With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score.

Cyber-Physical-Social Systems (CPSSs) represent a transformative paradigm that integrates human, machine, and environmental interactions to support intelligent services in smart spaces. However, providing accurate and efficient user activity analysis in such environments remains challenging due to the complex, high-dimensional, and noisy nature of sensory data. Although existing tensor-based models have shown promising accuracy in user activity analysis, they often suffer from low efficiency and reduced robustness to data noise, limiting their practicality in real-time applications. To address these challenges, this study proposes a Tensor-based Hidden Semi-Markov Model (T-HSMM) designed to efficiently analyze user activity durations and their dependencies using probabilistic distributions in tensor space. The main objective is to reduce redundant tensor computations while enhancing both the accuracy and robustness of activity analysis. Moreover, to effectively address the three basic micro-services in CPSSs—evaluation, learning, and prediction—we develop tensor-based algorithms, including the Forward-Backward, Baum-Welch, and Viterbi algorithms, for the proposed T-HSMM. These algorithms facilitate three computational subtasks of activity sequence probabilities, model parameter learning, and activity prediction. We evaluated the performance of the proposed model on three widely used open datasets. The results show that T-HSMM surpasses other models in terms of accuracy, precision, recall, and F1-score while maintaining acceptable time consumption. Additionally, we discuss the impact of varying parameters on the model's performance across different daily activities.

Idea mining is a new research topic which is gaining momentous attention among knowledge engineering research community. It aims to extend the benefits of the information retrieval by sifting through historical documents so that valuable machine-proposed ideas can be extracted. This paper is mainly focusing on the evaluation of candidate ideas generated by idea mining systems. The main challenge faced by judges is how to evaluate the extracted ideas. Different evaluation methods are critically explored, and evaluation criteria are proposed accordingly. The results showed that the Likert scale measurement is more reliable than binary scale measurement.

World Wide Web provides vast of resources to the public. Currently, many researches have been done on resources sharing among users through implementation of ontologies. Knowledge in an ontology are represented in the form of triple(s-p-o), where concepts are brought together by a relation. In a situation where there is a need to represent a resource which exist without IRI, blank node can be implemented in placed of the resource. Increase number of blank nodes implemented will increase the complexity of ontology structure. Since it is impossible to avoid blank nodes implementation in the ontology, increase used of it might lead to the intractable of data during the information retrieval. This paper presents a new clause-based structure that able to handle N-ary, container, collection and reified knowledge issues brought by the blank node application. The result shows that the structure able to store complicated knowledge without the need to implement blank node.

There are many linguistic morphology tools available in the market for commercial and research purposes. Morphology technique are incorporated into these tools to ensure its ability to study the internal structure of natural language words. This technique plays an important role in reducing the number of vocabularies used, at the same time retains the semantic meaning of the knowledge in NLP system. Among the algorithms implemented, majority of them only has to ability to carry out stemming process instead of a lemmatization process. Even with technology advancement, yet none of the available lemmatization algorithms able to produce 100% accurate result. Inappropriate words produced by the current algorithm might alter the overall meaning it tried to represent, which will directly affect the outcome of NLP system. This paper proposed a new method to handle lemmatization process during the morphological analysis. The method consist three layers of lemmatization process, which incorporate the implementation of a well known Stanford parser API, WordNet database and adaptive learning technique. Stanford parser API is implemented in the first layer of lemmatization process, whereas WordNet database and adaptive learning technique are implemented in the second layer and finally another lemmatization algorithm in the final layer. The lemmatised words yields from the proposed method are much more appropriate compare to the previous algorithms due to user participation in the adaptive learning technique, which will ultimately improve the semantic knowledge represented and stored in the knowledge base.

Providing easy access to Picture Archiving and Communications Systems (PACS) of selected image slices based on its diagnosis would be useful to teach medical students and educators and for government policies. This requires a simplified retrieval presenting only the key images to the doctors that has the diagnosis for every study of interest, thus saving doctors time. An automatic detection of diagnosis will help the radiographers in saving time since they consume a lot of time in the process of detecting skull fractures manually and with the automatic annotation of the pathological terms only to the key slices that has findings/diagnosis of the entire image set an efficient retrieval of specific key slices can be achieved. One important abnormality in the skull is its fracture. The proposed research goal concentrates on the automatic detection of normal and abnormal skull images as a part of our work. This paper presents a simple and fast automatic method to detect skull fracture in Digital Imaging and Communications in Medicine (DICOM) to extract the skull bone using histogram based thresholding and with the neighboring pixel connectivity search to identify the fracture. The experimental results of this approach are reliable with high detection rate.

Deceptive opinion classification has attracted a lot of research interest due to the rapid growth of social media users. Despite the availability of a vast number of opinion features and classification techniques, review classification still remains a challenging task. In this work we applied stylometric features, i.e. lexical and syntactic, using supervised machine learning classifiers, i.e. Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) and Naive Bayes, to detect deceptive opinion. Detecting deceptive opinion by a human reader is a difficult task because spammers try to write wise reviews, therefore it causes changes in writing style and verbal usage. Hence, considering the stylometric features help to distinguish the spammer writing style to find deceptive reviews. Experiments on an existing hotel review corpus suggest that using stylometric features is a promising approach for detecting deceptive opinions.

We present an alternative technique for discovering aggregate usage profiles from Web access logs. The technique is based on clustering information needs inferred from users' browsing paths. Browsing paths are extracted from users' access logs. Information need is inferred from each browsing path by using the Ostensive Model[1]. The technique is evaluated in a document recommendation application. We compare the performance of our technique against the well-established transaction-based technique proposed in [2]. Based on an initial evaluation, the results are encouraging.