Hasil untuk "Electronic computers. Computer science"

Adam M. Chekroud, J. Bondar, J. Delgadillo et al.

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Chen Gao, Chi Liu, Zhengquan Luo et al.

Large language models (LLMs) are increasingly applied in computer science education for tasks such as tutoring, content generation, and code assessment. However, systematic evaluations aligned with formal curricula and certification standards remain limited. This study benchmarked four recent models, including GPT-5, DeepSeek-R1, Qwen-Plus, and Llama-3.3-70B-Instruct, using a dataset of 1,068 questions derived from six certification exams covering networking, office applications, and Java programming. We evaluated performance across language (Chinese vs. English), cognitive levels based on Bloom's Taxonomy, domain knowledge, confidence-accuracy alignment, and robustness to input masking. Results showed that GPT-5 performed best on English-language certifications, while Qwen-Plus performed better in Chinese contexts. DeepSeek-R1 achieved the most balanced cross-lingual performance, whereas Llama-3.3 showed clear limitations in higher-order reasoning and robustness. All models performed worse on more complex tasks. These findings provide empirical support for the integration of LLMs into computer science education and offer practical implications for curriculum design and assessment.

SUN Haoran, WANG Zhihao, WU Yifan et al.

Computer education is one of the requirements of modern information society education. With the development of large language models, there has been increasing attention on applying of large language models to the computer education process. However, the hallucination problem associated with large language models poses significant challenges to their application. To solve the challenges, RAG techniques by incorporating external knowledge bases can effectively enhance the quality of responses generated by large language models. However, the traditional RAG techniques lack a fine screening mechanism for the retrieved information, which leads to the retention of a large amount of low-correlation knowledge, and the interference of irrelevant information makes the model hallucination problem not effectively solved. We collected computer-related textbooks and knowledge documents, dividing them into knowledge document blocks according to the content structure to construct an external knowledge database. On this base, we introduced the large educational models question-answering enhancement method based on re-ranking and post-retrieval reflection, which utilized a high-performance multilingual re-ranking model based on a cross-encoder to capture deep semantic information, filter the retrieval information, filter out irrelevant information to improve the retrieval quality. The proposed method applied RAG techniques for model reflection so that the model can further enhance the quality of the model's answers through self-examination, and effectively improve the accuracy of the large language model in computer question-answering. This approach significantly improves the accuracy of large language models in computer question-answering tasks. The proposed method has been tested on several popular current generative models, achieving promising results on CS-Bench, with an approximate 5% increase in accuracy for computer question-answering tasks.

Stanton Heister, Felix Kin Peng Hui, David Ian Wilson et al.

The 2015 Paris Agreement paved the way for the carbon trade economy, which has since evolved but has not attained a substantial magnitude. While carbon credit exchange is a critical mechanism for achieving global climate targets, it faces persistent challenges related to transparency, double-counting, and verification. This paper examines how Distributed Ledger Technology (DLT) can address these limitations by providing immutable transaction records, automated verification through digitally encoded smart contracts, and increased market efficiency. To assess DLT’s strategic potential for leveraging the carbon markets and, more explicitly, whether its implementation can reduce transaction costs and enhance market integrity, three alternative approaches that apply DLT for carbon trading were taken as case studies. By comparing key elements in these DLT-based carbon credit platforms, it is elucidated that these proposed frameworks may be developed for a scalable global platform. The integration of existing compliance markets in the EU (case study 1), Australia (case study 2), and China (case study 3) can act as a standard for a global carbon trade establishment. The findings from these case studies suggest that while DLT offers a promising path toward more sustainable carbon markets, regulatory harmonization, standardization, and data transfer across platforms remain significant challenges.

YU Yongtao, SUN Ao, LI Ang, ZHU Linlin

In industrial surface Quality Control (QC) scenarios, deep classification neural networks are widely used to classify product images for qualified judgment or quality grading. However, surface QC equipment equipped with deep classification neural networks must meet Attribute Reproducibility and Repeatability (AR&R) assessment requirements. Perturbations in product images, caused by assembly tolerance, equipment vibrations, and other factors, lead to variations in position, angle, brightness, and blurring. These perturbations result in inconsistent classification outputs, causing the surface QC equipment to fail the AR&R assessment, a problem referred to as the network output reproducibility issue. To address this issue, this study proposes a training method for classification neural networks based on Siamese networks. The Siamese primary network is trained using original samples for supervised learning to learn correct classification categories. The Siamese secondary network copies the weights of the primary network via exponential smoothing and generates feature embeddings of perturbed samples corresponding to the original ones. These embeddings are used for comparative learning training of the primary network, enabling it to output consistent classification probabilities for both original and perturbed sample inputs. During inference, only the primary network is retained for product defect classification. The results show that the classification accuracy reaches 99.346 2%, with a classification probability variance of 0.001 016. The described method effectively improves the output reproducibility of deep classification neural networks for industrial product image classification by reducing classification probability variance and enhancing accuracy.

Clément Aubert, Jean Krivine

Causality serves as an abstract notion of time for concurrent systems. A computation is causal, or simply valid, if each observation of a computation event is preceded by the observation of its causes. The present work establishes that this simple requirement is equally relevant when the occurrence of an event is invertible. We propose a conservative extension of causal models for concurrency that accommodates reversible computations. We first model reversible computations using a symmetric residuation operation in the general model of configuration structures. We show that stable configuration structures, which correspond to prime algebraic domains, remain stable under the action of this residuation. We then derive a semantics of reversible computations for prime event structures, which is shown to coincide with a switch operation that dualizes conflict and causality.

Kai Marquardt, Robert Hanak, Anne Koziolek et al.

This study offers new insights into students' interest in computer science (CS) education by disentangling the distinct effects of age and gender across a diverse adolescent sample. Grounded in the person-object theory of interest (POI), we conceptualize enthusiasm as a short-term, activating expression of interest that combines positive affect, perceived relevance, and intention to re-engage. Experiencing such enthusiasm can temporarily shift CS attitudes and strengthen future engagement intentions, making it a valuable lens for evaluating brief outreach activities. To capture these dynamics, we developed a theoretically grounded questionnaire for pre-post assessment of the enthusiasm potential of CS interventions. Using data from more than 400 students participating in online CS courses, we examined age- and gender-related patterns in enthusiasm. The findings challenge the prevailing belief that early exposure is the primary pathway to sustained interest in CS. Instead, we identify a marked decline in enthusiasm during early adolescence, particularly among girls, alongside substantial variability in interest trajectories across age groups. Crucially, our analyses reveal that age is a more decisive factor than gender in shaping interest development and uncover key developmental breakpoints. Despite starting with lower baseline attitudes, older students showed the largest positive changes following the intervention, suggesting that well-designed short activities can effectively re-activate interest even at later ages. Overall, the study highlights the need for a dynamic, age-sensitive framework for CS education in which instructional strategies are aligned with developmental trajectories.

LIU Yao, QIN Xun, LIU Tianji

To address the issue of re-analyzing and repeating development of natural language processing tools and resource ana-lysis plugins when new requirements arise during project development,this paper proposes a business-oriented on-demand resource analysis solution.Firstly,a demand-driven resource analysis method from requirement to code is proposed,focusing on the construction of a demand concept indexing model for the requirement text itself.The constructed demand concept indexing model outperforms other classification models in terms of accuracy,recall,and F1 score.Secondly,this paper establishes a mapping mechanism from requirement text to code library categories based on the correlation between requirement text and code.For the mapping results,the precison@K is used as an evaluation metric,with an ultimate accuracy rate of 60%,demonstrating a certain practical value.In summary,this paper explores a set of key technologies for on-demand resource analysis with demand parsing capabilities and implements the correlation between requirements and code,covering the entire process from requirement text classification,code library classification,code library retrieval to plugin generation.The proposed method forms a complete business loop of “requirement-code-plugin-analysis” and experimentally verifies to be effective for on-demand resource analysis.Compared to existing large language models for business requirement analysis and code generation,this method focuses on the implementation of the full process of plugin code reuse within specific business domains,containing business characteristics.

张钊(ZHANG Zhao), 毛义华(MAO Yihua), 王凯(WANG Kai) et al.

With the impact of the epidemic and the deepening of population aging in China, the distribution and quality of medical resources have become important factors affecting housing prices, gradually generating the capitalization effects of medical resources. In this study, the differences in the resident's preference for the infectious hospital and general 3A hospitals in Harbin were explored in depth through a questionnaire survey and a comparative analysis of their capitalization effects. Furthermore, the social heterogeneity of the capitalization effects of medical resources and the homogeneity of the two kinds of medical resources were analyzed based on quantile regression models and interaction effects tests. The results show that (1) the infectious hospital depresses the prices of nearby housings, and general 3A hospitals increase the prices of nearby housings. The capitalization effect of both medical resources gradually decreases with increasing distance. (2) Medium-priced housings are more sensitive to the proximity of the infectious hospital, and the capitalization effect of general 3A hospitals gradually increases as the price of housings increases. (3) There is an interaction between the capitalization effects of the two kinds of medical resources, and the proximity of general 3A hospitals enhances the NIMBY (not in my backyard) effect of infectious hospitals.(受新型冠状病毒感染冲击以及我国人口老龄化程度加深的影响，医疗资源的分布与质量成为影响住宅价格的重要因素，从而产生了医疗资源资本化效应。通过问卷调查，分析了城市居民对哈尔滨市传染病医院与全科三甲医院两种医疗资源的偏好差异。基于分位数回归模型与交互效应检验，深入探讨了医疗资源资本化效应的空间异质性、社会异质性以及两种医疗资源的同质性。结果表明：（1）传染病医院抑制了附近的住宅价格，全科三甲医院提升了附近的住宅价格，且随着距离的增加，两种医疗资源的资本化效应均逐渐减弱；（2）中等价位住宅对与传染病医院的距离更敏感，随着住宅价格的提高，全科三甲医院的资本化效应逐渐增强；（3）两种医疗资源具有交互资本化效应，住宅与全科三甲医院临近增强了传染病医院的邻避效应。)

Viktor Nazymko, Liudmila Zakharova, Denis Boulik

Parametric and structural uncertainties complicate the project management processes. The critical path is one of the pivotal parameters, which helps to control the project schedule and is used to determine the criticality of the tasks and activities that are the most decisive and should be treated during a project expediting or controlling. There may be a set of the critical paths in uncertain environment. Therefore, the main question is which of the critical paths to select. The aim of this paper is to answer to this question. We used Monte Carlo simulation to investigate the multiple critical paths. We revealed and explained several paradoxes that emerged as results of the multiple critical paths occurrence. They are inevitable late bias of the project duration under uncertainty, the tasks probability and their correlation effects, the impact of concurrent chains of the tasks on their criticality, multiplicity of the critical paths and especially multi-chain critical paths. We demonstrated that multiple critical paths are not negative effect. On the contrary, they play extraordinary useful role and are the reliable criterion of the project robustness and stability.

Arya Fernandi, Sofia Sa'idah, Rita Magdalena

Hiragana is one of the primary alphabets used in Japanese. Hiragana is a phonetic symbol; each letter represents one syllable. Hiragana letters are formed from curved lines and strokes. However, detecting Hiragana letters causes many errors because people still rely on their vision to detect the letters, especially people familiar with them for the first time. It will be difficult and not very clear to read the letters. Therefore, a Convolutional Neural Network (CNN) method is used to detect handwritten Hiragana letters and help people who first get to know Hiragana letters when the letters are too complicated for human eyes to detect. This research uses the YOLOv8 model as a handwritten Hiragana letter detection algorithm. The Hiragana letters to be detected are basic letters with 46 characters. This research uses the YOLOv8 model run on Google Collaboratory with the Ultralytics library version 8.0.20 using the Python programming language. The dataset is collected from the internet and annotated using the Roboflow framework and dataset 4600 Hiragana letters. From the test results, the best model is YOLOv8l using SGD optimizer and learning rate 0.01 with a precision value of 98.5%, recall value of 95.7%, f1-score value of 97.1%, and mAP value of 95.5%. In the future, we aim to expand the number of datasets and employ a broader range of hyperparameter values to optimize the classification precision and accuracy of the Hiragana Letter Detection system.

David H. Wolpert

The simulation hypothesis has recently excited renewed interest in the physics and philosophy communities. However, the hypothesis specifically concerns {\textit{computers}} that simulate physical universes. So to formally investigate the hypothesis, we need to understand it in terms of computer science (CS) theory. In addition we need a formal way to couple CS theory with physics. Here I couple those fields by using the physical Church-Turing thesis. This allow me to exploit Kleene's second recursion, to prove that not only is it possible for {us} to be a simulation being run on a computer, but that we might be in a simulation being run a computer \emph{by us}. In such a ``self-simulation'', there would be two identical instances of us, both equally ``real''. I then use Rice's theorem to derive impossibility results concerning simulation and self-simulation; derive implications for (self-)simulation if we are being simulated in a program using fully homomorphic encryption; and briefly investigate the graphical structure of universes simulating other universes which contain computers running their own simulations. I end by describing some of the possible avenues for future research. While motivated in terms of the simulation hypothesis, the results in this paper are direct consequences of the Church-Turing thesis. So they apply far more broadly than the simulation hypothesis.

Sergii Grybniak, Yevhen Leonchyk, Igor Mazurok et al.

Abstract A consensus protocol is a crucial mechanism of distributed networks by which nodes can coordinate their actions and the current state of data. This article describes a BlockDAG consensus algorithm based on the Proof of Stake approach. The protocol provides network participants with cross‐voting for the order of blocks, which, in the case of a fair vote, guarantees a quick consensus. Under conditions of dishonest behavior, cross‐voting ensures that violations will be quickly detected. In addition, the protocol assumes the existence of a Coordinating network containing information about the approved ordering, which qualitatively increases security and also serves to improve network synchronization.

Venelin Todorov, Slavi Georgiev, Ivan Dimov et al.

José Manuel Porras, Juan Alfonso Lara, Cristóbal Romero et al.

Predicting student dropout is a crucial task in online education. Traditionally, each educational entity (institution, university, faculty, department, etc.) creates and uses its own prediction model starting from its own data. However, that approach is not always feasible or advisable and may depend on the availability of data, local infrastructure, and resources. In those cases, there are various machine learning approaches for sharing data and/or models between educational entities, using a classical centralized machine learning approach or other more advanced approaches such as transfer learning or federated learning. In this paper, we used data from three different LMS Moodle servers representing homogeneous different-sized educational entities. We tested the performance of the different machine learning approaches for the problem of predicting student dropout with multiple educational entities involved. We used a deep learning algorithm as a predictive classifier method. Our preliminary findings provide useful information on the benefits and drawbacks of each approach, as well as suggestions for enhancing performance when there are multiple institutions. In our case, repurposed transfer learning, stacked transfer learning, and centralized approaches produced similar or better results than the locally trained models for most of the entities.

Ville Salo, Ilkka Törmä

Conway's Game of Life is a two-dimensional cellular automaton. As a dynamical system, it is well-known to be computationally universal, i.e.\ capable of simulating an arbitrary Turing machine. We show that in a sense taking a single backwards step of the Game of Life is a computationally universal process, by constructing patterns whose preimage computation encodes an arbitrary circuit-satisfaction problem, or, equivalently, any tiling problem. As a corollary, we obtain for example that the set of orphans is coNP-complete, exhibit a $6210 \times 37800$-periodic configuration whose preimage is nonempty but contains no periodic configurations, and prove that the existence of a preimage for a periodic point is undecidable. Our constructions were obtained by a combination of computer searches and manual design.

J. Drucker

YU Zhichao, LI Yangzhong, LIU Lei, FENG Shengzhong

Through superposition and entanglement, a quantum computing displays significant advantages over classical computers in dealing with problems that require large-scale parallel processing capabilities.At present, a physical quantum computer is limited in scalability, coherence time, and precision of quantum gate operations, so it is feasible to simulate quantum computing on a classical computer for studying quantum advantage and quantum algorithms.However, the computer resources required for quantum computing simulation grow exponentially with the number of qubits.Therefore, it is of great importance to study how to reduce the resources required for large-scale simulation with ensured computational accuracy, precision and efficiency.This paper describes the basic principles and background knowledge of quantum computing, including qubits, quantum gates, quantum circuits and quantum operating systems.Meanwhile, this paper summarizes the classical computer-based methods for simulating quantum computing, and analyzes their design ideas, advantages and disadvantages.Some commonly used simulators are also listed.On this basis, this paper discusses the communication overhead problem of quantum computing simulation, and presents some supercomputer-based methods for optimizing quantum computing simulation from the two aspects of node analysis and communication optimization.

Thomas J. Misa

This paper examines the historical dimension of gender bias in the US computing workforce. It offers new quantitative data on the computing workforce prior to the availability of US Census data in the 1970s. Computer user groups (including SHARE, Inc., and the Mark IV software user group) are taken as a cross-section of the computing workforce. A novel method of gender analysis is developed to estimate women's and men's participation in computing beginning in the 1950s. The data presented here are consistent with well-known NSF statistics that show computer science undergraduate programs enrolling increasing numbers of women students during 1965-1985. These findings challenge the 'making programming masculine' thesis, and serve to correct the unrealistically high figures often cited for women's participation in early computer programming. Gender bias in computing today is traced not to 1960s professionalization but to cultural changes in the 1980s and beyond.

Corey J. Maley

The relationship between brains and computers is often taken to be merely metaphorical. However, genuine computational systems can be implemented in virtually any media; thus, one can take seriously the view that brains literally compute. But without empirical criteria for what makes a physical system genuinely a computational one, computation remains a matter of perspective, especially for natural systems (e.g., brains) that were not explicitly designed and engineered to be computers. Considerations from real examples of physical computers-both analog and digital, contemporary and historical-make clear what those empirical criteria must be. Finally, applying those criteria to the brain shows how we can view the brain as a computer (probably an analog one at that), which, in turn, illuminates how that claim is both informative and falsifiable.