Reviewerly/exHarmony huggingface dataset:
A Strong Key Expansion Algorithm Based on Nondegenerate 2D Chaotic Map Over GF(2n)

The strength of a cryptosystem relies on the security of its key expansion algorithm, which is an important component of a block cipher. However, numerous block ciphers exhibit the vulnerability of reversibility and serialization. Therefore, it is necessary to design an irreversible parallel key expansion algorithm to generate independent round keys. First, a 2D nondegenerate exponential chaotic map (2D-NECM) is constructed, and the results of the dynamic analysis show that the 2D-NECM possesses ergodicity and superior randomness within a large range of parameters. Then, an irreversible parallel key expansion algorithm is designed based on 2D-NECM and primitive polynomial over GF([Formula: see text]). By injecting random perturbation into the initial key, the algorithm can generate different round keys even if the same initial key is used. Simulation results indicate that the algorithm has high security performance. It effectively satisfies the requirements of irreversibility and parallelism, while ensuring the mutual independence of round keys.
Exploiting CNN’s visual explanations to drive anomaly detection

Abstract Nowadays, deep learning is a key technology for many applications in the industrial area such as anomaly detection. The role of Machine Learning (ML) in this field relies on the ability of training a network to learn to inspect images to determine the presence or not of anomalies. Frequently, in Industry 4.0 w.r.t. the anomaly detection task, the images to be analyzed are not optimal, since they contain edges or areas, that are not of interest which could lead the network astray. Thus, this study aims at identifying a systematic way to train a neural network to make it able to focus only on the area of interest. The study is based on the definition of a loss to be applied in the training phase of the network that, using masks, gives higher weight to the anomalies identified within the area of interest. The idea is to add an Overlap Coefficient to the standard cross-entropy. In this way, the more the identified anomaly is outside the Area of Interest (AOI) the greater is the loss. We call the resulting loss Cross-Entropy Overlap Distance (CEOD). The advantage of adding the masks in the training phase is that the network is forced to learn and recognize defects only in the area circumscribed by the mask. The added benefit is that, during inference, these masks will no longer be needed. Therefore, there is no difference, in terms of execution times, between a standard Convolutional Neural Network (CNN) and a network trained with this loss. In some applications, the masks themselves are determined at run-time through a trained segmentation network, as we have done for instance in the "Machine learning for visual inspection and quality control" project, funded by the MISE Competence Center Bi-REX.
On the adoption and effects of source code reuse on defect proneness and maintenance effort

Abstract Software reusability mechanisms, like inheritance and delegation in Object-Oriented programming, are widely recognized as key instruments of software design that reduce the risks of source code being affected by defects, other than to reduce the effort required to maintain and evolve source code. Previous work has traditionally employed source code reuse metrics for prediction purposes, e.g., in the context of defect prediction. However, our research identifies two noticeable limitations of the current literature. First, still little is known about the extent to which developers actually employ code reuse mechanisms over time. Second, it is still unclear how these mechanisms may contribute to explaining defect-proneness and mainten0ance effort during software evolution. We aim at bridging this gap of knowledge, as an improved understanding of these aspects might provide insights into the actual support provided by these mechanisms, e.g., by suggesting whether and how to use them for prediction purposes. We propose an exploratory study, conducted on 12 Java projects–over 44,900 commits–of the Defects4J dataset, aiming at (1) assessing how developers use inheritance and delegation during software evolution; and (2) statistically analyzing the impact of inheritance and delegation on fault proneness and maintenance effort. Our results let emerge various usage patterns that describe the way inheritance and delegation vary over time. In addition, we find out that inheritance and delegation are statistically significant factors that influence both source code defect-proneness and maintenance effort.
Digital Discovery of 100 diverse Quantum Experiments with PyTheus

Photons are the physical system of choice for performing experimental tests of the foundations of quantum mechanics. Furthermore, photonic quantum technology is a main player in the second quantum revolution, promising the development of better sensors, secure communications, and quantum-enhanced computation. These endeavors require generating specific quantum states or efficiently performing quantum tasks. The design of the corresponding optical experiments was historically powered by human creativity but is recently being automated with advanced computer algorithms and artificial intelligence. While several computer-designed experiments have been experimentally realized, this approach has not yet been widely adopted by the broader photonic quantum optics community. The main roadblocks consist of most systems being closed-source, inefficient, or targeted to very specific use-cases that are difficult to generalize. Here, we overcome these problems with a highly-efficient, open-source digital discovery framework PyTheus, which can employ a wide range of experimental devices from modern quantum labs to solve various tasks. This includes the discovery of highly entangled quantum states, quantum measurement schemes, quantum communication protocols, multi-particle quantum gates, as well as the optimization of continuous and discrete properties of quantum experiments or quantum states. PyTheus produces interpretable designs for complex experimental problems which human researchers can often readily conceptualize. PyTheus is an example of a powerful framework that can lead to scientific discoveries – one of the core goals of artificial intelligence in science. We hope it will help accelerate the development of quantum optics and provide new ideas in quantum hardware and technology.
Patchy incentives: using law to encourage effective vulnerability response

Data breach reports suggest that managing patches is hard: too many major incidents are caused by well-known software vulnerabilities with available fixes. Legal sanctions – from mandates to liability – apparently have limited effect. This paper discusses how an effective vulnerability response process can help software users allocate their remediation effort to minimise overall risk and disruption. We analyse laws and regulations on liability, product quality and patching mandates to see why they fail to promote good practice. Recent cases under privacy laws highlight features that make risk-based patching a better basis for system managers, executives and regulators to agree a common approach to effective vulnerability response.
Conception and evaluation of anomaly detection models for monitoring analytical parameters in wastewater treatment plants

The exponential growth of technology in recent decades has led to the emergence of some challenges inherent to this growth. One of these challenges is the enormous amount of data collected by the different sensors in our society, namely in management processes such as Wastewater Treatment Plants (WWTPs). These infrastructures comprise several processes to treat wastewater and discharge clean water in water courses. Therefore, the concentration of pollutants must be below the allowable emissions limits. In this work, anomaly detection models were conceived, tuned and evaluated to monitor essential parameters such as nitrate and ammonia concentrations and pH to improve WWTP management. Four Machine Learning models were considered, particularly Local Outlier Fraction, Isolation Forest, One-Class Support Vector Machines and Long Short-Term Memory-Autoencoders (LSTM-AE), to detect anomalies in the three parameters mentioned. Through the different experiments, it was possible to verify that, in terms of F1-Score, the best candidate model for the three analyzed parameters was LSTM-AE-based, with a value consistently higher than 97%.
Current state and prospects of water supply and sanitation systems, methods for their calculation, design, and operation

The paper considers modern problems of developing a technology for managing water supply and sanitation systems. The main results achieved in this sphere are presented, and objectives for further research are outlined. Due to the ongoing processes of digitalization and intellectualization of social and technological systems, a new concept of design and operation management, development and conservation of water supply and sanitation systems of settlements, cities, and buildings is proposed. This concept is based on the creation of digital twins of all system and subsystem elements and their management in real and virtual space using cloud technologies and specific software systems. Forthese purposes, water supply and sanitation systems are considered from the perspective of general principles of organization, hydraulic calculation, formation of operation modes, the probabilistic nature of water withdrawals and wastewater inflows, uncertainty in the behavior and prospective loads of facilities, along with other characteristics typical of large and complex systems. The paper presents the main tasks for calculating and designing water supply and sanitation systems and briefly describes possible solutions, implemented in the relevant software packages, tested in practice in the actual process of designing, operating, and maintenance of piping systems of a housing and utilities infrastructure. All optimization models are based on the lifecycle cost criteria for water and sanitation systems.
An Experimental Evaluation of Conformance Testing Techniques in Active Automata Learning

Active automata learning is a technique for dynamically learning finite state machine models of black-box systems. Conformance testing is a well-known bottleneck during learning. While multiple conformance testing techniques (CTTs) for Finite State Machines have been proposed, there is a lack of empirical studies that assess the effects of these CTTs during learning. In this work, we compare the performance of eight different CTTs (W, Wp, HSI, H-ADS, H, SPY, SPY-H, I-ADS) while learning 46 models from different communication protocols. Moreover, we propose APFD <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> as a metric for characterizing the efficiency of automata learning experiments in terms of fault detection capacity. This metric allows identifying CTTs with a lower total cost regarding the number of symbols and resets and a higher rate of state discovery during learning. Our results indicate that while the total cost entailed by CTTs in learning tends to be negligible, we found a significant difference in fault detection rate in learning. Nevertheless, the differences in fault detection rates become negligible when CTTs are applied in randomized mode. These findings reveal the positive role that randomness can have in improving learning efficiency, despite compromising test completeness.
A Log‐Ratio‐Based Algorithm for Petrologic Mass‐Balance Problems and Uncertainty Assessment

Abstract We provide a new algorithm for mass‐balance calculations in petrology and geochemistry based on the log‐ratio approach championed initially by John Aitchison (e.g., Aitchison, 1982, https://doi.org/10.1111/j.2517-6161.1982.tb01195.x ; Aitchison, 1984, https://doi.org/10.1007/bf01029316 ) along with the underlying principles, mathematical frameworks, and data requirements. Log‐ratio Inversion of Mixed End‐members (LIME) is written in MATLAB and calculates phase proportions in an experiment or rock given a bulk composition, the composition of each phase, and the associated compositional uncertainties. An important advantage of LIME is that performing the mass‐balance calculation in inverse log‐ratio space constrains phase proportions to be between 0 and 100 wt.%. Further, the resulting LIME phase proportions provide realistic estimates of uncertainty regardless of data distribution. These two characteristics of LIME improve upon standard multiple linear regression techniques, which may yield negative values for phase proportions if non‐constrained or report oversimplified symmetric errors. Primary applications of LIME include estimating phase abundances, calculating melting and metamorphic reaction stoichiometries, and checking for open system behavior in phase equilibria experiments. The technique presented here covers whole‐rock analysis, mineralogy, and phase abundance, but could be extended to isotopic tracers, trace element modeling, and regolith component un‐mixing. We highlight the importance of uncertainty estimations for phase abundances to the fields of petrology and geochemistry by comparing our results from LIME to previously published mass‐balance calculations. Furthermore, we present case studies that demonstrate the role of mass‐balance calculations in determining magma crystallinity and defining melting reactions.
Piecewise nonlinear resistor-capacitor shunted josephson junction circuit: dynamical and microcontroller implementation probing

Abstract This paper exposes the theoretical and microcontroller implementation probing of the piecewise nonlinear resistor-capacitor shunted Josephson junction circuit (PNRCSJJC). The PNRCSJJC is characterized by no steady state when the applied current is greater than one and exhibits two steady states in which one is a focus and its counterpart a saddle-node for excitation current less than or equal to one with credit to the Routh–Hurwitz criterion. The PNRCSJJC exhibits periodic characteristics, quasi-periodic characteristics, varying structures of chaotic characteristics, and coexisting behaviors which is proved qualitatively by the microcontroller execution method. The polarity of the chaotic signal in the voltage state variable is flexibly altered by varying a constant parameter included in the rate equations of PNRCSJJC.
Extraction and imaging of indicator elements for non-destructive, in-situ, fast identification of adverse geology in tunnels

The lag in quantitative methods and detection techniques for geologic information has resulted in time-consuming and human-experienced geologic analysis in tunnels. Geochemical indicators of rocks can be used to identify adverse geology and to explain the intrinsic causes of damage to normal rocks. This study proposes a method to identify adverse geology by extracting and imaging the indicator elements. The mapping relationship between rock components and geologic bodies is quickly determined by indicator element extraction based on factor analysis, and then the data are gridded for image output. The location and size of the target adverse geology are visually identified through the distribution images of the indicator elements, thus reducing data dimensions and analysis time. A non-destructive, in-situ and fast element detection technique in tunnels was adopted to speed up the process of geology identification. The accuracy of the detection was validated by comparing field and laboratory test results. This study further confirms and refines the previous research, and the results provide references for geological, mining and underground projects.
A Generic Framework for Byzantine-Tolerant Consensus Achievement in Robot Swarms

Recent studies show that some security features that blockchains grant to decentralized networks on the internet can be ported to swarm robotics. Although the integration of blockchain technology and swarm robotics shows great promise, thus far, research has been limited to proof-of-concept scenarios where the blockchain-based mechanisms are tailored to a particular swarm task and operating environment. In this study, we propose a generic framework based on a blockchain smart contract that enables robot swarms to achieve secure consensus in an arbitrary observation space. This means that our framework can be customized to fit different swarm robotics missions, while providing methods to identify and neutralize Byzantine robots, that is, robots which exhibit detrimental behaviours stemming from faults or malicious tampering.
Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps

Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels. While impressive, these methods still require a relatively large amount of supervision and segmenting an object can take several minutes in practice. Such systems typically only optimize the representation on the scene they are fitting, without leveraging prior information from previously seen images. In this paper, we propose to use features extracted with models pre-trained on large existing datasets to improve segmentation performance on novel scenes. We bake this feature representation into a Neural Radiance Field (NeRF) by volu-metrically rendering feature maps and supervising on features extracted from each input image. We show that by baking this representation into the NeRF, we make the subsequent classification task much easier. Our experiments show that our method achieves higher segmentation accuracy with fewer semantic annotations than existing methods over a wide range of scenes.
Unsupervised OmniMVS: Efficient Omnidirectional Depth Inference via Establishing Pseudo-Stereo Supervision

Omnidirectional multi-view stereo (MVS) vision is attractive for its ultra-wide field-of-view (FoV), enabling machines to perceive 360°3D surroundings. However, the existing solutions require expensive dense depth labels for supervision, making them impractical in real-world applications. In this paper, we propose the first unsupervised omnidirectional MVS framework based on multiple fisheye images. To this end, we project all images to a virtual view center and composite two panoramic images with spherical geometry from two pairs of back-to-back fisheye images. The two 360° images formulate a stereo pair with a special pose, and the photometric consistency is leveraged to establish the unsupervised constraint, which we term “Pseudo-Stereo Supervision”. In addition, we propose Un-OmniMVS, an efficient unsupervised omnidirectional MVS network, to facilitate the inference speed with two efficient components. First, a novel feature extractor with frequency attention is proposed to simultaneously capture the non-local Fourier features and local spatial features, explicitly facilitating the feature representation. Then, a variance-based light cost volume is put forward to reduce the computational complexity. Experiments exhibit that the performance of our unsupervised solution is competitive to that of the state-of-the-art (SoTA) supervised methods with better generalization in real-world data. The code will be available at https://github.com/Chen-z-s/Un-OmniMVS.
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model

Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D uplifting approaches have achieved remarkable improvements. Still, monocular 3D HPE is a challenging problem due to the inherent depth ambiguities and occlusions. To handle this problem, many previous works exploit temporal information to mitigate such difficulties. However, there are many real-world applications where frame sequences are not accessible. This paper focuses on reconstructing a 3D pose from a single 2D keypoint detection. Rather than exploiting temporal information, we alleviate the depth ambiguity by generating multiple 3D pose candidates which can be mapped to an identical 2D keypoint. We build a novel diffusion-based framework to effectively sample diverse 3D poses from an off-the-shelf 2D detector. By considering the correlation between human joints by replacing the conventional denoising U-Net with graph convolutional network, our approach accomplishes further performance improvements. We evaluate our method on the widely adopted Human3.6M and HumanEva-I datasets. Comprehensive experiments are conducted to prove the efficacy of the proposed method, and they confirm that our model outperforms state-of-the-art multi-hypothesis 3D HPE methods.
Chat with the Environment: Interactive Multimodal Perception Using Large Language Models

Programming robot behavior in a complex world faces challenges on multiple levels, from dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. However, it remains challenging to ground LLMs in multimodal sensory input and continuous action output, while enabling a robot to interact with its environment and acquire novel information as its policies unfold. We develop a robot interaction scenario with a partially observable state, which necessitates a robot to decide on a range of epistemic actions in order to sample sensory information among multiple modalities, before being able to execute the task correctly. An interactive perception framework is therefore proposed with an LLM as its backbone, whose ability is exploited to instruct epistemic actions and to reason over the resulting multimodal sensations (vision, sound, haptics, proprioception), as well as to plan an entire task execution based on the interactively acquired information. Our study demonstrates that LLMs can provide high-level planning and reasoning skills and control interactive robot behavior in a multimodal environment, while multimodal modules with the context of the environmental state help ground the LLMs and extend their processing ability. The project website can be found at https://matcha-model.github.io/.
Neural Implicit Vision-Language Feature Fields

Recently, groundbreaking results have been presented on open-vocabulary semantic image segmentation. Such methods segment each pixel in an image into arbitrary categories provided at run-time in the form of text prompts, as opposed to a fixed set of classes defined at training time. In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method. Our method builds on the insight that we can fuse image features from a vision-language model into a neural implicit representation. We show that the resulting feature field can be segmented into different classes by assigning points to natural language text prompts. The implicit volumetric representation enables us to segment the scene both in 3D and 2D by rendering feature maps from any given viewpoint of the scene. We show that our method works on noisy real-world data and can run in real-time on live sensor data dynamically adjusting to text prompts. We also present quantitative comparisons on the ScanNet dataset.