belonging to these two categories have been proposed, only a few works actually conduct energy measurements. Others merely mention potential gains in efficiency or rely on alternative evaluation metrics such as FLOPs. In this paper, we conduct a series of experiments both on a synthetic dataset and on image classification benchmarks in order to compare the impact of pruning, architecture growing and data selection on training energy consumption and prediction quality. Our results show that growing maintains a high prediction quality but brings limited energy gains when the size of the resulting architecture is large. Pruning can offer high gains, but also impacts accuracy, making it more suited for large models. Data selection provides energy gains correlated with the selectivity rate but causes an accuracy loss. We find that the effectiveness of every technique depends on its hyperparameters and on the architecture size.
IArch: An AI Tool for Digging Deeper into Archaeological Data

The use of Artificial Intelligence (AI), notably Machine Learning (ML), is gaining momentum in archaeology, opening up new possibilities such as artifact classification, site location prediction, and remains analysis. One of the major challenges in this regard is the lack of qualified archaeologists who are experts in machine learning. In this study, we introduce IArch, a tool that enables eXplainable Artificial Intelligence (XAI) data analytics for archaeologists without requiring specific programming skills. It specifically allows data analysis performed to either validate existing data-supported hypotheses or generate new ones. The tool covers the entire workflow for applying ML, from data processing to explaining the final results. The tool allows the use of supervised and unsupervised ML algorithms, as well as the SHapley Additive exPlanations (SHAP) technique to provide archaeologists with global and individual explanations for the predictions. We demonstrate its use on data from a Xiongnu cemetery (100 BC/AD 100) in the Mongolian steppes.
Guided Hierarchical Reinforcement Learning for Safe Urban Driving

Designing a safe decision-making system for end-to-end urban driving is still challenging. Numerous contributions based on Deep Reinforcement Learning (DRL) were developed. However, they all suffer from the cold start issue and require extensive convergence training. Recent solutions for urban driving have emerged based on both Hierarchical Reinforcement Learning (HRL) and imitation learning to overcome these limitations. Nevertheless, they do not guarantee a safe exploration for an autonomous vehicle. In the literature, rule-based systems played a pivotal role in ensuring the safety of self-driving cars, but they require manual rule encoding. This paper introduces GHRL, a decision-making framework for vision-based urban driving that benefits from HRL, and a rule-based system for safe urban driving. The HRL algorithm learns the high-level policies, whereas the low-level policies are guided by the expert demonstration rules modeled with the Answer Set Programming (ASP) formalism. When a critical situation occurs, the system will shift to rely on ASP rules. The state of each policy includes visual features extracted by a convolutional neural network from a monocular camera, information on localization, and waypoints. GHRL is evaluated on the Carla NoCrash benchmark. The results show that by incorporating logical rules, GHRL achieved better performance over state-of-the-art HRL algorithms.
Illumination Conditions Adaptation for Data-Driven Keypoint Detection under Extreme Lighting Variations

In the domain of robotics vision, feature detection under challenging illumination conditions, especially in low-light or night settings, has emerged as a critical task. This paper presents a novel method that improves the repeatability and robustness of feature extraction in such challenging scenarios. The proposed approach introduces the concept of Illumination Conditions Adaptation (ICA); a technique that utilizes our Photo-Realistic Synthetic Illumination (PRSI) dataset. This dataset was created in a state-of-the-art game engine using ultra-high resolution textures and raytracing to resemble realistic environments in urban settings. It consists of image sequences of the same scenes captured with a script-based camera under different lighting conditions, which are then leveraged to enhance the training procedure of a feature detector based on Deep Neural Networks. Specifically, the ICA operates under a self-supervised scheme, harnessing ground truths extracted from day images and imposing them onto the corresponding night samples in order to minimize artifacts and errors in the detection process. Our experiments validate the efficacy of our approach, providing a notable improvement in feature detection reliability, even under extremely low lighting conditions, while also increasing the number of accurate keypoint matches. This development holds the potential to significantly enhance the performance of robotics vision applications in a wide range of real-world scenarios.
Reduced-rank spectral mixtures Gaussian processes for probabilistic time-frequency representations

Deterministic time-frequency representations are commonly used in signal processing, particularly in audio processing. Whilst presenting many potential advantages, their probabilistic counterparts are not widely used, essentially because of the computational load and the lack of clear interpretability of the different underlying models. However, using state space models, they have been shown recently to be equivalent to Spectral Mixtures Gaussian processes (SM-GP). This pioneer work unlocks this problem and opens the path for the development of tractable and interpretable probabilistic time-frequency analysis. In this paper, we propose a relatively simple yet a significant improvement of that work in terms of computational complexity, flexibility and practical application. To do so, we use a recent approach for covariance approximation to develop an algorithm for faster inference of SM-GP, while opting for a frequency-domain approach to hyperparameter learning. We illustrate the practical potential of our method using voiced speech data. We first show that key speech features can be accurately learned from the data. Second, we show that our method can yield better performances in denoising.
Exploration of Subject Service of Local University Library Based on New Media

This paper aims to explore the application and practice of new media in the subject service of local university libraries. As an important way for students and teachers to learn and obtain information, local university library is facing the challenge of digital transformation in the information age. The continuous development of new media technology provides new opportunities and possibilities for library subject service. By introducing diversified information resources, innovating subject service methods, improving subject service quality through data analysis, and establishing instant feedback mechanism with readers by means of interactive function of new media, the transformation and upgrading of subject service of local university libraries are realized. We should actively promote intelligent, global, diversified and personalized development, improve the accuracy and efficiency of services, expand the scope of resource acquisition, meet the needs of different readers, and achieve more accurate personalized recommendation.
Why Does the GRSS Need a Magazine? [From the Editor]

Since you are reading this editorial, you are well aware that this journal is quite different than the other ones published by the IEEE Geoscience and Remote Sensing Society (GRSS). Indeed, it is a magazine, which implies that it has some specific features. In the past two issues I introduced how the technical contents of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Geoscience and Remote Sensing Magazine</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GRSM</i> ) are selected and how authors may submit their contributions. In these few paragraphs I would like to highlight the specificity of a magazine as opposed to a technical journal and provide a more general explanation about the rationale for the choices innerving these procedures.
Analysis of Publications on Earthquake Research in Architecture Category and Analysis with R Studio-Biblioshiny Software

The purpose of this research is to examine the publications focusing on earthquakes in the category of architecture (web of science). The data of the research was analyzed with the Biblioshiny software program. This software program makes a bibliometric analysis on which topics and concepts earthquake research focuses. In addition, images and frequencies of publications related to architecture and earthquakes were revealed. The data of the research was collected between 1-15 July 2023. Results for Architecture (WoS Categories) AND earthquake* (Topic) OR earthquake AND architecture (Topic) OR earthquake AND house (Topic) OR earthquake AND structure (Topic) OR earthquake AND damage (Topic) OR earthquake AND city (Topic) OR earthquake AND urban (Topic) and Türkiye (Countries/Regions). Data were collected with keywords in the Web of Science database. According to the research findings, there are 1033 publications and in the country/region category (Türkiye), 83 publications are accessed. The most used words in the publications are earthquake, urban transformation, Istanbul, seismic, retrofit, assessment, structural, urban, damage, buildings and performance.
Machine Learning and Health Science Research: A Tutorial (Preprint)

Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.
Artificial Intelligence as a Game Changer Tool to Reshape the Banking Services in Digital Transformation

The banking industry is undergoing a profound transformation, driven by the integration of artificial intelligence (AI) into its operations and services. This study explores how AI is emerging as a game-changing tool, catalyzing a revolutionary shift in the way banks operate and interact with customers within the digital transformation landscape. AI's multifaceted applications in banking, including data analytics, natural language processing, and machine learning, have the potential to enhance customer experiences, streamline operations, and optimize decision-making processes. This research delves into the key roles played by AI in reshaping the financial sector. It investigates how AI is leveraged for personalized customer services, risk management, fraud detection, and compliance with regulatory standards. Additionally, it explores the ethical considerations and challenges that arise with the use of AI in banking, particularly related to bias and fairness. The study also addresses the need for talent development in AI and data science within the banking industry and how a skilled workforce is instrumental in harnessing AI's potential. Furthermore, the study examines the long-term impact of AI investments on the profitability and sustainability of banks, providing insights into measuring the return on investment in AI.
Quantum Key Distribution and Blockchain Based Secure Authentication in Medical Cyber-Physical Systems

Medical cyber-physical systems (MCPSs) enable flexible patient–medical system interaction as the foundation of application in the field of smart health care, realizing all-encompassing three-dimensional medical service. The security barrier must be built using a secure and trustworthy device identity authentication mechanism in order to meet the controlled and believable requirements of MCPS. This work, therefore, develops an efficient authentication and encryption system (EAES) to improve the security and authentication of MCPSs. This research suggests a secure authentication method to safeguard sensitive healthcare data. The suggested modified “elliptic-curve Diffie–Hellman” (ECDH) encryption is first applied to the medical data that is acquired from the patients and stored in the cloud in encrypted form. This is done in order to guarantee secure access to the stored data for use in medical data analysis. The key in the ECFH is controlled via a quantum key distribution system. Additionally, blockchain is considered for the mutual authentication of users in the MCPSs. The proposed methodology is implemented and evaluated by using performance measures. Additionally, it is compared with the conventional techniques.
VIPurPCA: Visualizing and Propagating Uncertainty in Principal Component Analysis

Variables obtained by experimental measurements or statistical inference typically carry uncertainties. When an algorithm uses such quantities as input variables, this uncertainty should propagate to the algorithm's output. Concretely, we consider the classic notion of principal component analysis (PCA): If it is applied to a finite data matrix containing imperfect (i.e., uncertain) multidimensional measurements, its output-a lower-dimensional representation-is itself subject to uncertainty. We demonstrate that this uncertainty can be approximated by appropriate linearization of the algorithm's nonlinear functionality, using automatic differentiation. By itself, however, this structured, uncertain output is difficult to interpret for users. We provide an animation method that effectively visualizes the uncertainty of the lower dimensional map. Implemented as an open-source software package, it allows researchers to assess the reliability of PCA embeddings.
Design of Automatic Identification System for Substation Monitoring Interface Based on Machine Vision

Substation is one of the key nodes in building a new energy system. The level of intelligence in substations directly affects the safety and efficiency of smart grids. In order to test and evaluate the working status of the substation monitoring interface, this work designs an automatic identification system based on machine vision. Firstly, an intelligent monitoring software and hardware platform for the substation monitoring interface is designed, utilizing a modular approach to decompose subsystems. At the same time, an intelligent entity recognition algorithm based on machine vision is proposed to accurately classify and recognize interface entities, and to compare and verify with test inputs. This system avoids manual verification and comparison, and achieves fully automatic testing of substation monitoring.
3D Intra-articular Dense Reconstruction from Arthroscopic Images

Arthroscopy is an important clinical technique for knee joint damage treatment, where a small fibre-optic camera is manipulated in a narrow space to provide a live 2D view of the inner joint. The small field of view from the surgical scope makes the 3D guidance for instruments manipulation very difficult. Therefore, a 3D reconstruction of the articular surface becomes very helpful. This paper provides a framework that recovers joint inner dense surface for arthroscopy using only a sequence of intra-articular images. A self-supervised convolutional neural network is trained pre-operatively to predict dense depth maps from the intra-articular images. The depth maps and images are fused together and a local-to-global SLAM pipeline is proposed for tissue surface reconstruction. Compared with existing works on 3D reconstruction using arthroscopic images, the proposed framework does not require additional sensors. Ex-vivo experiments are conducted and the result demonstrates reconstruction ability as well as the potential clinical value of this framework.
Towards Robust Modality Agnostic Pose Estimation via Unbiased Data Fusion

The ever-advancing field of visual odometry has always been plagued with limited robustness. This has led to many fusion schemes being utilised in conjunction with a second modality to enhance the robustness of the visual odometry pipeline. This paper produces an extension to this body of work by being the first practical application of the stereo thermal visual-inertial navigation system based on the extended kalman filter in the literature. This paper also examines the nature of the drift inherent in the system by means of contrast to the stereo-visual and multispectral versions of the solution. All on a novel dataset fashioned to enable the study of the effects of varying illumination and heating conditions on the systems. The construction of the dataset also enabled the employment of various props to alter the feature sets present in the scene. This is used to test the effects of the varying feature sets in isolation and in addition to the various illumination and heating changes introduced to the environment.
Binary Classification with Imbalanced Data

When the binary response variable contains an excess of zero counts, the data are imbalanced. Imbalanced data cause trouble for binary classification. To simplify the numerical computation to obtain the maximum likelihood estimators of the zero-inflated Bernoulli (ZIBer) model parameters with imbalanced data, an expectation-maximization (EM) algorithm is proposed to derive the maximum likelihood estimates of the model parameters. The logistic regression model links the Bernoulli probabilities with the covariates in the ZIBer model, and the prediction performance among the ZIBer model, LightGBM, and artificial neural network (ANN) procedures is compared by Monte Carlo simulation. The results show that no method can dominate the other methods regarding predictive performance under the imbalanced data. The LightGBM and ZIBer models are more competitive than the ANN model for zero-inflated-imbalanced data sets.
Blockchain and IOT for the sustainable agriculture census system development

The integration of Blockchain and the Internet of Things (IoT) technologies has revolutionized various sectors, including agriculture, by enhancing transparency, security, and efficiency. This abstract provides an overview of a groundbreaking project aimed at developing Blockchain and IoT technologies for agriculture. The conventional agriculture census process is often plagued by inefficiencies, data inaccuracies, and vulnerability to fraud. to fraud often plague the conventional agriculture census process. To address these challenges, this project leverages Blockchain's immutable ledger capabilities to ensure the integrity of agricultural data collected during the census. Furthermore, IoT devices, such as sensors and drones, are deployed to automate data collection and monitor various aspects of agricultural operations, promoting sustainability and resource optimization. Through the combination of Blockchain and IoT, the SACS provides several advantages. It offers secure, tamper-proof storage of agricultural data, ensuring its accuracy and reliability. Smart contracts on the Blockchain facilitate automated and transparent transactions, streamlining interactions among stakeholders in the agriculture ecosystem. Moreover, the real-time data collected by IoT devices enable farmers and policymakers to make data-driven decisions that improve crop yields, resource utilization, and environmental sustainability.
Advanced User Interactions

The Advanced User Interactions API in Selenium WebDriver provides a way to set up and perform complex user interactions, specifically those that group a series of keyboard and mouse operations and send them to the browser.
IoT-Facilitated Intimate Partner Abuse

IoT is an emerging technology that is being implemented into everyday life at a rapid rate. Interconnected devices such as smart home devices are prevalent and are only expected to increase. With this comes new security issues such as technology-facilitated intimate partner abuse (IPA) which is the abuse of a partner through the use of technology. The use of interconnected devices is expected to give rise to such forms of abuse as tech devices are easier to control and monitor than ever. Despite this concern, little research is done on how IoT can be used to instigate IPA. Although there exists technical research on security concerns of IoT and how it can be mitigated, it does not examine the technicality of how a user of the IoT device can misuse it and attack a partner sharing the same device. Some forms of abuse that can take place through IoT are monitoring a partner through video, controlling devices to coerce a partner, and more. Additionally, there is a lack of standards and regulations to mitigate such kind of misuse of technology which makes it difficult to find legal help when faced with it.
The Importance of Data Quality for Reducing the Uncertainty of Site-Adapted Solar Resource Datasets

In a 2016 study, Clean Power Research presented a statistically sound methodology for quantifying the uncertainty of satellite-derived solar resource datasets adapted to ground-measured data. Since that time, Clean Power Research has made numerous improvements to SolarAnywhere® data and reference data quality control (QC) procedures. Using the same methodology and site-adaptation procedure as the original study, we demonstrate a 40% reduction in the uncertainty of site-adapted solar resource data in North America. 83% of the reduction is attributable to the increased temporal consistency of Solar Anywhere V3.6 relative to prior versions. The remaining reduction in uncertainty (17%) is due to improved data QC. Together, the results demonstrate how using high-quality datasets reduces the uncertainty of site-adapted solar resource data.
Solar Forecasting: The Value of Using Satellite Derived Irradiance Data in Machine Learning Based Forecasts

Solar forecasts lower the cost of solar power and reduce the barriers to firm power generation. Solar forecasting conventionally has relied on advecting near real time observations and numerical weather predictions (NWPs). Observation based methods have limitations in cost and operational feasibility. NWPs generally have coarse spatial and temporal resolution, resulting in forecasts that may be overly general for a solar plant's location. Machine learning (ML) based forecasts have the potential to extract and blend observations and NWP data in an optimal blend, adding forecast skill. A primary drawback is ML based forecasts usually require training data. This paper will quantify the ML based forecast skill of using satellite derived irradiance data in lieu of ground, and the relationship between length of input training data and trained forecast skill gained. Climate and regional effects will be investigated by testing sites across the globe. Importantly these ML based forecasts will be compared to persistence forecasts and current NWP forecasts as a baseline.