Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD

Fall Detection and Motion Analysis Using Visual Approaches

Falls are considered one of the most ubiquitous problems leading to morbidity and disability in the elderly. This paper presents a vision-based approach toward the care and rehabilitation of the elderly by examining the important body symmetry features in falls and activities of daily living (ADL). The proposed method carries out human skeleton estimation and detection on image datasets for feature extraction to predict falls and to analyze gait motion. The extracted skeletal information is further evaluated and analyzed for the fall risk factors in order to predict a fall event. Four critical risk factors are found to be highly correlated to falls, including 2D motion (gait speed), gait pose, 3D trunk angle or body orientation, and body shape (width-to-height ratio). Different variants of deep architectures, including 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) Network, Gated Recurrent Units (GRU) model, and attention-based mechanism, are investigated with several fusion techniques to predict the fall based on human body balance study. A given test gait sequence will be classified into one of the three phases: non-fall, pre-impact fall, and fall. With the attention-based GRU architecture, an accuracy of 96.2% can be achieved for predicting a falling event

A review of abnormal behavior detection in activities of daily living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

Abnormal behavior recognition using SRU with attention mechanism

In response to the critical need for enhanced public safety measures, this study introduces an advanced intelligent surveillance system designed to autonomously detect abnormal behaviors within public spaces. Leveraging the computational efficiency and accuracy of a Simple Recurrent Unit (SRU) integrated with an attention mechanism, this research delivers a novel approach towards understanding and interpreting human interactions in real-time video footage. Distinctively, the model specializes in identifying two primary categories of abnormal behavior: aggressive two-person interactions such as physical confrontations and collective crowd dynamics, characterized by sudden dispersal patterns indicative of distress or danger. The incorporation of Attention mechanism precisely targets critical elements of behavior, thereby enhancing the model's focus and interpretative clarity. Empirical validation across five benchmark datasets reveals that our model not only outperforms traditional Long Short-Term Memory (LSTM) frameworks in terms of speed by a factor of 1.5 but also demonstrates superior accuracy in abnormal behavior recognition. These findings not only underscore the model's potential in preempting potential safety threats but also mark a significant advancement in the application of deep learning technologies for public security infrastructures. This research contributes to the broader discourse on public safety, offering actionable insights and robust technological solutions to enhance surveillance efficacy and response mechanisms in critical public domains

Non-invasive health prediction from visually observable features [version 2; peer review: 1 approved, 1 approved with reservations]

Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches

Review on Digital Signal Processing (DSP) Algorithm for Distributed Acoustic Sensing (DAS) for Ground Disturbance Detection

Fiber break because of third-party intrusion has become one of the challenges in maintaining the fiber-based communication link, especially those buried underground. Hence, we investigate the feasibility of using Distributed Acoustic Sensing (DAS) system to sense possible surrounding activities that might cause fiber break. This paper reviews the current digital signal processing (DSP) algorithm used in the DAS system designed to detect ground disturbance, highlighting the specific design parameters for each technique. These parameters include identification rate, classification accuracy, detection accuracy, training time, and signal-to-noise ratio (SNR). The algorithms used are near-field beamforming, phased-array beamforming, image edge detection, gaussian mixture model (GMM), gaussian mixture model - hidden Markov model (GMM-HMM), faster region-based convolutional neural networks (R-CNN), transfer learning, dual-stage recognition network, group convolutional neural network (100G-CNN), and support vector machine (SVM). By reviewing the existing techniques used in the DAS system for ground disturbance detection, we can determine the best DSP algorithm that should be implemented for fiber break prevention, enabling us to design a DAS system specifically for it in the near future

More than a piece of cake:Noun classifier processing in primary progressive aphasia

INTRODUCTION: Clinical understanding of primary progressive aphasia (PPA) has been primarily derived from Indo-European languages. Generalizing certain linguistic findings across languages is unfitting due to contrasting linguistic structures. While PPA patients showed noun classes impairments, Chinese languages lack noun classes. Instead, Chinese languages are classifier language, and how PPA patients manipulate classifiers is unknown. METHODS: We included 74 native Chinese speakers (22 controls, 52 PPA). For classifier production task, participants were asked to produce the classifiers of high-frequency items. In a classifier recognition task, participants were asked to choose the correct classifier. RESULTS: Both semantic variant (sv) PPA and logopenic variant (lv) PPA scored significantly lower in classifier production task. In classifier recognition task, lvPPA patients outperformed svPPA patients. The classifier production scores were correlated to cortical volume over left temporal and visual association cortices. DISCUSSION: This study highlights noun classifiers as linguistic markers to discriminate PPA syndromes in Chinese speakers. Highlights: Noun classifier processing varies in the different primary progressive aphasia (PPA) variants. Specifically, semantic variant PPA (svPPA) and logopenic variant PPA (lvPPA) patients showed significantly lower ability in producing specific classifiers. Compared to lvPPA, svPPA patients were less able to choose the accurate classifiers when presented with choices. In svPPA, classifier production score was positively correlated with gray matter volume over bilateral temporal and left visual association cortices in svPPA. Conversely, classifier production performance was correlated with volumetric changes over left ventral temporal and bilateral frontal regions in lvPPA. Comparable performance of mass and count classifier were noted in Chinese PPA patients, suggesting a common cognitive process between mass and count classifiers in Chinese languages.</p

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Adding 6 months of androgen deprivation therapy to postoperative radiotherapy for prostate cancer: a comparison of short-course versus no androgen deprivation therapy in the RADICALS-HD randomised controlled trial

Background Previous evidence indicates that adjuvant, short-course androgen deprivation therapy (ADT) improves metastasis-free survival when given with primary radiotherapy for intermediate-risk and high-risk localised prostate cancer. However, the value of ADT with postoperative radiotherapy after radical prostatectomy is unclear. Methods RADICALS-HD was an international randomised controlled trial to test the efficacy of ADT used in combination with postoperative radiotherapy for prostate cancer. Key eligibility criteria were indication for radiotherapy after radical prostatectomy for prostate cancer, prostate-specific antigen less than 5 ng/mL, absence of metastatic disease, and written consent. Participants were randomly assigned (1:1) to radiotherapy alone (no ADT) or radiotherapy with 6 months of ADT (short-course ADT), using monthly subcutaneous gonadotropin-releasing hormone analogue injections, daily oral bicalutamide monotherapy 150 mg, or monthly subcutaneous degarelix. Randomisation was done centrally through minimisation with a random element, stratified by Gleason score, positive margins, radiotherapy timing, planned radiotherapy schedule, and planned type of ADT, in a computerised system. The allocated treatment was not masked. The primary outcome measure was metastasis-free survival, defined as distant metastasis arising from prostate cancer or death from any cause. Standard survival analysis methods were used, accounting for randomisation stratification factors. The trial had 80% power with two-sided α of 5% to detect an absolute increase in 10-year metastasis-free survival from 80% to 86% (hazard ratio [HR] 0·67). Analyses followed the intention-to-treat principle. The trial is registered with the ISRCTN registry, ISRCTN40814031, and ClinicalTrials.gov, NCT00541047. Findings Between Nov 22, 2007, and June 29, 2015, 1480 patients (median age 66 years [IQR 61–69]) were randomly assigned to receive no ADT (n=737) or short-course ADT (n=743) in addition to postoperative radiotherapy at 121 centres in Canada, Denmark, Ireland, and the UK. With a median follow-up of 9·0 years (IQR 7·1–10·1), metastasis-free survival events were reported for 268 participants (142 in the no ADT group and 126 in the short-course ADT group; HR 0·886 [95% CI 0·688–1·140], p=0·35). 10-year metastasis-free survival was 79·2% (95% CI 75·4–82·5) in the no ADT group and 80·4% (76·6–83·6) in the short-course ADT group. Toxicity of grade 3 or higher was reported for 121 (17%) of 737 participants in the no ADT group and 100 (14%) of 743 in the short-course ADT group (p=0·15), with no treatment-related deaths. Interpretation Metastatic disease is uncommon following postoperative bed radiotherapy after radical prostatectomy. Adding 6 months of ADT to this radiotherapy did not improve metastasis-free survival compared with no ADT. These findings do not support the use of short-course ADT with postoperative radiotherapy in this patient population