Recognizing tool-based hand activities from a first-person view is a critical yet challenging task in computer vision, due to the complexity of hand-object interactions and often subtle, ambiguous motion patterns. In real-world manufacturing scenarios, these challenges are exacerbated by bidirectional action pairs whose visual cues are almost identical, with differences revealed only through subtle motion dynamics. However, existing datasets rarely capture these direction-sensitive interactions at scale, particularly in realistic tool-use contexts, limiting the ability of current models to learn fine-grained motion dynamics essential for accurate recognition. We introduce Ego-Bi (Egocentric-Bidirectional dataset), a large-scale, real-world egocentric RGB video dataset comprising 1,223 video sequences and 622,737 frames that cover diverse tool-use activities in unconstrained environments. Ego-Bi provides an extended 38-category hand type taxonomy, detailed object–tool labels, and challenging bidirectional action pairs, offering rich semantic and temporal cues for modeling complex hand–object interactions. In addition, to address the ambiguity in motion dynamics, we propose a BMP (Bidirectional Motion Prior module) that derives rotation and directional cues from predicted 3D hand poses to improve class separability of visually similar actions. Experimental results on Ego-Bi demonstrate that our approach improves bidirectional action recognition accuracy by + 8.96% over the baseline, while also yielding consistent gains across general action classes without requiring costly 3D pose annotations. Furthermore, the proposed motion priors generalize effectively to other egocentric benchmarks, underscoring their robustness in handling visually similar, direction-sensitive actions.
Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo
et al.
Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.
Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre
et al.
The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.
Abstract A cost-effective and large-scale method for synthesizing ZnCo2O4 nanoflowers with surface oxygen vacancies as electrode materials for supercapacitors is presented. The existence of oxygen vacancies on the surface of the ZnCo2O4 nanoflowers has been confirmed through X-ray photoelectron spectroscopy (XPS). The energy bands and density of states (DOS) of ZnCo2O4 are examined using density functional theory, revealing that treatment with NaBH4 reduces the band gap of ZnCo2O4 while increasing the DOS near the Fermi level compared to pristine ZnCo2O4. Furthermore, the specific capacitance of reduced ZnCo2O4 is nearly double that of its unmodified counterpart. This straightforward and practical approach significantly enhances both conductivity and specific capacitance in metal oxides, making it applicable to other similar materials.
Materials of engineering and construction. Mechanics of materials
A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.
Timo Kehrer, Robert Haines, Guido Juckeland
et al.
Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.
M. F. Nabiullina, G. R. Mingaleeva, O. V. Afanaseva
et al.
RELEVANCE. Agricultural enterprises generate vegetable waste, which is difficult to utilize. Such waste can be used for combustion in boilers, providing thermal and electric energy to an agricultural energy complex. A hybrid mini-thermal power plant combining renewable energy sources and plant biofuels will be able to provide more economical, environmentally friendly and reliable supplies of heat and electricity under any demand conditions compared to using one of these systems.OBJECTIVE. Determination of fuel consumption during combustion of various types of vegetative agricultural waste in a hybrid mini-thermal power plant with parallel connection of solar energy concentrators under conditions of solar insolation of the Republic of Tatarstan.METHODS. The article considers the chemical composition and characteristics of various types of plant waste from agriculture. The average total energy consumption load of an agricultural enterprise has been determined. RESULTS. To determine the consumption of biofuels for the operation of the KE10-14CO boiler, the calculation of the theoretical volumes of combustion products and the thermal calculation of the boiler were carried out. The need for auxiliary fuel at mini-thermal power plants with parallel connection of solar energy concentration plants has been determined. The calculation of the heat collected by the solar collector has been performed.CONCLUSION. The use of solar energy concentrators when connected in parallel with a biofuel boiler makes it possible to evenly supply energy to the enterprise and form biofuel reserves. Calculations have shown that the use of hybrid biomass combustion plants and solar collectors helps to reduce fuel consumption.
Production of electric energy or power. Powerplants. Central stations
Autonomous driving technology plays a key role in addressing traffic safety issues and relieving traffic congestion by virtue of its capabilities of enabling accurate environmental perception and real-time response. Aiming at the problem of limited computing power of mobile driving platform, an improved algorithm based on YOLOv8n: LBT-YOLO was proposed. The algorithm is improved in three aspects: firstly, replacing part of the traditional convolutional layers by linear deformable convolution, and designing a new C2L module by optimizing the C2F module, so as to reduce the number of model parameters and maintain the detection accuracy at the same time. Secondly, a new neck network structure BCFPN (Bidirectional Collocated Feature Pyramid Network) is designed based on the weighted bidirectional feature pyramid network, which enhances the feature fusion and the interaction of contextual information, and improves the detection accuracy of the model. Finally, a new detection head TADDH (Task Aligned Dynamic Detection Head) is proposed. This detection head reduces the number of parameters by sharing the neck network features, and performs task decomposition alignment to achieve high accuracy target detection using dynamic convolution and dynamic feature selection. After a series of improvements, LBT-YOLO outperforms YOLOv8n and other detection algorithms on the Autonomous Driving BDD100K dataset, with an average accuracy improvement of 2.4% while reducing the number of model parameters by 48.2%.
To investigate the impact of sulfate corrosion on the performance of shaft lining concrete under sustained compressive loads, this study immersed samples in a 10 % sodium sulfate solution after subjecting to sustained compressive loads of 0.2fcand 0.4fc. The variation in compressive strength, ultrasonic velocity, and dynamic elasticity modulus with corrosion age were examined. The corrosion mechanisms were analyzed using field emission scanning electron microscope and energy dispersive spectrometer. The impact tendency of concrete was also evaluated. The results indicated that under sustained compressive loads of 0.2fcand 0.4fc, the compressive strength, ultrasonic velocity, and dynamic elasticity modulus are all increased with age, in the order of 0.2fc> 0.4fc>no-load. When exposed to a 10 % sodium sulfate solution under sustained compressive loads, these properties initially increased and then decreased, peaking at 90 days of corrosion age, and the order was 0.2fc> no-load > 0.4fc when the corrosion age was longer than 240 days.The shaft lining concrete exhibited weak impact tendency, with the brittleness index increasing as the corrosion age progressed. The outcome of this work might provide a reference for the further research on the design of shaft lining concrete materials.
Materials of engineering and construction. Mechanics of materials
In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.
Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach
et al.
Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.
Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.
Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products.
Kelechi G. Kalu, Taylor R. Schorlemmer, Sophie Chen
et al.
The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies.
Hossein Safi, Ali Mohammad Montazeri, Javane Rostampoor
et al.
Abstract In this paper, the problem of opportunistic spectrum sharing for the next generation of wireless systems empowered by the cloud radio access network (C‐RAN) is studied. More precisely, low‐priority users employ cooperative spectrum sensing to detect a vacant portion of the spectrum that is not currently used by high‐priority users. The authors' aim is to maximize the overall throughput of the low‐priority users while guaranteeing the quality of service of the high‐priority users. This objective is attained by optimally adjusting spectrum sensing time, with respect to target probabilities of detection and false alarm, as well as dynamically allocating C‐RAN resources, that is, powers, sub‐carriers, remote radio heads, and base‐band units. To solve this problem, which is non‐convex and NP‐hard, a low‐complex iterative solution is proposed. Numerical results demonstrate the necessity of sensing time adjustment as well as effectiveness of the proposed solution.
Christina Junger, Richard Fütterer, Maik Rosenberger
et al.
In recent years, stereoscopic image processing algorithms have gained importance for a variety of applications. To capture larger measurement volumes, multiple stereo systems are combined into a multi-view stereo (MVS) system. To reduce the amount of data and the data rate, calculation steps close to the sensors are outsourced to Field Programmable Gate Arrays (FPGAs) as upstream computing units. The calculation steps include lens distortion correction, rectification and stereo matching. In this paper a FPGA-based MVS system with flexible camera arrangement and partly overlapping field of view is presented. The system consists of four FPGA-based passive stereoscopic systems (Xilinx Zynq-7000 7020 SoC, EV76C570 CMOS sensor) and a downstream processing unit (Zynq Ultrascale ZU9EG SoC). This synchronizes the sensor near processing modules and receives the disparity maps with corresponding left camera image via HDMI. The subsequent computing unit calculates a coherent 3D point cloud. Our developed FPGA-based 3D measurement system captures a large measurement volume at 24 fps by combining a multiple view with eight cameras (using Semi-Global Matching for an image size of 640 px × 460 px, up to 256 px disparity range and with aggregated costs over 4 directions). The capabilities and limitation of the system are shown by an application example with optical non-cooperative surface.
Electric apparatus and materials. Electric circuits. Electric networks
The Virtual Traffic Light (VTL) in a Vehicle-to-Vehicle (V2V) environment can negotiate the right-of-way allocation through the information directly exchanged between vehicles.When the equipment obtains relevant information, the vehicle can strategically provide information to obtain the priority right of way.To apply to a scene where unmeasurable factors affect the right of way, a virtual traffic light with group strategy protection characteristics is proposed.By abstracting the real information provided by vehicles into a cost allocation and cooperative game, a group strategy protection auction mechanism is designed, and the Shapley value is used to calculate the cost allocation of each vehicle as the payment of vehicles.On this basis, the green light signal is established according to the real evaluation value in the auction results, and the green light signal generated by multiple auctions is integrated through the signal merging algorithm to produce a reasonable right-of-way allocation.The experimental results show that the virtual traffic light has the characteristics of group strategy protection, which can prevent vehicles from forming an alliance of false information to obtain benefits and can also prevent vehicles from obtaining the right-of-way priority through false information.Compared with the virtual traffic light with a fixed threshold of the number of green lights, the virtual traffic lights protected by the group strategy show some improvement in the overall average driving time and the average driving time of high-value vehicles.
Software plays a central role in scientific discovery. Improving how we develop and use software for research can have both broad and deep impacts on a spectrum of challenges and opportunities society faces today. The emergence of Research Software Engineer (RSE) as a role correlates with the growing complexity of scientific challenges and diversity of software team skills. In this paper, we describe research software science (RSS), an idea related to RSE, and particularly suited to research software teams. RSS promotes the use of scientific methodologies to explore and establish broadly applicable knowledge. Using RSS, we can pursue sustainable, repeatable, and reproducible software improvements that positively impact research software toward improved scientific discovery.