Reviewerly/exHarmony huggingface dataset:
Precomputable Trade-off Between Error and Breakpoints in Piecewise Linearization for First-Order Loss Functions

Stochastic optimization often involves calculating the expected value of a first-order max or min function, known as a first-order loss function. In this context, loss functions are frequently approximated using piecewise linear functions. Determining the approximation error and the number of breakpoints (segments) becomes a critical issue during this approximation. This is due to a trade-off: increasing the number of breakpoints reduces the error but also increases the computational complexity of the embedded model. As this trade-off is unclear in advance, preliminary experiments are often required to determine these values. The objective of this study is to approximate the trade-off between error and breakpoints in piecewise linearization for first-order loss functions. To achieve this goal, we derive an upper bound on the minimum number of breakpoints required to achieve a given absolute error. This upper bound can be easily precomputed once the approximation intervals and error are determined, and serves as a guideline for the trade-off between error and breakpoints. Furthermore, we propose efficient algorithms to obtain a piecewise linear approximation with a number of breakpoints below the derived upper bound.
Fast-dRRT*: Efficient Multi-Robot Motion Planning for Automated Industrial Manufacturing

We present Fast-dRRT*, a sampling-based multi-robot planner, for real-time industrial automation scenarios. Fast-dRRT* builds upon the discrete rapidly-exploring random tree (dRRT*) planner, and extends dRRT* by using pre-computed swept volumes for efficient collision detection, deadlock avoidance for partial multi-robot problems, and a simplified rewiring strategy. We evaluate Fast-dRRT* on five challenging multi-robot scenarios using two to four industrial robot arms from various manufacturers. The scenarios comprise situations involving deadlocks, narrow passages, and close proximity tasks. The results are compared against dRRT*, and show Fast-dRRT* to outperform dRRT* by up to 94% in terms of finding solutions within given time limits, while only sacrificing up to 35% on initial solution cost. Furthermore, Fast-dRRT* demonstrates resilience against noise in target configurations, and is able to solve challenging welding, and pick and place tasks with reduced computational time. This makes Fast-dRRT* a promising option for real-time motion planning in industrial automation.
FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection

Natural language processing (NLP) applications such as named entity recognition (NER) for low-resource corpora do not benefit from recent advances in the development of large language models (LLMs) where there is still a need for larger annotated datasets. This research article introduces a methodology for generating translated versions of annotated datasets through crosslingual annotation projection. Leveraging a language agnostic BERT-based approach, it is an efficient solution to increase low-resource corpora with few human efforts and by only using already available open data resources. Quantitative and qualitative evaluations are often lacking when it comes to evaluating the quality and effectiveness of semi-automatic data generation strategies. The evaluation of our crosslingual annotation projection approach showed both effectiveness and high accuracy in the resulting dataset. As a practical application of this methodology, we present the creation of French Annotated Resource with Semantic Information for Medical Entities Detection (FRASIMED), an annotated corpus comprising 2'051 synthetic clinical cases in French. The corpus is now available for researchers and practitioners to develop and refine French natural language processing (NLP) applications in the clinical field (https://zenodo.org/record/8355629), making it the largest open annotated corpus with linked medical concepts in French.
DeFi composability as MEV non-interference

Complex DeFi services are usually constructed by composing a variety of simpler smart contracts. The permissionless nature of the blockchains where these smart contracts are executed makes DeFi services exposed to security risks, since adversaries can target any of the underlying contracts to economically damage the compound service. We introduce a new notion of secure composability of smart contracts, which ensures that adversaries cannot economically harm the compound contract by interfering with its dependencies.
Phase space analysis of two-wavelength interferometry

Multiple wavelength phase shifting interferometry is widely used to extend the unambiguous range (UR) beyond that of a single wavelength. Towards this end, many algorithms have been developed to calculate the optical path difference (OPD) from the phase measurements of multiple wavelengths. These algorithms fail when phase error exceeds a specific threshold. In this paper, we examine this failure condition. We introduce a "phase-space" view of multi-wavelength algorithms and demonstrate how this view may be used to understand an algorithm's robustness to phase measurement error. In particular, we show that the robustness of the synthetic wavelength algorithm deteriorates near the edges of its UR. We show that the robustness of de Groot's extended range algorithm [Appl. Opt. 33, 5948 (1994)] depends on both wavelength and OPD in a non-trivial manner. Further, we demonstrate that the algorithm developed by Houairi & Cassaing (HC) [J. Opt. Soc. Am. 26, 2503 (2009)] results in uniform robustness across the entire UR. Finally, we explore the effect that wavelength error has on the robustness of the HC algorithm.
Multi-Context Dual Hyper-Prior Neural Image Compression

Transform and entropy models are the two core components in deep image compression neural networks. Most existing learning-based image compression methods utilize convolutional-based transform, which lacks the ability to model long-range dependencies, primarily due to the limited receptive field of the convolution operation. To address this limitation, we propose a Transformer-based nonlinear transform. This transform has the remarkable ability to efficiently capture both local and global information from the input image, leading to a more decorrelated latent representation. In addition, we introduce a novel entropy model that incorporates two different hyperpriors to model cross-channel and spatial dependencies of the latent representation. To further improve the entropy model, we add a global context that leverages distant relationships to predict the current latent more accurately. This global context employs a causal attention mechanism to extract long-range information in a content-dependent manner. Our experiments show that our proposed framework performs better than the state-of-the-art methods in terms of rate-distortion performance.
Multi-spectral Entropy Constrained Neural Compression of Solar Imagery

Missions studying the dynamic behaviour of the Sun are defined to capture multi-spectral images of the sun and transmit them to the ground station in a daily basis. To make transmission efficient and feasible, image compression systems need to be exploited. Recently successful end-to-end optimized neural network-based image compression systems have shown great potential to be used in an ad-hoc manner. In this work we have proposed a transformer-based multi-spectral neural image compressor to efficiently capture redundancies both intra/inter-wavelength. To unleash the locality of window-based self attention mechanism, we propose an inter-window aggregated token multi head self attention. Additionally to make the neural compressor autoencoder shift invariant, a randomly shifted window attention mechanism is used which makes the transformer blocks insensitive to translations in their input domain. We demonstrate that the proposed approach not only outperforms the conventional compression algorithms but also it is able to better decorrelates images along the multiple wavelengths compared to single spectral compression.
The supersingular endomorphism ring problem given one endomorphism

Given a supersingular elliptic curve E and a non-scalar endomorphism $\alpha$ of E, we prove that the endomorphism ring of E can be computed in classical time about disc(Z[$\alpha$])^1/4 , and in quantum subexponential time, assuming the generalised Riemann hypothesis. Previous results either had higher complexities, or relied on heuristic assumptions. Along the way, we prove that the Primitivisation problem can be solved in polynomial time (a problem previously believed to be hard), and we prove that the action of smooth ideals on oriented elliptic curves can be computed in polynomial time (previous results of this form required the ideal to be powersmooth, i.e., not divisible by any large prime power). Following the attacks on SIDH, isogenies in high dimension are a central ingredient of our results.
Federated Classification Tasks in Long-tailed Data Environments via Classifier Representation Adjustment and Calibration

&lt;p&gt;This manuscript is Under Review of IEEE Transactions on Mobile Computing.&lt;/p&gt;
On the Reproducibility of Experiments achieved by TinyRCE

&lt;p&gt;TinyRCE is a hyperspherical classifier aimed at Continual Learning On-Tiny-Devices, a challenging task in which a Machine Learning model is required to learn from continuous streams of data while being directly installed on a (tiny) device with limited computational resources. The classifier has so far been applied to several use cases, including Human Activity Recognition, Ball Bearing Anomaly Classification, Keyword Spotting and Image Classification. The proposed work in this paper focuses on the reproducibility of TinyRCE’s experimental results already published on other papers. This to prove that all the published results are quantitatively reproducible. All the experiments have been executed on two independent computing machines to profile the impact on accuracy of the computations. As the outcomes are matching, the experimental reproducibility of TinyRCE’s accuracy over all the use cases has been positively verified.&lt;/p&gt;
A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

&lt;p&gt;Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.&lt;/p&gt;
Investigating The Impact Of Distance On Object Detection Accuracy in Unmanned Aerial Vehicle Systems Using MobileNetV3

&lt;p&gt;This study investigates the feasibility of using Raspberry Pi-equipped Unmanned Aerial Vehicles (UAVs) for object detection. The research focuses on analyzing the impact of the distance between the UAV and the target person on the accuracy of object detection. Experimental results reveal a decrease in detection accuracy as the distance between the UAV and the target person increases, emphasizing the critical role of distance in efficient object detection systems. By combining drones, Raspberry Pi, and machine learning algorithms, this research showcases the potential of advanced object detection systems. The findings contribute to the growing application of UAV technology by refining and optimizing UAV-based object detection systems.&lt;/p&gt;
Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip

&lt;p&gt;The feasibility study of deep learning (DL) approaches for reliable, flexible, and high throughput wireless physical layer (PHY) has received significant interest from academia and industry. Channel estimation (CE) is one of the critical signal processing units of the receiver PHY. Recent DLbased CE approaches have outperformed state-of-the-art statistical approaches such as least-square-based CE (LS) and linear minimum mean square error-based CE (LMMSE), requiring hardware-friendly arithmetic computations than LMMSE. The first contribution is optimizing the existing state-of-the-art CE algorithms for realization on system-on-chip (SoC) via hardwaresoftware co-design and fixed point analysis, followed by performance comparison for various signal-to-noise ratios (SNR) and wireless channels. We highlight the high complexity, execution time, and power consumption of existing DL-based CE and LMMSE approaches, along with the poor performance of the LS. We develop a novel compute-efficient LS-augmented interpolated deep neural network (LSiDNN) based CE algorithm and realize it on SoC. We demonstrate substantial savings in complexity and execution time without significant degradation in functional accuracy. Specifically, the proposed LSiDNN approach offers 88-90% lower execution time and 38-85% lower resources than state-of-the-art DL-based CE. LSiDNN significantly outperforms LMMSE, and the gain improves with increased mobility. It also offers 75% lower execution time and 90-94% lower resource utilization than LMMSE&lt;br&gt; &lt;/p&gt;
Mapping the Commercial VPN Ecosystem from Cost-Quality Perspective

&lt;p&gt;Reliance on commercial virtual private networks has increased manifold in the recent past. This paradigm shift of global internet users is primarily driven by their need of online privacy, censorship circumvention, and accessing geo-filtered content among other motivations. Variety of VPN services underscored by their opaque nature captures huge user base which is technically unsophisticated, and has very limited means to verify claims of a given VPN service. Capitalizing on extensive literature and online review, we have selected a sample of 60 x VPNs (from categories of free as well as paid services) which adequately represents the commercial VPN market from cost-quality perspective. An elaborate eco-system analysis reveals significant variations against the claims of service providers. Our crystallized infrastructure testing methodology executed over selected VPNs reveals: (1) Anonymity index of free versus paid VPNs differs widely at all levels i.e., IP, subnet, country and ISP; (2) 54% of selected VPN’s server locations in attractive geographic locations are virtual instead of physical; (3) 4/10 VPN tested services have been found to share their vantage points at subnet level; and (4) Selective co-location of 22% vantage points have also been observed. &lt;/p&gt;
Maximum-likelihood detection with QAOA for massive MIMO and Sherrington-Kirkpatrick model with local field at infinite size

&lt;p&gt;Quantum-approximate optimization algorithm (QAOA) is promising in Noisy Intermediate-Scale Quantum (NISQ) computers with applications for NP-hard combinatorial optimization problems. It is recently utilized for NP-hard maximum-likelihood (ML) detection problem with fundamental challenges of optimization, simulation and performance analysis for n x n multiple-input multiple output (MIMO) systems with large n. QAOA is recently applied by Farhi et al. on infinite size limit of Sherrington-Kirkpatrick (SK) model with a cost model including only quadratic terms. In this article, we extend application of QAOA on SK model by including also linear terms and then realize SK modeling of massive MIMO ML detection by ensuring independence from specific problem instance and size n while preserving computational complexity of O(16&lt;sup&gt;p&lt;/sup&gt;) designed by Farhi et al. We provide both optimized and extrapolated angles for p in the set [1, 14] and signal-to-noise (SNR) &lt; 12 dB achieving near-optimum ML performance for 25 x 25 MIMO system with p &gt;= 4 in extensive simulations where 236500 different QAOA circuits are simulated. We present two conjectures about the concentration properties of QAOA and its near-optimum performance for massive MIMO systems with large sizes covering n &lt; 300 promising significant performance for next generation massive MIMO systems.&lt;/p&gt;
Pars-HAO: Hate Speech and Offensive Language Detection on Persian Social Media Using Ensemble Learning

&lt;p&gt;As social networks continue to gain widespread popularity, an urgent requirement arises to automatically identify and detect offensive language and hate speech. While there is a wealth of research and datasets available for English in this domain, there is currently a scarcity of research and datasets focused on identifying hate speech and offensive language in Persian text. This article introduces a 3-class dataset named Pars-HAO, consisting of 8013 tweets, to fill the gap in existing research. We collected the dataset by combining comments from pages that are more exposed to hate speech and using a keyword-based approach. Three annotators then labeled the tweets. In this study, we employed a combination of the Convolutional Neural Network (CNN) model and two widely recognized machine learning models, namely Support Vector Machine (SVM) and Logistic Regression (LR), as a baseline. To improve the classification performance, we employed the Hard Voting ensemble learning technique. Experimental results on the Pars-HAO dataset demonstrated that the Hard voting ensemble learning technique yielded the best outcome, achieving a macro F1-score of 68.76%.&lt;/p&gt;
Blockchain for IoT: A Critical Review of the State-of-the-Art, Challenges, and Future Prospects

&lt;p&gt;This study delineates the convergence of blockchain technology with the Internet of Things (IoT), offering a critical analysis of the current state-of-the-art, prevailing challenges, and potential future directions. Initially, a comprehensive overview of the existing literature is provided, highlighting the progressive integration of blockchain with IoT networks. Subsequently, a meticulous evaluation of the present challenges, including security concerns and scalability issues, is conducted. Furthermore, this research explores the promising prospects of blockchain-enabled IoT systems, emphasizing innovations in data security, privacy, and decentralized control. Through a systematic approach, this investigation not only underscores the significant advancements in the field but also proposes potential strategies to overcome identified hurdles. The findings of this study aspire to pave the way for the development of more secure, scalable, and efficient blockchain-based IoT networks, fostering a new era of technological synergy. &lt;/p&gt;
Securing Advanced Metering Infrastructure Using Blockchain for Energy Trade

To effectively regulate the energy supply, advanced metering infrastructure (AMI) deployment is gaining momentum in various parts of the world. Energy distributors, service providers, and consumers must work together to address several issues. All the transactions need to be documented properly and securely. Third parties can be trusted in those transactions by using blockchain. With the deployment of advanced metering infrastructure (AMI) and distributed ledgers, blockchain may aid in safeguarding and facilitating the movement of data. This chapter discusses the viability of utilizing blockchain for advanced metering infrastructure and the security risks and threat landscape.
Retinotopy Improves the Categorisation and Localisation of Visual Objects in CNNs

Foveated vision is a trait shared by many animals, including humans, but its contribution to visual function compared to species lacking it is still under question. This study suggests that the retinotopic mapping which defines foveated vision may play a critical role in achieving efficient visual performance, notably for image categorisation and localisation. To test for this hypothesis, we transformed regular images by using a Log-polar mapping, and used this retinotopic images as the input of convolutional neural networks (CNNs). We then applied transfer learning on pre-trained networks on the ImageNet challenge dataset. Our results show that surprisingly, the network re-trained on images which were compressed by the retinotopic mapping performs as well as the re-trained network applied to regular images. Moreover, we observed that the retinotopic mapping improves the robustness and localisation of image classification, especially for isolated objects. This was specially acute on a custom version of the dataset which aimed to categorise images that contain or not an animal. In summary, these results suggest that such retinotopic mapping may be an important component of preattentive processes, a central cognitive characteristic of more advanced visual systems.
vRad PACS With Mammography

Biomedical Safety & Standards 53(18):p 130, October 15, 2023. | DOI: 10.1097/01.BMSAS.0000979596.81965.1d
The Effect of Organizational Identification on Green Organizational Behavior

There is a great deal of academic research that shows employees identified with their organization display behavior in favor of the organization.The subject of this research is based on the assumption that employees identified with the organization they work for will have more green organizational behavior.Simple random sampling method was used in the research.250 administrative staff willing to participate in the survey from 250 different businesses working in Konya Organized Industrial Zone 1 constituted the sample of the research.The survey technique was used as the data collection technique and data were collected by face-to-face interview method.The total of 250 questionnaires was analyzed through the SPSS 21.0 program.According to descriptive statistics, it was determined that the organizational identification levels of the participants were at a medium level whereas they had a high level of green organizational behavior.In order to evaluate the main hypothesis of the research, regression analysis was performed, and as a result, it was determined that organizational identification significantly predicted green organizational behavior, and organizational identification had a positive and significant effect on green organizational behavior.Accordingly, as the organizational identification level of the employees increases, the green organizational behavior also increases.