Search results for: knowledge based automated software engineering

Authors: Ilana Lavy, Rami Rashkovits

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In this study, we describe Information Systems students' learning model which was applied by students in order to cope with professional knowledge gaps in the context of their final project. The students needed to implement a software system according to specifications and design they have made beforehand. They had to select certain technologies and use them. Most of them decided to use programming environments that were learned during their academic studies. The students had to cope with various levels of knowledge gaps. For that matter they used learning strategies that were organized by us as a learning model which includes two phases each suitable for different learning tasks. We analyze the learning model, describing advantages and shortcomings as perceived by the students, and provide excerpts to support our findings.

Keywords: knowledge gaps, independent learner skills, self-regulated learning, final project

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Authors: Mehrnoosh Askarizadeh

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The customer’s value has become obvious for the leading companies in today’s competitive environment. Therefore they are constantly trying to improve their relationship with customers. Customer Knowledge has been recognized as a strategic resource and a key to the success of any company. Talking about the Customer Knowledge Management is closely associated with Knowledge Management and Customer Relationship Management (CRM). Recent studies conducted in the fields of Knowledge Management (KM) and Customer Relationship Management (CRM) has explained that the two approaches can have great synergies. In this paper, our aim is to provide an understanding of Customer Knowledge Management (CKM) as an integrated management approach and competence it requires. We describe CKM as an ongoing process of generating, disseminating and using customer knowledge within an organization and between an organization and its customers. In addition, we propose a comprehensive framework of CKM, the ability to integrate customer knowledge into customer relationship management processes.

Keywords: e-commerce, knowledge management (KM), customer relationship management (CRM), customer knowledge management (CKM)

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Authors: Khalaf Khatatneh, Nabeel Al-Milli, Amjad Hudaib, Monther Ali Tarawneh

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Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios.

Keywords: feature selection, neural network, particle swarm optimization, software fault prediction

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Authors: Malmi Amadoru, Chandana Gamage

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Sri Lanka is emerging as a global IT-BPO hub topping up among the 20 global outsourcing destinations. When setting up a new venture in Sri Lanka, talent sourcing plays one of the key functions due to the rapid growth of workforce. Getting competent people with right skills for right positions leads organizations achieving its vision, mission and objectives. It also drives in earning competitive advantage over industry competitors. Thus it is crucial to scan and recruit the best employees to an organization. However there is no published information available on recruitment methods utilized in Sri Lankan software industry, as a study of this nature had not being conducted previously in Sri Lanka. The main objective of this study was to explore various talent sourcing practices exploited in Sri Lankan software industry. Also this study analyses the extent which Sri Lanka has adopted different recruitment strategies utilized in worldwide and its deviations. The research outcome is beneficial for HR professionals to identify the current trends in recruitment practices. Moreover investors who are interested in IT-BPO engagements can gain a thorough knowledge about talent sourcing techniques in Sri Lankan software industry. Finally, this research clues trending areas which can be further investigated in future.

Keywords: IT-BPO, recruitment, Sri Lanka, software industry, talent

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Authors: Ruilian Zhao, Shukai Zhang, Yan Wang, Weiwei Wang

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Repairing broken test cases is an expensive and challenging task in evolving software systems. Although an automated repair technique with intent preservation has been proposed, but it does not take into account the association between test repairs and assertions, leading to a large number of irrelevant candidates and decreasing the repair capability. This paper proposes an assertion-driven test repair approach. Furthermore, an intent-oriented priority criterion is raised to guide the repair candidate generation, making the repairs closer to the intent of the test. In more detail, repair targets are determined through post-dominance relations between assertions and the methods that directly cause compilation errors. Then, test repairs are generated from the target in a bottom-up way, guided by the intent-oriented priority criteria. Finally, the generated repair candidates are prioritized to match the original test intent. The approach is implemented and evaluated on the benchmark of 4 open-source programs and 91 broken test cases. The result shows that the approach can fix 89% (81/91) of broken test cases, which is more effective than the existing intentpreserved test repair approach, and our intent-oriented priority criteria work well.

Keywords: test repair, test intent, software test, test case evolution

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Authors: Ahmed Mateen, Zhu Qingsheng, Faisal Shahzad

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Requirement Engineering (RE) is a part being created for programming structure during the software development lifecycle. Software product line development is a new topic area within the domain of software engineering. It also plays important role in decision making and it is ultimately helpful in rising business environment for productive programming headway. Decisions are central to engineering processes and they hold them together. It is argued that better decisions will lead to better engineering. To achieve better decisions requires that they are understood in detail. In order to address the issues, companies are moving towards Software Product Line Engineering (SPLE) which helps in providing large varieties of products with minimum development effort and cost. This paper proposed a new framework for software product line and compared with other models. The results can help to understand the needs in SPL testing, by identifying points that still require additional investigation. In our future scenario, we will combine this model in a controlled environment with industrial SPL projects which will be the new horizon for SPL process management testing strategies.

Keywords: requirements engineering, software product lines, scoping, process structure, domain specific language

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Authors: M. Takhtravanchi, C. Pathirage

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In the construction industry, project members are conveyor of project knowledge which is, often, not managed properly to be used in future projects. As construction projects are temporary and unique, project members are willing to be recruited once a project is completed. Therefore, poor management of knowledge across construction projects will lead to a considerable amount of knowledge loss; the ignoring of which would be detrimental to project performance. This issue is more prominent in projects undertaken through the traditional procurement system, as this system does not incentives project members for integration. Thus, disputes exist between the design and construction phases based on the poor management of knowledge between those two phases. This paper aims to highlight the challenges of the knowledge management that exists within the traditional procurement system. Expert interviews were conducted and challenges were identified and analysed by the Interpretive Structural Modelling (ISM) approach in order to summarise the relationships among them. Two identified key challenges are the Culture of an Organisation and Knowledge Management Policies. A knowledge of the challenges and their relationships will help project manager and stakeholders to have a better understanding of the importance of knowledge management.

Keywords: challenges, construction industry, knowledge management, traditional procurement system

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Authors: Mamoona Rasheed, Salman Iqbal, Muhammad Abdullah

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Organizational capability, in terms of employees’ knowledge is valuable, and difficult to reproduce; and help to build sustainable competitive advantages. Knowledge capability is linked with human resource (HR) practices of an organization. This paper investigates the relationship between HR practices, knowledge management and organization capability. In an organization, employees play key role for the effective organizational performance by sharing their knowledge with management and co-workers that contributes towards organization capability. Pakistan being a developing country has different HR practices and culture. The business opportunities give rise to the discussion about the effect of HR practices on knowledge management and organization capability as innovation performance. An empirical study is conducted through questionnaires form the employees in private banks of Lahore, Pakistan. The data is collected via structured questionnaire with a sample of 120 cases. Data is analyzed using Structure Equation Modeling (SEM), and results are depicted using AMOS software. Results of this study are tabulated, interpreted and crosschecked with other studies. Findings suggest that there is a positive relationship of training & development along with incentives on knowledge management. On the other hand, employee’s participation has insignificant association with knowledge management. In addition, knowledge management has also positive association with organization capability. In line with the previous research, it is suggested that knowledge management is important for improving the organizational capability such as innovation performance and knowledge capacity of firm. Organization capability may improve significantly once specific HR practices are properly established and implemented by HR managers. This Study has key implications for knowledge management and innovation fields theoretically and practically.

Keywords: employee participation, incentives, knowledge management, organization capability, training and development

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Authors: M. Erhan Orhan, Onur Ozdemir

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Expansion of area of operation and increasing diversity of threats forced the military organizations to change in many ways. However, tacit knowledge still is the most fundamental component of organizational knowledge. Since it is human oriented and in warfare human stands at the core of the organization. Therefore, military organizations should find effective ways of systematically utilizing tacit knowledge. In this context, this article suggest some methods for turning tacit knowledge into explicit in military organizations.

Keywords: tacit knowledge, military, knowledge management, warfare, technology

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Authors: Mohamed Fredj, Abdallah Hafsaoui, Radouane Nakache

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The study of the stability of the mining works in rock masses fractured is the major concern of the operating engineer. For geotechnical works in mines and quarries, it there is not today's general methodology for analysis and the quantification of the risks relating to the dangers inherent in these concrete types (falling boulders, landslides, etc.). The reasons for this are uncertainty, which weighs on available data or lack of knowledge of the values of the parameters required for this analysis type. Stability calculations must be based on reliable knowledge of the distribution of discontinuities that dissect the Rocky massif and the resistance to shear of the intact rock and discontinuities. This study is aimed to study the stability of slope of mine (Kef Sennoun - Tebessa, Algeria). The problem is analyzed using a numerical model based on the finite elements (software Plaxis 3D).

Keywords: stability, discontinuities, finite elements, rock mass, open-pit mine

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Authors: Saleh Gorji, Sahil Gulati, Ana Pakzad

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Continuous electron diffraction tomography, also known as microcrystal electron diffraction (MicroED) or three-dimensional electron diffraction (3DED), is a powerful technique, which in combination with cryo-electron microscopy (cryo-ED), can provide atomic-scale 3D information about the crystal structure and composition of different classes of crystalline materials such as proteins, peptides, and small molecules. Unlike the well-established X-ray crystallography method, 3DED does not require large single crystals and can collect accurate electron diffraction data from crystals as small as 50 – 100 nm. This is a critical advantage as growing larger crystals, as required by X-ray crystallography methods, is often very difficult, time-consuming, and expensive. In most cases, specimens studied via 3DED method are electron beam sensitive, which means there is a limitation on the maximum amount of electron dose one can use to collect the required data for a high-resolution structure determination. Therefore, collecting data using a conventional scintillator-based fiber coupled camera brings additional challenges. This is because of the inherent noise introduced during the electron-to-photon conversion in the scintillator and transfer of light via the fibers to the sensor, which results in a poor signal-to-noise ratio and requires a relatively higher and commonly specimen-damaging electron dose rates, especially for protein crystals. As in other cryo-EM techniques, damage to the specimen can be mitigated if a direct detection camera is used which provides a high signal-to-noise ratio at low electron doses. In this work, we have used two classes of such detectors from Gatan, namely the K3® camera (a monolithic active pixel sensor) and Stela™ (that utilizes DECTRIS hybrid-pixel technology), to address this problem. The K3 is an electron counting detector optimized for low-dose applications (like structural biology cryo-EM), and Stela is also a counting electron detector but optimized for diffraction applications with high speed and high dynamic range. Lastly, data collection workflows, including crystal screening, microscope optics setup (for imaging and diffraction), stage height adjustment at each crystal position, and tomogram acquisition, can be one of the other challenges of the 3DED technique. Traditionally this has been all done manually or in a partly automated fashion using open-source software and scripting, requiring long hours on the microscope (extra cost) and extensive user interaction with the system. We have recently introduced Latitude® D in DigitalMicrograph® software, which is compatible with all pre- and post-energy-filter Gatan cameras and enables 3DED data acquisition in an automated and optimized fashion. Higher quality 3DED data enables structure determination with higher confidence, while automated workflows allow these to be completed considerably faster than before. Using multiple examples, this work will demonstrate how to direct detection electron counting cameras enhance 3DED results (3 to better than 1 Angstrom) for protein and small molecule structure determination. We will also show how Latitude D software facilitates collecting such data in an integrated and fully automated user interface.

Keywords: continuous electron diffraction tomography, direct detection, diffraction, Latitude D, Digitalmicrograph, proteins, small molecules

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: Waleed Al-Nuaimy, Luke Anastassiou, Manjinder Kainth

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As the process of teaching has evolved with the advent of new technologies over the ages, so has the process of learning. Educators have perpetually found themselves on the lookout for new technology-enhanced methods of teaching in order to increase learning efficiency and decrease ever expanding workloads. Shortly after the invention of the internet, web-based learning started to pick up in the late 1990s and educators quickly found that the process of providing learning material and marking assignments could change thanks to the connectivity offered by the internet. With the creation of early web-based virtual learning environments (VLEs) such as SPIDER and Blackboard, it soon became apparent that VLEs resulted in higher reported computer self-efficacy among students, but at the cost of students being less satisfied with the learning process . It may be argued that the impersonal nature of VLEs, and their limited functionality may have been the leading factors contributing to this reported dissatisfaction. To this day, often faced with the prospects of assigning colossal engineering cohorts their homework and assessments, educators may frequently choose optimally curated assessment formats, such as multiple-choice quizzes and numerical answer input boxes, so that automated grading software embedded in the VLEs can save time and mark student submissions instantaneously. A crucial skill that is meant to be learnt during most science and engineering undergraduate degrees is gaining the confidence in using, solving and deriving mathematical equations. Equations underpin a significant portion of the topics taught in many STEM subjects, and it is in homework assignments and assessments that this understanding is tested. It is not hard to see that this can become challenging if the majority of assignment formats students are engaging with are multiple-choice questions, and educators end up with a reduced perspective of their students’ ability to manipulate equations. Artificial intelligence (AI) has in recent times been shown to be an important consideration for many technologies. In our paper, we explore the use of new AI based software designed to work in conjunction with current VLEs. Using our experience with the software, we discuss its potential to solve a selection of problems ranging from impersonality to the reduction of educator workloads by speeding up the marking process. We examine the software’s potential to increase learning efficiency through its features which claim to allow more customized and higher-quality feedback. We investigate the usability of features allowing students to input equation derivations in a range of different forms, and discuss relevant observations associated with these input methods. Furthermore, we make ethical considerations and discuss potential drawbacks to the software, including the extent to which optical character recognition (OCR) could play a part in the perpetuation of errors and create disagreements between student intent and their submitted assignment answers. It is the intention of the authors that this study will be useful as an example of the implementation of AI in a practical assessment scenario insofar as serving as a springboard for further considerations and studies that utilise AI in the setting and marking of science and engineering assignments.

Keywords: engineering education, assessment, artificial intelligence, optical character recognition (OCR)

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Authors: Nongyao Intasaso, Atchara Rattanama, Navarat Pewnual

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This research is survey research purposed to study the factor affected to knowledge management efficiency of personnel in Rajamangala University of Technology Srivijaya, and study the problem of knowledge management affected to knowledge development of personnel in the university. The tool used in this study is structures questioner standardize rating scale in 5 levels. The sample selected by purposive sampling and there are 137 participation calculated in 25% of population. The result found that factor affected to knowledge management efficiency in the university included (1) result from the organization factor found that the university provided project or activity that according to strategy and mission of knowledge management affected to knowledge management efficiency in highest level (x̅ = 4.30) (2) result from personnel factor found that the personnel are eager for knowledge and active to learning to develop themselves and work (Personal Mastery) affected to knowledge management efficiency in high level (x̅ = 3.75) (3) result from technological factor found that the organization brought multimedia learning aid to facilitate learning process affected to knowledge management efficiency in high level (x̅ = 3.70) and (4) the result from learning factor found that the personnel communicated and sharing knowledge and opinion based on acceptance to each other affected to knowledge management efficiency in high level (x̅ = 3.78). The problem of knowledge management in the university included the personnel do not change their work behavior, insufficient of collaboration, lack of acceptance in knowledge and experience to each other, and limited budget. The solutions to solve these problems are the university should be support sufficient budget, the university should follow up and evaluate organization development based on knowledge using, the university should provide the activity emphasize to personnel development and assign the committee to process and report knowledge management procedure.

Keywords: knowledge management, efficiency, personnel, learning process

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Authors: Nina Dzamashvili Fogelström

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This paper examines concepts of Game-Based Learning and Gamification. Conducted literature survey found an increased interest in the academia in these concepts, limited evidence of a positive effect on student motivation and academic performance, but also certain scepticism for adding games to traditional educational activities. A small-scale empirical study presented in this paper aims to evaluate student experience and usefulness of GameBased Learning and Gamification for a better understanding of the threshold concepts in software engineering project courses. The participants of the study were 22 second year students from bachelor’s program in software engineering at Blekinge Institute of Technology. As a part of the course instruction, the students were introduced to a digital game specifically designed to simulate agile software project. The game mechanics were designed as to allow manipulation of the agile concept of team velocity. After the application of the game, the students were surveyed to measure the degree of a perceived increase in understanding of the studied threshold concept. The students were also asked whether they would like to have games included in their education. The results show that majority of the students found the game helpful in increasing their understanding of the threshold concept. Most of the students have indicated that they would like to see games included in their education. These results are encouraging. Since the study was of small scale and based on convenience sampling, more studies in the area are recommended.

Keywords: agile development, gamification, game based learning, digital games, software engineering, threshold concepts

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Authors: Amna Batool

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Agile methodology is the current most popular technique in software development projects. Agile methods in software development bring optimistic impact on software performances, quality and customer satisfaction. There are some organizations and small-medium enterprises adopting agile into their local software development projects as well as in distributed software development projects. Adopting agile methods in local software development projects is valuable. However, agile global software deployment needs an attention. There are different key challenges in agile global software development that need to resolve and enhance the global software development cycles. The proposed systematic literature review investigates all key challenges of agile in global software development. Moreover, a quantitative methodology (an actual survey) targeted to present a real case scenario of these particular key challenges faced by one of the software houses that is BestWeb Malaysia. The outcomes of systematic literature and the results of quantitative methodology are compared with each other to evaluate if the key challenges pointed out in systematic review still exist. The proposed research and its exploratory results can assist small medium enterprises to avoid these challenges by adopting the best practices in their global software development projects. Moreover, it is helpful for novice researchers to get valuable information altogether.

Keywords: agile software development, ASD challenges, agile global software development, challenges in agile global software development

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Zhengrong Wu, Haibo Yang

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In the context of escalating global climate change and environmental degradation, the complexity and frequency of natural disasters are continually increasing. Confronted with an abundance of information regarding natural disasters, traditional knowledge graph construction methods, which heavily rely on grammatical rules and prior knowledge, demonstrate suboptimal performance in processing complex, multi-source disaster information. This study, drawing upon past natural disaster reports, disaster-related literature in both English and Chinese, and data from various disaster monitoring stations, constructs question-answer templates based on large language models. Utilizing the P-Tune method, the ChatGLM2-6B model is fine-tuned, leading to the development of a disaster knowledge graph based on large language models. This serves as a knowledge database support for disaster emergency response.

Keywords: large language model, knowledge graph, disaster, deep learning

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Authors: Jan Barak, Karel Frana, Joerg Stiller

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This work presents results of moist air condensation in heat exchanger. It describes theoretical knowledge and definition of moist air. Model with geometry of square canal was created for better understanding and post processing of condensation phenomena. Different approaches were examined on this model to find suitable software and model. Obtained knowledge was applied to geometry of real heat exchanger and results from experiment were compared with numerical results. One of the goals is to solve this issue without creating any user defined function in the applied code. It also contains summary of knowledge and outlook for future work.

Keywords: condensation, exchanger, experiment, validation

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Authors: V. Prodocimo, A. Malucelli, S. Reinehr

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Organizations, driven by business digitization, have in software the main core of value generation and the main channel of communication with their clients. The software, as well as responding to momentary market needs, spans an extensive product family, ranging from mobile applications to multilateral platforms. Thus, the software specification needs to represent solutions focused on consumer problems and market needs. However, requirements engineering, whose approach is strongly linked to technology, becomes deficient and ineffective when the problem is not well defined or when looking for an innovative solution, thus needing a complementary approach. Research has cited the combination of design thinking and requirements engineering, many correlating design thinking as a support technique for the elicitation step, however, little is known about the real benefits and challenges that this combination can bring. From the point of view of the development process, there is little empirical evidence of how Design Thinking interactions with requirements engineering occur. Given this scenario, this paper aims to understand how design thinking practices are applied in each of the requirements engineering stages in software projects. To elucidate these interactions, a qualitative and exploratory research was carried out through the application of the case study method in IT organizations in Brazil that work in the development of software projects. The results indicate that design thinking has aided requirements engineering, both in projects that adopt agile methods and those that adopt the waterfall process, bringing a complementary thought that seeks to build the best software solution design for business problems. It was also possible to conclude that organizations choose to use design thinking not based on a specific software family (e.g. mobile or desktop applications), but given the characteristics of the software projects, such as: vague nature of the problem, complex problems and/or need for innovative solutions.

Keywords: software engineering, requirements engineering, design thinking, innovative solutions

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Authors: Pavneet Kaur, Palak Sharma

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Virtualization is a term that basically describe creation of virtual version of something like operating system, network, etc. Virtualization is a technology which is in use from 1970, but with new developments and inventions, it is now useful in education, software development etc. This paper will describe basic introduction of virtualization, along with its various categories. It will also describe use of virtualization in software engineering, its various benefits and shortcomings.

Keywords: virtualization, hardware, software, os

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference

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Authors: Sang Ho Lee, Tae Heon Moon, Youn Taik Leem, Kwang Woo Nam

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Knowledge and IT inputs to other industrial production have become more important as a key factor for the competitiveness of national and regional economies, such as knowledge economies in smart cities. Knowledge and IT industries lead the industrial innovation and technical (r)evolution through low cost, high efficiency in production, and by creating a new value chain and new production path chains, which is referred as knowledge and IT dynamics. This study aims to investigate the knowledge and IT dynamics in Korea, which are analyzed through the input-output model and structural path analysis. Twenty-eight industries were reclassified into seven categories; Agriculture and Mining, IT manufacture, Non-IT manufacture, Construction, IT-service, Knowledge service, Non-knowledge service to take close look at the knowledge and IT dynamics. Knowledge and IT dynamics were analyzed through the change of input output coefficient and multiplier indices in terms of technical innovation, as well as the changes of the structural paths of the knowledge and IT to other industries in terms of new production value creation from 1985 and 2010. The structural paths of knowledge and IT explain not only that IT foster the generation, circulation and use of knowledge through IT industries and IT-based service, but also that knowledge encourages IT use through creating, sharing and managing knowledge. As a result, this paper found the empirical investigation on the knowledge and IT dynamics of the Korean economy. Knowledge and IT has played an important role regarding the inter-industrial transactional input for production, as well as new industrial creation. The birth of the input-output production path has mostly originated from the knowledge and IT industries, while the death of the input-output production path took place in the traditional industries from 1985 and 2010. The Korean economy has been in transition to a knowledge economy in the Smart City.

Keywords: knowledge and IT industries, input-output model, structural path analysis, dynamics of knowledge and it, knowledge economy, knowledge city and smart city

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: R. Byler

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Innovation, particularly in technology development, is a crucial aspect of nanotechnology R&D and, although several approaches to effective innovation management exist, organizational structures that promote knowledge exchange have been found to be most effect in supporting new and emerging technologies. This paper discusses Integrated Knowledge Networks (IKNs) and evaluates its use within nanotechnology R&D to increase technology innovation. Specifically, this paper reviews the role of IKNs in bolstering national and international nanotechnology development and in enhancing nanotechnology innovation. Both physical and virtual IKNs, particularly IT-based network platforms for community-based innovation, offer strategies for enhanced technology innovation, interdisciplinary cooperation, and enterprise development. Effectively creating and managing technology R&D networks can facilitate successful knowledge exchange, enhanced innovation, commercialization, and technology transfer. As such, IKNs are crucial to technology development processes and, thus, in increasing the quality and access to new, innovative nanoscience and technologies worldwide.

Keywords: community-based innovation, integrated knowledge networks, nanotechnology, technology innovation

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Authors: Aloysius Hardoko, Susilo

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The root cause of environmental damage post coal mining activities as determined by the province of East Kalimantan as a corridor of economic activity masterplan acceleration of economic development expansion (MP3EI) is the behavior of adults. Adult behavior can be changed through knowledge management and attitude. Based on the root of the problem, the objective of the research is to apply knowledge management and attitude based on holistic approach in learning andragogy as an effort to solve environmental problems after coal mining activities. Research methods to achieve the objective of using quantitative research with pretest posttest group design. Knowledge management and attitudes based on a holistic approach in adult learning are applied through initial learning activities, core and case-based cover of environmental damage. The research instrument is a description of the case of environmental damage. The data analysis uses t-test to see the effect of knowledge management attitude based on holistic approach before and after adult learning. Location and sample of representative research of adults as many as 20 people in Kutai Kertanegara District, one of the districts in East Kalimantan province, which suffered the worst environmental damage. The conclusion of the research result is the application of knowledge management and attitude in adult learning influence to adult knowledge and attitude to overcome environmental problem post coal mining activity.

Keywords: knowledge management and attitude, holistic approach, andragogy learning, environmental damage

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Authors: Hanin Suleiman Alqam

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The knowledge-based economy, as its name implies relies on knowledge, information and high levels of skills made available for all economic agents. Delving a bit more deeply, the concept of a knowledge-based economy is showcasing four main pillars, which are: Education and Training, Information and Communication Technology, Economic incentives and Institutional regimes, and Research and Development (R&D) and Innovation system. A good number of researches are showing its positive contribution to economic diversification underpinning sustainable development and growth. The present paper aimed at assessing the role of SMEs in moving Oman’s economy from a traditional economy to a knowledge-based economy. To lay down a groundwork that should lead to future studies, the methodology selected is based on exploratory research. Hence, the interview was conducted as a data collection tool. Based on a purposive sampling technique, seven handpicked experts have partaken in the study as they are working in different key organizations considered to be directly or indirectly the backbone of the Omani national economy. A thematic approach is employed for the purpose of data analysis. Results of the study showed that SMEs are not really contributing in the knowledge-based economy due to a lack of awareness about its importance to the country and to the enterprise within SMEs in Oman. However, it was shown that SMEs owners are interested in innovation and are trying to support innovative individuals by attracting them to their enterprises. On the other hand, the results revealed that SMEs' performance in e-solution is still not up to the level as 32% of SMEs only are using e-solutions in their internal processes and procedures like accounting systems. It is recommended to SMEs owners to use new and modern technologies in marketing and customer relation, encourage creativity, research and development, and allow the youth to have opportunities and facilitate the procedure in terms of innovation so that their role in contributing to the knowledge-based economy could be improved.

Keywords: knowledge-based economy, SMEs, ICT pillars, research and innovation

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Authors: Agnese Natali, Francesco Morelli, Walter Salvatore, José Humberto Matias de Paula Filho, Patrick Pol

Abstract:

In the framework of the evaluation of the applicability of high strength steel to produce thin-walled elements to be used in Automated Rack Supported Warehouses, an experimental campaign is carried outto evaluate the structural performance of typical profile shapes adopted for such purposes and made of high strength steel. Numerical models are developed to fit the observed failure modes, stresses, and deformation patterns, and proper directions are proposed to simplify the numerical simulations to be used in further applications and to evaluate the mechanical behavior and performance of profiles.

Keywords: Steel racks, Automated Rack Supported Warehouse, thin walled cold-formed elements, high strength steel.

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Authors: Shraddha Chaudhary, Raksha Agarwal, Niladri Chatterjee

Abstract:

This work demonstrates a web crawler-based generalized end-to-end open domain Question Answering (QA) system. An efficient QA system requires a significant amount of domain knowledge to answer any question with the aim to find an exact and correct answer in the form of a number, a noun, a short phrase, or a brief piece of text for the user's questions. Analysis of the question, searching the relevant document, and choosing an answer are three important steps in a QA system. This work uses a web scraper (Beautiful Soup) to extract K-documents from the web. The value of K can be calibrated on the basis of a trade-off between time and accuracy. This is followed by a passage ranking process using the MS-Marco dataset trained on 500K queries to extract the most relevant text passage, to shorten the lengthy documents. Further, a QA system is used to extract the answers from the shortened documents based on the query and return the top 3 answers. For evaluation of such systems, accuracy is judged by the exact match between predicted answers and gold answers. But automatic evaluation methods fail due to the linguistic ambiguities inherent in the questions. Moreover, reference answers are often not exhaustive or are out of date. Hence correct answers predicted by the system are often judged incorrect according to the automated metrics. One such scenario arises from the original Google Natural Question (GNQ) dataset which was collected and made available in the year 2016. Use of any such dataset proves to be inefficient with respect to any questions that have time-varying answers. For illustration, if the query is where will be the next Olympics? Gold Answer for the above query as given in the GNQ dataset is “Tokyo”. Since the dataset was collected in the year 2016, and the next Olympics after 2016 were in 2020 that was in Tokyo which is absolutely correct. But if the same question is asked in 2022 then the answer is “Paris, 2024”. Consequently, any evaluation based on the GNQ dataset will be incorrect. Such erroneous predictions are usually given to human evaluators for further validation which is quite expensive and time-consuming. To address this erroneous evaluation, the present work proposes an automated approach for evaluating time-dependent question-answer pairs. In particular, it proposes a metric using the current timestamp along with top-n predicted answers from a given QA system. To test the proposed approach GNQ dataset has been used and the system achieved an accuracy of 78% for a test dataset comprising 100 QA pairs. This test data was automatically extracted using an analysis-based approach from 10K QA pairs of the GNQ dataset. The results obtained are encouraging. The proposed technique appears to have the possibility of developing into a useful scheme for gathering precise, reliable, and specific information in a real-time and efficient manner. Our subsequent experiments will be guided towards establishing the efficacy of the above system for a larger set of time-dependent QA pairs.

Keywords: web-based information retrieval, open domain question answering system, time-varying QA, QA evaluation

Procedia PDF Downloads 74

Authors: Yihao Kuang, Bowen Ding

Abstract:

With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference, supervised learning

Procedia PDF Downloads 34