Data safety concerns are critical during the whole lifecycle of any built asset from the design stage until destruction. Information security in Building Information Modeling (BIM) enabled projects represents a key factor for the whole process success. Unsuitable distribution of sensitive information may cause financial loss, lack of trust and even physical security threats. This paper would propose a novel solution to secure BIM workflow through a Blockchain technology-based lifecycle approach. The aim in this solution is to integrate a blockchain-based lifecycle management platform with BIM software. The interactions and system transactions between the two entities will be managed by distributed ledger technology and smart contracts. This will ensure workflow's security by strengthening the transparency, traceability and securing information exchange.

Nowadays, Blockchains are considered the go-to solution when it comes to secure and trust-ensuring platforms for critical data exchange. However, integrating this technology with existing systems is quite a challenging task as there is an unclear path to achieve the integration. Usually, the main motivation behind integrating blockchain with information systems is the fact that it ensures trustworthiness, security, and traceability by design. In this paper, we investigate the integration of this technology from an information system perspective in the context of manufacturing supply chains. We propose a proof-of-concept of a manufacturing supply chain scenario integration between Odoo (Supply Chain processes) and Hyperledger Fabric (blockchain development platform). We demonstrate how Odoo workflows can be optimized, secured, and made trustworthy using smart contracts and highlight the impact of this solution on current and future applications especially in the Industry 4.0 context where data is an abundant and critical resource.

The distribution and immutability properties of blockchains made it possible to use them in various fields, such as Supply Chain, finance and health. The automation of the creation and execution of transactions in a blockchain in a decentralized and transparent manner is realized through Smart Contracts programming codes. This paper presents the implementation of Smart Contracts in specific manufacturing Supply Chains and discusses their life cycle and impact on the Supply Chain management. The presented application deals with the possibility of transforming natural language contracts of a given Supply Chain to automated Smart Contracts that makes the Supply Chain management faster and safer. A first solution is proposed based on Bidirectional Encoder Representations from Transformers (BERT) model and limited to the implementation of Smart Contracts of the Supply Chain legal contracts. Also described here is the ways of extracting contract elements from legal contracts by applying the BERT Deep Learning method on annotated contract dataset of a corpus of 13000 annotations over 510 contracts.

Production of goods has reached record numbers in the last decades as it became more efficient and effective than ever before due to the automation and digitalization of the production process. This gave customers more choice and the delivery process became faster and more satisfying. However, supply chain management remains a bottleneck due to the limitations of the supply chain management systems which suffer from poor traceability, product tampering, lack of timely information sharing, and delays. These issues can be addressed by the blockchain as a digital platform with features like immutability, transparency, and decentralization of information. In this paper, we propose a case study for improving the manufacturing supply chain management through a blockchain-based solution. The proposed system would provide a more transparent supply chain with improved product traceability because of the tamper-proof and decentralized nature of blockchains.

Since its revolution in the financial sector, the blockchain technology disrupted the majority of collaboration-based applications including supply chain management. The supply chain is one of the most important sectors benefiting from all the advantages of blockchain. Through this paper, we are mainly focusing on the practical aspects of integrating blockchain technology with traditional and existing business applications. Indeed, most businesses and corporations will find it hard to shift from traditional architectures to a decentralized one with fears related to upgrading risks, unknown tools, and resistance to change. Thus, we mainly propose several scenarios for blockchain integration focusing on the most used Enterprise Resource Planning (ERP) platforms. Besides, we present our proof of concept integration that uses the HyperLedger Fabric blockchain platform and the Odoo ERP framework. The selection of these two solutions for our study was mainly influenced by the fact that Hyperledger is the most used open-source blockchain platform in the industry and that Odoo is the most used open-source ERP framework. Nevertheless, we believe that our proposed solution can easily be used with proprietary ERP platforms and business applications.

Nowadays data analytics and Artificial Intelligence (AI) tools are used at all levels of the extended enterprise, from the shop floor level to run and improve operations, to the strategic process level to make high levels decisions. Failing to provide a unique and fit for all solution, the system providers focus on joining the dots along the digital threads with data continuity in mind. Unfortunately, the existing separate solutions contribute to data overlaps and involve data safety issues. Re-defining the place of the Enterprise Information System components such as Product Lifecycle Management (PLM) and Supply Chain Management (SCM) solutions in a wider digitalization landscape, from product creation to smart factory/Industry 4.0 is the scope of many current works. This includes enhancement in terms of traceability and timely information sharing, addressed through blockchain as digital platforms with features like immutability, transparency, and decentralization of data and information. In this paper, we show how blockchain can overcome such barriers and propose a case study for improving the Enterprise Information System through a blockchain-based solution. The solution would provide more transparent supply chains with improved product traceability as a consequent result of the tamper-proof and decentralization nature of blockchains.

Through this paper, we aim at investigating the impact of artificial intelligence technologies on cultural heritage promotion and long-term preservation in terms of digitization effectiveness, attractiveness of the assets, and value empowering. Digital tools have been validated to yield sustainable and yet effective preservation for multiple types of content. For cultural data, however, there are multiple challenges in order to achieve sustainable preservation using these digital tools due to the specificities and the high-quality requirements imposed by cultural institutions. With the rise of machine learning and data science technologies, many researchers and heritage organizations are nowadays searching for techniques and methods to value and increase the reliability of cultural heritage digitization through machine learning. The present study investigates some of these initiatives highlighting their added value and potential future improvements. We mostly cover the aspects related to our context which is the long-term cost-effective digital preservation of the Qatari cultural heritage through the CEPROQHA project.

The concept of Ontologies has been used in a wide range of application domains, due to the fact that ontologies provide a useful mean for establishing a formal, shared and collective understanding of the concepts and their underlying relations at a certain domain of interest, which allows for interoperability and information exchange in a formal an understandable way for both humans and machines. In Cultural Heritage (CH) domain, ontologies serve as a fundamental building block for the traceability of the cultural heritage objects, especially with the increasing demand of providing digital formats for cultural objects and make them available for public. In this paper we implement OntoM; an Ontology model that incorporates the relevant concepts of the Cultural Heritage (CH) domain in Qatar. Then, we will use such an ontology to perform inferences about cultural object classifications via two approaches: string matching, that allows for direct matching between the object and the ontology concepts, and semantic matching, in which we use WordNet lexical database to find all possible synonyms for properties of a given anonymous object.

Cultural heritage takes an important part of the history of humankind as it is one of the most powerful tools for the transfer and preservation of moral identity. As a result, these cultural assets are considered highly valuable and sometimes priceless. Digital technologies provided multiple tools that address challenges related to the promotion and information access in the cultural context. However, the large data collections of cultural information have more potential to add value and address current challenges in this context with the recent progress in artificial intelligence (AI) with deep learning and data mining tools. Through the present paper, we investigate several approaches that are used or can potentially be used to promote, curate, preserve and value cultural heritage through new and evolutionary techniques based on deep learning tools. The deep learning approaches entirely developed by our team are intended to classify and annotate cultural data, complete missing data, or map existing data schemes and information to standardized schemes with language processing tools.

In this paper, we discuss how Long Short Time Memory (LSTM) neural networks can be applied to cyber security knowledge base population. Assuming we have an empty ontology that models the field of vulnerabilities description management using ontology concepts such as classes and properties, we want to populate it from online unstructured textual resources. More precisely, the task involves predicting instances of the classes in the ontology and the semantic relationship between them from a text describing a vulnerability in a software. As opposed to the statistical inference approach, we adopt a neural networks approach to predict the structure of the text. Given an input as a sequence of words, the model predicts the most likely classification of the words and extracts the relationship between the words that are relevant to the domain. The proposed system is decomposed into named entry recognition, relation extraction, ontology population. In this paper, we show how these tasks fit together and how they are implemented as unified framework.

Extracting cybersecurity entities and the relationships between them from online textual resources such as articles, bulletins, and blogs and converting these resources into more structured and formal representations has important applications in cybersecurity research and is valuable for professional practitioners. Previous works to accomplish this task were mainly based on utilizing feature-based models. Feature-based models are time-consuming and need labor-intensive feature engineering to describe the properties of entities, domain knowledge, entity context, and linguistic characteristics. Therefore, to alleviate the need for feature engineering, we propose the usage of neural network models, specifically the long short-term memory (LSTM) models to accomplish the tasks of Named Entity Recognition (NER) and Relation Extraction (RE). We evaluated the proposed models on two tasks. The first task is performing NER and evaluating the results against the state-of-the-art Conditional Random Fields (CRFs) method. The second task is performing RE using three LSTM models and comparing their results to assess which model is more suitable for the domain of cybersecurity. The proposed models achieved competitive performance with less feature-engineering work. We demonstrate that exploiting neural network models in cybersecurity text mining is effective and practical.

The automated and timely conversion of cybersecurity information from unstructured online sources, such as blogs and articles to more formal representations has become a necessity for many applications in the domain nowadays. Named Entity Recognition (NER) is one of the early phases towards this goal. It involves the detection of the relevant domain entities, such as product, version, attack name, etc. in technical documents. Although generally considered a simple task in the information extraction field, it is quite challenging in some domains like cybersecurity because of the complex structure of its entities. The state of the art methods require time-consuming and labor intensive feature engineering that describes the properties of the entities, their context, domain knowledge, and linguistic characteristics. The model demonstrated in this paper is domain independent and does not rely on any features specific to the entities in the cybersecurity domain, hence does not require expert knowledge to perform feature engineering. The method used relies on a type of recurrent neural networks called Long Short-Term Memory (LSTM) and the Conditional Random Fields (CRFs) method. The results we obtained showed that this method outperforms the state of the art methods given an annotated corpus of a decent size.

It is often difficult to meaningfully convey concepts like security incident management cycle, information sharing, cooperation, as well as the roles of people, processes and technology in information and cybersecurity courses. Such complexity requires immersive and interactive learning based on continuous cooperation between industry and academia. In this paper we highlight the ongoing industry/university cooperative effort towards an cooperative schema to enforce the Information Security and Cybersecurity Curriculum development within an existing Master of Computing.

As the main supplier of the workforce to the industry, higher education is increasingly criticized for not being abreast with the digital revolution and being disconnected from the industry. Competency-based education was developed to address this issue and bridge the gap between what the university is producing and the requirements of the industry. Hence, tools need to be developed that assists in the analysis process. This paper focuses on proposing a system that models the competencies required by occupations in the industry and higher education curricula and assists in matching profiles from the two domains. The different concepts in the domain are modeled as a semantic web ontology, and an inference engine performs the profile matching. In addition to the profile matching, the system calculates a score for the matching degree using the analytic hierarchy process (AHP) method.

As the main supplier of workforce to the industry, higher education is increasingly challenged for not being abreast with the digital revolution and somehow disconnected from the industry. Competency-based education was therefore developed to address this issue and bridge the gap between what the university is producing and the requirements of the industry. To analyze this gap, tools need to be developed that assists in the analysis process. This paper focuses on proposing a system that models the competencies of occupations in the industry and higher education curricula and assist in matching profiles from the two domains. The different concepts in the domain are modelled as a semantic web ontology and the profile matching is performed by an inference engine. In addition to the profile matching, the system calculates a matching score using the Analytic Hierarchy Process (AHP) method.

in this research work we propose a framework of experiential learning to enhance student work skills and experience. This research main to contribute to the development and expansion of local industry, through the conduct of long-term fundamental research that contributes to the science base and understanding needs of national economy through industrial by providing an adapted method, enhance the teaching contents and pedagogical organization to be more accurate and adapted to the competency requirements of local employers.

one of the main suppliers of the workforce for the engineering industry and the economy in general is the higher education sector. The higher education sector is consistently being challenged by the fast evolving industry and hence it is under constant pressure to fulfill the industry's ever-changing needs. It needs to adapt its academic curricula to supply the industry with students who have up-to-date and relevant competencies. Therefore, it is crucial to find an efficient solution to bridge the gap between the two worlds. Bridging the gap helps the industry to cut the costs of training university graduates and assists in higher education advancement. This paper describes how ontologies could be a relevant tool for an initial analysis and assessment of the competencies needed by the industry.