Lucas Romao, Luiz Xavier, Júlia Condé Araújo
et al.
Machine Learning (ML)-enabled systems challenge traditional Requirements Engineering (RE) and agile management due to data dependence, experimentation, and uncertain model behavior. Existing RE and agile practices remain poorly integrated and insufficiently tailored to these characteristics. This paper reports on the practical experience of applying RefineML, a requirements-focused approach for the continuous and agile refinement of ML-enabled systems, which integrates ML-tailored specification and agile management approaches with best practices derived from a systematic mapping study. The application context concerns an industry-academia collaboration project between PUC-Rio and EXA, a Brazilian cybersecurity company. For evaluation purposes, we applied questionnaires assessing RefineML's suitability and overall acceptance and semi-structured interviews. We applied thematic analysis to the collected qualitative data. Regarding suitability and acceptance, the results of the questionnaires indicated high perceived usefulness and intention to use. Based on the interviews, stakeholders perceived RefineML as improving communication and facilitating early feasibility assessments, as well as enabling dual-track governance of ML and software work, allowing continuous refinement of the model while evolving the overall software project. However, some limitations remain, particularly related to difficulties in operationalizing ML concerns into agile requirements and in estimating ML effort.
Brian Freeman, Adam Kicklighter, Matt Erdman
et al.
Hallucinations in large language models (LLMs) are outputs that are syntactically coherent but factually incorrect or contextually inconsistent. They are persistent obstacles in high-stakes industrial settings such as engineering design, enterprise resource planning, and IoT telemetry platforms. We present and compare five prompt engineering strategies intended to reduce the variance of model outputs and move toward repeatable, grounded results without modifying model weights or creating complex validation models. These methods include: (M1) Iterative Similarity Convergence, (M2) Decomposed Model-Agnostic Prompting, (M3) Single-Task Agent Specialization, (M4) Enhanced Data Registry, and (M5) Domain Glossary Injection. Each method is evaluated against an internal baseline using an LLM-as-Judge framework over 100 repeated runs per method (same fixed task prompt, stochastic decoding at tau = 0.7. Under this evaluation setup, M4 (Enhanced Data Registry) received ``Better'' verdicts in all 100 trials; M3 and M5 reached 80% and 77% respectively; M1 reached 75%; and M2 was net negative at 34% when compared to single shot prompting with a modern foundation model. We then developed enhanced version 2 (v2) implementations and assessed them on a 10-trial verification batch; M2 recovered from 34% to 80%, the largest gain among the four revised methods. We discuss how these strategies help overcome the non-deterministic nature of LLM results for industrial procedures, even when absolute correctness cannot be guaranteed. We provide pseudocode, verbatim prompts, and batch logs to support independent assessment.
Bryanna A.H. Sherbo, Marianne Marcoux, Cortney A. Watt
Remote sensing technologies have expanded methods for monitoring wildlife. Very High Resolution (VHR) satellite imagery is becoming more widely used for animal detection. This is especially important for remotely based and hard to detect species such as narwhal (Monodon monoceros Linnaeus, 1758). Narwhal are a data deficient species due to their large geographic distribution and elusive nature. During the summer, narwhal from the Baffin Bay population migrate to fiords and inlets in Canada and Greenland but their spatial use and density in fiords in the high Arctic is relatively unknown. Makinson Inlet, an inlet on Ellesmere Island in northern Canada, was surveyed using aerial methods in 2013 and estimated a surface abundance of 812 narwhal (adjusted to 2387). Another aerial survey was attempted but unsuccessful due to inclement weather in 2022; however, satellite imagery offers another method for estimating abundance of narwhal in this remote fiord. In this study the World-View 3 satellite (31 cm resolution) was tasked to obtain optical imagery from Makinson Inlet and 5752 km2 was imaged between August 2 to 5, 2022. Imagery readers with previous satellite imagery analysis experience manually analyzed the imagery and identified 406 narwhal. The estimated number of narwhal in Makinson Inlet was adjusted for availability bias to account for deeper whales that would not be visible in the imagery (>1 m deep). The adjusted estimated abundance for narwhal in Makinson Inlet was 1987 (CV = 0.12; 95 % CI: 1578-2502). This study demonstrates the first use of VHR satellite imagery as a remotely-based non-invasive method to obtain information on narwhal abundance in the Canadian high Arctic.
Predictions of student performance are important to the education system as a whole, helping students to know how their learning is changing and adjusting teachers' and school policymakers' plans for their future growth. However, selecting meaningful features from the huge amount of educational data is challenging, so the dimensionality of student achievement features needs to be reduced. Based on this motivation, this paper proposes an improved Binary Snake Optimizer (MBSO) as a wrapped feature selection model, taking the Mat and Por student achievement data in the UCI database as an example, and comparing the MBSO feature selection model with other feature methods, the MBSO is able to select features with strong correlation to the students and the average number of student features selected reaches a minimum of 7.90 and 7.10, which greatly reduces the complexity of student achievement prediction. In addition, we propose the MDBO-BP-Adaboost model to predict students' performance. Firstly, the model incorporates the good point set initialization, triangle wandering strategy and adaptive t-distribution strategy to obtain the Modified Dung Beetle Optimization Algorithm (MDBO), secondly, it uses MDBO to optimize the weights and thresholds of the BP neural network, and lastly, the optimized BP neural network is used as a weak learner for Adaboost. MDBO-BP-Adaboost After comparing with XGBoost, BP, BP-Adaboost, and DBO-BP-Adaboost models, the experimental results show that the R2 on the student achievement dataset is 0.930 and 0.903, respectively, which proves that the proposed MDBO-BP-Adaboost model has a better effect than the other models in the prediction of students' achievement with better results than other models.
Cultural tourism is important for preserving cultural history and giving visitors immersive experiences, but tailoring it to each visitor's needs is still a major problem. It offers a distinct method of improving cultural tourism by combining Virtual Reality (VR), Genetic Algorithm (GA), and individual customization. Premature convergence and inadequate population variety are addressed by the Dynamic variety-Enhanced Genetic Algorithm (DDE-GA), a variation of the conventional GA. DDE-GA improves the investigation of possible solutions by dynamically modifying selection pressure according to population diversity, it makes it particularly useful for tackling optimization problems that are complicated, multi-modal, and highly dimensional. Creating an immersive environment that enables visitors to experience cultural heritage in a manner that is entirely tailored to their preferences, interests, and schedules is the objective of virtual reality technology. By adjusting to these individual parameters, the algorithm cleverly optimizes tourist itineraries. The DDE-GA-powered VR system works better than current methods, according to experimental data, with improvements in reaction time (1.1 s), accuracy (98 %), precision (97 %), and modeling error (0.10). When compared to convolutional algorithms, the suggested approach specifically enhances accuracy and drastically lowers error. This invention assists not only in satisfying tourists with individualized experiences but also in popularizing and preserving cultural traditions via the use of modern technology. The research concludes that integrating DDE-GA with VR technology substantially enhances personalized cultural tourism by optimizing routes based on user-specific preferences. This approach yields notable improvements in accuracy, precision, and response time while minimizing modeling errors. Furthermore, it contributes to both enriching tourist experiences and advancing cultural heritage conservation through innovative technological applications.
Information technology, Electronic computers. Computer science
Agentic AI is poised to usher in a seismic paradigm shift in Software Engineering (SE). As technologists rush head-along to make agentic AI a reality, SE researchers are driven to establish agentic SE as a research area. While early visions of agentic SE are primarily focused on code-related activities, early empirical evidence calls for a consideration of a wider range of socio-technical activities and concerns to make it work in practice. This paper contributes to the emerging visions by: (a) recommending an expansion of its scope beyond code, toward a 'whole of process' vision, grounding it in SE foundations and evolution and emerging agentic SE frameworks, (b) proposing a preliminary set of values and principles to guide community efforts, and (c) sharing guidance on designing and using well-defined vocabulary for agentic SE. It is hoped that these ideas will encourage collaborations and steer the SE community toward laying strong foundations of agentic SE so it is not limited to enabling coding acceleration but becomes the next process-level paradigm shift.
PT. XYZ adalah cabang perusahaan dibidang retail fashion seperti kosmetik, sepatu, tas aksesoris, koleksi pakaian pria maupun wanita serta koleksi pakaian anak. Permasalahan yang ada di perusahanan tersebut adalah kualitas pelayanan yang diberikan karyawan terhadap pelanggan yang belum sesuai dengan yang diharapkan pelanggan. Dalam penelitian ini menggunakan metode servqual, dan IPA atribut-atribut mana yang perlu dan penting untuk dilakukan perbaikan. Selain itu Penelitian ini bertujuan untuk mengukur kualitas pelayanan di mana untuk mengetahui kesenjangan dan faktor yang mempengaruhi kepuasan konsumen terhadap kualitas pelayanan yang diperoleh sesuai dengan harapan konsumen. Untuk mendapatkan data maka dilakukan dengan penyebaran kuisioner kepada 95 responden. Yang kemudian dianalisis dengan software Microsoft excel. Berdasarkan hasil analisis metode servqual menggunakan software Microsoft excel maka didapatkan GAP masing-masing dimensi, tangible memiliki nilai (Q) -0.23, Reliability memiliki nilai (Q) -1.16, kemudian responsiveness nilai nilai (Q) -1.23, dimensi assurance nilai (Q) -0.68, dimensi emphaty nilai (Q)- 0.99. Sedangkan hasil dari analisis IPA didapatkan bahwa atribut 6,8,10,11,17 tergolong kuadran I yang artinya menjadi prioritas utama yang perlu diperbaiki karena kinerja tidak sesuai dengan yang diharapakan. Atribut 1 dan 2 masuk dalam kuadran 2 (pertahankan prioritas) yang artinya kinerja sudah sesuai/stabil dan harus dipertahankan, atribut 5,9,13,14,15,16 masuk dalam kuadran III (Prioritas Rendah) yang artinya atribut yang memiliki prioritas rendah dan tidak terlalu penting tetapi kinerja tidak sesuai, atribut 4 dan 7 masuk dalam kuadran IV (Berlebihan).
Kata Kunci: Kualitas Pelayanan, Servqual, GAP, IPA
In this paper, we introduce a new class of generalized (h,φ)-G-type I vector-valued functions, by combining the notions of (h,φ)-differentiable functions, G-invex functions, and type I functions. By using these new concepts, we formulate and prove the sufficient optimality conditions for the considered problem (GMP)h,φ. In addition, we investigate a dual problem of Mond–Weir type, called (GMWD)h,φ, and establish several duality results.
Dissimilar welds between ferritic and austenitic stainless steels are widely used in industrial applications. Taking into account the issues inherent to arc welding, such as the high heat input and the need to carry out multiple passes in the case of thick plates, a procedure with two simultaneous laser beams (working in a single pass) and consumable inserts as filler metal has been considered. Particular attention was paid to the choice of the filler metal (composition and amount), as well as welding parameters, which are crucial to obtain the right dilution necessary for a correct chemical composition in the weld zone. The first experimental investigations confirmed the achievement of a good weldability of the dissimilar pair ASTM A387 ferritic/AISI 304L austenitic steel, having ascertained that the microstructure of the weld zone is austenitic with a little amount of residual primary ferrite, which is the best condition to minimize the risk of hot cracking.
Daylí Covas Varela, Gilberto Dionisio Hernández Pérez, Juan José Cabello Eras
et al.
Este artículo presenta un modelo de ecuaciones estructurales que mide la calidad de vida urbana (CVU), tomando como referencia la ciudad de Cienfuegos (Cuba). El objetivo es determinar las variables que influyen en la CVU mediante un procedimiento que permite el diseño de dos modelos: uno desde la dimensión objetiva y otro desde la dimensión subjetiva. Los resultados muestran un modelo de relaciones entre indicadores de gestión, basado en la información de los decisores locales, y otro modelo que relaciona indicadores de percepción obtenidos de los ciudadanos. La comparación de ambos modelos generaliza que variables como salud, vivienda, ingresos personales y carga contaminante son factores determinantes en la CVU.
Mechanical engineering and machinery, Industrial engineering. Management engineering
Jose Dixon, Oluwatunmise Akinniyi, Abeer Abdelhamid
et al.
The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagnosis. This work introduces an enhanced AI-based architecture for improved brain tumor classification. We introduce a hybrid architecture that integrates vision transformer (ViT) and deep neural networks to create an ensemble classifier, resulting in a more robust brain tumor classification framework. The analysis pipeline begins with preprocessing and data normalization, followed by extracting three types of MRI-derived information-rich features. The latter included higher-order texture and structural feature sets to harness the spatial interactions between image intensities, which were derived using Haralick features and local binary patterns. Additionally, local deeper features of the brain images are extracted using an optimized convolutional neural networks (CNN) architecture. Finally, ViT-derived features are also integrated due to their ability to handle dependencies across larger distances while being less sensitive to data augmentation. The extracted features are then weighted, fused, and fed to a machine learning classifier for the final classification of brain MRIs. The proposed weighted ensemble architecture has been evaluated on publicly available and locally collected brain MRIs of four classes using various metrics. The results showed that leveraging the benefits of individual components of the proposed architecture leads to improved performance using ablation studies.
This study investigates the impact of Hofstede's cultural dimensions on abnormal core earnings management in multiple national cultural contexts. We employ an Ordinary Least Squares (OLS) regression model with abnormal core earnings as the dependent variable. The independent variables analyzed include Hofstede's dimensions: Power Distance Index (PDI), Individualism (IDV), Masculinity (MAS), and Uncertainty Avoidance Index (UAI). Our findings reveal that individualism is positively associated with abnormal core earnings, suggesting that cultures characterized by high individualism may encourage practices that inflate earnings due to the prominence of personal achievement and rewards. In contrast, masculinity negatively correlates with abnormal core earnings, indicating that the risk-taking attributes associated with masculine cultures may deter earnings management. Interestingly, uncertainty avoidance is positively linked to abnormal core earnings, supporting the notion that managers tend to engage more in earnings management to minimize fluctuations in financial reports in cultures with high uncertainty avoidance. The relationship between power distance and abnormal core earnings is found to be non-significant, indicating no substantial effect in this context. These findings contribute to the literature on cultural influences in financial reporting, providing valuable insights for policymakers and multinational firms concerning the cultural contexts within which financial decisions and reporting occur.
Huimin Jiang, Xianhui Wu, Farzad Sabetzadeh
et al.
Abstract In online sales platforms, product design attributes influence consumer preferences, and consumer preferences also have a significant impact on future product design optimization and iteration. Online review data are the most intuitive feedback from consumers on products. Using the value of online review information to explore consumer preferences is the key to optimize the products, improve consumer satisfaction and meet consumer requirements. Therefore, the study of consumer preferences based on online reviews is of great importance. However, in previous research on consumer preferences based on online reviews, few studies have modeled consumer preferences. The models often suffer from the nonlinear structure and the fuzzy coefficients, making it challenging to build explicit models. Therefore, this study adopts a fuzzy regression approach with a nonlinear structure to model consumer preferences based on online reviews to provide reference and insight for subsequent studies. First, smartwatches were selected as the research object, and the sentiment scores of product reviews under different topics were obtained by text mining on the product online data. Second, a polynomial structure between product attributes and consumer preferences was generated to investigate the association between them further. Afterward, based on the existing polynomial structure, the fuzzy coefficients of each item in the structure were determined by the fuzzy regression approach. Finally, the mean relative error and mean systematic confidence of the fuzzy regression with nonlinear structure method were numerically calculated and compared with fuzzy least squares regression, fuzzy regression, adaptive neuro fuzzy inference system (ANFIS) and K-means-based ANFIS, and it was found that the proposed method was relatively more effective in modeling consumer preferences.
Electronic computers. Computer science, Information technology
Michael Dorner, Maximilian Capraro, Oliver Treidler
et al.
The engineering of complex software systems is often the result of a highly collaborative effort. However, collaboration within a multinational enterprise has an overlooked legal implication when developers collaborate across national borders: It is taxable. In this article, we discuss the unsolved problem of taxing collaborative software engineering across borders. We (1) introduce the reader to the basic principle of international taxation, (2) identify three main challenges for taxing collaborative software engineering making it a software engineering problem, and (3) estimate the industrial significance of cross-border collaboration in modern software engineering by measuring cross-border code reviews at a multinational software company.
Noise: an enemy to be dealt with and a major factor limiting communication system performance. However, what if there is gold in that garbage? In conventional engineering, our focus is primarily on eliminating, suppressing, combating, or even ignoring noise and its detrimental impacts. Conversely, could we exploit it similarly to biology, which utilizes noise-alike carrier signals to convey information? In this context, the utilization of noise, or noise-alike signals in general, has been put forward as a means to realize unconditionally secure communication systems in the future. In this tutorial article, we begin by tracing the origins of thermal noise-based communication and highlighting one of its significant applications for ensuring unconditionally secure networks: the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange scheme. We then delve into the inherent challenges tied to secure communication and discuss the imperative need for physics-based key distribution schemes in pursuit of unconditional security. Concurrently, we provide a concise overview of quantum key distribution (QKD) schemes and draw comparisons with their KLJN-based counterparts. Finally, extending beyond wired communication loops, we explore the transmission of noise signals over-the-air and evaluate their potential for stealth and secure wireless communication systems.
Semi-structured explanation depicts the implicit process of a reasoner with an explicit representation. This explanation highlights how available information in a specific query is utilised and supplemented with information a reasoner produces from its internal weights towards generating an answer. Despite the recent improvements in generative capabilities of language models, producing structured explanations to verify a model's true reasoning capabilities remains a challenge. This issue is particularly pronounced for not-so-large LMs (e.g., FLAN-T5-XXL). In this work, we first underscore the limitations of supervised fine-tuning (SFT) in tackling this challenge, and then introduce a carefully crafted reward engineering method in reinforcement learning (RL) to better address this problem. We investigate multiple reward aggregation methods and provide a detailed discussion which sheds light on the promising potential of RL for future research. Our proposed method on two semi-structured explanation generation benchmarks (ExplaGraph and COPA-SSE) achieves new state-of-the-art results.
Matthew Tyler, Hengyun Zhou, Leigh S. Martin
et al.
We introduce a framework for designing Hamiltonian engineering pulse sequences that systematically accounts for the effects of higher-order contributions to the Floquet-Magnus expansion. Our techniques result in simple, intuitive decoupling rules, despite the higher-order contributions naively involving complicated, non-local-in-time commutators. We illustrate how these rules can be used to efficiently design improved Hamiltonian engineering pulse sequences for a wide variety of tasks, such as dynamical decoupling, quantum sensing, and quantum simulation.
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.