Fuzzy logic based intention recognition in STS processes

This paper represents a fuzzy logic based classifier that is able to recognise human users' intention of standing up from their behaviours in terms of the force they apply to the ground. The research reported focused on the selection of meaningful input data to the classifier and on the determination of fuzzy sets that best represent the intention information hidden in the force data. The classifier is a component of a robot chair which provides the users with assistance to stand up based on the recognised intention by the classifier

Automated ontology framework for service robots

This paper presents an automated ontology framework for service robots. The framework is designed to automatically create an ontology and an instance of concept in dynamic environment. Ontology learning from text is applied to build a concept hierarchy using WordNet which provides a rich semantic processing for physical objects. The Automated Ontology is composed of four modules: Concept Creation, Property Creation, Relationship Creation and Instance of Concept Creation. The automated ontology algorithm was implemented in order to create the concept hierarchy in the Robot Ontology. The Semantic Knowledge Acquisition represents knowledge of physical objects in dynamic environments. In simulation experiments, the list of object names and property names was identified. The result shows the concept hierarchy which represents explicit terms and the semantic knowledge of physical objects for performing everyday manipulation tasks

Visualising Arabic sentiments and association rules in financial text

Text mining methods involve various techniques, such as text categorization, summarisation, information retrieval, document clustering, topic detection, and concept extraction. In addition, because of the difficulties involved in text mining, visualisation techniques can play a paramount role in the analysis and pre-processing of textual data. This paper will present two novel frameworks for the classification and extraction of the association rules and the visualisation of financial Arabic text in order to realize both the general structure and the sentiment within an accumulated corpus. However, mining unstructured data with natural language processing (NLP) and machine learning techniques can be arduous, especially where the Arabic language is concerned, because of limited research in this area. The results show that our frameworks can readily classify Arabic tweets. Furthermore, they can handle many antecedent text association rules for the positive class and the negative class

Tuning of Customer Relationship Management (CRM) via Customer Experience Management (CEM) using sentiment analysis on aspects level

This study proposes a framework that combines a supervised machine learning and a semantic orientation approach to tune Customer Relationship Management (CRM) via Customer Experience Management (CEM). The framework extracts data from social media first and then integrates CRM and CEM by tuning and optimising CRM to reflect the needs and expectations of users on social media. In other words, in order to reduce the gap between the users' predicted opinions in CRM and their opinions on social media, the existing data from CEM will be applied to determine the similar behavioural patterns of customers towards similar outcomes within CRM. CRM data and extracted data from social media will be consolidated by the unsupervised data mining method (association). The framework will lead to a quantitative approach to uncover relationships between the extracted data from social media and the CRM data. The results show that changing some aspects of the e-learning criteria that were required by students in their social media posts can help to enhance the classification accuracy in the learning management system (LMS) data and to understand more students' studying statuses. Furthermore, the results show matching between students' opinions in CRM and CEM, especially in the negative and neutral classes

Sentiment analysis of Arabic tweets in e-learning

In this study, we present the design and implementation of Arabic text classification in regard to university students' opinions through different algorithms such as Support Vector Machine (SVM) and Naive Bayes (NB). The aim of the study is to develop a framework to analyse Twitter "tweets" as having negative, positive or neutral sentiments in education or, in other words, to illustrate the relationship between the sentiments conveyed in Arabic tweets and the students' learning experiences at universities. Two experiments were carried out, one using negative and positive classes only and the other one with a neutral class. The results show that in Arabic, a sentiments SVM with an n-gram feature achieved higher accuracy than NB both with using negative and positive classes only and with the neutral class

Techniques for improving the labelling process of sentiment analysis in the Saudi stock market

Sentiment analysis is utilised to assess users' feedback and comments. Recently, researchers have shown an increased interest in this topic due to the spread and expansion of social networks. Users' feedback and comments are written in unstructured formats, usually with informal language, which presents challenges for sentiment analysis. For the Arabic language, further challenges exist due to the complexity of the language and no sentiment lexicon is available. Therefore, labelling carried out by hand can lead to mislabelling and misclassification. Consequently, inaccurate classification creates the need to construct a relabelling process for Arabic documents to remove noise in labelling. The aim of this study is to improve the labelling process of the sentiment analysis. Two approaches were utilised. First, a neutral class was added to create a framework of reliable Twitter tweets with positive, negative, or neutral sentiments. The second approach was improving the labelling process by relabelling. In this study, the relabelling process applied to only seven random features (positive or negative): "earnings" (Arabic source), "losses" (Arabic source), "green colour" (Arabic source:Arabic source), "growing" (Arabic source), "distribution" (Arabic source), "decrease" (Arabic source), "financial penalty" (Arabic source), and "delay" (Arabic source). Of the 48 tweets documented and examined, 20 tweets were relabelled and the classification error was reduced by 1.34%

Capture and sorting of multiple cells by polarization-controlled three-beam interference

For the capture and sorting of multiple cells, a sensitive and highly efficient polarization-controlled three-beam interference set-up has been developed. With the theory of superposition of three beams, simulations on the influence of polarization angle upon the intensity distribution and the laser gradient force change with different polarization angles have been carried out. By controlling the polarization angle of the beams, various intensity distributions and different sizes of dots are obtained. We have experimentally observed multiple optical tweezers and the sorting of cells with different polarization angles, which are in accordance with the theoretical analysis. The experimental results have shown that the polarization angle affects the shapes and feature sizes of the interference patterns and the trapping force

Research on borehole transient electromagnetic positioning method of azimuthal coil scanning detection

The borehole transient electromagnetic method is used to detect the long-range water-bearing bodies around the borehole wall by using the advanced water exploration borehole in the heading roadway, which avoids the “one’s opinion in one hole” because the normal logging methods can only detect the rock layer of the borehole wall. At present, because of the whole space effect, the current borehole transient electromagnetic method cannot distinguish the orientation of abnormal bodies by the received single-component response, and it can approximate the orientation of abnormal bodies by multi-component response, but it is difficult to precisely locate and interpret the profiles by the visual and effective means such as imaging. Based on the theory of borehole transient electromagnetic method and azimuthal electromagnetic logging, this paper proposes a borehole transient electromagnetic positioning method with azimuthal coil scanning detection. The azimuth coil is used as the detection device to reduce the mutual inductance of the coil and enhance the detection effect of the borehole transient electromagnetic method. At the same time, by changing the rotation angle of the coil, the borehole wall is scanned by 360° to form a radial all-round detection of the borehole, aiming at a precise localization and profile interpretation of low-resistance abnormal bodies in the rock mass at the periphery of the borehole wall. Firstly, the authors derive an analytical expression for the mutual inductance of an azimuth coil, discuss the effect of azimuth angle on azimuth coil mutual inductance, and determine the optimum azimuth angle by numerical calculation. Secondly, a full-space 3D geological-geophysical model of the borehole with homogeneous medium and low-resistance abnormal bodies are established respectively and the numerical simulation of transient electromagnetic field is carried out. The multi-component response characteristics of the azimuthal coil scanning detection transient electromagnetic response are analyzed, and the law of transient electromagnetic response of azimuthal coil scanning detection is summarized, and the borehole transient electromagnetic positioning method with azimuthal coil scanning detection is determined. Namely, through the transient electromagnetic response characteristics of the axial and radial directions of the borehole, the position of the low-resistance abnormal bodies in the rock body at the periphery of the borehole wall is determined. Finally, by establishing the geological-geophysical model with two low-resistance bodies and using azimuth coils set to the best azimuth angle for numerical experiments, it is verified that the method can intuitively and effectively determine the orientation of low resistance abnormal bodies. The study shows that the azimuth coil scanning detection results of the borehole transient electromagnetic method can better reflect the transient electromagnetic field anomaly caused by the low-resistance abnormal bodies in the rock body at the periphery of the borehole wall, and the resolution of the full-space apparent resistivity imaging results is high. Based on the transient electromagnetic response characteristics of the axial and radial directions of the borehole, the borehole transient electromagnetic positioning method with azimuthal coil scanning detection has a high resolution and localization accuracy for the low-resistance abnormal bodies in the rock body at the periphery of the borehole wall. The research results provide a theoretical basis for the practical application of the borehole transient electromagnetic positioning method with azimuthal coil scanning detection

Determination of beam incidence conditions based on the analysis of laser interference patterns

Beam incidence conditions in the formation of two-, three- and four-beam laser interference patterns are presented and studied in this paper. In a laser interference lithography (LIL) process, it is of importance to determine and control beam incidence conditions based on the analysis of laser interference patterns for system calibration as any slight change of incident angles or intensities of beams will introduce significant variations of periods and contrasts of interference patterns. In this work, interference patterns were captured by a He-Ne laser interference system under different incidence conditions, the pattern period measurement was achieved by cross-correlation with, and the pattern contrast was calculated by image processing. Subsequently, the incident angles and intensities of beams were determined based on the analysis of spatial distributions of interfering beams. As a consequence, the relationship between the beam incidence conditions and interference patterns is revealed. The proposed method is useful for the calibration of LIL processes and for reverse engineering applications

Against The Achilles' Heel: A Survey on Red Teaming for Generative Models

Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed. Faced with the problem, the field of red teaming is experiencing fast-paced growth, which highlights the need for a comprehensive organization covering the entire pipeline and addressing emerging topics for the community. Our extensive survey, which examines over 120 papers, introduces a taxonomy of fine-grained attack strategies grounded in the inherent capabilities of language models. Additionally, we have developed the searcher framework that unifies various automatic red teaming approaches. Moreover, our survey covers novel areas including multimodal attacks and defenses, risks around multilingual models, overkill of harmless queries, and safety of downstream applications. We hope this survey can provide a systematic perspective on the field and unlock new areas of research