Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Crowdsourcing subjective annotations using pairwise comparisons reduces bias and error compared to the majority-vote method

How to better reduce measurement variability and bias introduced by subjectivity in crowdsourced labelling remains an open question. We introduce a theoretical framework for understanding how random error and measurement bias enter into crowdsourced annotations of subjective constructs. We then propose a pipeline that combines pairwise comparison labelling with Elo scoring, and demonstrate that it outperforms the ubiquitous majority-voting method in reducing both types of measurement error. To assess the performance of the labelling approaches, we constructed an agent-based model of crowdsourced labelling that lets us introduce different types of subjectivity into the tasks. We find that under most conditions with task subjectivity, the comparison approach produced higher $f_1$ scores. Further, the comparison approach is less susceptible to inflating bias, which majority voting tends to do. To facilitate applications, we show with simulated and real-world data that the number of required random comparisons for the same classification accuracy scales log-linearly $O(N \log N)$ with the number of labelled items. We also implemented the Elo system as an open-source Python package.Comment: Accepted for publication at ACM CSCW 202

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

How does language inform our downstream thinking? In particular, how do humans make meaning from language -- and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In this paper, we propose \textit{rational meaning construction}, a computational framework for language-informed thinking that combines neural models of language with probabilistic models for rational inference. We frame linguistic meaning as a context-sensitive mapping from natural language into a \textit{probabilistic language of thought} (PLoT) -- a general-purpose symbolic substrate for probabilistic, generative world modeling. Our architecture integrates two powerful computational tools that have not previously come together: we model thinking with \textit{probabilistic programs}, an expressive representation for flexible commonsense reasoning; and we model meaning construction with \textit{large language models} (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language. We illustrate our framework in action through examples covering four core domains from cognitive science: probabilistic reasoning, logical and relational reasoning, visual and physical reasoning, and social reasoning about agents and their plans. In each, we show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings, while Bayesian inference with the generated programs supports coherent and robust commonsense reasoning. We extend our framework to integrate cognitively-motivated symbolic modules to provide a unified commonsense thinking interface from language. Finally, we explore how language can drive the construction of world models themselves

Application of single nucleotide polymorphisms (SNPs) to forensic casework in Malaysia

The analysis of degraded DNA can be problematic. Recent advances in the identification and analysis of single nucleotide polymorphisms (SNPs) have demonstrated the advantage of these markers over short tandem repeats (STRs) in that they only require small amplicons. However, before applying to casework, it is important to develop allele frequency databases from relevant populations. The main aim of the study is to characterize three Malaysian major ethnic groups; Malay, Chinese and Indian, using 52 autosomal SNP markers that have been identified in the SNPforID project. Sanchez et. al., 2006 reported a multiplex of 52 SNP markers in one PCR reaction with two single base reaction (SBE) in the detection of SNPs using capillary electrophoresis (CE). The amplicons for PCR ranged from 59 bp to 115 bp. Whilst for SBE reactions ranged from 16 nt to 92 nt. In their study, full complete profile was obtained from 500 pg DNA input. The study was carried out on three major populations: African, Asian and European. As in this study, a total of 325 Malaysian samples (109 from Malays, 107 from Chinese and 109 from Indians) were genotyped. In order to genotype the population samples reliably and robustly, four sets of 13-plex SNPs were developed. Internal validation was carried out using both genetic analyzers, ABI PRISM® 310 and 3500 Genetic Analyzer. Sensitivity and reproducibility studies demonstrated that the assays were highly sensitive, requiring as little as 30 pg of DNA. Full, complete and clear profiles were generated. Data were collected and evaluated statistically for forensic usefulness. Across the three ethnic groups, few significant departures from HWE, at p values <0.05 were observed at 3 SNP markers in Malays, 4 SNP markers in Chinese and 9 SNP markers in Indian samples. Five markers (rs2107612, rs722098, rs2076848, rs907100 and rs1528460) in the Indians and one marker (rs1528460) in the Chinese, showed the lowest p value, that is p=0.0. However, after Bonferroni correction at p <0.00096 significant deviation(s) from HWE was observed at 1 SNP marker (code marker 26) in the Malays, 2 SNP markers (code marker 46 and 54) in the Chinese and 5 SNP markers (code marker 12, 21, 36, 38 and 54) in the Indians. In addition, a pair of loci (at code markers 3 and 53) was found to be associated in the Malays after the Bonferroni correction (at p <0.0000377). As for forensic parameters, the combined mean match probabilities for the 52 SNPs of Malay, Chinese, and Indian were 1 in 3.9654e-19, 5.3964e-18, and 1.7459e-19, corresponding to a combined power of discrimination of >99.99999999%, respectively. Paired Fst values obtained in the study showed, as expected, that Malay group is closely related to the Chinese population, with the Indian population being more distant

Advances in Forensic Genetics

The book has 25 articles about the status and new directions in forensic genetics. Approximately half of the articles are invited reviews, and the remaining articles deal with new forensic genetic methods. The articles cover aspects such as sampling DNA evidence at the scene of a crime; DNA transfer when handling evidence material and how to avoid DNA contamination of items, laboratory, etc.; identification of body fluids and tissues with RNA; forensic microbiome analysis with molecular biology methods as a supplement to the examination of human DNA; forensic DNA phenotyping for predicting visible traits such as eye, hair, and skin colour; new ancestry informative DNA markers for estimating ethnic origin; new genetic genealogy methods for identifying distant relatives that cannot be identified with conventional forensic DNA typing; sensitive DNA methods, including single-cell DNA analysis and other highly specialised and sensitive methods to examine ancient DNA from unidentified victims of war; forensic animal genetics; genetics of visible traits in dogs; statistical tools for interpreting forensic DNA analyses, including the most used IT tools for forensic STR-typing and DNA sequencing; haploid markers (Y-chromosome and mitochondria DNA); inference of ethnic origin; a comprehensive logical framework for the interpretation of forensic genetic DNA data; and an overview of the ethical aspects of modern forensic genetics

Policing Identity

Identity has long played a critical role in policing. Learning “who” an individual is not only affords police knowledge of possible criminal history, but also of “what” an individual might have done. To date, however, these matters have eluded sustained scholarly attention, a deficit that has assumed ever greater significance as government databases have become more comprehensive and powerful. Identity evidence, in short, has and continues to suffer from an identity crisis, which this Article seeks to remedy. The Article does so by first surveying the methods historically used by police to identify individuals, from nineteenth-century efforts to measure bodies and note physical marks to today’s sophisticated biometric identifiers. As this history makes clear, the American justice system has not kept pace with evolving developments and has failed to impose meaningful limits on identity evidence. The Article highlights this shortcoming and offers a remedy, focusing on two central, yet unresolved questions: (1) whether and how limits should be placed on the collection, retention and use of legally obtained identity evidence, DNA in particular, and (2) whether identity evidenced illegally secured by police should be subject to suppression. In doing so, the Article provides a much-needed analytic framework for courts as they seek to balance social control needs and individual civil liberties

Biometrics Institute 20th Anniversary Report

The purpose of this report is to mark the 20-year anniversary of the Biometrics Institute on the 11 October 2021. More importantly, however, this report celebrates the work of the Biometrics Institute over the past twenty years, which together with the support of its members, has provided a platform for a balanced discussion promoting the responsible and ethical use of biometrics and a deeper understanding of the biometrics industry

Program and abstracts

We are pleased that the program in 2022 will be more interesting than ever and it will include the following topics: Mathematical Modeling in Cancer Therapy, Gene Therapy, Archaeological Genetics, New perspectives in Human Forensic Molecular Biology, Genomics in Medicine, Pharmacogenomics and Drug Development, Stem Cells in Medicine, Regenerative Medicine, Ribosomes in Medicine, Epigenomics, Crime Scene Investigation, Forensic Genetics, and Mass Catastrophes Managements. This year, the third "Nobel Spirit" will provide a forum to the three Nobel laureates to stimulate public discussion on the role of science in solving global health issues, acute regional problems such as brain drain, demographic decline, as well as cultural and social change. In addition, we are organizing a very stimulating Session on Bioanthropology and global health in the times of crisis, as well as Joint Event ISABS and Ministry of the Interior - Crime Scene Investigation Training Course: Mystery on the ship —Investigation of the water-related crime scene