Reviewerly/exHarmony huggingface dataset:
Trustworthy and Scalable Federated Edge Learning for Future Integrated Positioning, Communication and Computing System: Attacks and Defenses

The emergence of Integrated Positioning, Communication, and Computing (IPC2) technology has paved the way for advanced capabilities in physical-digital spatial positioning, intelligent communication, and computing. This paper delves into an in-depth exploration of a federated learning-assisted multi-dimensionality fusion IPC2 system. Within this system, edge nodes collaboratively harness their locally distributed multi-dimensionality positioning and communication data to coordinate edge computing resources for model training. Throughout the process of fully distributed collaborative training, we focus on addressing two specific security concerns: data tampering and model tampering attacks. In pursuit of bolstering the system’s resilience against potential attacks, we introduce a novel federated-blockchain edge learning framework. This framework capitalizes on the inherent features of the blockchain, namely, its non-tampering and traceability attributes. In addition, we present a meticulously designed verification algorithm tailored for the parameters aggregation process. Specifically, an aggregation algorithm is developed to enhance the efficiency and accuracy of the training model’s fitting. To assess the effectiveness of our proposed approach, comprehensive simulations are conducted using an openly accessible wireless Artificial Intelligence (AI) dataset. The outcomes of these simulations clearly demonstrate that the proposed scheme adeptly combats data tampering attacks initiated by multiple malicious nodes and high-intensity model tampering attacks, all while maintaining minimal accuracy loss.
Optimal Order Routing with Reinforcement Learning

Many asset managers use sell-side brokers to buy and sell shares of common stock. Typically, orders are distributed between several brokers using so-called algo wheels. This allows asset managers to compare brokers, and, thereby, ensure they receive the best possible execution. This paper describes how reinforcement learning techniques can be used to optimally distribute orders between brokers. The methodologies find a balance between exploitation and exploration, i.e., the best performing broker receives most orders, and others receive sufficient orders to determine which broker performs best.
Exploring Possibilities of Integrating Version Control Platforms in Higher Education Through GitHub Data Analysis

Working in the software development industry in the modern world cannot be imagined without integration with some code versioning platform. In addition to allowing copies of the code to be kept, these platforms provide the ability to control changes that occur in the code itself, so at any time it can be determined which member of the development team made the change. The aim of this paper is to determine the possibilities of using code versioning platforms in education, i.e. conducting teaching in the field of software development at faculties, through the analysis of data generated on one of these platforms. In order to gain insight into the dynamics of software solution development, data analysis from the GitHub platform was performed using the GitHub REST API on the example of a public GitHub organization. The results of the analysis indicate that the data generated on these platforms can be used to better understand the learning process and monitor the dynamics of project work. In this way, lecturers have a better insight into the dynamics of the development of a given software solution. It is possible to follow iteratively how much and in what way each team member contributed to the solution through the analysis of different groups of data and thus better understand the state of individual activities, but also the state of the entire project.
Post-LLM Academic Writing Considerations

Academic publishers would not be naive to have a concern for how scientific records are increasingly vulnerable in the Age of Large Language Models. Vulnerabilities can come in the form of DDOS-like attacks to repositories or guidance in the creation of malware that can serve to infiltrate their systems. Actors from mere pranksters to malicious actors who are anti-science or want to affect national security could very well affect the publishers and endanger collected human knowledge. It is important for stakeholders to become realistic about the risk environment and the increasingly leveled playing field for the simplest of tasks and how academia may be affected. This exploration and commentary paper suggests some complications in this era for academic publishing and is not meant to be taken as a scientific work, in hopes of generating conversation. Failure to consider the new landscape may result in the permanent loss of the value that scientific records could offer, down the line.
Enhanced Relative Comparison of Traditional Sorting Approaches towards Optimization of New Hybrid Two-in-One (OHTO) Novel Sorting Technique

In the world of computer technology, sorting is an operation on a data set that involves ordering it in an increasing or decreasing fashion according to some linear relationship among the data items. With the rise in the generation of big data, the concept of big numbers has come into existence. When the number of records to be sorted is limited to thousands, traditional sorting approaches can be used; in such cases, complexities in their execution time can be ignored. However, in the case of big data, where processing times for billions or trillions of records are very long, time complexity is very significant. Therefore, an optimized sorting technique with efficient time complexity is very much required. Hence, in this paper an optimized sorting technique is proposed, named Optimized Hybrid Two-in-One Novel Sorting Technique (OHTO, a mixed approach of the Insertion Sort technique and the Bubble Sort technique. The proposed sorting technique uses the procedure of both Bubble Sort and Insertion Sort, resulting in fewer comparisons, fewer data movements, fewer data insertions, and less time complexity for any given input data set compared to existing sorting techniques.
VFCFinder: Seamlessly Pairing Security Advisories and Patches

Security advisories are the primary channel of communication for discovered vulnerabilities in open-source software, but they often lack crucial information. Specifically, 63% of vulnerability database reports are missing their patch links, also referred to as vulnerability fixing commits (VFCs). This paper introduces VFCFinder, a tool that generates the top-five ranked set of VFCs for a given security advisory using Natural Language Programming Language (NL-PL) models. VFCFinder yields a 96.6% recall for finding the correct VFC within the Top-5 commits, and an 80.0% recall for the Top-1 ranked commit. VFCFinder generalizes to nine different programming languages and outperforms state-of-the-art approaches by 36 percentage points in terms of Top-1 recall. As a practical contribution, we used VFCFinder to backfill over 300 missing VFCs in the GitHub Security Advisory (GHSA) database. All of the VFCs were accepted and merged into the GHSA database. In addition to demonstrating a practical pairing of security advisories to VFCs, our general open-source implementation will allow vulnerability database maintainers to drastically improve data quality, supporting efforts to secure the software supply chain.
Exploring the Hyperparameter Space of Image Diffusion Models for Echocardiogram Generation

This work presents an extensive hyperparameter search on Image Diffusion Models for Echocardiogram generation. The objective is to establish foundational benchmarks and provide guidelines within the realm of ultrasound image and video generation. This study builds over the latest advancements, including cutting-edge model architectures and training methodologies. We also examine the distribution shift between real and generated samples and consider potential solutions, crucial to train efficient models on generated data. We determine an Optimal FID score of $0.88$ for our research problem and achieve an FID of $2.60$. This work is aimed at contributing valuable insights and serving as a reference for further developments in the specialized field of ultrasound image and video generation.
Preserving the knowledge of long clinical texts using aggregated ensembles of large language models

Clinical texts, such as admission notes, discharge summaries, and progress notes, contain rich and valuable information that can be used for various clinical outcome prediction tasks. However, applying large language models, such as BERT-based models, to clinical texts poses two major challenges: the limitation of input length and the diversity of data sources. This paper proposes a novel method to preserve the knowledge of long clinical texts using aggregated ensembles of large language models. Unlike previous studies which use model ensembling or text aggregation methods separately, we combine ensemble learning with text aggregation and train multiple large language models on two clinical outcome tasks: mortality prediction and length of stay prediction. We show that our method can achieve better results than baselines, ensembling, and aggregation individually, and can improve the performance of large language models while handling long inputs and diverse datasets. We conduct extensive experiments on the admission notes from the MIMIC-III clinical database by combining multiple unstructured and high-dimensional datasets, demonstrating our method's effectiveness and superiority over existing approaches. We also provide a comprehensive analysis and discussion of our results, highlighting our method's applications and limitations for future research in the domain of clinical healthcare. The results and analysis of this study is supportive of our method assisting in clinical healthcare systems by enabling clinical decision-making with robust performance overcoming the challenges of long text inputs and varied datasets.
Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.
Optimal Zeno Dragging for Quantum Control: A Shortcut to Zeno with Action-based Scheduling Optimization

The quantum Zeno effect asserts that quantum measurements inhibit simultaneous unitary dynamics when the "collapse" events are sufficiently strong and frequent. This applies in the limit of strong continuous measurement or dissipation. It is possible to implement a dissipative control that is known as "Zeno Dragging", by dynamically varying the monitored observable, and hence also the eigenstates which are attractors under Zeno dynamics. This is similar to adiabatic processes, in that the Zeno dragging fidelity is highest when the rate of eigenstate change is slow compared to the measurement rate. We demonstrate here two methods for using such dynamics to achieve control of quantum systems. The first, which we shall refer to as "shortcut to Zeno", is analogous to the shortcuts to adiabaticity (counterdiabatic driving) that are frequently used to accelerate unitary adiabatic evolution. In the second approach we apply the Chantasri Dressel Jordan (2013, CDJ) stochastic action, and demonstrate that the extremal-probability readout paths derived from this are well suited to setting up a Pontryagin-style optimization of the Zeno dragging schedule. Implementing these methods on the Zeno dragging of a qubit, we find that both approaches yield the same solution, namely, that the optimal control is a unitary that matches the motion of the Zeno-monitored eigenstate. These methods open up new pathways toward systematically developing dynamic control of Zeno subspaces to realize dissipatively-stabilized quantum operations.
MARRS: Multimodal Reference Resolution System

Successfully handling context is essential for any dialog understanding task. This context maybe be conversational (relying on previous user queries or system responses), visual (relying on what the user sees, for example, on their screen), or background (based on signals such as a ringing alarm or playing music). In this work, we present an overview of MARRS, or Multimodal Reference Resolution System, an on-device framework within a Natural Language Understanding system, responsible for handling conversational, visual and background context. In particular, we present different machine learning models to enable handing contextual queries; specifically, one to enable reference resolution, and one to handle context via query rewriting. We also describe how these models complement each other to form a unified, coherent, lightweight system that can understand context while preserving user privacy.
Simulating Heavy-Hex Transverse Field Ising Model Magnetization Dynamics Using Programmable Quantum Annealers

Recently, a Hamiltonian dynamics simulation was performed on a kicked ferromagnetic 2D transverse field Ising model with a connectivity graph native to the 127 qubit heavy-hex IBM Quantum architecture using ZNE quantum error mitigation. We demonstrate that one of the observables in this Trotterized Hamiltonian dynamics simulation, namely magnetization, can be efficiently simulated on current superconducting qubit-based programmable quantum annealing computers. We show this using two distinct methods: reverse quantum annealing and h-gain state encoding. This simulation is possible because the 127 qubit heavy-hex connectivity graph can be natively embedded onto the D-Wave Pegasus quantum annealer hardware graph and because there exists a direct equivalence between the energy scales of the two types of quantum computers. We derive equivalent anneal pauses in order to simulate the Trotterized quantum circuit dynamics for varying Rx rotations $\theta_h \in (0, \frac{\pi}{2}]$, using quantum annealing processors. Multiple disjoint instances of the Ising model of interest can be embedded onto the D-Wave Pegasus hardware graph, allowing for parallel quantum annealing. We report equivalent magnetization dynamics using quantum annealing for time steps of 20, 50 up to 10,000, which we find are consistent with exact classical 27 qubit heavy-hex Trotterized circuit magnetization dynamics, and we observe reasonable, albeit noisy, agreement with the existing simulations for single site magnetization at 20 Trotter steps. The quantum annealers are able to simulate equivalent magnetization dynamics for thousands of time steps, significantly out of the computational reach of the digital quantum computers on which the original Hamiltonian dynamics simulations were performed.
Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations

The escalating risk of collisions and the accumulation of space debris in Low Earth Orbit (LEO) has reached critical concern due to the ever increasing number of spacecraft. Addressing this crisis, especially in dealing with non-cooperative and unidentified space debris, is of paramount importance. This paper contributes to efforts in enabling autonomous swarms of small chaser satellites for target geometry determination and safe flight trajectory planning for proximity operations in LEO. Our research explores on-orbit use of the You Only Look Once v5 (YOLOv5) object detection model trained to detect satellite components. While this model has shown promise, its inherent lack of interpretability hinders human understanding, a critical aspect of validating algorithms for use in safety-critical missions. To analyze the decision processes, we introduce Probabilistic Explanations for Entropic Knowledge extraction (PEEK), a method that utilizes information theoretic analysis of the latent representations within the hidden layers of the model. Through both synthetic in hardware-in-the-loop experiments, PEEK illuminates the decision-making processes of the model, helping identify its strengths, limitations and biases.
Universal Perturbation-based Secret Key-Controlled Data Hiding

Deep neural networks (DNNs) are demonstrated to be vulnerable to universal perturbation, a single quasi-perceptible perturbation that can deceive the DNN on most images. However, the previous works are focused on using universal perturbation to perform adversarial attacks, while the potential usability of universal perturbation as data carriers in data hiding is less explored, especially for the key-controlled data hiding method. In this paper, we propose a novel universal perturbation-based secret key-controlled data-hiding method, realizing data hiding with a single universal perturbation and data decoding with the secret key-controlled decoder. Specifically, we optimize a single universal perturbation, which serves as a data carrier that can hide multiple secret images and be added to most cover images. Then, we devise a secret key-controlled decoder to extract different secret images from the single container image constructed by the universal perturbation by using different secret keys. Moreover, a suppress loss function is proposed to prevent the secret image from leakage. Furthermore, we adopt a robust module to boost the decoder's capability against corruption. Finally, A co-joint optimization strategy is proposed to find the optimal universal perturbation and decoder. Extensive experiments are conducted on different datasets to demonstrate the effectiveness of the proposed method. Additionally, the physical test performed on platforms (e.g., WeChat and Twitter) verifies the usability of the proposed method in practice.
ProPane: Image Warping with Fire

In this paper we introduce the software package ProPane, written for the R data analysis language. ProPane combines the full range of wcslib projections with the C++ image manipulation routines provided by the CImg library. ProPane offers routines for image warping and combining (including stacking), and various related tasks such as image alignment tweaking and pixel masking. It can stack an effectively unlimited number of target frames using multiple parallel cores, and offers threading for many lower level routines. It has been used for a number of current and upcoming large surveys, and we present a range of its capabilities and features. ProPane is already available under a permissive open-source LGPL-3 license at github.com/asgr/ProPane (DOI: 10.5281/zenodo.10057053).
Solving Kernel Ridge Regression with Gradient Descent for a Non-Constant Kernel

Kernel ridge regression, KRR, is a generalization of linear ridge regression that is non-linear in the data, but linear in the parameters. The solution can be obtained either as a closed-form solution, which includes a matrix inversion, or iteratively through gradient descent. Using the iterative approach opens up for changing the kernel during training, something that is investigated in this paper. We theoretically address the effects this has on model complexity and generalization. Based on our findings, we propose an update scheme for the bandwidth of translational-invariant kernels, where we let the bandwidth decrease to zero during training, thus circumventing the need for hyper-parameter selection. We demonstrate on real and synthetic data how decreasing the bandwidth during training outperforms using a constant bandwidth, selected by cross-validation and marginal likelihood maximization. We also show theoretically and empirically that using a decreasing bandwidth, we are able to achieve both zero training error in combination with good generalization, and a double descent behavior, phenomena that do not occur for KRR with constant bandwidth but are known to appear for neural networks.
RDE: A Hybrid Policy Framework for Multi-Agent Path Finding Problem

Multi-agent path finding (MAPF) is an abstract model for the navigation of multiple robots in warehouse automation, where multiple robots plan collision-free paths from the start to goal positions. Reinforcement learning (RL) has been employed to develop partially observable distributed MAPF policies that can be scaled to any number of agents. However, RL-based MAPF policies often get agents stuck in deadlock due to warehouse automation's dense and structured obstacles. This paper proposes a novel hybrid MAPF policy, RDE, based on switching among the RL-based MAPF policy, the Distance heat map (DHM)-based policy and the Escape policy. The RL-based policy is used for coordination among agents. In contrast, when no other agents are in the agent's field of view, it can get the next action by querying the DHM. The escape policy that randomly selects valid actions can help agents escape the deadlock. We conduct simulations on warehouse-like structured grid maps using state-of-the-art RL-based MAPF policies (DHC and DCC), which show that RDE can significantly improve their performance.
Holography Transformer

We have constructed a generative artificial intelligence model to predict dual gravity solutions when provided with the input of holographic entanglement entropy. The model utilized in our study is based on the transformer algorithm, widely used for various natural language tasks including text generation, summarization, and translation. This algorithm possesses the ability to understand the meanings of input and output sequences by utilizing multi-head attention layers. In the training procedure, we generated pairs of examples consisting of holographic entanglement entropy data and their corresponding metric solutions. Once the model has completed the training process, it demonstrates the ability to generate predictions regarding a dual geometry that corresponds to the given holographic entanglement entropy. Subsequently, we proceed to validate the dual geometry to confirm its correspondence with the holographic entanglement entropy data.