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Building Information Modelling (BIM) is a working methodology for the design and management of construction projects that facilitates collaboration and communication between the different parties involved. It is based on the digital three-dimensional representation of infrastructures within the field of architecture and civil engineering. The generated models are true data containers, capable of storing the geometric and semantic information necessary to support the design, construction and maintenance operations of the assets. This methodology promotes data exchange between different software tools through interoperability and the use of standards. Usually in the development of projects throughout their life cycle, commercial, closed-source BIM software is used. This generally leads to limitations in interoperability and data reuse. This paper details advances in the use of Blender, a free and open-source software (FOSS), to support BIM workflows in construction projects. The paper addresses the automation of the process to export a 3D Blender model to a BIM model in IFC (Industry Foundation Classes). The IFC Class Assignment add-on is developed, which allows the automated assignment of IFC classes to the different elements of the 3D model in Blender to convert it into a BIM model, using scripts written in Python. The proposed extension is able to generate, in an agile and simple way, digital models in IFC4, adequately storing the geometric and semantic information of its components. The results provide a tool that contributes to automate the creation of BIM models in an open-source software such as Blender, simplifying modelling processes and supporting collaboration and standardisation in architectural and engineering projects. [ABSTRACT FROM AUTHOR]

This paper introduces a kind of intelligent lubricating oil quality detector integrating computer technology. On the basis of the traditional lubricating oil quality detection, through the introduction of advanced computer technology, the equipment realizes the automation of the detection process, the intelligence of data processing and the convenience of remote monitoring and management. This paper not only expounds the system structure and working principle of the detector, but also discusses the application of computer technology in the detection process. [ABSTRACT FROM AUTHOR]

As artificial intelligence becomes more prevalent, regulators are increasingly turning to legal measures, like "a right to explanation," to protect against potential risks raised by AI systems. However, are eXplainable AI (XAI) tools-the artificial intelligence tools that provide such explanations-up for the task? This paper critically examines XAI's potential to facilitate the right to explanation by applying the prism of explanation's role in law to different stakeholders. Inspecting the underlyingfunctions of reason-giving reveals different objectives for each of the stakeholders involved. From the perspective of a decision-subject, reason-giving facilitates due process and acknowledges human agency. From a decision-maker's perspective, reason-giving contributes to improving the quality of the decisions themselves. From an ecosystem perspective, reason-giving may strengthen the authority of the decision-making system toward different stakeholders by promoting accountability and legitimacy and by providing better guidance. Applying this analytical framework to XAI's generated explanations reveals that XAI fails to fulfilll the underlying objectives of the right to explanation from the perspective of both the decisionsubject and the decision-maker. In contrast, XAI is found to be extremely well-suited to fulfill the underlying functions of reason-giving from an ecosystem's perspective, namely, strengthening the authority of the decision-making system. However, lacking all other virtues, this isolated ability may be misused or abused, eventually harming XAI's intended human audience. The disparity between human decision-making and automated decisions makes XAI an inst®cient and even risky tool, rather than serving as a guardian Of human rights. After conducting a rigorous analysis of these ramifications, this paper concludes by urging regulators and the XAI community to reconsider the pursuit of explainability and the right to explanation of AI systems. [ABSTRACT FROM AUTHOR]

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Automatic electroplating production lines have been widely used in electronics industries to reduce the labour intensity and improve the production efficiency. In the multi-variety and low-volume electroplating production, it is known that the task loading sequence and hoist scheduling are coupled with each other, and they codetermine the production efficiency, while all the existing scheduling methods consider them separately, and thus the optimal production schemes become unavailable. Therefore, this paper develops a Task sequence-Hoist scheduling Coupled Optimization (THCO) model which simultaneously considers the requirements and practical constrains of task sequence and hoist scheduling, having an optimization objective of minimizing the maximum completion time. For this model, a double-layer code is developed and an Improved Salp Swarm Algorithm (ISSA) is developed by introducing three improvement strategies: the random spare strategy which is used to increase the population diversity, the nonlinear adaptive weight strategy which is used to balance the exploration and exploitation capacities, and a golden sine algorithm which is used to improve the convergence rate. Experiments based on 23 benchmark functions are then conducted. The obtained results show that ISSA has better convergence and solving quality than existing algorithms. Furthermore, several production cases prove that THCO can generate production schemes that better meet the requirements of production lines. [ABSTRACT FROM AUTHOR]

Surface finishing applications are still cumbersome manual tasks, therefore, robotization of the process is of great interest as it allows for automation and increased versatility. However, finishing processes are difficult to automate, mainly because of the variation in material removal. In particular, the variables involved undergo changes that modify the material removal rate. This paper proposes a methodology for modeling material removal automatically based on experimental data. The procedure consists of monitoring the material removed from the parts under study that are in an automated precision measuring system during the finishing process. Based on the experimental models, a control algorithm for continuous material removal is presented. It guarantees a homogeneous surface finish by varying the robot feed rate. Finally, the results of several experimental material removal models under different process conditions and the validation of the proposed control algorithm are presented. The results show that the proposed method achieves a substantial improvement in the homogeneity of the finish. [ABSTRACT FROM AUTHOR]

Shared micromobility - or short-term access to shared bikes and scooters - provides a flexible alternative for households living in urban areas, individuals seeking first- and last-mile connections to public transportation, and those without access to a private vehicle trying to reach jobs and essential services. In this paper, the authors discuss the history, growth, and evolution of bike and scooter sharing in North America; summarize the demographics and impacts of shared micromobility; and explore shared micromobility policies and practices for managing devices and operations such as: device caps, service area limitations, designated parking areas, fees, equipment/operational requirements, and enforcement. In the future, enhancements in device automation, battery range, charging times, and weight are likely to contribute to the evolution and development of additional devices and service models, which could allow improved range and e-hail for shared micromobility devices. [ABSTRACT FROM AUTHOR]

In the realm of five-axis Computer Numerical Control (CNC) machining, the challenge of minimizing velocity, acceleration, and jerk fluctuations has prompted the development of various local corner smoothing methods. However, existing techniques often rely on symmetrical spline curves or impose pre-set transition length constraints, limiting their effectiveness in reducing curvature and maintaining velocity at critical corners. To comprehensively address these limitations comprehensively, a novel approach for local smoothing of five-axis linear toolpaths is presented in this paper. The proposed method introduces two asymmetrical B-splines at corners, effectively smoothing both the tool-tip position in the workpiece coordinate system and the tool orientation in the machine coordinate system. To fine-tune transition curve scales and minimize velocity disparities between adjacent corners, an overlap elimination scheme is employed. Furthermore, the two-step strategy emphasizes the synchronization of tool-tip position and tool orientation while considering maximal approximation error. The outcome is a blended five-axis toolpath that significantly reduces curvature extremes in the smoothed path, achieving reductions ranging from 36.28 % to 45.51 %. Additionally, the proposed method streamlines the interpolation process, resulting in time savings ranging from 5.68 % to 8.78 %, all the while adhering to the same geometric and kinematic constraints. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety. • Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance. • AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated. [ABSTRACT FROM AUTHOR]

Pneumatic Systems are widely used in industries for a variety of manufacturing processes and conveyor-based automation for lighter payloads. Actuator's position-holding ability, along with ease of maintenance and cleaner medium, makes them the right choice for low-cost automation. The accurate, repeatable, and instantaneous position control of the actuator is required in such applications. Different configurations of pneumatic systems can be built using different orientations of cylinders. Due to the inertia of the actuator and the compressibility of air, it is difficult to predict the actual position of the piston rod at an intermediate position of the Directional control valve. Hence, the accurate estimation of the piston rod is significant. In this paper, an optimized mathematical model concerning the position of the actuator is developed using the transfer function approach. The paper also presents a model-based technique for prediction of positioning accuracy and Settling time of double acting single rod pneumatic cylinder for horizontal and vertical orientations using SIMULINK®. The mathematical model will be useful to predict the instantaneous position and settling time of the cylinder at a given orientation. The paper also comments on the trade-off between the accuracy and stability of pneumatic actuators at these orientations. The quantitative model can be used by the industry and researchers for achieving accurate position control and minimal settling time in industrial applications such as palletizing, stacking, and pneumatic press for better productivity and throughput. [ABSTRACT FROM AUTHOR]

This paper explores the parallel collaboration of multimodal physiological signals, combining eye tracker output signals, motor imagery, and error-related potentials to control a computer mouse. Specifically, a parallel working mechanism is implemented in the decision layer, where the eye tracker manages cursor movements, and motor imagery manages click functions. Meanwhile, the eye tracker output signals are integrated with electroencephalography data to detect the idle state for asynchronous control. Additionally, error-related potentials evoked by visual feedback, are detected to reduce the cost of error corrections. To efficiently collect data and provide continuous evaluations, we performed offline training and online testing in the designed paradigm. To further validate the practicability, we conducted online experiments on the real-world computer, focusing on a scenario of opening and closing files. The experiments involved seventeen subjects. The results showed that the stability of the eye tracker was optimized from 67.6% to 95.2% by the designed filter, providing the support for parallel control. The accuracy of motor imagery conducted simultaneously with fixations reached 93.41 ± 2.91%, proving the feasibility of parallel control. Furthermore, the real-world experiments took 45.86 ± 14.94 s to complete three movements and clicks, and showed a significant improvement compared to the baseline experiment without automatic error correction, validating the practicability of the system and the efficacy of error-related potentials detection. Moreover, this system freed users from the stimulus paradigm, enabling a more natural interaction. To sum up, the parallel collaboration of multimodal physiological signals is novel and feasible, the designed mouse is practical and promising. [ABSTRACT FROM AUTHOR]

Short-term automation solutions for industrial companies have potential to compensate for volatile demand, employee turnover or shortages. Current solutions are either not available at short notice or economically unviable, as integration in existing control systems and machine tools would be necessary. The paper proposes a cognitive robotic system that circumvents this problem by emulating the behaviour of operators through automated, cognitive skills. The approach uses a perception system, handling and skill modules and a skill-based control to automate in an industrial brownfield use case without additional interfaces to the existing control systems to reduce integration effort to a minimum. [ABSTRACT FROM AUTHOR]

The year 2008 is etched in human history as the year of the 'Global Financial Crises'. Post the crises, Historians and financial commentators alike rushed to impute blame. Some blamed securitizations, some the banks and some Lehman Brothers and AIG. However, in the midst of all of this humbug, a key epicentre of the crises escaped academic scrutiny; 'Automation Technology'. The paper therefore aims to present an alternative view of financial history; one which impleads 'automation technology in finance' i.e., Risk Modelling Algorithms and RegTech. However, the underlying aim of this paper is to make a case against systemic automation bias in finance and to achieve that end, the paper employs an inter-disciplinary approach and uses history, law and data science to show case the multifarious perils of using automation technology blindfold in finance whilst also proposing possible solutions such as the incorporating of design thinking and systems theory in finance. Expired data sets, human assumptions, turning code in law, and a lack of standardized financial semantics as but some of these 'perils'. On the law front; it presents a twofold challenge under constitutional and anti-trust law and aims to reconcile law and technology. Lastly the paper aims to guide regulators by categorizing multiple stages of technological complexity and recommends application of different regulatory approaches to regulating automation. Therefore, the paper shall maintain a 'solution' oriented approach throughout. [ABSTRACT FROM AUTHOR]

The design of an automation controller for a “Electro-Hydro-Mechanical Object Laying System” presented in this paper is multidisciplinary equipment. It consists of Electromechanical, Hydraulic actuators several sensors for process feedback. The conventional object-laying process is a time and labour-intensive manual process. Another way of laying the objects is through mechanical layers, in which most operations are mechanical, require constant human intervention, lack operator safety and don’t provide location marking data of laid objects. Commercial-Off-The-Shelf (COTS) method is adopted to develop the apparatus. There are complex mechanisms and processes involved in this apparatus, which are required to be operated/executed in a sequential and parallel manner in real-time. Based on the computational requirement for the Controller, the cPCI bus-based rugged architecture has been adopted with software built on RTOS. Operation synchronization of spatially distributed Electromechanical & Hydraulic actuators is required. Safe handling of the objects is one of the critical requirements. All the activities are automated with minimum human intervention to avoid risk to the crew. The resultant outcome of this process is the time reduction for object readiness by 54 % without compromising safety. We achieved average object marking accuracy up to 30cm in dynamic conditions on the vehicle platform using differential GNSS with multi-constellation and augmentation system support. This paper mainly focuses on electronic controller hardware design for military environments and process optimization to achieve a faster object laying rate. [ABSTRACT FROM AUTHOR]

Text-to-SQL is the task of translating a natural language query into a structured query language. Existing text-to-SQL approaches focus on improving the model' s architecture while ignoring the relationship between queries and table schemas and the differences in difficulty between examples in the dataset. To tackle these challenges, a twostage curriculum learning framework for text-to-SQL (TSCL-SQL) is proposed in this paper. To exploit the relationship between the queries and the table schemas, a schema identification pre-training task is proposed to make the model choose the correct table schema from a set of candidates for a specific query. To leverage the differences in difficulty between examples, curriculum learning is applied to the text-to-SQL task, accompanied by an automatic curriculum learning solution, including a difficulty scorer and a training scheduler. Experiments show that the framework proposed in this paper is effective. [ABSTRACT FROM AUTHOR]

With the rocketing progress of the Internet, it is easier for people to get information about the objects that they are interested in. However, this information usually has conflicts. In order to resolve conflicts and get the true information, truth discovery has been proposed and received widespread attention. Many algorithms have been proposed to adapt to different scenarios. This paper aims to investigate these algorithms and summarize them from the perspective of algorithm models and specific concepts. Some classic datasets and evaluation metrics are given in this paper. Some future directions for readers are also provided to better understand the field of truth discovery. [ABSTRACT FROM AUTHOR]

In this paper, an optimized Genetic Algorithm (GA) based internal model controller-proportional integral derivative (IMC-PID) controller has been designed for the control variable to output variable transfer function of dc-dc boost converter to mitigate the effect of non-minimum phase (NMP) behavior due to the presence of a right-half plane zero (RHPZ). This RHPZ limits the dynamic performance of the converter and leads to internal instability. The IMC PID is a streamlined counterpart of the standard feedback controller and easily achieves optimal set point and load change performance with a single filter tuning parameter ?. Also, this paper addresses the influences of the model-based controller with model plant mismatch on the closed-loop control. The conventional IMC PID design is realized as an optimization problem with a resilient controller being determined through a genetic algorithm. Computed results suggested that GA-IMC PID coheres to the optimum designs with a fast convergence rate and outperforms conventional IMC PID controllers. [ABSTRACT FROM AUTHOR]

This paper presents research performed on set-based multi-criteria optimization for the preliminary design of slab frame bridges. As a result of immense CO2 emissions emerging from concrete production, there is a need for optimization methods decreasing the volume of concrete without affecting the function of the structure. Previous research suggests a general correlation between CO2 footprint- and cost reduction, due to minimized material use. However, the aspect of buildability may conflict with lessened material, as optimized designs might simultaneously be less buildable. This research aimed to develop an optimization method with respect to the investment cost and environmental impact while also considering buildability cost aspects. The optimization algorithm shows the possibilities of reducing the environmental impact by up to 13.7% for a slightly increased cost of 2.3%. Thus, by implementing optimization procedures in the early stages of the planning process the holistic cost effects related to green solutions can be presented, favoring the choice of sustainable designs amongst clients during tendering procedures. [ABSTRACT FROM AUTHOR]

The monitoring of complex industrial systems through the implementation of smart approaches provides unique opportunities, such as the characterisation of their real-time performance. Within this scope, there exists the need to support decision-making during maintenance processes, due to the presence of a multitude of faults in real-world systems and the difficulty of identifying the appropriate mitigating solutions. The proposed solution uses intelligent optimisation techniques to identify the ideal control solution within these complex systems when faults arise. This paper presents a framework based on an intelligent optimisation approach, which provides a workflow process for the support of decision-making during faulty situations. It is adapted and implemented to the demand-side of a Compressed Air System (CAS), thus providing a holistic approach in automating fault mitigation during real-time system operation. In implementing this framework, multiple intelligent optimisation techniques such as the Genetic Algorithm and the Particle Swarm Optimisation algorithms were adopted and implemented. Both algorithms were successful in providing the ideal control solution under fault conditions. For a typical production case study, the proposed optimisation approach results in a reduction of 40% in its air consumption, which directly improves its environmental performance and energy costs. This result demonstrates that this approach contributes a suitable control strategy for CAS when experiencing a pneumatic fault, which has a direct positive effect on the operational energy performance and costs. [ABSTRACT FROM AUTHOR]

The deployment of information and communication technologies in organizations is on the rise. Many organizations consider the application of technologies to be a crucial key to improve their processes. However, traditional costing systems are not suitable for cost estimation of business processes due to the use of volume-based cost drivers, which are often not adequate for the structure of today's organizations. In this research, we present an overview of how the TDABC (time-driven activity-based costing) model can be combined with process mining and business process simulation for cost estimation of such processes. The objective of this paper is to use the cost dimension as a major attribute for the potential implementation of robotic process automation (RPA) in companies. However, information and communication technologies could be considered in general. We demonstrate our approach in a case study that takes advantage of a real-life event log containing transactional data representing the loan application process in an insurance company. The event log is analyzed and processed using process mining techniques. Based on the preprocessing, a simulation model representing the original loan application process is designed. The designed simulation model is then used for simulation of partial and full implementation of RPA through separate scenarios. Then, we add the cost dimension to the simulation by enriching the event log with cost data based on a formalized cost model. We show that even though partial implementation of RPA might not deliver significant increase in efficiency in the process, it might still represent significant cost savings. [ABSTRACT FROM AUTHOR]

The automation and control of industrial machines and processes is a task traditionally performed by PLCs and SCADA systems. However, with the advent of Industry 4.0 paradigm, which merges OT and IT domains, this task can also be performed inside the IT domain. This paper proposes an edge architecture where a flexible manufacturing line is led by a state machine programmed and running on the IT domain inside the Manufacturing Execution System. Each industrial asset has been modelled as a black-box, where security and hard real-time functionalities are programmed and controlled inside each black-box by traditional OT tools. Functionalities and variables required to synchronize each black-box, have been modelled as inputs and outputs. These input and output variables and methods are available using Industry 4.0 industrial communication protocols, such as OPC UA or MQTT. Then, a state machine programmed on a high-level IT language model inside the MES, automates the manufacturing line reacting to the inputs and outputs of each black-box. This architecture has been successfully validated automating a manufacturing line composed by a conveyor, a robot and a vision based quality control system. [ABSTRACT FROM AUTHOR]

On November 30,2022, OpenAI released an AI based chatbot called ChatGPT (Generative Pre-trained Transformer). This Al system uses natural language processing (NLP) techniques to generate written text by learning the patterns and structures of natural language. ChatGPT is a versatile chatbot with the ability to write speeches, news articles, and essays either wholly or partially. Some users acknowledge ChatGPT as a co-author of the generated text, while others only acknowledge its use in writing the document. In fact, this issue became so pressing in acade# nia that Springer Nature, the world's largest academic publisher, announced that software like ChatGPT cannot be credited as an author in papers published in its thousands of journals. However, the question of authorship when working with text generator Chatbots remains an open issue. Following the release of ChatGPT other Chatbots, such as Google's Bard and AutoGPT have emerged. It is now clear that these Chatbots are here to stay, and so the issue of co-authorship and IP rights in the final products of these Chatbots became an important legal question. Can ChatGTP and the like be your co-author or whether it is just a tool that assists in writing? This Article further discusses the possibility of granting algorithms with a separated legal personhood. [ABSTRACT FROM AUTHOR]

With the global growth of Al applications and automated decision-making systems across public and private sectors, countries globally are grappling with how to govern AI responsibly and encourage Al innovation to promote the Sustainable Development Goals ("SDGs"), while still protecting key rights and interests. While national, regional, and global governance models are starting to emerge, it is too early to assess trends that could work as "good practice models." The sheer volume, scope, and pace of AI regulatory efforts across the globe, and especially in the Global North, may pose a challenge for Low- and Middle-income countries CLMICs") seeking to address the risks of AI in their local contexts. While trying to develop appropriate governance frameworks for rapidly developing Al technologies, many LMICs still jace gaps in access to digital infrastructure, broadband connectivity, electricity, availability of quality data sets, digital literacy, and overall capacity. Such digital divides risk being compounded by a global Al divide, as a handful of private sector actors in the developed world continue to entrench their control over key data and Al infrastructures. In seeking to avoid mimicry and enable sustainable development and technological innovation, what should LMICs consider in developing their Al governance frameworks? Through our analytical and operational experience, we have learned that Low- and Middle-income countries design and enforcement of effective governance approaches and institutions in LMICs requires considering the spect*c context, values, social, economic, political landscape as well as capacity and resource constraints. LMICs may have the opportunity to engage in more "greenfield" approaches to governance, given the relative lack of legacy policy, regulatory, and institutional frameworks. This Paper will seek to provide an overview of some of these tradeoffs, as well as a structured approach for LMICs to consider putting in place the building blocks of an Al governance regime that can support sustainable development byfostering "trust". [ABSTRACT FROM AUTHOR]

Due to the cultural and traditional diversity of the Indian environment, the successful implementation of self-automated and smart architectures is very challenging. As the IoT expands the network of devices, it also increases the hazards with increasing demands. This paper aims to propose an efficient model with the combination of interdisciplinary aspects of Indian building automation and numerous factors of Indian cultural differences. The proposed fuzzy-colored Petri-nets-based ubiquitous framework utilizes the capabilities of ubiquity for the diversified culture of Indian Building Automation systems. The proposed algorithms establish the Wi-Fi component connectivity and the working of the LED pin. Further, the modules of the Wi-Fi component connectivity, set up function and get values function to illustrate the working principle of the proposed model. Lastly, the integrated system using the FCPN tools. The significance of developing the proposed model is in its usefulness, usage of maximum ideas of different modeling techniques, cultural support, and its conversion into a formal qualitative and quantitative model. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) technology can organically combine data modeling and virtual reality, and compare them with actual construction objects to realize the smart collaboration of entity and the model in the construction processes, greatly reducing early stage mistakes and improve construction efficiency. This paper proposed an automation system framework of smart construction based on BIM technology and collaboration platform, and discussed the element configuration of smart construction based on BIM technology and collaboration platform, and finally elaborated the working mechanism of automated system based on BIM technology and collaboration platform. The study results show that the automation system based on the BIM technology and collaboration platform supports the rapid construction of large-scale three-dimensional scenes and the precise integration of multi-source data, and can bring together construction information and asset information from different sources to form an open, safe, and accessible digital environment. The application of smart construction technology in construction cost management has brought huge economic benefits, construction efficiency has been increased by 65%, construction period has been shortened by 30%, labor intensity has been reduced by 27%, and labor productivity has been increased by 39%, which has brought considerable indirect benefits to construction projects. [ABSTRACT FROM AUTHOR]

This paper presents new methods for analyzing and evaluating generalized plans that can solve broad classes of related planning problems. Although synthesis and learning of generalized plans has been a longstanding goal in AI, it remains challenging due to fundamental gaps in methods for analyzing the scope and utility of a given generalized plan. This paper addresses these gaps by developing a new conceptual framework along with proof techniques and algorithmic processes for assessing termination and goal-reachability related properties of generalized plans. We build upon classic results from graph theory to decompose generalized plans into smaller components that are then used to derive hierarchical termination arguments. These methods can be used to determine the utility of a given generalized plan, as well as to guide the synthesis and learning processes for generalized plans. We present theoretical as well as empirical results illustrating the scope of this new approach. Our analysis shows that this approach significantly extends the class of generalized plans that can be assessed automatically, thereby reducing barriers in the synthesis and learning of reliable generalized plans. [ABSTRACT FROM AUTHOR]

Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution -- a machine learning pipeline -- tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. At the same time, AutoML is being criticized for its high resource consumption as many approaches rely on the (costly) evaluation of many machine learning pipelines, as well as the expensive large-scale experiments across many datasets and approaches. In the spirit of recent work on Green AI, this paper proposes Green AutoML, a paradigm to make the whole AutoML process more environmentally friendly. Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool. Afterward, different strategies on how to design and benchmark an AutoML tool w.r.t. their "greenness", i.e., sustainability, are summarized. Finally, we elaborate on how to be transparent about the environmental footprint and what kind of research incentives could direct the community in a more sustainable AutoML research direction. As part of this, we propose a sustainability checklist to be attached to every AutoML paper featuring all core aspects of Green AutoML. [ABSTRACT FROM AUTHOR]

Evaluation practices in natural language generation (NLG) have many known flaws, but improved evaluation approaches are rarely widely adopted. This issue has become more urgent, since neural generation models have improved to the point where their outputs can often no longer be distinguished based on the surface-level features that older metrics rely on. This paper surveys the issues with human and automatic model evaluations and with commonly used datasets in NLG that have been pointed out over the past 20 years. We summarize, categorize, and discuss how researchers have been addressing these issues and what their findings mean for the current state of model evaluations. Building on those insights, we lay out a long-term vision for evaluation research and propose concrete steps for researchers to improve their evaluation processes. Finally, we analyze 66 generation papers from recent NLP conferences in how well they already follow these suggestions and identify which areas require more drastic changes to the status quo. [ABSTRACT FROM AUTHOR]

In this paper we describe the selection of a modern build automation tool for an industry research partner of ours, namely an insurance company. Build automation has become increasingly important over the years. Today, build automation became one of the central concepts in topics such as cloud native development based on microservices and DevOps. Since more and more products for build automation have entered the market and existing tools have changed their functional scope, there is nowadays a large number of tools on the market that differ greatly in their functional scope. Based on requirements from our partner company, a build server analysis was conducted. This paper presents our analysis requirements, a detailed look at one of the examined tools and a summarized comparison of two tools. [ABSTRACT FROM AUTHOR]

Automated Fibre Placement (AFP) is receiving increasing attention from academia and industry. This is due to its widespread application in major sectors such as the automotive, marine, aerospace, renewables, and rail industry. AFP defects pose a significant barrier to efficient part throughput due to their lengthy processing time in inspection and rework. While there is a plethora of research on the causes and effects of various defect types, the subsequent reworking of these defects has not been thoroughly investigated. Although some papers have examined manual rework, their scope is limited. Insights from manual hand layup and similar methods can be applied to this context. This article presents the first comprehensive review of the academic literature in the field of defect rework, alongside an analysis of rework process experiments conducted by the authors to provide insight into current industry best practices. Following this, the article focuses on analysing the rework process in AFP and proposes guidelines and best practices. It emphasises the need for further research in this area, along with the findings of other researchers, to demystify the rework activity and inspire new investigations. Such research can offer valuable insights into enhancing reworking techniques and exploring automation possibilities. Automation can assist in manual reworking or even eliminate the need for it entirely, thereby reducing manufacturing time. The suggested directions for future work include developing automated rework processes to achieve near-full automation of AFP. [ABSTRACT FROM AUTHOR]

The food supply chain (FSC) comprises food production, storage, delivery, and retailing of food to reach the final customers by the due date. In general, the modern FSC is extraordinarily complex and challenging to manage; therefore, an efficient and effective supply chain requires appropriate management. All actors in the FSC have to keep track of the most recent trends and challenges and respond to changes quickly to keep up production and stay competitive. This paper explains some selected trends and challenges in the modern FSC. Moreover, the influence of the ongoing COVID-19 pandemic and the impact of the Ukraine war on the FSC are investigated. Despite the new trends and massive improvements in the FSC in terms of efficiency, some current problems remain unsolved, whereas new issues will arise in the future. The COVID-19 outbreak and the Ukraine war are two examples that imposed new challenges and significant consequences on the FSC. [ABSTRACT FROM AUTHOR]

In the modern business world, where hybrid business processes are widespread, effective resource and operations management defines the success of companies. One of the most critical areas is logistics, which is a critical component for many organizations, regardless of size or industry. The management of logistics processes is becoming more complex every year, as the demands on the industry are becoming more complicated and today consists not only of efficiency issues but also of environmental friendliness, and globalization. In this situation, RPA technology is one of the main solutions, enabling companies to achieve new levels of efficiency, accuracy, and speed in managing warehouse processes. In this article, we will consider modern approaches to managing hybrid business processes using the example of warehouses and explore how RPA technology has become the main optimization tool. The paper observes specific examples of the use of RPA in the context of warehouse management, identifies the benefits and risks associated with automation, and defines the development prospects and future of this innovative technology in logistics. [ABSTRACT FROM AUTHOR]

Written answers to open-ended questions can have a higher long-term effect on learning than multiple-choice questions. However, it is critical that teachers immediately review the answers, and ask to redo those that are incoherent. This can be a difficult task and can be time-consuming for teachers. A possible solution is to automate the detection of incoherent answers. One option is to automate the review with Large Language Models (LLM). They have a powerful discursive ability that can be used to explain decisions. In this paper, we analyze the responses of fourth graders in mathematics using three LLMs: GPT-3, BLOOM, and YOU. We used them with zero, one, two, three and four shots. We compared their performance with the results of various classifiers trained with Machine Learning (ML). We found that LLMs perform worse than MLs in detecting incoherent answers. The difficulty seems to reside in recursive questions that contain both questions and answers, and in responses from students with typical fourth-grader misspellings. Upon closer examination, we have found that the ChatGPT model faces the same challenges. [ABSTRACT FROM AUTHOR]

The growing complexity of the logistics chain, the need to take on an ever longer and distributed logistic chain, which in some cases even reaches the final stages of the production of the same goods, highlights the problem of managing the post-production phases of products and goods, but also of the personnel who work on these activities, charged to the companies that deal with logistics. We present in this paper the Integrated Logistic Platform (ILP 4.0), a software architectural model, whose purpose is to lead warehouse logistics to new levels of efficiency, not reachable as the sum of the operational functions within a single logistic company, but reachable only through a unique and cross-functional system, which surpasses the usual fragmented vision and consequent approaches, in favor a strategic coordination of all activities. In this paper it is described the general architecture of the realized platform, with its research contributions and innovations, in particular related to some typical problems of warehouse environments, solved with the help of machine learning and computer vision approaches, but also with the integration of augmented reality (AR) and virtual reality (VR) devices. The aim of the integration of these technologies in the project is the definition of a "smart" warehouse model environment, capable of mitigate problems of warehouse logistics such as: automation of the inventory process (through an UAV), control of warehouse movements, and management of logical / physical security of the premises. Based on the experiences made on the project, and focusing the attention on the safety and security aspects in logistics, that the platform covers, we discuss further a possible federated learning approach in order to mitigate the lack of data in order to solve some security and safety problems in the logistic area. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) and machine learning (ML) is increasingly becoming prevalent in libraries and information centres, promising to improve service delivery and efficiency. However, the use of these technologies also poses significant ethical challenges and risks, including bias and discrimination, privacy and security, automation and job displacement, and the lack of human interaction in service delivery. This conceptual paper provides an overview of the key ethical frameworks and principles relevant to the use of AI and ML in libraries and information centres, and analyses how these frameworks can be applied to this specific context. The paper also discusses the potential benefits and risks of these technologies, and provides best practices and strategies for mitigating the ethical risks associated with their use. Finally, the paper highlights the implications of these findings for libraries and information centres, and recommends future research and practice in this area, including on-going evaluation and monitoring of the ethical implications of AI and ML. Overall, this paper underscores the importance of taking a proactive and ethical approach to the use of AI and ML in libraries and information centres to ensure that these technologies are used in a way that aligns with their mission and values, and serves the best interests of their users and society as a whole. [ABSTRACT FROM AUTHOR]

The vast volume of currently available unstructured text data, such as research papers, news, and technical report data, shows great potential for ecological research. However, manual processing of such data is labour-intensive, posing a significant challenge. In this study, we aimed to assess the application of three state-of-the-art prompt-based large language models (LLMs), GPT-3.5, GPT-4, and LLaMA-2-70B, to automate the identification, interpretation, extraction, and structuring of relevant ecological information from unstructured textual sources. We focused on species distribution data from two sources: news outlets and research papers. We assessed the LLMs for four key tasks: classification of documents with species distribution data, identification of regions where species are recorded, generation of geographical coordinates for these regions, and supply of results in a structured format. GPT-4 consistently outperformed the other models, demonstrating a high capacity to interpret textual data and extract relevant information, with the percentage of correct outputs often exceeding 90% (average accuracy across tasks: 87–100%). Its performance also depended on the data source type and task, with better results achieved with news reports, in the identification of regions with species reports and presentation of structured output. Its predecessor, GPT-3.5, exhibited slightly lower accuracy across all tasks and data sources (average accuracy across tasks: 81–97%), whereas LLaMA-2-70B showed the worst performance (37–73%). These results demonstrate the potential benefit of integrating prompt-based LLMs into ecological data assimilation workflows as essential tools to efficiently process large volumes of textual data. • Large Language Models (LLMs) are tested for extracting biodiversity data from text. • Tasks include identification of relevant sources, geocoding, and data structuring. • LLMs, especially GPT-4, show over 90% accuracy on multiple tasks. • LLMs promise effective data assimilation from unstructured text in ecology. [ABSTRACT FROM AUTHOR]

The automation system of the future will consist of an increasing amount of complex resources, such as collaborative robots and/or autonomously roaming robots for material handling. To control these devices in an environment shared with human operators require state of the art computer perception and motion planning algorithms to be used as part of the automation system. This new type of intelligent automation system, where intelligent machines and learning algorithms are replacing more traditional automation solutions, requires new methods and workflows to keep up with the increase in complexity. This paper presents an interactive and iterative framework for solving some of these new challenges. The framework supports model-based control system preparation performed simultaneously to preparation of 3D geometries, positioning of robots, and tool design. The workflow enables an interactive preparation process, where new resources and constraints can be added to a live (real or simulated) automation system and control system failures can be analyzed in familiar tools for virtual preparation. Additionally, the paper describes how the integrated preparation process was applied to reconfiguring an industrial use case that includes a collaborative robot working side by side with a human operator, smart tools, and a vision system for localizing both work objects and tools. [ABSTRACT FROM AUTHOR]

Automation systems are soaring due to the innovation of artificial intelligence (AI) technologies. In this paper, we mainly concentrate on the security and efficiency of data transmission in AI-based automation systems, especially, for data sharing in a group manner in distributed networks. To this end, an authenticated group key agreement protocol is proposed for secure data transmission in AI-based automation systems. In order to release the computational overhead of distributed nodes, a semi-trusted authority (STA) is introduced to enable precomputation operations. Moreover, to overcome the predominantly distributed denial of service (DDoS) attack, a dynamic batch verification mechanism is devised. The presented dynamic batch verification mechanism guarantees that the proposed protocol can be run properly among legitimate nodes regardless of the existence of nodes who suffered from the DDoS attack. Finally, the session key security of the proposed protocol is proved and the performance is evaluated. [ABSTRACT FROM AUTHOR]

The academic accreditation for an engineering program in higher education institution is helpful to enhance comprehensive qualities of graduates. Taking the Electrical Engineering and Automation (EEA) Program as an example, a framework is constructed based on the concept of Outcome Based Education (OBE) for a high quality measurement, which ensures the program to satisfy the requirement of engineering education accreditation. Particular steps are designed by taking into account the evaluation methods for educational objectives and students' outcomes. Then, a closed-loop continuous improvement process is applied to develop the program based on the assessment result of students' performances in collage and society. The achievement level of the program outcomes is evaluated by efficient methods periodically, and the assessment results are presented in the paper. The situation of graduates' competencies developed by the program is indicated, and improvements of the program are implemented based on shortcomings. In this research, work experiences for the engineering education accreditation are shown, which can be studied by institutions and universities offering corresponding programs. [ABSTRACT FROM AUTHOR]

Roadway noise is the collective sound energy emanating from motor vehicles. It consists chiefly on road surfaces, tire, engine/transmission, aerodynamic, and braking elements. Noise of rolling tires driving on pavement is found to be the biggest contributor of highway noise and increases with higher vehicle speeds. This study explores the use of a rather unconventional form of energy (Sound). An application is proposed for the same in which a distinctly designed circuitry is used to convert the sound produced by a loudspeaker. Based on the law of electromagnetic induction, the vibrations produced by the speaker can be converted into electrical energy. The use of sound energy is both clean and unconventional. It is an entire paradigm shift from the concept of noise cancellation to a new idea of noise utilization. This paper takes a step forward in this direction, using sound as a source of energy to provide a viable electronic source in a vehicle, converting the sound waves into electrical energy indicator used to power streetlight. The result of this study shows the relationship between the spring displacement and the DC voltage generated, and the relationship between the sound source and the generated voltage. It follows that the relationship is directly proportional, and a 95 dB sound generated of 1.3V, which is regulated by using an XL4016 8A regulator to maintain a constant voltage of 2V required to lighten the LED indicator used to power the prototype streetlight implemented in this study. [ABSTRACT FROM AUTHOR]

The demand for image retrieval with text manipulation exists in many fields, such as e-commerce and Internet search. Deep metric learning methods are used by most researchers to calculate the similarity between the query and the candidate image by fusing the global feature of the query image and the text feature. However, the text usually corresponds to the local feature of the query image rather than the global feature. Therefore, in this paper, we propose a framework of image retrieval with text manipulation by local feature modification (LFM-IR) which can focus on the related image regions and attributes and perform modification. A spatial attention module and a channel attention module are designed to realize the semantic mapping between image and text. We achieve excellent performance on three benchmark datasets, namely Color-Shape-Size (CSS), Massachusetts Institute of Technology ( MIT ) States and Fashion200K (+8.3%, +0.7% and +4.6% in R@1). [ABSTRACT FROM AUTHOR]

Digital twin (DT) is a virtual replica of a physical world that has become one of the most important ideas in the manufacturing industry's digital revolution. DT modeling is a vital issue in building a DT of a production line. In this paper, a method is proposed to address the difficulties of complicated production line business and data heterogeneity. The method focuses on essential data in the production line and creates conceptual and information models based on the ArtiFlow model and AutomationML (AML). Conceptual models are mainly used to describe and analyze the business activities of the production line, and information models describe real production lines in the form of XML files. The proposed modeling approach has been applied to a real-world clothing production line to demonstrate its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads to industrial revolution 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, this IoT device is used to compute the receivedWSN data from devices and transfer it to remote locations for assistance. In general, WSNs, the gateways are a long distance from the base station (BS) and are communicated through the gateways nearer to the BS. At the gateway, which is closer to the BS, energy drains faster because of the heavy load, which leads to energy issues around the BS. Since the sensors are battery-operated, either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas. In that situation, energy plays a vital role in sensor survival. Concerning reducing the network energy consumption and increasing the network lifetime, this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set (ISSRS) and routing path selection to reduce the network load using the Improved Grey wolf optimization (IGWO) approach. (i) Using ISSRS, the initial clusters are formed with the local nodes, and the cluster head is chosen. (ii) Load balancing through routing path selection using IGWO. The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency, packet delivery ratio, network throughput, and packet loss percentage. [ABSTRACT FROM AUTHOR]

In this paper, two systematic literature searches are used to pursue questions about digitization in management reporting on the one hand and role-specific questions on the other. We assume that greater digitization in terms of efficiency and effectiveness will change the role of management accountants in general and in reporting in particular, as this should lead to more automation and possibly more time for consulting activities from management accountants/controllers. After a strictly documented filtering process, a total of 51 papers remained for the analysis of the research questions posed. As a result, in addition to research gaps in management reporting, the most important information technology (IT) tools in management accounting and management reporting can be identified. Concerning corporate performance and management accounting performance, the research also reveals a connection with digitization/IT trends in controlling, controller roles, and role conflicts. The systematic literature evaluations serve as a basis for further research to verify the possible causal relationships found. [ABSTRACT FROM AUTHOR]

LoRa (Long-Range) has become the Deoxyribo Nucleic Acid (DNA) of the Internet of things (IoT) for equipping smart solutions. Home automation is responsible for providing a safe and stylish home. This paper proposes a capable architecture of home automation for both short-range and long-range utilizing multiple communication technologies, namely LoRaWAN, server-based LoRa gateway, and Bluetooth connectivity. This integrated system effectively controls distinct types of home appliances and keeps smart management among all the electronics components. A regular user can easily manage these unified systems by using an Android application. This paper also presents experimental data analysis. The results and discussion section provide a set of experiments like estimated transmission delay calculation for LoRa, Wi-Fi, and Bluetooth, a coverage area calculation for LoRa with RSSI and SNR values, and a System Usability Scale (SUS). The scheme has achieved a SUS score of 93%. However, the proposed architecture can be called an outright package for smart home and will be very workable, abuzz, and handy. [ABSTRACT FROM AUTHOR]

In additive manufacturing with metallic base material, Laser Powder Bed Fusion (L-PBF) is an industrially established technology for tool-free production of complex and individualized components and products by applying material layer by layer. In addition to in-processing, the L-PBF process chain consists of several other process steps in pre- and post-processing. A holistic view of the additive manufacturing process chain shows that many process steps currently involve non-value-adding (NVA) operations and that there is generally great potential for reducing throughput times and costs. Due to the great importance of efficiency in industrial manufacturing process chains, this paper proposes a concept for analyzing the L-PBF process chain and deriving measures to eliminate NVA operations and increase the efficiency of value-adding (VA) operations. First, this paper presents a methodology for identifying VA and NVA operations that encompasses the entire chain. After the process mapping, the process steps are divided into sub-tasks to subsequently analyze them with regard to the level of automation. This analysis is the basis for selecting the appropriate automation strategy and optimizing the individual process steps by applying automation-aligned lean production methods to increase the proportion of VA operations. The potential for a significant increase in the productivity of the additive manufacturing process chain is demonstrated through systematic analysis and requirements-based optimization of the level of automation. The approach is structured in an adaptive manner so that, beyond the possibilities of lean production and automation, other optimization alternatives from further disciplines, such as digitization and networking, can be integrated. [ABSTRACT FROM AUTHOR]

Reconfigurable Pilot Lines (RPLs) are one practical concept for implementing the test-before-invest strategy to improve skills and capabilities of European small and medium enterprises (SMEs). SMEs typically have limited resources for systematically exploring the potential benefits of available technologies. By utilizing the RPLs offered by research institutes and universities, companies can get both commercially uncommitted and realistic proof-of-concept studies for their applications, and valuable information about the skills and personnel competences needed for successful implementation of the new technologies. Test-before-invest services offered by RPLs can also foster the process of adopting new digital technologies in manufacturing industry. RPLs are versatile. RPL can act as a test bed for manufacturing company's investment study, industry-scale product development laboratory for technology provider, as well as a platform for scientific research and education. Due to versatile roles and functions, defining pilot line requirements specifications call for systematic contribution from both industry and academia. This paper introduces a collaborative approach for defining requirements specifications for RPLs from the future technology skills development perspective. As a basis for this research, we combine results of three joined industry-academia workshops and one academic workshop with total 87 participants utilizing Affinity Wall method. This data was supplemented with an online survey for 74 companies from manufacturing industry. The analysis indicates that defining requirements specifications and development roadmaps for RPLs is a complex process, requiring multidisciplinary collaboration. Based on the analysis, a conceptual model supporting the requirements specification work is proposed in this paper. The model was applied to an existing testbed environment for further evaluation. [ABSTRACT FROM AUTHOR]

With the increasing performance of computer hardware and the resulting computing capabilities, machine learning more and more transforms into a common tool for automating a wide range of applications. While today's society is aware of the potential and possibilities this technology provides, most small companies often lack the manpower, problem solving imagination or basic knowledge to implement and use machine learning for those purposes, which is why many process steps are still carried out manually. Consequences are increasing expenses and manufacturing time caused by process delays. The use case described in this paper regards the manufacturability check (feasibility of the coating process) of sheet metal objects; a task that still has to be performed by an expert. The system proposed in this paper aims to fully automate this process by applying convolutional neural networks (CNN) on transformed computer-aided design (CAD) objects in order to determine their coatability. [ABSTRACT FROM AUTHOR]

In this paper, we present two projects of a printing system. First, we present a project of automation. In fact, industrial automation is the art of using machines to reduce the workload of the worker while maintaining productivity and quality. Second, we present a project of Information System development. In fact, the information has now become a strategic weapon; its duration has become very short. Therefore, the development of an Information System is necessary. In this paper, we adopt a system approach of analysis and modeling to respond to issues of automation and Information System development projects of a printing system. [ABSTRACT FROM AUTHOR]