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Primary cultures of glial and endothelial cells are important tools for basic and translational neuroscience research. Primary cell cultures are usually generated from rodent brain although considerable differences exist between human and rodent glia and endothelial cells. Because many translational research projects aim to identify mechanisms that eventually lead to diagnostic and therapeutic approaches to target human diseases, glia, and endothelial cultures are needed that better reflect the human central nervous system (CNS). Pig brain is easily accessible and, in many aspects, close to the human brain. We established an easy and cost-effective method to isolate and culture different primary glial and endothelial cells from adult pig brain. Oligodendrocyte, microglia, astrocyte, and endothelial primary cell cultures were generated from the same brain tissue and grown for up to 8 weeks. Primary cells showed lineage-specific morphology and expressed specific markers with a purity ranging from 60 to 95%. Cultured oligodendrocytes myelinated neurons and microglia secreted tumor necrosis factor alpha when induced with lipopolysaccharide. Endothelial cells showed typical tube formation when grown on Matrigel. Astrocytes enhanced survival of co-cultured neurons and were killed by Aquaporin-4 antibody positive sera from patients with Neuromyelitis optica. In summary, we established a new method for primary oligodendrocyte, microglia, endothelial and astrocyte cell cultures from pig brain that provide a tool for translational research on human CNS diseases.

Electrified transportation technology has matured in different parts of the globe. However, this technology is in an advent stage in the Indian market. Due to this fact, a lot more challenges are being encountered in the development of electrified transportation in India; with the scarcity of viable charging stations posing as a significant bottleneck. In this study, the techno-economic analysis of different solar-based charging schemes that are available in the existing environment and present a modest, economical and reliable method of charging an electric vehicle (EV) )(e.g. e-rickshaw) through a solar panel that ultimately enhances the driving range and overall reliability of the system has been done. To validate the performance, the prototype of vehicle-integrated photovoltaic (PV) charging system has been developed and test results are demonstrated. Economic analysis is done based on the yearly average solar irradiance profile in Aligarh, India. Further, this work presents a comparative analysis of CO2 emission for 100 km driving range from the EVs charge by different charging schemes and internal combustion engine vehicles.

Doc number: O38

Biometrics has become a viable and popular solution for applications which require secure authentication. In spite of the advantages of biometrics as an automatic authentication technology, the usage of biometric characteristics raises significant concerns regarding personal data and privacy of subjects in these systems. To address these concerns, several biometric template protection schemes have been proposed in the literature to achieve trustworthy, reliable and privacy-preserving systems. In this paper, we benchmark several cancelable biometrics (CB) schemes on different biometric characteristics. We consider BioHashing, Multi-Layer Perceptron (MLP) hashing, Bloom filters, and two schemes based on Index-of-Maximum (IoM) hashing (i.e. IoM-URP and IoM-GRP). In addition to the mentioned CB schemes, we introduce a CB scheme (as a baseline) based on user-specific random transformations followed by binarization. We evaluate the unlinkability, irreversibility, and recognition performance (which are the required criteria by the ISO/IEC 24745 standard) of these CB schemes on deep learning-based templates extracted from different physiological and behavioural biometric characteristics including face, voice, finger vein, and iris. Our experiments show that all the studied CB schemes are almost unlinkable for different characteristics. We also observe that the mutual information (MI) between protected and unprotected templates varies according to the scenario and biometric characteristic. In terms of recognition accuracy, our study shows that deep templates protected by Bloom filters suffer from a drop in performance, while other CB schemes achieve competitive accuracies for different biometric characteristics. We provide an open-source implementation of all the experiments presented to facilitate the reproducibility of our results: https://github.com/otroshi/benchmark_cb.

In this paper, we undertake a novel two-pronged investigation into the human recognition of deepfake speech, addressing critical gaps in existing research. First, we pioneer an evaluation of the impact of prior information on deepfake recognition, setting our work apart by simulating real-world attack scenarios where individuals are not informed in advance of deepfake exposure. This approach simulates the unpredictability of real-world deepfake attacks, providing unprecedented insights into human vulnerability under realistic conditions. Second, we introduce a novel metric to evaluate the quality of deepfake audio. This metric facilitates a deeper exploration into how the quality of deepfake speech influences human detection accuracy. By examining both the effect of prior knowledge about deepfakes and the role of deepfake speech quality, our research reveals the importance of these factors, contributes to understanding human vulnerability to deepfakes, and suggests measures to enhance human detection skills.

A large number of images are available on the Internet with the growth of social networking services, and many of them are face photos or contain faces. It is necessary to protect the privacy of face images to prevent their malicious use by face image de-identification techniques that make face recognition difficult, which prevent the collection of specific face images using face recognition. In this paper, we propose a face image de-identification method that generates a de-identified image from an input face image by embedding facial features extracted from that of another person into the input face image. We develop the novel framework for embedding facial features into a face image and loss functions based on images and features to de-identify a face image preserving its appearance. Through a set of experiments using public face image datasets, we demonstrate that the proposed method exhibits higher de-identification performance against unknown face recognition models than conventional methods while preserving the appearance of the input face images.

Contactless hand biometrics has emerged as an alternative to traditional biometric characteristics, e.g., fingerprint or face, as it possesses distinctive properties that are of interest in forensic investigations. As a result, several hand-based recognition techniques have been proposed with the aim of identifying both wanted criminals and missing victims. The great success of deep neural networks and their application in a variety of computer vision and pattern recognition tasks has led to hand-based algorithms achieving high identification performance on controlled images with few variations in, e.g., background context and hand gestures. This article provides a comprehensive review of the scientific literature focused on contactless hand biometrics together with an in-depth analysis of the identification performance of freely available deep learning-based hand recognition systems under various scenarios. Based on the performance benchmark, the relevant technical considerations and trade-offs of state-of-the-art methods are discussed, as well as further topics related to this research field.

This paper is roughly in two parts. The first deals with whether knowhow is constituted by propositional knowledge, as discussed primarily by Gilbert Ryle (1949) The concept of mind. London: Hutchinson, Jason Stanley and Timothy Williamson (2001). Knowing how. \"Journal of Philosophy,\" 98, pp. 411-444 as well as Stephen Hetherington (2006). How to know that knowledge-that is knowledge-how. In S. Hetherington (Ed.) \"Epistemology futures.\" Oxford: Oxford University Press. The conclusion of this first part is that know-how sometimes does and sometimes does not consist in propositional knowledge. The second part defends an analysis of know-how inspired by Katherine Hawley (2003). Success and knowledge-how. \"American Philosophical Quarterly,\" 40, pp. 19-31, insightful proposal that know-how requires counterfactual success. I conclude by showing how this analysis helps to explain why know-how sometimes does and sometimes does not consist of propositional knowledge.

In cryptocurrency transactions, effective wallet key management by users is crucial for asset protection. Fuzzy Signature (FS) schemes enhance the binding between signers and their keys by using biometric information instead of traditional signing keys, offering secure and convenient key management. However, FS faces two significant privacy concerns. First, using biometric information as the signing key restricts users’ ability to manage multiple distinct keys for different applications. Second, the design that absorbs biometric fuzziness during verification results in publicly available verification keys that contain data dependent on biometric information. This paper introduces Biometrically-Independent Fuzzy Signature (BIFS), a variant addressing existing FS scheme problems. We propose a generic construction for BIFS and present a BIFS scheme compatible with BLS signatures supported by Ethereum 2.0. Experimental results confirm that the success rate of signature generation maintains the same authentication accuracy as the employed facial recognition system. Moreover, our results show that the proposed scheme is practical since the signature generation process requires approximately 2 ms.

Finger vein recognition is an emerging biometric trait known for its privacy features. Despite the remarkable performance of deep learning methods like convolutional neural networks on challenging finger vein datasets, their reliability and robustness need further examination. This study evaluates the robustness of three recognition methods—the traditional Miura Method, a supervised convolutional neural network, and an unsupervised convolutional auto-encoder—through the challenging and more realistic scenario of cross-dataset comparisons. We also analyse the reliability of these methods in terms of sample quality. We introduce a novel vein quality metric to measure vein clarity and complexity and compare it against an existing image quality metric, natural image quality evaluator. Our findings reveal differences in how these recognition methods utilise finger vein images for comparisons, highlighting the need for robust recognition techniques in more realistic scenarios. In addition, our vein quality metric effectively detects defective images, reducing the zero false-match rate from 34.98% to 8.18% on the SDUMLA-HMT dataset. These results indicate the need for metrics more focussing on finger vein image characteristics for effective quality assessment for finger vein images.