Reviewerly/exHarmony huggingface dataset:
Adversarial Attacks on Machine Learning-Based State Estimation in Power Distribution Systems

We examine the robustness of machine learning-based distribution system state estimation (DSSE) techniques to a class of adversarial attacks, known as the black-box evasion attack. In these attacks, the attacker manipulates real-time measurements from sensors installed in the distribution grid by adding carefully crafted perturbations to diminish the accuracy of DSSE. We devise a stealthy attack based on the Fast Gradient Sign Method (FGSM), dubbed Sneaky-FGSM, by analyzing the statistical properties of real-time measurements and adding perturbations accordingly. Using simulation on a standard test distribution system, we show that this attack would remain largely unidentified and the error introduced in the DSSE process could propagate to a voltage control scheme that takes the DSSE result as input. Our result suggests that incorporating machine learning models in DSSE is a double-edged sword and calls for more research to ensure the robustness of these models to adversarial samples.
Patch-based Monte Carlo Terrain Upsampling via Gaussian Laplacian Pyramids

Terrain upsampling techniques are aiming at solving the changing problem of synthesizing arbitrary dimensional high-resolution terrain heightmaps from given low-resolution terrain heightmaps. In this paper, we propose a novel patch-based Monte Carlo terrain upsampling approach using Gaussian Laplacian pyramids. In our approach, given low-resolution terrain heightmaps as input, we generate its Gaussian Laplacian pyramids. Then, we upsample the Laplacian pyramids using a Monte Carlo image patch quilting approach. Finally, we reconstruct realistic terrains from the upsampled terrain Laplacian pyramids. In the end, We conduct a series of numerical experiments to show the effectiveness of our approach in upsampling terrain heightmaps.
A Framework to Secure Electronic Health Records using Privacy-Enabled Hyperledger Fabric

Data sharing is critical in the healthcare industry. A Patient may experience an exchange of medical history with various medical organizations throughout his healthcare journey. Their medical record may not be recorded confidential and integral way by any authority. Medical practitioners may require medical history to make good decisions for treatment. So, a malfunction or disclosure of these medical records could have damaging consequences. In this work, we have applied a Hyperledger fabric based EHR that can offer a way to share the Patient’s medical data with various healthcare-related organizations while maintaining data integrity, confidentiality most importantly patient privacy. The comparative study of the work indicates the significance of the framework, and shows that it is having low cost, high transaction speed compared to public blockchain network.
Geospatial big data analysis using neural networks

Geospatial information system (GIS) produces large and complex data. Geo-spatial big data analysis is a critical field nowadays because a large amount of data is generated every day by various space mission programs running through space agencies all over the globe. It requires robust data storage and retrieval systems for decision-making in various GIS-based systems. This paper introduces a method for data analysis by adding a fog layer in the cloud-based GIS. The Fog environment is integrated mainly for data pre-processing and data cleaning for GIS systems. The load on the cloud environment will reduce when the pre-processing tasks are executed on the fog layer. The Weather dataset is used for weather prediction using an artificial neural network in a cloud environment.
Software framework for spatiotemporal data analysis and mining of earth observation data

Spatiotemporal data are generated in various fields: such as agriculture, defence, meteorology, crop sciences, medicine, and transportation. Data can be captured in various formats at multiple levels of granularity both in space and time. Such data can be analyzed using various visualization methods, spatiotemporal On Line Analytic Process (OLAP) operations and data mining. Separate tools exist for each possible way of analysis, which lacks in some other way in fulfilling generic functionalities to analyze each sort of spatiotemporal data. The paper proposes a framework, spatiotemporal data analysis and mining environment (ST-DAME) for effective analysis of spatiotemporal data by integrating various components of geographic visualization, information visualization, OLAP server, and data mining server in a single software. Machine learning and deep learning techniques can be used for clustering purposes using spatiotemporal data to expedite the work faster. Moreover, other than a generalized framework, the need for application-specific analysis is also realized and incorporated.
Deep Reinforcement Learning for Pin-Point Autonomous Lunar Landing: Trajectory Recalculation for Obstacle Avoidance

This work aims to present a method to perform autonomous precision landing—pin-point landing—on a planetary environment and perform trajectory recalculation for fault recovery where necessary. In order to achieve this, we choose to implement a Deep Reinforcement Learning—DRL—algorithm, i.e. the Soft Actor-Critic—SAC—architecture. In particular, we select the lunar environment for our experiments, which we perform in a simulated environment, exploiting a real-physics simulator modeled by means of the Bullet/PyBullet physical engine. We show that the SAC algorithm can learn an effective policy for precision landing and trajectory recalculation if fault recovery is made necessary—e.g. for obstacle avoidance.
File type identification tools for digital investigations

Digital forensics is the process of identifying, preserving, analyzing, and documenting digital evidence for investigation purposes. Building or using file analysis tools is of great interest for a forensic expert to collect high-level information in a short time. In this paper, we consider the examination of files contained in digital media, especially files with possible incorrect types. This often reveals a simple way to hide sensitive content such as porn images, passwords, or accounts. Many commercial and free forensic tools are available for file type identification (FTI). In this work, we assess the performance of ten of them on two significant datasets and scenarios. The main issue we address is the relevance of the tools for forensic purposes. The underlying question is: do expectations meet reality? Our experiments highlight the significant disparity in the accuracy and behavior of the studied tools.
Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training

Hyperparameter tuning of deep learning models can lead to order-of-magnitude performance gains for the same amount of compute. Despite this, systematic tuning is uncommon, particularly for large models, which are expensive to evaluate and tend to have many hyperparameters, necessitating difficult judgment calls about tradeoffs, budgets, and search bounds. To address these issues and propose a practical method for robustly tuning large models, we present Cost-Aware Pareto Region Bayesian Search (CARBS), a Bayesian optimization algorithm that performs local search around the performance-cost Pareto frontier. CARBS does well even in unbounded search spaces with many hyperparameters, learns scaling relationships so that it can tune models even as they are scaled up, and automates much of the "black magic" of tuning. Among our results, we effectively solve the entire ProcGen benchmark just by tuning a simple baseline (PPO, as provided in the original ProcGen paper). We also reproduce the model size vs. training tokens scaling result from the Chinchilla project (Hoffmann et al. 2022), while simultaneously discovering scaling laws for every other hyperparameter, via an easy automated process that uses significantly less compute and is applicable to any deep learning problem (not just language models).
Safe Use of Neural Networks

Neural networks in modern communication systems can be susceptible to internal numerical errors that can drastically effect decision results. Such structures are composed of many sections each of which generally contain weighting operations and activation function evaluations. The safe use comes from methods employing number based codes that can detect arithmetic errors in the network's processing steps. Each set of operations generates parity values dictated by a code in two ways. One set of parities is obtained from a section's outputs while a second comparable set is developed directly from the original inputs. The parity values protecting the activation functions involve a Taylor series approximation to the activation functions. We focus on using long numerically based convolutional codes because of the large size of data sets. The codes are based on Discrete Fourier Transform kernels and there are many design options available. Mathematical program simulations show our error-detecting techniques are effective and efficient.
Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerations

Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information about the material composition of the imaged scene. However, these images can be affected by various sources of noise, distortions, or data losses, which can significantly degrade their quality and usefulness. To address these problems, we introduce two novel self-supervised Hyperspectral Images (HSI) inpainting algorithms: Low Rank and Sparsity Constraint Plug-and-Play (LRS-PnP), and its extension LRS-PnP-DIP, which features the strong learning capability, but is still free of external training data. We conduct the stability analysis under some mild assumptions which guarantees the algorithm to converge. It is specifically very helpful for the practical applications. Extensive experiments demonstrate that the proposed solution is able to produce visually and qualitatively superior inpainting results, achieving state-of-the-art performance. The code for reproducing the results is available at \url{https://github.com/shuoli0708/LRS-PnP-DIP}.
Robust Contrastive Learning With Dynamic Mixed Margin

One of the promising ways for the representation learning is contrastive learning. It enforces that positive pairs become close while negative pairs become far. Contrastive learning utilizes the relative proximity or distance between positive and negative pairs. However, contrastive learning might fail to handle the easily distinguished positive-negative pairs because the gradient of easily divided positive-negative pairs comes to vanish. To overcome the problem, we propose a dynamic mixed margin (DMM) loss that generates the augmented hard positive-negative pairs that are not easily clarified. DMM generates hard positive-negative pairs by interpolating the dataset with Mixup. Besides, DMM adopts the dynamic margin incorporating the interpolation ratio, and dynamic adaptation improves representation learning. DMM encourages making close for positive pairs far away, whereas making a little far for strongly nearby positive pairs alleviates overfitting. Our proposed DMM is a plug-and-play module compatible with diverse contrastive learning loss and metric learning. We validate that the DMM is superior to other baselines on various tasks, video-text retrieval, and recommender system task in unimodal and multimodal settings. Besides, representation learned from DMM shows better robustness even if the modality missing occurs that frequently appears on the real-world dataset. Implementation of DMM at downstream tasks is available here: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/teang1995/DMM</uri>
SonicGest: Ultrasonic Gesture Recognition System Combined with GAN on Smartphones

In recent years, with the widespread popularity of mobile devices, gesture recognition as a way of human-computer interaction has received more and more attention. However, existing gesture recognition methods have their limitations, such as requiring additional hardware devices, invading user privacy, and causing difficulty in data collection. To address these issues, we propose SonicGest, a recognition system that utilizes acoustic signals to sense in-air gestures. The system only needs the built-in speaker and microphone of the smartphone, without any additional hardware and no privacy disclosure. SonicGest transforms the features of the acoustic Doppler effect caused by gesture movements into a spectrogram, uses spectrogram enhancement techniques to remove noise interference, and then builds a convolutional neural network (CNN) classification model to recognize different gestures. To solve the problem of data collection difficulties, we utilize the Wasserstein distance based on gradient penalty to optimize the loss function of the generative adversarial network (GAN) to generate high-quality spectrograms to expand the dataset. The experimental results show that SonicGest has a recognition accuracy of 98.9% for ten kinds of gestures.
Learning from deep learning and pathomics

The suffix -omics has been applied to a variety of approaches with the overarching aim to identify, describe, and quantify processes that contribute to the form and function of cells and tissues. Historically, histopathologic approaches have relied on assessment of lesions to describe a pattern of injury and when possible, give a specific etiologic diagnosis. Broad pathologic entities have been split into smaller groups to provide more individualized diagnostic, prognostic, and etiologic information as a step towards precision medicine. More recently, pathomics has emerged as a complement to traditional histopathology, applying deep learning algorithms to digital histopathology images for detailed quantitative mining of visible or subvisual features that correlate with specific diagnoses, disease trajectories, or shared injury patterns. In a recent publication in Nature Communications, Hölscher and colleagues describe this exciting new approach of pathomics, with the potential to provide insights into kidney disease diagnosis, prognosis, and mechanisms ( 1 Hölscher D.L. Bouteldja N. Joodaki M. et al. Next-Generation Morphometry for pathomics-data mining in histopathology. Nat Commun. 2023; 14: 470 Crossref PubMed Scopus (1) Google Scholar ).
Towards a Blockchain-Based Crowdsourcing Method for Robotic Ontology Evolution

All domain knowledge may change over time due to the addition or updating of new concepts, rules or requirements, especially when the domain is in its infancy such as robotics. This mandates the need to evolve the corresponding ontologies. In this paper, we propose a new blockchain-based robotic ontology evolution method using a crowdsourcing approach. The proposed method integrates the use of a crowdsourcing approach to allow a crowd of robotic experts to evolve the ontology and then use blockchain as a storage mechanism to store and manage the different versions of the ontology. We aim to solve some of the issues emanating from the methods proposed in the literature, such as preventing manipulation in ontologies, accessing different versions of ontologies, and involving a good number of domain experts in proposing changes and seeking their approval.
Browsing the Latent Space: A New Approach to Interactive Design Exploration for Volumetric Generative Systems

We introduce an interactive tool called "Browsing the Latent Space" (BLS), which explores the potential of latent space visualization and interpolation in 3D generative systems for design space exploration. By visualizing and interpreting the abstract space in which the outputs produced by generative systems operate (the latent space), users gain a better understanding of how the model generates its outputs, and can explore underlying patterns and structures in the data. Using the GET3D generative model, this demonstration showcases how latent space manipulation can enhance the creative process and provides an intuitive and interactive way to explore, remix, and create new chair designs. Allowing interaction with and manipulation of the generative system’s outputs in real-time, this tool supports increased awareness of the latent space as a resource for a designer’s understanding of generative systems and creative exploration.
Technologies of remote laboratory practical training in engineering disciplines

The article presents the results of the development and implementation of distance learning technologies for conducting a laboratory workshop in engineering disciplines. Among them, ways to ensure remote laboratory practice, organizational and technical problems associated with distance learning, and solutions to these problems based on the experience gained. The technology and software solution based on Project Jupiter components are proposed, providing effective remote interaction in the process of conducting laboratory classes.
CARINA Project: Visual Perception Systems Applied for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS)

Autonomous mobile robots use computational techniques of great complexity so that to allow navigation in various types of dynamic environments, avoiding collisions with obstacles and always seeking to optimize the best route, ultimately enabling them to operate in a safe and precise manner.In order for navigation at this level to be possible, a variety of intelligent sensing techniques and computer vision are used.The potential of an intelligent computer vision system to detect and predict the actions of dynamic agents on the streets is applied to increase traffic safety with intelligent robotic vehicles.In this paper we present a systematic review of computer vision models for the detection and tracking of obstacles in traffic environments.Specifically, we will cover works involving 2D and 3D data fusion for both internal and external perception, as well as current trends regarding efficient model design and temporally-aware architectures.Alongside our review, we also provide a thorough discussion on the main positive and negative points of the state-of-the-art for detecting and tracking obstacles in Visual Robotic Attention works, as well as share our experience in applying visual perception for external obstacle detection and tracking, as well as internal (driver) monitoring.The results presented should serve as a compilation of the history of visual perception for autonomous mobile robots, and our contributions to the field, thus enabling an advance for research in the area of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles.
The Influence of Transformational Education Prediction on Softskills of Madrasa Student using Data Mining

&#x0D; Currently, transformative education is oriented towards students' independence in solving a problem they face. In other words, transformative education has its own influence on the soft skills of students. This research was conducted with the aim of providing predictions regarding the effect of transformative education on the soft skills possessed by students. The subjects in this study were students of Madrasah Aliyah Negeri 2 Bandung, ranging from grade 10 to grade 12. The research method used was the Naïve Bayes and K-Nearest Neighbor algorithm by taking datasets from an independent survey that had been carried out previously. The Naïve Bayes and K-Nearest Neighbor algorithms themself are included in supervised learning and can be used to predict with a high degree of accuracy. Supervised learning is one of the existing methods in machine learning by means of labeling the data trained by the machine. Testing the data using the Naïve Bayes and K-Nearest Neighbor algorithm obtains predictions that transformative education affects students' soft skills and produces a very high level of accuracy for the Naïve Bayes algorithm, namely 98% of the 100 existing datasets and accuracy of the K-Nearest Neighbor is 76,67%.&#x0D;
Impact of Digitalization on Sustainable Supply Chain Management

There are only so many resources available; it is essential to put into action strategies that will lead to sustainable growth if one wishes to ensure their continued success over the long run. Despite this, a significant number of scholars have investigated the prospect that digital technologies may be able to increase sustainable performance in this age of digitalization and globalization. This research maintains collaboration and coordination in a digitally connected supply chain (SC) could contribute to sustainability is still in its early phases, and there is still a long way to go before it can be considered complete. Using SC, it is possible to cut down on the amount of energy that is consumed, cut down on the amount of time that is spent traveling, and make better use of the assets that are employed in logistics. Case studies conducted with a variety of manufacturers form the basis of this investigation and will serve as its primary focus. Researchers nevertheless give equal weight to the social and environmental sustainability components, even though the majority of studies in this subject concentrate on the financial aspect of the topic. The research concluded that incorporating SC into logistics and supply chain management led to a moderate improvement in terms of both environmental and social sustainability.
SPEEDZO: Vehicle speed detection using Video Processing

In the modern world, reckless driving has been a major contributing factor in many road accidents. Various researches and studies have already been done on speed detection and published on the Internet. Proposed approach roughly involves three steps: background removal, feature extraction, and vehicle tracking. Our model will help in detecting the over speeding of vehicles on the highways by mainly presenting an algorithm for estimating vehicles speed based on target detection in video surveillance. The proposed algorithm uses various techniques such as Kalman filtering, contour tracing, centroid tracking, zth image, background subtraction, vehicle segmentation, computing centroid, speed detection, and calculation. This study describes how to find a vehicle, track its speed using video processing, and mathematical technology for detecting, tracking, and calculating vehicle speed. Our approach towards this problem is mainly done through a video input to make the system work to analyze the vehicle traveling speed using computer visioning to exploit and manipulate data from videos to extract and estimate useful data. The algorithm can be applied to various types of video surveillance systems and can be used for real-time monitoring of traffic violations.
Joint User Cooperation and Scheduling for Federated Learning in CFmMIMO Networks

This paper proposes a joint user cooperation and scheduling (JUCS) method to minimize the maximum transmission time among users in the synchronous federated learning (FL) architecture, since the user with the worst quality link limits the training performance of FL. To this end, an optimization problem is formulated to find the optimal user cooperation pairing and the power control coefficients. Meanwhile, as the links among users operate on the same frequency bands, the interface between them is taken into account in this paper. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fair scheduling (FS)-based algorithm to schedule the helping users to pair with the ones who are required to be assisted. For the second sub-problem, an algorithm based on deterministic policy gradient (DDPG) is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCS method shortens about 34.48% transmission time of the worst user in FL compared to the traditional one without JUCS.