Hasil untuk "Electronic computers. Computer science"

M. Johnston, K. Langton, R. Haynes et al.

Fabrizio Montesi, Marco Peressotti, Alexandre Rademaker

We present a library-level formalisation of Hennessy-Milner Logic (HML) - a foundational logic for labelled transition systems (LTSs) - for the Lean Computer Science Library (CSLib). Our development includes the syntax, satisfaction relation, and denotational semantics of HML, as well as a complete metatheory including the Hennessy-Milner theorem - bisimilarity coincides with theory equivalence for image-finite LTSs. Our development emphasises generality and reusability: it is parametric over arbitrary LTSs, definitions integrate with CSLib's infrastructure (such as the formalisation of bisimilarity), and proofs leverage Lean's automation (notably the grind tactic). All code is publicly available in CSLib and can be readily applied to systems that use its LTS API.

John Millar Carroll

Bo Feng, Yangrui Zhang, Chao Zhang et al.

Abstract Cloud storage systems in smart grids face dual challenges: ensuring data integrity while maintaining real-time responsiveness when managing large-scale power data. Conventional static key management strategies, due to their fixed update patterns, are prone to predictability. Meanwhile, standalone data integrity verification mechanisms often introduce substantial computational and communication overhead, rendering them unsuitable for real-time grid monitoring requirements. To address these issues, this study proposes a collaborative anti-tampering strategy that integrates reinforcement learning with dynamic hash chains. The approach employs a deep Q-network (DQN) to dynamically optimize the update timing and strategy of Advanced Encryption Standard (AES) keys, thereby enhancing the adaptability of key management. Simultaneously, by constructing a dynamic hash chain, it achieves chain-style cross-verification between data blocks to ensure traceability and rapid localization of tampering incidents. Simulation results demonstrate that, compared with conventional key rotation and Secure Hash Algorithm (SHA)-based methods, the proposed scheme improves the tamper detection rate by 67.2%, reduces the average system latency by 38.5 ms, and significantly decreases computational and communication overhead by 52% and 88%, respectively. This study provides an effective technical pathway for addressing dynamic security challenges in smart grid cloud storage environments.

Shreyan Banerjee, Luna Gava, Aasifa Rounak et al.

The emerging field of neuromorphic computing for edge control applications poses the need to quantitatively estimate and limit the number of spiking neurons, to reduce network complexity and optimize the number of neurons per core and hence, the chip size, in an application-specific neuromorphic hardware. While rate-encoding for spiking neurons provides a robust way to encode signals with the same number of neurons as an ANN, it often lacks precision. To achieve the desired accuracy, a population of neurons is often needed to encode the complete range of input signals. However, using population encoding immensely increases the total number of neurons required for a particular application, thus increasing the power consumption and on-board resource utilization. A transition from two neurons to a population of neurons for the linear quadratic regulator (LQR) control of a cartpole is shown in this work. The near-linear behavior of a leaky-integrate-and-fire neuron can be exploited to achieve the LQR control of a cartpole system. This has been shown in simulation, followed by a demonstration on a single-neuron hardware, known as Lu.i. The improvement in control performance is then demonstrated by using a population of varying numbers of neurons for similar control in the Nengo neural engineering framework (NEF), on CPU and on Intel’s Loihi neuromorphic chip. Finally, linear control is demonstrated for four multi-linked pendula on cart systems, using a population of neurons in Nengo, followed by an implementation of the same on Loihi. This study compares LQR control in the NEF using 7 control and 7 neuromorphic performance metrics, followed by a comparison with other conventional spiking and non-spiking controllers.

Jessica Gronsbell, Hilary Thurston, Lillian Dong et al.

Computational phenotyping has emerged as a practical solution to the incomplete collection of data on gender in electronic health records (EHRs). This approach relies on algorithms to infer a patient's gender using the available data in their health record, such as diagnosis codes, medication histories, and information in clinical notes. Although intended to improve the visibility of trans and gender-expansive populations in EHR-based biomedical research, computational phenotyping raises significant methodological and ethical concerns related to the potential misuse of algorithm outputs. In this paper, we review current practices for computational phenotyping of gender and examine its challenges through a critical lens. We also highlight existing recommendations for biomedical researchers and propose priorities for future work in this domain.

Keyang Qian, Kaixun Yang, Wei Dai et al.

Using LLMs to give educational feedback to students for their assignments has attracted much attention in the AI in Education field. Yet, there is currently no large-scale open-source dataset of student assignments that includes detailed assignment descriptions, rubrics, and student submissions across various courses. As a result, research on generalisable methodology for automatic generation of effective and responsible educational feedback remains limited. In the current study, we constructed a large-scale dataset of Synthetic Computer science Assignments for LLM-generated Educational Feedback research (SCALEFeedback). We proposed a Sophisticated Assignment Mimicry (SAM) framework to generate the synthetic dataset by one-to-one LLM-based imitation from real assignment descriptions, student submissions to produce their synthetic versions. Our open-source dataset contains 10,000 synthetic student submissions spanning 155 assignments across 59 university-level computer science courses. Our synthetic submissions achieved BERTScore F1 0.84, PCC of 0.62 for assignment marks and 0.85 for length, compared to the corresponding real-world assignment dataset, while ensuring perfect protection of student private information. All these results of our SAM framework outperformed results of a naive mimicry method baseline. The LLM-generated feedback for our synthetic assignments demonstrated the same level of effectiveness compared to that of real-world assignment dataset. Our research showed that one-to-one LLM imitation is a promising method for generating open-source synthetic educational datasets that preserve the original dataset's semantic meaning and student data distribution, while protecting student privacy and institutional copyright. SCALEFeedback enhances our ability to develop LLM-based generalisable methods for offering high-quality, automated educational feedback in a scalable way.

David M. Berry

If an active citizen should increasingly be a computationally enlightened one, replacing the autonomy of reason with the heteronomy of algorithms, then I argue in this article that we must begin teaching the principles of critiquing the computal through new notions of what we might call digital Bildung. Indeed, if civil society itself is mediated by computational systems and media, the public use of reason must also be complemented by skills for negotiating and using these computal forms to articulate such critique. Not only is there a need to raise the intellectual tone regarding computation and its related softwarization processes, but there is an urgent need to attend to the likely epistemic challenges from computation which, as presently constituted, tends towards justification through a philosophy of utility rather than through a philosophy of care for the territory of the intellect. We therefore need to develop an approach to this field that uses concepts and methods drawn from philosophy, politics, history, anthropology, sociology, media studies, computer science, and the humanities more generally, to try to understand these issues - particularly the way in which software and data increasingly penetrate our everyday life and the pressures and fissures that are created. We must, in other words, move to undertake a critical interdisciplinary research program to understand the way in which these systems are created, instantiated, and normatively engendered in both specific and general contexts.

Erika Ábrahám, Miguel Goulão, Milena Vujošević Janičić et al.

Computer science attracts few women, and their proportion decreases through advancing career stages. Few women progress to PhD studies in CS after completing master's studies. Empowering women at this stage in their careers is essential to unlock untapped potential for society, industry and academia. This paper identifies students' career assumptions and information related to PhD studies focused on gender-based differences. We propose a Women Career Lunch program to inform female master students about PhD studies that explains the process, clarifies misconceptions, and alleviates concerns. An extensive survey was conducted to identify factors that encourage and discourage students from undertaking PhD studies. We identified statistically significant differences between those who undertook PhD studies and those who didn't, as well as gender differences. A catalogue of questions to initiate discussions with potential PhD students which allowed them to explore these factors was developed and translated to 8 languages. Encouraging factors toward PhD study include interest and confidence in research arising from a research involvement during earlier studies; enthusiasm for and self-confidence in CS in addition to an interest in an academic career; encouragement from external sources; and a positive perception towards PhD studies which can involve achieving personal goals. Discouraging factors include uncertainty and lack of knowledge of the PhD process, a perception of lower job flexibility, and the requirement for long-term commitment. Gender differences highlighted that female students who pursue a PhD have less confidence in their technical skills than males but a higher preference for interdisciplinary areas. Female students are less inclined than males to perceive the industry as offering better job opportunities and more flexible career paths than academia.

Reshma Devi, Keith T. Butler, Gopalakrishnan Sai Gautam

Abstract A pathway to overcome limited data availability in materials science is to use the framework of transfer learning, where a pre-trained (PT) machine learning model (on a larger dataset) can be fine-tuned (FT) on a target (smaller) dataset. We systematically explore the effectiveness of various PT/FT strategies to learn and predict material properties and create generalizable models by PT on multiple properties (MPT) simultaneously. Specifically, we leverage graph neural networks (GNNs) to PT/FT on seven diverse curated materials datasets, with sizes ranging from 941 to 132,752. Besides identifying optimal PT/FT strategies and hyperparameters, we find our pair-wise PT-FT models to consistently outperform models trained from scratch on target datasets. Importantly, our MPT models outperform pair-wise models on several datasets and, more significantly, on a 2D material band gap dataset that is completely out-of-domain. Finally, we expect our PT/FT and MPT frameworks to accelerate materials design and discovery for various applications.

Sergii Degtyar, Oleh Kopiika, Yurii Shusharin

Branching processes as a mathematical concept has applications in various fields, including information technology. In information technology, branching processes can be used to model and analyze various scenarios, such as the propagation of data or information in a network, the growth of computer viruses, the spread of software bugs, and more. Branching processes are particularly useful for understanding the dynamics of systems where events can lead to multiple new events in a probabilistic manner. Overall, branching processes provide a valuable mathematical framework for modeling and analyzing various aspects of information technology, helping to make informed decisions and optimize IT systems and networks. We have studied transient phenomena for branching processes with an infinite number of types close to critical. The analytical apparatus for this study is Markov renewal theorems. Branched processes were used to evaluate the performance of IT systems and predict their behavior under different conditions. This is important for capacity planning and resource allocation.

Václav Diviš, Bastian Spatz, Marek Hrúz

Recent research has drawn attention to the ambiguity surrounding the definition and learnability of Out-of-Distribution recognition. Although the original problem remains unsolved, the term “Out-of-Model Scope” detection offers a clearer perspective. The ability to detect Out-of-Model Scope inputs is particularly beneficial in safety-critical applications such as autonomous driving or medicine. By detecting Out-of-Model Scope situations, the system’s robustness is enhanced and it is prevented from operating in unknown and unsafe scenarios. In this paper, we propose a novel approach for Out-of-Model Scope detection that integrates three sources of information: (1) the original input, (2) its latent feature representation extracted by an encoder, and (3) a synthesized version of the input generated from its latent representation. We demonstrate the effectiveness of combining original and synthetically generated inputs to defend against adversarial attacks in the computer vision domain. Our method, TRust Your GENerator (TRYGEN), achieves results comparable to those of other state-of-the-art methods and allows any encoder to be integrated into our pipeline in a plug-and-train fashion. Through our experiments, we evaluate which combinations of the encoder’s features are most effective for discovering Out-of-Model Scope samples and highlight the importance of a compact feature space for training the generator.

Anila Mjeda, Hazel Murray

Quantum computing presents a transformative potential for the world of computing. However, integrating this technology into the curriculum for computer science students who lack prior exposure to quantum mechanics and advanced mathematics remains a challenging task. This paper proposes a scaffolded learning approach aimed at equipping computer science students with essential quantum principles. By introducing foundational quantum concepts through relatable analogies and a layered learning approach based on classical computation, this approach seeks to bridge the gap between classical and quantum computing. This differs from previous approaches which build quantum computing fundamentals from the prerequisite of linear algebra and mathematics. The paper offers a considered set of intuitive analogies for foundation quantum concepts including entanglement, superposition, quantum data structures and quantum algorithms. These analogies coupled with a computing-based layered learning approach, lay the groundwork for a comprehensive teaching methodology tailored for undergraduate third level computer science students.

Kade Head-Marsden, Johannes Flick, C. Ciccarino et al.

Discoveries in quantum materials, which are characterized by the strongly quantum-mechanical nature of electrons and atoms, have revealed exotic properties that arise from correlations. It is the promise of quantum materials for quantum information science superimposed with the potential of new computational quantum algorithms to discover new quantum materials that inspires this Review. We anticipate that quantum materials to be discovered and developed in the next years will transform the areas of quantum information processing including communication, storage, and computing. Simultaneously, efforts toward developing new quantum algorithmic approaches for quantum simulation and advanced calculation methods for many-body quantum systems enable major advances toward functional quantum materials and their deployment. The advent of quantum computing brings new possibilities for eliminating the exponential complexity that has stymied simulation of correlated quantum systems on high-performance classical computers. Here, we review new algorithms and computational approaches to predict and understand the behavior of correlated quantum matter. The strongly interdisciplinary nature of the topics covered necessitates a common language to integrate ideas from these fields. We aim to provide this common language while weaving together fields across electronic structure theory, quantum electrodynamics, algorithm design, and open quantum systems. Our Review is timely in presenting the state-of-the-art in the field toward algorithms with nonexponential complexity for correlated quantum matter with applications in grand-challenge problems. Looking to the future, at the intersection of quantum information science and algorithms for correlated quantum matter, we envision seminal advances in predicting many-body quantum states and describing excitonic quantum matter and large-scale entangled states, a better understanding of high-temperature superconductivity, and quantifying open quantum system dynamics.

Meri Azmi, Deni Satria, Farhan Rinsky Mulya et al.

The industrial sector's growth has led to an increase in the number of industrial products available in the market. However, this has made it more challenging for retail merchants to choose which items to sell due to the overwhelming number of options. The seller must carefully consider various factors such as the type, quality, and probability of selling the goods to turn a profit. This research proposes a group decision support system to assist retail sellers in selecting the products to sell. The system is designed to process various information on comparing retail products against specific criteria, enabling sellers to make quick and accurate decisions. To achieve optimal results, this study combines three methods in the decision-making calculation process: Fuzzy Logic, EDAS, and Borda methods. The Fuzzy Logic method is used to assign a value to an unclear criterion, followed by the EDAS method ranking process, and ending with the combination of the decision-making results using the Borda method. The group decision support system is web-based and has been proven to provide effective solutions for retail business actors to increase sales and reduce losses. By using this system, retail sellers can make informed decisions about their products, enabling them to optimize their profits and reduce their risks. In conclusion, the increase in the number of industrial products has created challenges for retail merchants, but this research proposes a solution in the form of a group decision support system. Combining Fuzzy Logic, EDAS, and Borda methods results in an effective decision-making process that allows retail sellers to make informed decisions and achieve their business goals.

Yan Qian Lim, Yim Ling Loo

This paper presents a detailed analysis of the linguistic characteristics connected to specific levels of suicide risks, providing insight into the impact of the feature extraction techniques on the effectiveness of the predictive models of suicide ideation. Prevalent initiatives of research works had been observed in the detection of suicide ideation from social media posts through feature extraction and machine learning techniques but scarcely on the multiclass classification of suicide risks and analysis of linguistic characteristics' impact on predictability. To address this issue, this paper proposes the implementation of a machine learning framework that is capable of analyzing multiclass classification of suicide risks from social media posts with extended analysis of linguistic characteristics that contribute to suicide risk detection. A total of 552 samples of a supervised dataset of Twitter posts were manually annotated for suicide risk modeling. Feature extraction was done through a combination of feature extraction techniques of term frequency-inverse document frequency (TF-IDF), Part-of-Speech (PoS) tagging, and valence-aware dictionary for sentiment reasoning (VADER). Data training and modeling were conducted through the Random Forest technique. Testing of 138 samples with scenarios of detections in real-time data for the performance evaluation yielded 86.23% accuracy, 86.71% precision, and 86.23% recall, an improved result with a combination of feature extraction techniques rather than data modeling techniques. An extended analysis of linguistic characteristics showed that a sentence's context is the main contributor to suicide risk classification accuracy, while grammatical tags and strong conclusive terms were not.

ZANG Jie, ZHOU Wanlin, WANG Yan

Considering the matching problem of enterprise resources and customer requirements,the existing methods have the problems that the resource and requirement encapsulation is not accurate enough and the matching effect can't satisfy uses' requirement.In order to solve the problem of diversity and ambiguity of enterprise resource and requirement description,this paper proposes the dynamic user-defined template encapsulation.According to the feature that most of the encapsulated requirements and resources are Chinese short texts,an interactive text matching model which integrates multi-head attention mechanism and sia-mese network is proposed.The semantic differences and similarities between sentences are considered in this model.It uses word mixing vectors as input to enhance the semantic information of the text,combines the Siamese network with the multi-head attention mechanism,and extractes the semantic features of the context as an independent unit to fully interact with the semantic features.In order to verify the effectiveness of the model,the classical data set LCQMC and the self-constructed CSMD data set are used to conduct experiments on the model.The results show that the accuracy and performance of the model are improved in different degrees,which provides a more accurate matching method for enterprise resources and requirements.

Firdews A.Alsalman, Shler Khorshid, Amira Sallow

Machine learning and deep learning algorithms have become increasingly important in the medical field, especially for diagnosing disease using medical databases. Techniques developed within these two fields are now used to classify different diseases. Although the number of Machine Learning algorithms is vast and increasing, the number of frameworks and libraries that implement them is also vast and growing.  TensorFlow is a well-known machine learning library that has been used by several researchers in the field of disease classification. With the help of TensorFlow (Google's framework), a complex calculation can be addressed effectively by modeling it as a graph and properly mapping the graph segments to the machine in the form of a cluster. In this review paper, the role of the TensorFlow-Python framework- for disease classification is discussed.

SUNWenbing(孙文兵), XIEWenping(谢文平)

构造了一个带参数的Riemann-Liouville分数阶积分恒等式，得到几个关于h-预不变凸函数的带参数的分数阶积分不等式。当参数取特殊值时，分别得到了“中点型”“梯形型”和“Simpson型”积分不等式。利用构建的不等式得到了几个经典数值积分的误差估计式。

Ibrahim Alameri, Jitka Komarkova, Tawfik Al-Hadhrami et al.

Mobile ad-hoc networks (MANETs) and wireless mesh networks (WMNs) are used in a variety of research areas, including the military, industry, healthcare, agriculture, the Internet of Things (IoT), transportation, and smart cities. The swift advancement in MANET technology is the driving force behind this rising adoption rate. Routing over MANET is a critical problem due to the dynamic nature of the link qualities, even when nodes are static. A key challenge in MANETs is the need for an efficient routing protocol that establishes a route according to certain performance metrics related to the link quality. The routing protocols utilised by the nodes in WMNs and MANETs are distinct. Nodes in both types of networks exchange data packets through the routing protocols. For this highly mobile network, the ad-hoc On-Demand Distance Vector (AODV) routing protocol has been suggested as a possible solution. Recent years have attracted researchers’ attention to AODV since it is a routing technique for ad-hoc networks that prevents looping. The architecture of this routing protocol considers several factors, including the mobility of nodes, the failure of connection links, and the loss of packets. In this systematic review, one of the key focuses is bringing attention to the classic AODV, which was developed after discussing the recent development of several versions of AODV. The AODV routing protocol performs a path strength check to generate a more reliable and secure route between the source and destination nodes. In AODV, investigations demonstrate advances in both the format protocol approach and the network simulation-2 (NS-2), and these improvements were made in the same scenario used to revitalise AODV. It has been discovered that the AODV is more effective in several aspects, such as throughput, end-to-end delay, packet delivery ratio (PDR), energy consumption, jitter, packet loss ratio, and network overhead. Furthermore, this paper presents this systematic review based on AODV modifications in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It also provides a methodological framework for the papers’ selection.