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Research on the Identification and Analysis of Technological Opportunities Utilizing the BERT Model and MULTIMOORA Approach 24th

Research on the Identification and Analysis of Technological...

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24th International TRIZ Future Conference

作者： Sun, Jianguang Miao, Runze Du, YuJuan Zhang, Delong Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China Hebei Univ Technol Natl Engn Res Ctr Technol Innovat Method & Tool Tianjin 300401 Peoples R China China North Engine Res Inst Tianjin 300401 Peoples R China

ISBN: (纸本)9783031759215;9783031759192;9783031759185

Product innovation is considered a crucial component of enhancing corporate competitiveness. Identifying technological opportunities plays a pivotal role in the success of a company's research and development (R&D) activities. Technological opportunities are defined as the potential for technological advancements in specific areas. Historically, product innovation often relied on identifying singular technological opportunities. However, the current challenge lies in recognizing and leveraging a series of interacting multiple technological opportunities. To address this, our study introduces a novel approach based on the Transformer-based Bidirectional Encoder Representations from Transformers (BERT) model. This method transforms multiple technological objectives into generalized functional behaviors by restructuring them. An in-depth analysis of patent databases is conducted using the semantic search tool from Patsnap, extracting patent information related to these functional requirements. Subsequently, the data undergoes a transition from qualitative to quantitative analysis using spherical fuzzy sets. Finally, the quantified technologies are ranked for opportunities using the MULTIMOORA method, thus completing the identification of single or multiple interacting technological opportunities.

关键词： TRIZ BERT Model MULTIMOORA Deep Learning technology Opportunity Analysis

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Research on Disruptive technology Prediction Methods Based on BERT Model and Graph Theory Analysis 24th

Research on Disruptive Technology Prediction Methods Based o...

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24th International TRIZ Future Conference

作者： Wang, Zhongyou Zhang, Jianhui Li, Rongjian Guo, Xiangdong Hebei Univ Technol Sch Mech & Engn Tianjin Peoples R China

ISBN: (纸本)9783031759215;9783031759192;9783031759185

With the acceleration of the technological revolution, disruptive technologies have become a key factor in global technological competition. However, existing prediction methods are limited by single technology fields, semantic analysis limitations, and subjective factors, making it difficult to effectively predict these technologies. In this paper, we studied the current disruptive technology prediction methods using the ideal solution analysis and resource analysis tools of TRIZ theory and proposed a new prediction method. This method combines the BERT model and graph theory analysis for the first time, and it analyzes patent text, mines the inherent relationships between cross-domain technologies, extracts disruptive technology features, and evaluates them through expert judgments. This method fills the gap in existing research. Our method demonstrates unique innovation in cross-domain technology integration and can more accurately predict disruptive technologies. The research results show that the patent technologies selected after being fused perform excellently in terms of performance and advantages, verifying the scientificity and effectiveness of our research framework. This study provides a new and effective method for exploring and predicting disruptive technologies, which is expected to drive further development in related fields.

关键词： Disruptive technology Forecasting BERT Models Graph Theory Analysis Patent Text Analysis Cross-Domain Technologies

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On Opportunities and Challenges of Large Language Models and GPT for Problem Solving and TRIZ Education 24th

On Opportunities and Challenges of Large Language Models and...

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24th International TRIZ Future Conference

作者： Avogadri, Simone Russo, Davide Univ Bergamo Viale Marconi 5 I-24044 Dalmine BG Italy

ISBN: (纸本)9783031759215;9783031759192;9783031759185

The advent of GPT has caused a real revolution in many application contexts. Even the TRIZ community has had to face up to this new technology, questioning the possible integrations with traditional paths and tools. Many problem-solving experts have for some time been proposing specific prompts based on the methodology's tools such as functional analysis, reconstruction of cause-effect relationships, identification of Resources, 40 inventive principles, etc., in order to support the problem solver, or even replace him altogether, during the inventive process. The free generation of LLM content has been applied for very different purposes such as, for example, to contextualize general purpose heuristics in specific domains, or as a search engine to answer technical questions, to suggest creative ideas or improve the formulation and redefinition of a problem, or finally to find connections between different application contexts. This article proposes a critical analysis of the real effectiveness of these prompts according to the different needs of users. The analysis was carried out using a software application that was developed in-house and for which a testing phase was conducted on a variegated sample covering both the academic and industrial fields, with more experienced users and users who have been approaching TRIZ for less time.

关键词： CAI - Computer Aided Innovation AI - Artificial Intelligence Problem-Solving TRIZ GPT LLM Large Language Model

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Improving Android Malware Detection in Imbalanced Data Scenarios 20th

Improving Android Malware Detection in Imbalanced Data Scena...

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20th ifip WG 11.9 International Conference on Digital Forensics

作者： Qin, Shengzhi Chow, Kam-Pui Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783031710278;9783031710254;9783031710247

The massive storage capacity of electronic devices has significantly increased the volume of evidentiary data encountered in digital forensic investigations. As a result, the automation of digital evidence analysis employing deep learning techniques has become a trend. The training data distributions for deep learning techniques are typically balanced, which means that the data size of each class is roughly the same. However, in the digital forensics domain, evidence is mostly imbalanced, containing a large proportion of normal data and a small proportion of anomalous data. As a result, deep learning algorithms have to be enhanced to achieve good performance on imbalanced evidentiary data. This chapter focuses on a common digital evidence analysis task - identifying Android malware dispersed in a large number of normal files. An imbalance tuning method leveraging bidirectional encoder representations from transformers (BERT) is employed to overcome the imbalanced data problem. The BERT model is trained with balanced training data and subsequently tuned using imbalanced data. The model achieves a low false positive rate and a high malware detection rate with an overall performance (F-score) of 90.26% when the positive sample population in the testing set is set to 1%.

关键词： Digital Evidence Analysis Android Malware Detection Deep Learning Imbalanced Learning Model

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AI Based Search Engine to Deploy a TRIZ Pointer to Chemical Effects 24th

AI Based Search Engine to Deploy a TRIZ Pointer to Chemical ...

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24th International TRIZ Future Conference

作者： Russo, Davide Cattaneo, Matteo Avogadri, Simone Univ Bergamo Viale Marconi 5 I-24044 Dalmine Italy

ISBN: (纸本)9783031759215;9783031759192;9783031759185

Pointers to effects are a group of TRIZ tools which helps the inventor to master a greater knowledge of scientific phenomena and laws, so to suggest him or her different directions to reach possibilities of solution. Pointers to geometric, chemical, and technological effects have been theorized, but only those to physical effects have ever had concrete developments at the research level and as commercial applications. The aim of this work is twofold: on the one hand, to bring the pointer back to chemical effects (CE), recovering little-known texts that are difficult to find but also difficult to interpret, as they have never been translated from Russian. The other aim is to contextualize these tools in the light of the recent achievements of artificial intelligence technologies in the field of information retrieval. A combination of AI tools, as NER (Named Entity Recognition), RAG (Retrieval Augmented Generation) and LLM (Large Language Model) have been combined in order to identify chemical features from several chemical sources, to index documents in order to answer user's questions, to interact with this Knowledge-Base by a chatbot and finally to generate a complete and standardized output. A comparison is presented between recent commercial applications of AI and traditional pointers to CE from TRIZ literature. In this paper it is explained how the system works, which are the potentialities according to the AI technologies evolution and a comparative study between a SW infrastructure developed by the authors in collaboration with university spin-off software house and others current AI commercial players like GPT or Gemini based applications.

关键词： Pointer to chemical effects AI - Artificial Intelligence TRIZ Patents

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GreenMov: A Fiware Based Interoperable Solution to Reduce the Environmental Impact of Mobility 7th

GreenMov: A Fiware Based Interoperable Solution to Reduce th...

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7th ifip WG 5.5 International Cross-Domain Conference on Internet of Things

作者： Couraud, Benoit Nafkha, Mehdi Dechavanne, Franck El Youssfi, Azeddine Moura, Paulo Univ Cote Azur IMREDD Nice France Univ Glasgow James Watt Sch Engn Glasgow Lanark Scotland

ISBN: (纸本)9783031819025;9783031819001;9783031818998

The recent advancements in the Internet of Things (IoT) have facilitated the deployment of numerous applications that enhance urban intelligence by improving the monitoring and control of city assets, such as lighting systems, traffic signals, and public transportation. However, these applications are often tailored to the specific needs of individual cities, limiting their replicability in other locations primarily due to the lack of interoperability in communication signals between assets. Despite various initiatives to establish standards for interoperability, real-world implementations frequently fall short of achieving fully interoperable systems that can be universally replicated, largely due to the absence of comprehensive solutions and implementation examples. This paper presents a fully interoperable use case for Green Mobility solutions in a smart city, utilizing air quality, traffic, and noise intensity data to provide transport recommendations to end-users. The implementation employs the NGSI-LD standard, Fiware data storage tools, and developed artificial intelligence-based algorithms to predict the transport situation for the following day and offer relevant traffic recommendations. This work has resulted in the development of several data models and standardized forecast algorithms with accuracy exceeding 75% on the noise and traffic datasets at our disposal, thereby enabling the potential for replication in other locations.

关键词： Data models forecasting services green mobility Fiware

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The Role of Ethics in Smart Homes - A Workshop-Based Approach 7th

The Role of Ethics in Smart Homes - A Workshop-Based Approac...

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7th ifip WG 5.5 International Cross-Domain Conference on Internet of Things

作者： Bagheri, Sally Jacobsson, Andreas Malmo Univ Malmo Sweden Dept Comp Sci & Media Technol S-21425 Malmo Sweden Internet Things & People Res Ctr Malmo Sweden

ISBN: (纸本)9783031819025;9783031819001;9783031818998

Smart homes are increasingly popular and offer users multiple benefits, such as increased security, entertainment, health, and energy efficiency. But smart homes also raise ethical challenges. Analyzing ethical risks in smart homes requires an approach that can reveal and analyze the complex consequences of unethical IoT use. Such an analysis, however, is cumbersome and requires including many aspects and stakeholder perspectives. There is a lack of methods to analyze smart homes ethically and document such research results for continual evaluation over time as the smart home and our understanding of its ethics inevitably evolve and change. This work aims to design a workshop methodology to support systematic ethical analyses of smart homes. It builds on previous work considering smart homes as digital ecosystems to contextually examine ethical risks and challenges. A group of research participants were asked to undergo the workshop to evaluate its usefulness in supporting ethical discussions and documenting insights systematically. The results show the feasibility of the workshop design in conducting ethical analyses and eliciting system requirements for smart homes. Several unethical use cases are discussed, such as IoT gaslighting and surveillance concerns related to child users.

关键词： Smart Homes Ethics Workshops IoT Digital Ecosystems

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Integrating Generative AI with TRIZ for Evolutionary Product Design 24th

Integrating Generative AI with TRIZ for Evolutionary Product...

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24th International TRIZ Future Conference

作者： Iuga, Marin Brad, Stelian Intertechnica SRL Viseut 26B Borsa Maramures Romania Tech Univ Cluj Napoca Memorandumului 28 Cluj Napoca 400114 Romania

ISBN: (纸本)9783031759215;9783031759192;9783031759185

Democratizing, scaling, and automating creativity and innovation are essential for boosting competitiveness, efficiency, and cost-effectiveness in product creation. This capability will be a key differentiator for organizations seeking a competitive edge and a catalyst for ensuring a better future for humanity. We demonstrate how the TRIZ methodology and Generative AI provide a solid foundation for solving product design challenges. We show how technology and human feedback enable the automated discovery of product design improvements. To prove this, we have developed a technological proof-of-concept solution that leverages the strengths of both TRIZ and Generative AI. This solution explores product reviews, identifies product strengths and weaknesses, and generates an initial palette of potential product design problems that can be iteratively refined to drive product improvement decisions. We provide the theoretical foundation by utilizing TRIZ's systematic problem-solving approach and specific examples of generated data, creating a consistent basis for evaluating the tremendous potential of Generative AI and TRIZ in product design and improvement processes.

关键词： Generative AI TRIZ Product Design Product Improvement Processes Innovation Democratization Creativity Automation

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Multiple-Bayesian-Network-Based Risk Assessment Methodology for Industrial Control Systems 18th

Multiple-Bayesian-Network-Based Risk Assessment Methodology ...

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18th International Conference on Critical Infrastructure Protection

作者： Guarino, Simone Ansaldi, Silvia Setola, Roberto Univ Campus BioMed Rome Rome Italy Natl Inst Insurance Against Accidents Work Rome Italy

ISBN: (纸本)9783031818905;9783031818882;9783031818875

Cyber attacks on industrial control systems have increased dramatically due to the adoption of open, heterogeneous industrial protocols that enable remote connectivity to industrial plants. Of particular concern are attacks that impair physical processes, leading to production loss, service outages, equipment damage, human injury and environmental pollution. Given the plethora of consequences induced by cyber attacks on industrial control systems, it is vital to compute the risks associated with the attacks to enable operators to implement appropriate recovery measures. This chapter presents a probabilistic risk assessment methodology that leverages multiple Bayesian networks to measure the risks associated with cyber attacks on industrial control systems. The Bayesian networks are designed to compute the probabilities of detected cyber attacks propagating to cyber and physical assets that have not yet been compromised, thereby providing an estimate of the overall risk to the integrity of the entire industrial control system. The proposed risk assessment methodology is evaluated using a real hardware-in-the-loop case study involving a water distribution testbed.

关键词： Industrial Control Systems Cyber Attacks Risk Assessment Bayesian Networks

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Leveraging Client-Side User Account Data in Digital Forensic Investigations 20th

Leveraging Client-Side User Account Data in Digital Forensic...

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20th ifip WG 11.9 International Conference on Digital Forensics

作者： Garg, Pulkit Srivastava, Vishal Bharadwaj, Nitesh Gupta, Gaurav Indian Inst Technol Jodhpur Rajasthan India Natl Inst Technol Raipur Madhya Pradesh India Minist Elect & Informat Technol New Delhi India

ISBN: (纸本)9783031710278;9783031710254;9783031710247

Smartphones have become integral to daily routines, generating a wealth of user data with every interaction. Client-side data from smartphones is invaluable in digital forensic investigations, especially data generated while using social media apps and Google services. However, large volumes of the generated data - called user account data - are not stored on the mobile devices, but on enterprise servers. This chapter explores the user account data maintained by three prominent service providers, Meta Platforms, Google and Microsoft, and highlights its pivotal role in digital forensic investigations. Additionally, the chapter describes the information Extraction and Collation Tool, an automated solution designed to extract relevant information from large user account datasets. The tool facilitates comparisons with data from other users, enabling the identification of shared activities and enhancing the efficiency of investigations.

关键词： User Account Data Android Data Forensics Forensic Tool

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