Shaurya Purohit, Manimaran Govindarasu, Benjamin Blakely
Abstract With the ongoing development of Distributed Energy Resources (DER) communication networks, the imperative for strong cybersecurity and data privacy safeguards is increasingly evident. DER networks, which rely on protocols such as Distributed Network Protocol 3 and Modbus, are susceptible to cyberattacks such as data integrity breaches and denial of service due to their inherent security vulnerabilities. This paper introduces an innovative Federated Learning (FL)‐based anomaly detection system designed to enhance the security of DER networks while preserving data privacy. Our models leverage Vertical and Horizontal Federated Learning to enable collaborative learning while preserving data privacy, exchanging only non‐sensitive information, such as model parameters, and maintaining the privacy of DER clients' raw data. The effectiveness of the models is demonstrated through its evaluation on datasets representative of real‐world DER scenarios, showcasing significant improvements in accuracy and F1‐score across all clients compared to the traditional baseline model. Additionally, this work demonstrates a consistent reduction in loss function over multiple FL rounds, further validating its efficacy and offering a robust solution that balances effective anomaly detection with stringent data privacy needs.
Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the "memory wall" issue. By utilizing nonvolatile memory (NVM) devices in a crossbar structure, CiM efficiently accelerates multiply-accumulate (MAC) computations, the crucial operations in neural networks and other AI models. Among various NVM devices, Ferroelectric FET (FeFET) is particularly appealing for ultra-low-power CiM arrays due to its CMOS compatibility, voltage-driven write/read mechanisms and high ION/IOFF ratio. Moreover, subthreshold-operated FeFETs, which operate at scaling voltages in the subthreshold region, can further minimize the power consumption of CiM array. However, subthreshold-FeFETs are susceptible to temperature drift, resulting in computation accuracy degradation. Existing solutions exhibit weak temperature resilience at larger array size and only support 1-bit. In this paper, we propose TReCiM, an ultra-low-power temperature-resilient multibit 2FeFET-1T CiM design that reliably performs MAC operations in the subthreshold-FeFET region with temperature ranging from 0 to 85 degrees Celcius at scale. We benchmark our design using NeuroSim framework in the context of VGG-8 neural network architecture running the CIFAR-10 dataset. Benchmarking results suggest that when considering temperature drift impact, our proposed TReCiM array achieves 91.31% accuracy, with 1.86% accuracy improvement compared to existing 1-bit 2T-1FeFET CiM array. Furthermore, our proposed design achieves 48.03 TOPS/W energy efficiency at system level, comparable to existing designs with smaller technology feature sizes.
Computational Thinking (CT) is a key skill set for students in higher education to thrive and adapt to an increasingly technology-driven future and workplace. While research on CT education has gained remarkable momentum in K12 over the past decade, it has remained under-explored in higher education, leaving higher education teachers with an insufficient overview, knowledge, and support regarding CT education. The proliferation and adoption of artificial intelligence (AI) by educational institutions have demonstrated promising potential to support instructional activities across many disciplines, including CT education. However, a comprehensive overview outlining the various aspects of integrating AI in CT education in higher education is lacking. To mitigate this gap, we conducted this systematic literature review study. The focus of our study is to identify initiatives applying AI in CT education within higher education and to explore various educational aspects of these initiatives, including the benefits and challenges of AI in CT education, instructional strategies employed, CT components covered, and AI techniques and models utilized. This study provides practical and scientific contributions to the CT education community, including an inventory of AI-based initiatives for CT education useful to educators, an overview of various aspects of integrating AI into CT education such as its benefits and challenges (e.g., AI potential to reshape CT education versus its potential to diminish students creativity) and insights into new and expanded perspectives on CT in light of AI (e.g., the decoding approach alongside the coding approach to CT).
Ivan Baburin, Matthew Cook, Florian Grötschla
et al.
In this work, we investigate the computational aspects of asynchronous cellular automata (ACAs), a modification of cellular automata in which cells update independently, following an asynchronous schedule. We introduce flip automata networks (FAN), a simple modification of automata networks that remain robust under any asynchronous update schedule. We show that asynchronous automata can efficiently simulate their synchronous counterparts with a linear memory overhead, which improves upon the previously established quadratic bound. Additionally, we address the universality gap for (a)synchronous cellular automata -- the boundary separating universal and non-universal automata, which is still not fully understood. We tighten this boundary by proving that all one-way asynchronous automata lack universal computational power. Conversely, we establish the existence of a universal 6-state first-neighbor automaton in one dimension and a 3-state von Neumann automaton in two dimensions, which represent the smallest known universal constructions to date.
This paper addresses the problem of improving the query performance of the triangular expansion algorithm (TEA) for computing visibility regions by finding the most advantageous instance of the triangular mesh, the preprocessing structure. The TEA recursively traverses the mesh while keeping track of the visible region, the set of all points visible from a query point in a polygonal world. We show that the measured query time is approximately proportional to the number of triangle edge expansions during the mesh traversal. We propose a new type of triangular mesh that minimizes the expected number of expansions assuming the query points are drawn from a known probability distribution. We design a heuristic method to approximate the mesh and evaluate the approach on many challenging instances that resemble real-world environments. The proposed mesh improves the mean query times by 12-16% compared to the reference constrained Delaunay triangulation. The approach is suitable to boost offline applications that require computing millions of queries without addressing the preprocessing time. The implementation is publicly available to replicate our experiments and serve the community.
The circulant twin column parity mixer (TCPM) is a type of mixing layer for the round function of cryptographic permutations designed by Hirch et al. at CRYPTO 2023. It has a bitwise differential branch number of 12 and a bitwise linear branch number of 4, which makes it competitive in applications where differential security is required. Hirch et al. gave a concrete instantiation of a permutation using such a mixing layer, named Gaston, and showed the best 3-round differential and linear trails of Gaston have much higher weights than those of Ascon. In this paper, we first prove why the TCPM has linear branch number 4 and then show that Gaston’s linear behavior is worse than Ascon for more than 3 rounds. Motivated by these facts, we aim to enhance the linear security of the TCPM. We show that adding a specific set of row cyclic shifts to the TCPM can make its differential and linear branch numbers both 12. Notably, by setting a special relationship between the row shift parameters of the modified TCPM, we obtain a special kind of mixlayer called the symmetric circulant twin column parity mixer. The symmetric TCPM has a unique design property that its differential and linear branch histograms are the same, which makes the parameter selection process and the security analysis convenient. Using the symmetric TCPM, we present two new 320-bit cryptographic permutations, namely (1) Gaston-S where we replace the mixing layer in Gaston with the symmetric TCPM and (2) SBD which uses a low-latency degree-4 S-box as the non-linear layer and the symmetric TCPM as the mixing layer. We evaluate the security of these permutations considering differential, linear and algebraic analysis, and then provide the performance comparison with Gaston in both hardware and software. Our results indicate that Gaston-S and SBD are competitive with Gaston in both security and performance.
La automatización de decisiones morales es un tema que suscita profundo interés en la sociedad contemporánea, caracterizada por un creciente uso de la tecnología. Esta investigación se propone analizar los factores cruciales que deben considerarse al establecer límites para la automatización de juicios y acciones con implicaciones éticas. Los investigadores se plantearon la siguiente pregunta central: ¿Qué tipos de decisiones éticas no deberían delegarse a sistemas automatizados y cómo se puede mantener la responsabilidad humana en procesos morales automatizados? El estudio reveló que existen categorías específicas de decisiones éticas que no deberían ser completamente automatizadas. Entre estas se encuentran aquellas que implican dilemas morales complejos, las que afectan directamente la vida humana, o las que requieren un alto grado de empatía y comprensión contextual. Además, los hallazgos subrayan la importancia de mantener la supervisión y responsabilidad humana, incluso en procesos que están parcialmente automatizados. Los investigadores concluyeron que es fundamental alcanzar un equilibrio entre aprovechar la eficiencia que ofrece la automatización y preservar el juicio ético humano. Esto implica el diseño de sistemas capaces de identificar situaciones que requieren intervención humana y el establecimiento de mecanismos claros de rendición de cuentas. En síntesis, el estudio determina que, si bien la automatización presenta ventajas en términos de eficiencia y consistencia, existen límites éticos que deben ser respetados. Las decisiones morales más complejas y con mayor impacto en la vida humana deben permanecer bajo control humano, mientras que la automatización puede aplicarse de forma limitada en procesos más rutinarios, siempre bajo supervisión humana y con mecanismos de responsabilidad claramente definidos.
Alberto Bacchin, Davide Allegro, Stefano Ghidoni
et al.
Out-of-Distribution (OOD) detection in computer vision is a crucial research area, with related benchmarks playing a vital role in assessing the generalizability of models and their applicability in real-world scenarios. However, existing OOD benchmarks in the literature suffer from two main limitations: (1) they often overlook semantic shift as a potential challenge, and (2) their scale is limited compared to the large datasets used to train modern models. To address these gaps, we introduce SOOD-ImageNet, a novel dataset comprising around 1.6M images across 56 classes, designed for common computer vision tasks such as image classification and semantic segmentation under OOD conditions, with a particular focus on the issue of semantic shift. We ensured the necessary scalability and quality by developing an innovative data engine that leverages the capabilities of modern vision-language models, complemented by accurate human checks. Through extensive training and evaluation of various models on SOOD-ImageNet, we showcase its potential to significantly advance OOD research in computer vision. The project page is available at https://github.com/bach05/SOODImageNet.git.
Pablo Flores-Siguenza, Jose Antonio Marmolejo-Saucedo, Rodrigo Guamán
Textile MSMEs are characterized by their high influence on the economy of the countries, both for their contribution to the gross domestic product as well as for the generation of employment, in recent years the complexity of their operations, instability and lack of balance between economic, environmental and social factors, axes of sustainable development, stand out. It is necessary to implement approaches such as sustainable manufacturing and production planning, which seeks the creation of products with minimal environmental impact, safe for workers, and economically robust. In this context, this study aims to develop a multi-objective optimization model that enhances sustainable production planning in textile MSMEs. The methodology is based on two phases, the first one focused on the acquisition of information and the second one dedicated to the mathematical formulation of the model, where three objective functions focused on economic, environmental and social factors are proposed. The model is validated with real data from a textile MSME in Ecuador and different production alternatives are generated by proposing the implementation and use of photovoltaic energy as well as a greater use of personal protective equipment. One of the relevant conclusions of the study is the contribution to the textile industry with a sustainable decision support tool, where different scenarios for production planning and their respective economic, environmental and social consequences are shown.
Computer engineering. Computer hardware, Systems engineering
The production capacity of compact reactors with micrometer or millimeter-scale channels or tubes is increased by numbering-up. In previous studies, a multi-channel plate type reactor and a multi-tube type reactor (MTR) were developed and applied to extraction and reaction operations. Fluid distribution has often been evaluated to design these reactors, but temperature control, which is critical to the reaction, has not been fully considered. It is important to solve this problem and establish a design method. In this study, computational fluid dynamics (CFD)-based design was performed so as to achieve the uniform flow and temperature distributions among the reaction tubes in the MTR, where an exothermic reaction proceeds in each tube with an immobilized catalyst and the reaction temperature is controlled by a coolant flowing outside the tubes. Effects of multi-tube arrangement of lattice, concentric circles and single circle, shell cross-sectional shape of circle, rectangle and ring, and reaction tubes with or without catalyst-free inert sections on the reactor performance were investigated by CFD. The usefulness of a two-step approach of designing the MTR after designing the double-tubular reactor was confirmed through a case study on parallel reactions.
Chemical engineering, Computer engineering. Computer hardware
Universal hash functions play a prominent role in the design of message authentication codes and the like. Whereas it is known how to build highly efficient sequential universal hash functions, parallel non-algebraic universal hash function designs are always built on top of a PRP. In such case, one employs a relatively strong primitive to obtain a function with a relatively weak security model. In this work, we present EliHash, a construction of a parallel universal hash function from non-compressing universal hash functions, and we back it up with supporting security analysis. We use this construction to design EliMAC, a message authentication code similar to LightMAC. We consider a heuristic instantiation of EliMAC with roundreduced AES, and argue that this instantiation of EliMAC is much more efficient than LightMAC, it is around 21% faster, and additionally allows for precomputation of the keys, albeit with a stronger assumption on the AES primitive than in LightMAC. These observations are backed up with an implementation of our scheme.
Joffrey L. Leevy, John Hancock, Taghi M. Khoshgoftaar
Abstract The yearly increase in incidents of credit card fraud can be attributed to the rapid growth of e-commerce. To address this issue, effective fraud detection methods are essential. Our research focuses on the Credit Card Fraud Detection Dataset, which is a widely used dataset that contains real-world transaction data and is characterized by high class imbalance. This dataset has the potential to serve as a benchmark for credit card fraud detection. Our work evaluates the effectiveness of two supervised learning classification techniques, binary classification and one-class classification, for credit card fraud detection. The performance of five binary-class classification (BCC) learners and three one-class classification (OCC) learners is evaluated. The metrics used are area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUC). Our results indicate that binary classification is a better approach for detecting credit card fraud than one-class classification, with the top binary classifier being CatBoost.
Computer engineering. Computer hardware, Information technology
Karen Wintersperger, Florian Dommert, Thomas Ehmer
et al.
We present an industrial end-user perspective on the current state of quantum computing hardware for one specific technological approach, the neutral atom platform. Our aim is to assist developers in understanding the impact of the specific properties of these devices on the effectiveness of algorithm execution. Based on discussions with different vendors and recent literature, we discuss the performance data of the neutral atom platform. Specifically, we focus on the physical qubit architecture, which affects state preparation, qubit-to-qubit connectivity, gate fidelities, native gate instruction set, and individual qubit stability. These factors determine both the quantum-part execution time and the end-to-end wall clock time relevant for end-users, but also the ability to perform fault-tolerant quantum computation in the future. We end with an overview of which applications have been shown to be well suited for the peculiar properties of neutral atom-based quantum computers.
For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engineering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to AI4SE, particularly highlighting the need to realize trustworthy and synergistic AI4SE. Progressing, the paper paints a vision for the potential leaps achievable if AI4SE's key challenges are surmounted, suggesting a transition towards Software Engineering 2.0. Two strategic roadmaps are then laid out: one centered on realizing trustworthy AI4SE, and the other on fostering synergistic AI4SE. While this paper may not serve as a conclusive guide, its intent is to catalyze further progress. The ultimate aspiration is to position AI4SE as a linchpin in redefining the horizons of software engineering, propelling us toward Software Engineering 2.0.
Least Mean Square(LMS) adaptive filtering algorithms with mean square error as the cost function have the advantages of simple structure, easy implementation, low computational complexity, and good stability.During estimation of the impulse response of an unknown system, the traditional Diffusion LMS(DLMS) algorithm is usually corrupted by noise, thereby reducing its estimation accuracy.To address this problem, a Frequency-domain Correlation DLMS(FCDLMS) algorithm is proposed.Because the correlation coefficient of the uncorrelated signals approaches zero, the autocorrelation function of the input signal and the cross-correlation function of the input and the desired signal in the DLMS algorithm are used as new observation data to propose a Correlation DLMS(CDLMS) algorithm.This CDLMS algorithm is then extended to the frequency domain, and a multiplication operation rather than a convolution operation is adopted to update the tap coefficients, reducing computational complexity.Experimental results show that, compared with the traditional DLMS algorithm, the FCDLMS algorithm has a better estimation result for the impulse response of an unknown system over distributed adaptive networks in a noisy environment, and its performance improved.It can also better adapt to complex environments such as multi-tap number, multi-node number, and strong noise.
El proyecto de investigación que se detalla sobre la gestión administrativa y el fortalecimiento organizacional busca mejorar el conocimiento de la sociedad, el cual tiene como enfoque servir para los procesos administrativos. En este sentido, el propósito es brindar conocimiento para que futuras generaciones conozcan sobre estos temas de interés general y poder ponerlos en práctica. El objetivo de investigación es determinar la gestión administrativa y su incidencia en el fortalecimiento organizacional en la asociación el sombrerito Machalilla, del cantón Puerto López, año 2021. Se pretende beneficiar a los socios de El Sombrerito en Machalilla, y de la misma forma al estudiante y autor del presente estudio de carácter científico. La investigación sigue un enfoque cualitativo, descriptivo, de campo y revisión documental. La población tomada en cuenta es de 65 personas inscritas, de esta forma se toma como muestra un total de 45 personas. Se constató en los resultados que un 67% de los encuestados refirieron que si influye el comportamiento del individuo en el desempeño administrativo.
Yeong Heon Jeong, Chul Woong Joo, Hyein Jeong
et al.
A new 2-(5,9-dioxa-13b-boranaphtho[3,2,1-de]anthracene-8-yl)-4-(trimethylsilyl)pyridine-based iridium complex was synthesized for efficient solution-processed sky-blue phosphorescent organic light-emitting diodes (PhOLEDs). The effect of dioxaboranaphthoanthracene instead of phenyl with the electron-accepting group, as well as the bulky pyridine with the trimethylsilyl group, on the ligand was investigated. The new dopant was found to have an extremely high photoluminescence quantum yield of 94% when doped in an emissive layer. As a result, the solution-processed blue PhOLED consisting of a simple structure without any interlayer exhibited remarkable light-emitting performance with an external quantum efficiency of 8.93% and a current efficiency of 23.56 cd/A.
Claudio Cicconetti, Marco Conti, Andrea Passarella
Offloading computation from user devices to nodes with processing capabilities at the edge of the network is a major trend in today's network/service architectures. At the same time, serverless computing has gained a huge traction among the cloud computing technologies and has, thus, promoted the adoption of Function-as-a-Service (FaaS). The latter has some characteristics that make it generally suitable to edge applications, except for its cumbersome support of stateful applications. This work is set to provide a broad view on the options available for supporting stateful FaaS, which are distilled into four reference execution models that differ on where the state resides. While further investigation is needed to advance our understanding of the opportunities offered by in-network computing through stateful FaaS, initial insights are provided by means of a qualitative analysis of the four alternatives and their quantitative comparison in a simulator.
During the production of titanium slag by melting ilmenite concentrate, a large amount of dust is formed, most of which is taken to the landfill. The problem of dust disposal and recycling is the most important task of titanium production, since valuable components that can be extracted are lost along with the dust. In addition, the storage of dust in open landfills causes great harm to the environment. The use of deep eutectic solvents as green solvents for dust leaching has great prospects. The possibility of extracting vanadium from the dust of ore-thermal melting using deep eutectic solvents based on choline chloride and carboxylic acids is investigated. The highest yield of vanadium was during leaching in the Oxaline system and amounted to 89 %. The results of leaching were compared with leaching with equivalent aqueous acid solutions. The effect of ultrasound on the time of the leaching process was also studied. The resulting vanadium-containing material can be used as an additive to the charge in the accompanying technology for the production of vanadium pentoxide.
Chemical engineering, Computer engineering. Computer hardware