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

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

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

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

Application of Graph-Based Technique to Identity Resolution

These days the ability to prove an individual identity is crucial in social, eco-nomic and legal aspects of life. Identity resolution is the process of semantic reconciliation that determines whether a single identity is the same when be-ing described differently. The importance of identity resolution has been greatly felt these days in the world of online social networking where per-sonal details can be fabricated or manipulated easily. In this research a new graph-based approach has been used for identity resolution, which tries to resolve an identity based on the similarity of attribute values which are relat-ed to different identities in a dataset. Graph analysis techniques such as cen-trality measurement and community detection have been used in this ap-proach. Moreover, a new identity model has been used for the first time. This method has been tested on SPIRIT policing dataset, which is an anony-mized dataset used in SPIRIT project funded by European Union’s Horizon 2020. There are 892 identity records in this dataset and two of them are ‘known’ identities who are using two different names, but they are both be-longing to the same person. These two identities were recognized successful-ly after using the presented method in this paper. This method can assist po-lice forces in their investigation process to find criminals and those who committed a fraud. It can also be useful in other fields such as finance and banking, marketing or customer service

A Graph-Based Method for Identity Resolution to Assist Police Force Investigative Process

The ability to prove an individual identity has become crucial in social, economic, and legal aspects of life. Identity resolution is the process of semantic reconciliation that determines whether a single identity is the same when being described differently. This paper introduces a novel graph-based methodology for identity resolution, designed to reconcile identities by analysing the similarity of attribute values associated with different identities within a policing dataset. The proposed methodology employs graph analysis techniques, including centrality measurement and community detection, to enhance the identity resolution process. This paper also presents a new identity model for identity resolution. SPIRIT policing dataset was used for testing the proposed methodology. This dataset is an anonymised dataset used in SPIRIT project funded by EU Horizon. It contains 892 identity records and among these, two 'known' identities utilize different names but actually represent the same individual. The presented method successfully recognised these two identities. Additionally, another experimental evaluation was conducted on a refined and extended version of the dataset and the false identities were successfully detected. This method can assist police forces in identifying criminals and fraudsters using fake identities and has applications across finance, marketing, and customer service

Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning

This paper explores the application of machine learning in battery recycling, aiming to enhance sustainability and process efficiency. The research focuses on three key areas: (i) Investigating machine learning's potential in predicting battery recycling viability, optimizing processes, and improving resource recovery. (ii) Assessing machine learning's impact on addressing engineering challenges within recycling. (iii) Introducing a streamlined framework for the application of machine learning in this domain. The study comprehensively analyzes scientific principles, methodologies, and algorithms relevant to battery recycling. Furthermore, it examines practical implications and challenges associated with implementing machine learning techniques in real-world scenarios. Our comparative analysis reveals that the proposed framework offers numerous advantages and effectively addresses common limitations seen in previous models. Notably, this framework provides detailed insights into pre-processing, feature engineering, and evaluation phases, catering to researchers with varying technical skills for effective model application in analysis and product development