2,390 research outputs found

    A Robust Fault-Tolerant and Scalable Cluster-wide Deduplication for Shared-Nothing Storage Systems

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    Deduplication has been largely employed in distributed storage systems to improve space efficiency. Traditional deduplication research ignores the design specifications of shared-nothing distributed storage systems such as no central metadata bottleneck, scalability, and storage rebalancing. Further, deduplication introduces transactional changes, which are prone to errors in the event of a system failure, resulting in inconsistencies in data and deduplication metadata. In this paper, we propose a robust, fault-tolerant and scalable cluster-wide deduplication that can eliminate duplicate copies across the cluster. We design a distributed deduplication metadata shard which guarantees performance scalability while preserving the design constraints of shared- nothing storage systems. The placement of chunks and deduplication metadata is made cluster-wide based on the content fingerprint of chunks. To ensure transactional consistency and garbage identification, we employ a flag-based asynchronous consistency mechanism. We implement the proposed deduplication on Ceph. The evaluation shows high disk-space savings with minimal performance degradation as well as high robustness in the event of sudden server failure.Comment: 6 Pages including reference

    Bridging the Spoof Gap: A Unified Parallel Aggregation Network for Voice Presentation Attacks

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    Automatic Speaker Verification (ASV) systems are increasingly used in voice bio-metrics for user authentication but are susceptible to logical and physical spoofing attacks, posing security risks. Existing research mainly tackles logical or physical attacks separately, leading to a gap in unified spoofing detection. Moreover, when existing systems attempt to handle both types of attacks, they often exhibit significant disparities in the Equal Error Rate (EER). To bridge this gap, we present a Parallel Stacked Aggregation Network that processes raw audio. Our approach employs a split-transform-aggregation technique, dividing utterances into convolved representations, applying transformations, and aggregating the results to identify logical (LA) and physical (PA) spoofing attacks. Evaluation of the ASVspoof-2019 and VSDC datasets shows the effectiveness of the proposed system. It outperforms state-of-the-art solutions, displaying reduced EER disparities and superior performance in detecting spoofing attacks. This highlights the proposed method's generalizability and superiority. In a world increasingly reliant on voice-based security, our unified spoofing detection system provides a robust defense against a spectrum of voice spoofing attacks, safeguarding ASVs and user data effectively

    Securing Voice Biometrics: One-Shot Learning Approach for Audio Deepfake Detection

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    The Automatic Speaker Verification (ASV) system is vulnerable to fraudulent activities using audio deepfakes, also known as logical-access voice spoofing attacks. These deepfakes pose a concerning threat to voice biometrics due to recent advancements in generative AI and speech synthesis technologies. While several deep learning models for speech synthesis detection have been developed, most of them show poor generalizability, especially when the attacks have different statistical distributions from the ones seen. Therefore, this paper presents Quick-SpoofNet, an approach for detecting both seen and unseen synthetic attacks in the ASV system using one-shot learning and metric learning techniques. By using the effective spectral feature set, the proposed method extracts compact and representative temporal embeddings from the voice samples and utilizes metric learning and triplet loss to assess the similarity index and distinguish different embeddings. The system effectively clusters similar speech embeddings, classifying bona fide speeches as the target class and identifying other clusters as spoofing attacks. The proposed system is evaluated using the ASVspoof 2019 logical access (LA) dataset and tested against unseen deepfake attacks from the ASVspoof 2021 dataset. Additionally, its generalization ability towards unseen bona fide speech is assessed using speech data from the VSDC dataset

    Neuroimaging of Tuberculosis- Modalities, Imaging Protocols and Radiomics: A Review

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    Background and objective: A large number of review and research articles exists in literature which describe the radiological appearance of various manifestations of nervous system tuberculosis, however there is paucity of text which describes the application of each and every imaging modality in the workup of the entire spectrum of this pathology. The intent of this article is to review the existing literature on the role of different radiological modalities in the stepwise work up of CNS TB. The article focuses on the role of plain radiograph, fluoroscopy, computed tomography, magnetic resonance imaging along with its advanced sequences and nuclear medicine in imaging of the many faces of tuberculosis in CNS. The article also aims to review the existing literature on the role of MR based textural analysis (Radiomics) as a problem-solving tool in various nervous system pathologies. Methods: We searched PubMed central databases for articles published in English from January 1 2000 to February 28 2021 along with references from the relevant articles. The search terms included “imaging in central nervous system tuberculosis” “Radiomics in tuberculosis “, “Radomics in central nervous system ”. In total 95 articles including case reports, case series, original articles and review articles were included in this review. Results: Conventional imaging modalities including radiograph and fluoroscopy are becoming extinct in work up of tuberculosis in the nervous system itself, however a plain radiograph still holds a key position in screening the chest for presence of subclinical respiratory tract infection in patients presenting with brain tuberculosis. In addition, it is a sensitive tool as baseline investigation in workup of spinal tuberculosis (T.B). Fluoroscopy is a useful tool in image guided procedures for collection of samples for histopathology and CSF analysis. Cross sectional imaging modalities including computed tomography and magnetic resonance imaging have revolutionized imaging of central nervous system pathologies in particular tuberculosis. Computed tomography acts as a screening tool to identify the presence of intracranial tuberculosis and recognize its complications. In addition it is an important tool to determine the extent of spinal T.B. Magnetic Resonance Imaging (MRI) along with its advanced sequences including spectroscopy, Magnetization transfer T1 sequence (MT T1), perfusion imaging, and magnetic resonance angiography (MRA) and magnetic resonance venography (MRV) is an ideal imaging method to work up CNS TB. It can identify numerous manifestations of tuberculosis in the brain, work up its associated complication, and explain the extent of neurological symptoms. Moreover, it has the capability to differentiate TB from other nervous system infections. Furthermore, it can differentiate neoplastic and inflammatory brain disorders from CNS TB. Radiomics, particularly the textural features based on MR imaging is the future of neuroimaging. Its role is getting established in the work up of several intracranial pathologies including brain tumors and neurodegenerative disorders. Certainly, it has significant potential in the imaging work up of CNS tuberculosis, which is underexplored and therefore requires the central attention of upcoming researchers focusing on this topic. Conclusion: Cross sectional imaging is the mainstay of imaging workup. Nuclear imaging is becoming an essential adjuvant to determine the burden of the disease. Role of radiomics is evolving in intracranial pathologies and certainly needs the central attention of future researches to establish its role in CNS TB imaging

    Ontology Evolution Using Recoverable SQL Logs

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    Logs of SQL queries are useful for building the system design, upgrading, and checking which SQL queries are running on certain applications. These SQL queries provide us useful information and knowledge about the system operations. The existing works use SQL query logs to find patterns when the underlying data and database schema is not available. For this purpose, a knowledge-base in the form of an ontology is created which is then mined for knowledge extraction. In this paper, we have proposed an approach to create and evolve an ontology from logs of SQL queries. Furthermore, when these SQL queries are transformed into the ontology, they loose their original form/shape i.e., we do not have original SQL queries. Therefore, we have further proposed a strategy to recover these SQL queries in their original form. Experiments on real world datasets demonstrate the effectiveness of the proposed approach
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