This paper presents a technical curriculum on language-oriented artificial intelligence (AI) in the language and translation (L&T) industry. The curriculum aims to foster domain-specific technical AI literacy among stakeholders in the fields of translation and specialised communication by exposing them to the conceptual and technical/algorithmic foundations of modern language-oriented AI in an accessible way. The core curriculum focuses on 1) vector embeddings, 2) the technical foundations of neural networks, 3) tokenization and 4) transformer neural networks. It is intended to help users develop computational thinking as well as algorithmic awareness and algorithmic agency, ultimately contributing to their digital resilience in AI-driven work environments. The didactic suitability of the curriculum was tested in an AI-focused MA course at the Institute of Translation and Multilingual Communication at TH Koeln. Results suggest the didactic effectiveness of the curriculum, but participant feedback indicates that it should be embedded into higher-level didactic scaffolding - e.g., in the form of lecturer support - in order to enable optimal learning conditions.
Francisco Leiva, Claudio Canales, Michelle Valenzuela
et al.
This paper presents a data-driven methodology for the control of static hydraulic impact hammers, also known as rock breakers, which are commonly used in the mining industry. The task addressed in this work is that of controlling the rock-breaker so its end-effector reaches arbitrary target poses, which is required in normal operation to place the hammer on top of rocks that need to be fractured. The proposed approach considers several constraints, such as unobserved state variables due to limited sensing and the strict requirement of using a discrete control interface at the joint level. First, the proposed methodology addresses the problem of system identification to obtain an approximate dynamic model of the hydraulic arm. This is done via supervised learning, using only teleoperation data. The learned dynamic model is then exploited to obtain a controller capable of reaching target end-effector poses. For policy synthesis, both reinforcement learning (RL) and model predictive control (MPC) algorithms are utilized and contrasted. As a case study, we consider the automation of a Bobcat E10 mini-excavator arm with a hydraulic impact hammer attached as end-effector. Using this machine, both the system identification and policy synthesis stages are studied in simulation and in the real world. The best RL-based policy consistently reaches target end-effector poses with position errors below 12 cm and pitch angle errors below 0.08 rad in the real world. Considering that the impact hammer has a 4 cm diameter chisel, this level of precision is sufficient for breaking rocks. Notably, this is accomplished by relying only on approximately 68 min of teleoperation data to train and 8 min to evaluate the dynamic model, and without performing any adjustments for a successful policy Sim2Real transfer. A demonstration of policy execution in the real world can be found in https://youtu.be/e-7tDhZ4ZgA.
There is tremendous reduction of the available fresh water sources due to competing uses and uncontrolled pollution leading to global water scarcity. Worldwide, urban areas are well served with improved quality water unlike the rural areas, with a wide gap in the rural developing regions suffering from limited financial resources and other competing needs. Developing regions’ rural population has resorted to using raw surface water, rendering them vulnerable to water-related infections. This study aims to achieving improved drinking water quality using an easy to build horizontal roughing filter (HRF), to be used as a pre-treatment unit for variably turbid surface water before using slow sand filtration (SSF) units. The HRF comprises locally sourced gravel of different sizes filled into different compartments. The system was operated at filtration rates of 0.35, 0.70, 1.10, 1.45, and 2.00 m/h, with a model suspension of varied turbidity level ranging between 300–450 NTU. The results showed a significant reduction of turbidity level to an average range of 8.8–25.02 NTU for the respective filtration rates that conform to the WHO requirements of <50 NTU for subsequent treatment using SSF units. An overall average removal efficiency of 93.68% was observed for the system operation. It was concluded that the HRF system is a suitable alternative to conventional treatment units for use in rural developing regions, for improving the quality of surface water before using SSF as a final purification unit. Compared to conventional systems, the HRF system impresses with its simple design and low costs.
Klara Borowa, Andrzej Ratkowski, Roberto Verdecchia
Microservice architectures provide an intuitive promise of high maintainability and evolvability due to loose coupling. However, these quality attributes are notably vulnerable to technical debt (TD). Few studies address TD in microservice systems, particularly on a large scale. This research explores how TD manifests in a large-scale microservice-based industrial system. The research is based on a mixed-method case study of a project including over 100 microservices and serving over 15k locations. Results are collected via a quantitative method based static code analyzers combined with qualitative insights derived from a focus group discussion with the development team and a follow-up interview with the lead architect of the case study system. Results show that (1) simple static source code analysis can be an efficient and effective entry point for holistic TD discovery, (2) inadequate communication significantly contributes to TD, (3) misalignment between architectural and organizational structures can exacerbate TD accumulation, (4) microservices can rapidly cycle through TD accumulation and resolution, a phenomenon referred to as "microservice architecture technical debt gamble". Finally, we identify a set of fitting strategies for TD management in microservice architectures.
Representation of systems using the SAPPhIRE model of causality can be an inspirational stimulus in design. However, creating a SAPPhIRE model of a technical or a natural system requires sourcing technical knowledge from multiple technical documents regarding how the system works. This research investigates how to generate technical content accurately relevant to the SAPPhIRE model of causality using a Large Language Model, also called LLM. This paper, which is the first part of the two-part research, presents a method for hallucination suppression using Retrieval Augmented Generating with LLM to generate technical content supported by the scientific information relevant to a SAPPhIRE con-struct. The result from this research shows that the selection of reference knowledge used in providing context to the LLM for generating the technical content is very important. The outcome of this research is used to build a software support tool to generate the SAPPhIRE model of a given technical system.
Breeze-7B is an open-source language model based on Mistral-7B, designed to address the need for improved language comprehension and chatbot-oriented capabilities in Traditional Chinese. This technical report provides an overview of the additional pretraining, finetuning, and evaluation stages for the Breeze-7B model. The Breeze-7B family of base and chat models exhibits good performance on language comprehension and chatbot-oriented tasks, reaching the top in several benchmarks among models comparable in its complexity class.
Abdoul Jalil Djiberou Mahamadou, Artem A. Trotsyuk
Efforts to mitigate bias and enhance fairness in the artificial intelligence (AI) community have predominantly focused on technical solutions. While numerous reviews have addressed bias in AI, this review uniquely focuses on the practical limitations of technical solutions in healthcare settings, providing a structured analysis across five key dimensions affecting their real-world implementation: who defines bias and fairness; which mitigation strategy to use and prioritize among dozens that are inconsistent and incompatible; when in the AI development stages the solutions are most effective; for which populations; and the context in which the solutions are designed. We illustrate each limitation with empirical studies focusing on healthcare and biomedical applications. Moreover, we discuss how value-sensitive AI, a framework derived from technology design, can engage stakeholders and ensure that their values are embodied in bias and fairness mitigation solutions. Finally, we discuss areas that require further investigation and provide practical recommendations to address the limitations covered in the study.
Slurry transport pumps, the central equipment of deep-sea mining (DSM) systems, provide the lifting power required for lifting mineral ores from the seafloor to the surface. The current technical challenges are associated with transport security and the economic aspects of coarse ore particles in pumps and pipelines. This paper focuses on the transportation characteristics of slurry pumps and uses theoretical methods, numerical calculations, and experimental methods to identify a feasible working mode. The geometric parameters of impeller channels in pump hydraulics significantly influence the migration properties of particles which in turn affects the overall security and economy of the system. The ratio of the impeller cross-sectional area F2/F1 (F1: cross-sectional area of the impeller outlet; F2: cross-sectional area of the impeller inlet) affects the particle passing capacity but negatively impacts pump efficiency. The percent of particles in the excellent passage interval of 0.2 s to 0.25 s increases from 25 to 43% when the number increases from 1.57 to 2.51. The pump behavior increases of the head by 5–10 m, and the efficiency decreases by 5–10%. So, the recommended span of F2/F1 is 1.57–2.00, and satisfying particle passing ability and efficiency can be achieved in this range. This study can provide a reference for the commercial transportation of slurry ores for deep-sea mining systems.
Rana Muhammad Adnan, Hong-Liang Dai, Reham R. Mostafa
et al.
The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), and the jellyfish search optimizer (JFO) in groundwater level estimation. Daily precipitation and temperature datasets acquired from two stations in northern Bangladesh were used as inputs to the models, which were evaluated based on different quantitative statistics and assessed based on RMSE, MAE, R2, and some new graphical inspection methods. The outcomes of the applications revealed that the efficiency of ELM models was considerably improved by using metaheuristic algorithms. The ELM-JSO improved the RMSE of the standalone ELM model by 13% for the optimal precipitation, temperature, and groundwater level inputs in the testing stage. Among the implemented methods, the ELM-JFO performed the best in estimating the daily groundwater level, and the ELM-WOA and ELM-HHO, respectively, followed it. Viability of a new extreme machine learning (ELM) method tuned with Jellyfish search optimizer (JFO) is investigated in groundwater level estimation. The ELM-JFO is compared with hybrid ELM-PSO, ELM-WOA and ELM-HHO models using daily precipitation and temperature data acquired from two stations of Bangladesh. The ELM-JSO improves the root mean square error of the standalone ELM model by 13% for the optimal precipitation, temperature and groundwater level inputs.
Oleksandra Klymenko, Stephen Meisenbacher, Florian Matthes
Modern privacy regulations provide a strict mandate for data processing entities to implement appropriate technical measures to demonstrate compliance. In practice, determining what measures are indeed "appropriate" is not trivial, particularly in light of vague guidelines provided by privacy regulations. To exacerbate the issue, challenges arise not only in the implementation of the technical measures themselves, but also in a variety of factors involving the roles, processes, decisions, and culture surrounding the pursuit of privacy compliance. In this paper, we present 33 challenges faced in the implementation of technical measures for privacy compliance, derived from a qualitative analysis of 16 interviews with privacy professionals. In addition, we evaluate the interview findings in a survey study, which gives way to a discussion of the identified challenges and their implications.
Giada Pistilli, Carlos Munoz Ferrandis, Yacine Jernite
et al.
The growing need for accountability of the people behind AI systems can be addressed by leveraging processes in three fields of study: ethics, law, and computer science. While these fields are often considered in isolation, they rely on complementary notions in their interpretation and implementation. In this work, we detail this interdependence and motivate the necessary role of collaborative governance tools in shaping a positive evolution of AI. We first contrast notions of compliance in the ethical, legal, and technical fields; we outline both their differences and where they complement each other, with a particular focus on the roles of ethical charters, licenses, and technical documentation in these interactions. We then focus on the role of values in articulating the synergies between the fields and outline specific mechanisms of interaction between them in practice. We identify how these mechanisms have played out in several open governance fora: an open collaborative workshop, a responsible licensing initiative, and a proposed regulatory framework. By leveraging complementary notions of compliance in these three domains, we can create a more comprehensive framework for governing AI systems that jointly takes into account their technical capabilities, their impact on society, and how technical specifications can inform relevant regulations. Our analysis thus underlines the necessity of joint consideration of the ethical, legal, and technical in AI ethics frameworks to be used on a larger scale to govern AI systems and how the thinking in each of these areas can inform the others.
Este artículo se basa en la generación de información de la variación estacional en la calidad del recurso hídrico del río que abastece de agua potable a la población de Salamá, en el cual se determinaron los índices de calidad del río Cachil que pertenece a la cuenca del mismo nombre, ubicada en el área del corredor seco en el municipio de Salamá departamento de Baja Verapaz. En este estudio se hace énfasis en el análisis de la variación de la calidad del agua según la contaminación que esté presente en la época lluviosa calculando los índices de calidad mensuales, a través de los cuales se identificaron variaciones causadas por el aumento de material suspendido procedente de arrastre en la época lluviosa. Para poder determinar la variación de la calidad de agua se establecieron 4 puntos de muestreo; uno ubicado en la parte alta de la cuenca (Llano Largo), el segundo en la parte media (El Carmen), el tercero en la parte baja (Cachil) y el cuarto en el área de captación para la planta de tratamiento. Se realizaron muestreos mensuales en donde se tomaron parámetros in situ y parámetros que se analizaron a nivel de laboratorio estos proveyeron información que permitió calcular los índices de calidad de agua por medio de WQINSF, metodología que asigna valores de ponderación a cada una de las variables en las que se fundamenta, para que este índice pueda determinarse como: (Krendel y Novontny, 1980); donde Wi: denota el factor de importancia o ponderación de la variable (i) respecto a las restantes variables que conforman el índice, y ponderados entre 0 y 1, de tal forma que se cumpla que la sumatoria sea igual a uno. (15) Estos permitieron definir que las variaciones de calidad se dan principalmente por los niveles de conductividad eléctrica, nitratos, fosfatos, oxígeno disuelto, sólidos totales, coliformes totales y fecales que muestran una variación estacional marcada. Las aguas del río Cachil en época de lluvia se clasifican dentro de los índices de calidad para consumo entre (40.21 a 60.21) índices de calidad de media a mala, con estas características podrían utilizarse con tratamientos de potabilización. Y para uso agrícola se encuentra dentro del mismo rango la cual podría utilizarse para la mayoría de los cultivos y en algunos meses se debe realizar tratamientos para la utilización en sistemas de riego. Es importante resaltar que estos datos se pudieron obtener asociando parámetros físico-químicos y bacteriológicos (Temperatura, solidos disueltos, turbiedad, fosfatos, % de saturación de oxígeno, pH, nitratos, coliformes fecales, y DBO). También se determinó la morfometría de la cuenca, definiéndola como alargada, con una densidad de drenajes baja que refleja un área pobremente drenada con respuesta hidrológica muy lenta, con pendiente media de 43.21% y pendiente del cauce principal de 5.724 %. Se observó que el caudal disminuye considerablemente de febrero al mes de abril (caudal mínimo es 0.001 m3 /s en el mes de febrero en la parte baja de la cuenca) y el mes que reporta mayor caudal es agosto (caudal máximo 2.33 m3 /s en el punto medio de la cuenca El Carmen).
Willis Awandu, Robin Ruff, Jens-Uwe Wiesemann
et al.
Energy accessibility, reliability and availability are key components of improved quality of life and human development in all spheres. As the United Nations’ SDG 7 calls for access to electricity for all by 2030, Africa still has a wide gap to fill as the statistics show that 85% of the population that will not have access to electricity is in Africa. As the world tries to wean itself off non-renewable energy and transition to green through use of renewable energy sources, hydropower energy remains at the heart of Africa for this venture. With majority of the rural population in Africa lacking electricity, there is need for a low-tech system that utilizes river flow to generate just enough energy for normal operation in these regions. Micro-hydrokinetic river turbine technology (µ-HRT), which offers less intermittency, can potentially contribute to sustainably electrifying Africa rural areas. The technology has been adopted by few countries worldwide, with limited comprehensive study in Africa even though the technology seems viable for use in African rivers. This paper reviewed the status of the µ-HRT applications in Africa and some of the barriers to its development. The study found out that the technology has not been vastly developed in Africa. Despite numerous barriers, the technology is simply a low-tech technology that requires the use of local resources and capacity building for its sustainability in terms of construction, operation and maintenance requirements. It is therefore recommended that R&D and field trials be conducted for its possible adoption.
We consider a special type of hydraulic jumps (internal bores) which, in the vertically bounded system of two immiscible fluids with slightly different densities, conserve not only the mass and impulse but also the circulation and energy. This is possible only at specific combinations of the upstream and downstream states. Two such combinations are identified with arbitrary upstream and downstream interface heights. The first has a cross symmetry between the interface height and shear on both sides of the jump. This symmetry, which is due to the invariance of the two-layer shallow-water system with swapping the interface height and shear, ensures the automatic conservation of the impulse and energy as well as the continuity of characteristic velocities across the jump. The speed at which such jumps propagate is uniquely defined by the conservation of the mass and circulation. The other possibility is a marginally stable shear flow which can have fully conservative jumps with discontinuous characteristic velocities. Both types of conservative jumps are shown to represent a long-wave approximation to the so-called solibores which appear as smooth permanent-shape solutions in a weakly non-hydrostatic model. A new analytical solution for solibores is obtained and found to agree very well with the previous DNS results for partial-depth lock release flow. The finding that certain large-amplitude hydraulic jumps can be fully conservative, while most are not such even in the inviscid approximation, points toward the wave dispersion as a primary mechanism behind the lossy nature of internal bores.
Rayed Alghamdi, Seyed M. Buhari, Madini O. Alassafi
One of the leading university goals is to provide the students with the necessary skills for better functioning in their future studies. Gaining and developing skills, both technical and soft skills, are the critical building blocks for a successful career. The traditional teaching process, which includes delivering lectures and conducting exams, emphasizes the upliftment of technical knowledge rather than building self-esteem and enhancing skills development among students. Development of non-technical skills like self-esteem, life-long learning among students is vital for a successful career. This paper targets to achieve the objective of identifying ways to empower students with non-technical skills along with technical skills. The approach adopted in this research work is to transform the delivery of a faculty-wide course from teaching to coaching. One salient difference between teaching and coaching is to move students motivation away from grades towards life-long learning. Thus, university students maximize skills through coaching like course structure, course delivery, course assessment, and student involvement. As a case study, the proposed approach was successfully tested and validated on a course taught in the Faculty of Computing and Information Technology (FCIT) at King Abdulaziz University (KAU). The outcome of this case study reveals that there is substantial potential towards enhancement of self-responsibility. In the future, this approach can be extended for other courses that aim to develop skills such as English language courses, computer, and communication skills courses in the preparatory/foundation year.
Lukáš Svoboda, Tomáš Picek, Štěpán Zrostlík
et al.
Experimentalinvestigation of the effect of particle geometry on the values of hydraulic conductivity using 5 plastic particle fractions ofdifferent shapes and sizes is presented in this paper. Conducted experiments serveasabackgroundfor ongoing researchfocused onsediment transport of erodible bed in tilting flume. When a flowing mixture of water and particles evolves above astationary bed, thedetermination of flow rate of waterfiltered through stationary bed is very important in order to estimatethe inaccuracyof total flow ratetroughtheflume.In order to evaluate the amount of infiltrate water,experimentswere carried out inbothlaminar and turbulent flowregimes. The transition area was identified,and the results show only small effect of particle geometry on the transition between regimes. Experimentally identified valuesofhydraulic conductivity wereused to calculatethe amountof infiltrate water inthetiltingflumefor different inclination angles.Experimentally identified values of hydraulic conductivityare, for example,useful in designing industrial water filters,as the materials used to conduct experiments and those in filters are similar.
Technical support problems are often long and complex. They typically contain user descriptions of the problem, the setup, and steps for attempted resolution. Often they also contain various non-natural language text elements like outputs of commands, snippets of code, error messages or stack traces. These elements contain potentially crucial information for problem resolution. However, they cannot be correctly parsed by tools designed for natural language. In this paper, we address the problem of segmentation for technical support questions. We formulate the problem as a sequence labelling task, and study the performance of state of the art approaches. We compare this against an intuitive contextual sentence-level classification baseline, and a state of the art supervised text-segmentation approach. We also introduce a novel component of combining contextual embeddings from multiple language models pre-trained on different data sources, which achieves a marked improvement over using embeddings from a single pre-trained language model. Finally, we also demonstrate the usefulness of such segmentation with improvements on the downstream task of answer retrieval.
Valentina Lenarduzzi, Antonio Martini, Davide Taibi
et al.
The concept of technical debt has been explored from many perspectives but its precise estimation is still under heavy empirical and experimental inquiry. We aim to understand whether, by harnessing approximate, data-driven, machine-learning approaches it is possible to improve the current techniques for technical debt estimation, as represented by a top industry quality analysis tool such as SonarQube. For the sake of simplicity, we focus on relatively simple regression modelling techniques and apply them to modelling the additional project cost connected to the sub-optimal conditions existing in the projects under study. Our results shows that current techniques can be improved towards a more precise estimation of technical debt and the case study shows promising results towards the identification of more accurate estimation of technical debt.
Georgiana Dunca, Diana Maria Bucur, Alexandru Aldea
et al.
A complex hydraulic facility is modelled in EPANET. The system consists in an underground high head Hydro Power Plant (HPP) and a Pumping Station (PS), operating in a pumped-storage hydropower scheme with three reservoirs. The complexity of the system is due to its unusual configuration, where the PS discharges the water directly into the HPP penstock. The PS is equipped with 2 × 10 MW pumps. The HPP is equipped with 2 × 75 MW Francis turbines. The simulations allow assessing the energy production and/or consumption in various scenarios, offering a tool to decision makers, to wittingly choose the operation mode of the facility.