A Machine Learning Approach to Identify the Preferred Representational System of a Person

Whenever people think about something or engage in activities, internal mental processes will be engaged. These processes consist of sensory representations, such as visual, auditory, and kinesthetic, which are constantly being used, and they can have an impact on a person’s performance. Each person has a preferred representational system they use most when speaking, learning, or communicating, and identifying it can explain a large part of their exhibited behaviours and characteristics. This paper proposes a machine learning-based automated approach to identify the preferred representational system of a person that is used unconsciously. A novel methodology has been used to create a specific labelled conversational dataset, four different machine learning models (support vector machine, logistic regression, random forest, and k-nearest neighbour) have been implemented, and the performance of these models has been evaluated and compared. The results show that the support vector machine model has the best performance for identifying a person’s preferred representational system, as it has a better mean accuracy score compared to the other approaches after the performance of 10-fold cross-validation. The automated model proposed here can assist Neuro Linguistic Programming practitioners and psychologists to have a better understanding of their clients’ behavioural patterns and the relevant cognitive processes. It can also be used by people and organisations in order to achieve their goals in personal development and management. The two main knowledge contributions in this paper are the creation of the first labelled dataset for representational systems, which is now publicly available, and the use of machine learning techniques for the first time to identify a person’s preferred representational system in an automated way

A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data

In criminal records, intentional manipulation of data is prevalent to create ambiguous identity and mislead authorities. Registering data electronically can result in misspelled data, variations in naming order, case sensitive data and inconsistencies in abbreviations and terminology. Therefore, trying to obtain the true identity (or identities) of a suspect can be a challenge for law enforcement agencies. We have developed a targeted approach to identity resolution which uses a rule-based scoring system on physical and official identity attributes and a graph-based analysis on social identity attributes to interrogate policing data and resolve whether a specific target is using multiple identities. The approach has been tested on an anonymized policing dataset, used in the SPIRIT project, funded by the European Union’s Horizon 2020. The dataset contains four ‘known’ identities using a total of five false identities. 23 targets were inputted into the methodology with no knowledge of how many or which had false identities. The rule-based scoring system ranked four of the five false identities with the joint highest score for the relevant target name with the remaining false identity holding the joint second highest score for its target. Moreover, when using graph analysis, 51 suspected false identities were found for the 23 targets with four of the five false identities linked through the crimes they had been involved in. Therefore, an identity resolution approach using both a rule-based scoring system and graph analysis, could be effective in facilitating the investigation process for law enforcement agencies and assisting them in finding criminals using false identities

Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment

The purpose of this research is to investigate the impact of social media sentiments on predicting the Bitcoin price using machine learning models, with a focus on integrating on-chain data and employing a Multi Modal Fusion Model. For conducting the experiments, the crypto market data, on-chain data, and corresponding social media data (Twitter) has been collected from 2014 to 2022 containing over 2000 samples. We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting and a Multi Modal Fusion. Next, we added Twitter sentiment data to the models, using the Twitter-roBERTa and VADAR models to analyse the sentiments expressed in social media about Bitcoin. We then compared the performance of these models with and without the Twitter sentiment data and found that the inclusion of sentiment feature resulted in consistently better performance, with Twitter-RoBERTa-based sentiment giving an average F1 scores of 0.79. The best performing model was an optimised Multi Modal Fusion classifier using Twitter-RoBERTa based sentiment, producing an F1 score of 0.85. This study represents a significant contribution to the field of financial forecasting by demonstrating the potential of social media sentiment analysis, on-chain data integration, and the application of a Multi Modal Fusion model to improve the accuracy and robustness of machine learning models for predicting market trends, providing a valuable tool for investors, brokers, and traders seeking to make informed decisions

An AI powered system to enhance self-reflection practice in coaching

Self-reflection practice in coaching can help with time management by promoting self-awareness. Through this process, a coach can identify habits, tendencies and behaviours that may be causing distraction or make them less productive. This insight can be used to make changes in behaviour and establish new habits that promote effective use of time. This can also help the coach to prioritise goals and create a clear roadmap. An AI powered system has been proposed that maps the conversion onto topics and relations that could help the coach with note-taking and progress identification throughout the session. This system enables the coach to actively self-reflect on time management and make sure the conversation follows the target framework. This will help the coach to better understand the goal setting, breakthrough moment, and client accountability. The proposed end-to-end system is capable of identifying coaching segments (Goal, Option, Reality, and Way forward) across a session with 85% accuracy. Experimental evaluation has also been conducted on the coaching dataset which includes over 1k one-to-one English coaching sessions. In regards to the novelty, there are no datasets of such nor study of this kind to enable self-reflection actively and evaluate in-session performance of the coach

Multifrequency Wilkinson power divider using microstrip nonuniform transmission lines

A new idea is proposed to modify the conventional Wilkinson power dividers to operate at two or several desired frequencies. The proposed structure contains two Microstrip Nonuniform Transmission Lines (MNTLs) instead of two uniform ones with nearly the same length at the minimum frequency. The strip width of MNTLs is considered variable and is written as a truncated Fourier series. Three nonuniform power dividers are designed and one of them operating at frequencies 1.0, 2.8, and 4.5 GHz is fabricated and measured. The measured results of the fabricated diplexer have a good agreement with the theoretical results. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down

Design of Nonuniformly Spaced Antenna Arrays Using Orthogonal Coefficients Equating Method

Orthogonal Coefficients Equating (OCE) method as an analytic method is proposed to synthesize nonuniformly spaced antenna arrays to have array factors nearly equal to that of a previously designed uniformly spaced antenna arrays. In this method, the orthogonal coefficients of array factors of nonuniformly space array are equated to those of uniformly space array. To this end, three orthogonal functions including Chebyshev polynomials, Legendre polynomials and exponential functions are discussed. Some examples are brought to verify the performance of the OCE method for all three orthogonal functions

An artificial intelligence approach to predicting personality types in dogs

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable’s contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: “Excitable/Hyperattached”, “Anxious/Fearful”, “Aloof/Predatory”, “Reactive/Assertive”, and “Calm/Agreeable”. Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles

Subwavelength focusing and resolution with hyperbolic transmission line metamaterial

We show the potential of using two dimensional hyperbolic metamaterials (HMs) made of microstrip transmission line (TL) grids loaded by lumped components for achieving subwavelength focusing and resolution at microwave frequencies. The designed planar HM exhibits a very flat wavevector-dispersion diagram over a wide frequency range, signature of the so-called canalization regime. The canalization regime allows us to transfer the field profile of a single point source at the interface through the HM, with full width half maximum of λg / 69, where λg is the guided wavelength in the isotropic TL grid at 200 MHz. We also report the ability to resolve two point sources with subwavelength distance of λg / 15. © 2013 IEEE

Diclofenac Hypersensitivity: Antibody Responses to the Parent Drug and Relevant Metabolites

Background: Hypersensitivity reactions against nonsteroidal antiinflammatory drugs (NSAIDs) like diclofenac (DF) can manifest as Type I-like allergic reactions including systemic anaphylaxis. However, except for isolated case studies experimental evidence for an IgE-mediated pathomechanism of DF hypersensitivity is lacking. In this study we aimed to investigate the possible involvement of drug-and/or metabolite-specific antibodies in selective DF hypersensitivity. Methodology/Principal Findings: DF, an organochemically synthesized linkage variant, and five major Phase I metabolites were covalently coupled to carrier proteins. Drug conjugates were analyzed for coupling degree and capacity to crosslink receptor-bound IgE antibodies from drug-sensitized mice. With these conjugates, the presence of hapten-specific IgE antibodies was investigated in patients' samples by ELISA, mediator release assay, and basophil activation test. Production of sulfidoleukotrienes by drug conjugates was determined in PBMCs from DF-hypersensitive patients. All conjugates were shown to carry more than two haptens per carrier molecule. Immunization of mice with drug conjugates induced drug-specific IgE antibodies capable of triggering mediator release. Therefore, the conjugates are suitable tools for detection of drug-specific antibodies and for determination of their anaphylactic activity. Fifty-nine patients were enrolled and categorized as hypersensitive either selectively to DF or to multiple NSAIDs. In none of the patients' samples evidence for drug/metabolite-specific IgE in serum or bound to allergic effector cells was found. In contrast, a small group of patients (8/59, 14%) displayed drug/metabolite-specific IgG. Conclusions/Significance: We found no evidence for an IgE-mediated effector mechanism based on haptenation of protein carriers in DF-hypersensitive patients. Furthermore, a potential involvement of the most relevant metabolites in DF hypersensitivity reactions could be excluded