Inconsistency Detection-Based LOD in Smart Homes

The emergence of the internet of things in the smart homes has given rise to many services to meet the user's expectations. It is possible to control the temperature, the brightness, the sound system, and even the security of the house via a smartphone, at the request of the inhabitant or by scheduling it. This growing number of “things” must deal with material constraints such as home network infrastructure, but also applicative due to the number of proposed services. The heterogeneity of users' preferences often creates conflicts between them like turn on and off light or using a heater and an air conditioner in the same time. To manage these conflicts, the authors proposed a solution based on linked open data (LOD). The LOD allows defining the relation between the different services and things in the house and a better exploitation of the attributes of the inhabitant's profile and services. It consists to find inconsistency relation between the equipment using the antonym thesaurus.

Building the Semantic Layer of the Józef Piłsudski Digital Archive With an Ontology-Based Approach

Using semantic web technologies is becoming an efficient way to overcome metadata storage and data integration problems in digital archives, thus enhancing the accuracy of the search process and leading to the retrieval of more relevant results. In this paper, the results of the implementation of the semantic layer of the Józef Piłsudski Institute of America digital archive are presented. In order to represent and integrate data about the archival collections housed by the institute, the authors developed arkivo, an ontology that accommodates the archival description of records but also provides a reference schema for publishing linked data. The authors describe the application of arkivo to the digitized archival collections of the institute, with emphasis on how these resources have been linked to external datasets in the linked data cloud. They also show the results of an experiment focused on the query answering task involving a state-of-the-art triple store system. The dataset related to the Piłsudski Institute archival collections has been made available for ontology benchmarking purposes.

Provably Secure Data Sharing Approach for Personal Health Records in Cloud Storage Using Session Password, Data Access Key, and Circular Interpolation

Personal health record (PHR) system has become the most important platform to exchange health information, in which the patients can share and manage personal health information more effectively in cloud storage. However, the cloud server is unreliable, and the secure data of users may be disclosed. Therefore, a secure data sharing mechanism is developed in this research using the proposed session password, data access key, and circular interpolation (SKC)-based data-sharing approach for the secure sharing of PHR in the cloud. The proposed SKC-based data sharing approach provides high efficiency and high-security guarantee. It effectively satisfies various security properties, such as tamper resistance, openness, and decentralization. The proposed SKC-based data sharing approach is the reliable mechanism created for the doctors to share the PHR and to access the patient historical data while meeting the privacy preservation.

Enhanced Learning Experiences Based on Regulatory Fit Theory Using Affective State Detection

Predicting learners' affective states through the internet has great impact on their learning experiences. Hence, it is important for an intelligent tutoring system (ITS) to consider the learners' affective state in their learning models. This research work focuses on finding learners' frustration levels during learning. Motivating the learners appropriately can enhance their learning experiences. Therefore, the authors also bring in a strategy to respond to learners' affective states in order to motivate them. This work uses Behavioral theory for goal generation, and frustration index is calculated. Based on the frustration level of the learner, motivational messages are displayed to the learners using Regulatory fit theory. The authors evaluated the model using t-test by collecting learners' data from MoodleCloud. The results of the evaluation demonstrate that 80% of the learners' performance significantly increases statistically as an impact of motivational messages provided in response to the learners' frustration.

Ranked Deep Web Page Detection Using Reinforcement Learning and Query Optimization

This paper proposes a novel algorithm based on reinforcement learning-entitled asynchronous advantage actor-critic (A3C). Overflow queries are optimized to crawl the ranked deep web. A3C assigns the reward and penalty to the various queries. Queries are derived from the domain-based taxonomy that helps to fill the search forms. Overflow queries are the collection of queries that match with more than k number of results and only top k matched results are retrieved. Low ranked documents beyond k results are not accessible and lead to low coverage. Overflow queries are optimized to convert into non-overflow queries based on the proposed technique and lead to more coverage. As of yet, no research work has been explored by using A3C with taxonomy in the domain of ranked deep web. The experimental results show that the proposed technique outperforms the three other techniques (i.e., document frequency, random query, and high frequency) in terms of average improvement metric by 26%, 69%, and 92%, respectively.

Discovering Curriculum Components for a New Product Development Course

Presently, new product development is deemed the core activity of an organization's competitive strategy, but there is a surprising dearth of research on what curriculum components should be embedded in the new product development course. This study aims to introduce a methodology that can carry out curriculum design with limited prior knowledge. First, latent semantic analysis is applied to extract the main research themes from the subject matter literature, which are considered as potential curriculum components. Next, the revised Bloom's taxonomy is applied to develop a questionnaire for establishing the learning objectives. Finally, the study uses the modified Delphi method to verify the eligibility of these curriculum components, concluding that the most effective approach for imparting knowledge and skills regarding new product development is to adopt a capstone course and equip students with creativity and advanced technology and design for sustainability through a project-based learning approach.

Blog Backlinks Malicious Domain Name Detection via Supervised Learning

Web spam is the unwanted request on websites, low-quality backlinks, emails, and reviews which is generated by an automated program. It is the big threat for website owners; because of it, they can lose their top keywords ranking from search engines, which will result in huge financial loss to the business. Over the years, researchers have tried to identify malicious domains based on specific features. However, lighthouse plugin, Ahrefs tool, and social media platforms features are ignored. In this paper, the authors are focused on detection of the spam domain name from a mixture of legit and spam domain name dataset. The dataset is taken from Google webmaster tools. Machine learning models are applied on individual, distributed, and hybrid features, which significantly improved the performance of existing malicious domain machine learning techniques. Better accuracy is achieved for support vector machine (SVM) classifier, as compared to Naïve Bayes, C4.5, AdaBoost, LogitBoost.

Prospecting the Effect of Topic Modeling in Information Retrieval

Enormous records and data are gathered every day. Organization of this data is a challenging task. Topic modeling provides a way to categorize these documents, where high dimensionality of the corpus affects the result of topic model, making it important to apply feature selection or information retrieval process for dimensionality reduction. The requirement for efficient topic modeling includes the removal of unrelated words that might lead to specious coexistence of the unrelated words. This paper proposes an efficient framework for the generation of better topic coherence, where term frequency-inverse document frequency (TF-IDF) and parsimonious language model (PLM) are used for the information retrieval task. PLM extracts the important information and expels the general words from the corpus, whereas TF-IDF re-estimates the weightage of each word in the corpus. The work carried out in this paper improved the topic coherence measure to provide a better correlation among the actual topic and the topics generated from PLM.

Government Services Bus (GSB)

The Kingdom of Saudi Arabia (KSA) consists of 26 agencies. Each agency is composed of branches that are spread across the KSA. Some have the authority to strategically attract and hire people. Most agencies and branches do not interchange data with each other. People who want to apply to a university for study or to the public government for work have to enter all of their personal data each time. A government services bus (GSB) is a platform that enables agencies and the associated branches to integrate and interlink in order to share data. Most agencies are unaware of the advantages of GSB. COVID-19 has locked down the world in many aspects. This study aims to identify the purpose of GSB and its advantages in order to raise awareness in the agencies, branches, and communities. A questionnaire was conducted to measure the awareness of GSB. Awareness is an essential factor that allows a government agency to understand why it should interlink with GSB. There are numerous advantages to sharing and interchanging data among agencies and providing e-services to other agencies and citizens.

Certain and Uncertain Temporal Data Representation and Reasoning in OWL 2

Temporal data given by Alzheimer's patients are mostly uncertain. Many approaches have been proposed to handle certain temporal data and lack uncertain ones. This paper proposes an approach to represent and reason about quantitative time intervals and points and qualitative relations between them. It is suitable to handle certain and uncertain temporal data. It includes three parts. (1) The authors extend the 4D-fluents approach with certain components to represent certain and uncertain temporal data. (2) They extend the Allen's interval algebra to reason about certain and uncertain time intervals. They adapt these relations to relate a time interval and a time point, and two time points. All relations can be used for temporal reasoning by means of transitivity tables. (3) They propose a certain ontology based on the extensions. A prototype is implemented and integrated into an ontology-based memory prosthesis for Alzheimer's patients to handle uncertain data inputs. The evaluation proves the usefulness of the approach as all the inferences are well established and the precision results are promising.