Miracle-dz/UniFAR-FTUs huggingface dataset:
Lip Reading-Based User Authentication Through Acoustic Sensing on Smartphones

To prevent users’ privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, and so on, to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although the state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Toward this end, we explore liveness verification of user authentication leveraging users’ mouth movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users’ speaking mouths through acoustic sensing on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users’ speaking mouths and find that there are unique mouth movement patterns for different individuals. To characterize the mouth movements, we propose a deep learning-based method to extract efficient features from Doppler profiles and employ softmax function, support vector machine, and support vector domain description to construct multi-class identifier, binary classifiers, and spoofer detectors for mouth state identification, user identification, and spoofer detection, respectively. Afterward, we develop a balanced binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.2% accuracy in user identification and 93.1% accuracy in spoofer detection.
Functional Brain Areas Mapping in Patients with Glioma Based on Resting-State fMRI Data Decomposition

In current work we propose a three-step approach to automatic and efficient functional brain areas mapping as well demonstrate in case studies on three patients with gliomas the potential applicability of constrained source separation technique (semiblind Independent Component Analysis, ICA) to brain networks discovery and the similarity of task-based-fMRI (t-fMRI) and resting state-fMRI (rs-fMRI) results. Blind and semiblind ICA-analysis was applied for both methods t-fMRI and rs-fMRI. To measure similarity between spatial maps we used Dice coefficient, which shows the ratio of overlapping voxels and all active voxels in two compared maps for each patient Based on the analysis of Dice coefficients, there was a fairly high degree of overlap between the t-fMRI active areas, Broca and Wernicke and the language network obtained from rs-fMRI. The degree of motor areas overlap with sensorimotor network is less pronounced, but the activation sites correspond to anatomical landmarks - a complex of central gyri and supplementary motor area. In general, in comparisons of the functional brain areas obtained with t-fMRI and rs-fMRI, there is a greater specificity of semiblind ICA compared to blind ICA. RSNs of interest (motor and language) discovered by rs-fMRI highly correlate with t-fMRI reference and are located in anticipated anatomical regions. As a result, rs-fMRI maps seem as a good approximation of t-fMRI maps, especially in case of semiblind ICA decomposition. We hope that further our research of individual changes in sensorimotor and language networks based on functional rs-MRI will allow predicting the activity of neural network architectures and non-invasive mapping of functional areas for preoperative planning.
Human-Assisted Computation for Auto-Grading

In this paper, we present a novel auto-grading framework that can automatically grade student assignments without prior knowledge of the answers. The idea is crowd-sourcing or human-assisted computation that extract knowledge from a large number of people to make predictions using hypothesis testing and Bayesian analysis. We also explore the possibilities of combining this framework with an educational chatbot software interface (e.g., the Facebook Messenger chatbot platform), in order to utilize the built-in image annotation feature that facilitates the assignment submission process in large classes.
Automated 3-D Retinal Layer Segmentation From SD-OCT Images With Neurosensory Retinal Detachment

Neurosensory retinal detachment (NRD) is a separation of the neurosensory retina from the retinal pigment epithelium (RPE) because of the subretinal fluid that can result in significant vision loss. The detachment of the neurosensory retina is known to alter the topology as well as the intensity continuity of the retinal layers. This nature of NRD makes the layer segmentation of NRD affected eyes difficult. In this paper, we presented a fully automated three-dimensional (3D) method to segment the retinal layers and NRD associated subretinal fluid from a spectral domain optical coherence tomography (SD-OCT) image. The proposed method has three phases, including a prior information model; an NRD associated subretinal fluid segmentation; and layer segmentation. The graph search and graph cut techniques were employed to segment the retinal layers and NRD associated sub-retinal fluid, respectively. To reduce the computational cost of graph-based optimization, the ‘divide and merge’ approach was introduced. The experiment shows that while maintaining the segmentation accuracy, the ‘divide and merge’ approach considerably decreases the computational cost. Our method was evaluated on 20 SD-OCT cubes diagnosed with NRD, and the results were compared with the manual segmentation results from experts. The layer evaluation showed an overall absolute surface position difference of $6.34~\pm ~2.6\mu \text{m}$ , which is comparable with the inter-expert variability of $6.39~\pm ~5.9~\mu \text{m}$ . The segmentation result of the NRD associated sub-retinal fluid was assessed in terms of the dice coefficient and achieved means of 90.78% and 92.04% in comparison to two experts.
Inhalation of Bacterial Cellulose Nanofibrils Triggers an Inflammatory Response and Changes Lung Tissue Morphology of Mice

In view of the growing industrial use of Bacterial cellulose (BC), and taking into account that it might become airborne and be inhaled after industrial processing, assessing its potential pulmonary toxic effects assumes high relevance. In this work, the murine model was used to assess the effects of exposure to respirable BC nanofibrils (nBC), obtained by disintegration of BC produced by Komagataeibacter hansenii . Murine bone marrow-derived macrophages (BMMΦ) were treated with different doses of nBC (0.02 and 0.2 mg/mL, respectively 1 and 10 μg of fibrils) in absence or presence of 0.2% Carboxymethyl Cellulose (nBCMC). Furthermore, mice were instilled intratracheally with nBC or nBCMC at different concentrations and at different time-points and analyzed up to 6 months after treatments. Microcrystaline Avicel-plus® CM 2159, a plant-derived cellulose, was used for comparison. Markers of cellular damage (lactate dehydrogenase release and total protein) and oxidative stress (hydrogen peroxidase, reduced glutathione, lipid peroxidation and glutathione peroxidase activity) as well presence of inflammatory cells were evaluated in brochoalveolar lavage (BAL) fluids. Histological analysis of lungs, heart and liver tissues was also performed. BAL analysis showed that exposure to nBCMC or CMC did not induce major alterations in the assessed markers of cell damage, oxidative stress or inflammatory cell numbers in BAL fluid over time, even following cumulative treatments. Avicel-plus® CM 2159 significantly increased LDH release, detected 3 months after 4 weekly administrations. However, histological results revealed a chronic inflammatory response and tissue alterations, being hypertrophy of pulmonary arteries (observed 3 months after nBCMC treatment) of particular concern. These histological alterations remained after 6 months in animals treated with nBC, possibly due to foreign body reaction and the organism’s inability to remove the fibers. Overall, despite being a safe and biocompatible biomaterial, BC-derived nanofibrils inhalation may lead to lung pathology and pose significant health risks.
Efficacy and Safety of 90,000 IU versus 300,000 IU Single Dose Oral Vitamin D in Nutritional Rickets: A Randomized Controlled Trial

Aim: To compare efficacy and safety of 90,000 IU versus 300,000 IU oral single dose vitamin D for treatment of nutritional rickets. Study Design: Randomized controlled trial. Setting: Tertiary care hospital. Participants: One hundred ten children (6 months to 5 years, median age 10.5 months) with rickets. Exclusion criteria were disease affecting absorption, intake of calcium/vitamin D preparation in last 6 months, abnormal renal function, and rickets other than nutritional. Intervention: Vitamin D3 as a single oral dose 90,000 IU (group A, n = 55) or 300,000 IU (group B, n = 55). Methodology: Severity of rickets was scored on knee and wrist X-ray as per Thacher's radiographic score. Baseline serum levels of calcium, SAP, 25(OH)D, iPTH were measured. Follow up was done at 1 week, 4 weeks, and 12 weeks. Outcome Variable: Primary – Radiographic score at 3 months. Secondary – Serum levels of 25(OH)D, SAP, and iPTH at 3 months, clinical and biochemical adverse effects. Results: Eighty-six subjects (43 in each group) completed the study. The radiographic score reduced from 6.90 to 0.16 in group A and from 6.93 to 0.23 in group B. The levels of 25(OH)D, ALP, and PTH were similar between the groups at baseline and follow up. Hypercalciuria and hypercalcemia were seen more often in group B as was hypervitaminosis D. There were no clinical adverse events. Conclusions: Single oral dose vitamin D3 90,000 IU is safe and effective in achieving healing of rickets.
A Network-Based Approach to Enhance Electricity Load Forecasting

In the field of energy analysis, time series forecasting techniques are widely used to predict customer electricity consumptions. To enhance the electricity forecasting accuracy, in current approaches, clustering techniques are first applied to identify groups of customers exhibiting the same electricity load profile, from which a representative consumption pattern can be extracted. This pattern is later used to predict customers' subsequent electricity consumption. In the vast majority of clustering approaches, authors use the entire data set as input to identify customer consumption groups. However, electricity load data vary extremely rapidly and can thus be dominated by outdated historical information which may influence the effective cluster status at a given time-stamp. To overcome this constraint, instead of using the entire data set, we propose an adaptive process which involves tracking the evolution of identified customer consumption groups at different time-stamps. A network structure is used to model the interrelation between customer electricity load profiles. The network is then split into subnetworks that are treated as customer electricity consumption clusters. Representative subseries, called master subseries, are extracted to track the evolution of clusters over time. Finally, the master subseries are used as a knowledge base for forecasting customers' electricity consumption at later time-stamps and automatically predicting future cluster status. The load forecasting is done using a seasonal autoregressive integrated moving average model, which is compared to a multi-layer perceptron, support vector regression, lasso regression, bayesian ridge regression and K-nearest neighbor regression models.
Computer Assisted Language Learning For Spanish Oral Proficiency

The purpose of this mixed-method study was to determine the effect of integrating computer-assisted language learning (CALL) with a voice-recording software into a Spanish curricular unit of study in order to improve native English speakers’ foreign language oral proficiency. Second language acquisition theory and post-modern curriculum framework guided this study. Fourteen Spanish level-two high school students participated in this eightweek long study. The intervention consisted of having students engage in voice-recordings of their oral language using eight successive CALL tasks. A post-intervention focus group interview as well as self-reflective journals were used to identify the qualitatively differing ways the students perceived their experiences using the CALL curricular unit for developing their Spanish oral proficiency. The student scores for each of the eight CALL task voicerecordings were collected to assess the development of students’ oral proficiency. The qualitative results of the CALL tasks oral recordings revealed that the students’ Spanish oral language proficiency meaningful output was high, medium, or low. Specific areas of students’ growth in verb conjugation or weakness in pronunciation, expressions and vocabulary, and grammatical structures were also identified. The qualitatively differing ways students perceived CALL task recordings were as follows: anxiety decrease, motivation and confidence increase, and speaking improvement. Friedman’s tests were run to find significant differences in the students’ oral proficiency from one to eight consecutive tasks. The p-values 0.003, 0.007, 0.002, 0.003, and 0.007 of the students’ scores on CALL tasks (2, 4, 5, 6, and 7) respectively, when compared to task 8, show a significant difference. Too, these quantitative results indicate that students’
Hierarchical Routing for Vehicular Ad Hoc Networks via Reinforcement Learning

Vehicular ad hoc network is a collection of vehicles and associated road-side infrastructure, which is able to provide mobile wireless communication services. This highly dynamic topology structure is still open to many routing and message forwarding challenges. This paper addresses the issue of message delivery from vehicle to a fixed destination, by hopping over neighboring vehicles. We propose a reinforcement-learning-based hierarchical protocol called QGrid to improve the message deliver ratio with minimum possible delay and hops. The protocol works at two levels. First, it divides the geographical area into smaller grids and finds the next optimal grid toward the destination. Second, it discovers a vehicle inside or moving toward the next optimal grid for message relaying. There is no need of routing tables as the protocol builds a Q-value table based on the traffic flow in neighbor grids, which is then used for the grid selection. The vehicle selection process can employ different strategies, like, greedy selection of nearest neighbor, or solution based on the two-order Markov chain prediction of neighbor movement. This combination makes QGrid an offline and online solution. QGrid is further improved giving higher priority to vehicles with fixed routes and better communication capabilities, like buses, when making the vehicle selection. We have carried out extensive simulation evaluation by using real-world vehicular traces to measure the performance of our proposed schemes. The simulation comparisons among QGrid with/without bus aid, and existing position-based routing protocols, show the great improvement in the delivery percentage by our proposed routing protocol.
Magnification Rate in 3D Optical Scanning Holography Recording

Optical scanning holography (OSH) is a powerful holographic recording technique that employs a single-pixel photo-detector and 2D scanning mechanism to capture the hologram of a wide-view and 3D object. However, in OSH experiments, we found that 2D <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> scanning mirrors would cause unavoidable magnification and lateral resolution issues in 3D recording or sectioning imaging. In this letter, we will discuss this problem both theoretically and experimentally. The experiment results show that our results have a good performance on these issues.
Phishing Email Detection Using Robust NLP Techniques

Even with many successful phishing email detectors, phishing emails still cost businesses and individuals millions of dollars per year. Most of these models seem to ignore features like word count, stopword count, and punctuations; they use features like n-grams and part of speech tagging. Previous phishing email research ignores or removes the stopwords, and features relating to punctuation only count as a minor part of the detector. Even with a strong unconventional focus on features like word counts, stopwords, punctuation, and uniqueness factors, an ensemble learning model based on a linear kernel SVM gave a true positive rate of 83% and a true negative rate of 96%. Moreover, these features are robustly detected even in noisy email data. It is much easier to detect our features than correct part-of-speech tags or named entities in emails.
Endoscopic Ultrasound‐Guided Angiotherapy for Gastric Varices: A Single Center Experience

There are limited efficacious therapeutic options for management of gastric variceal bleeding. Treatment modalities include transjugular intrahepatic portosystemic shunt, surgical shunts, and endoscopic interventions, including the recent advancement of endoscopic ultrasound (EUS)‐guided coiling. We present a case series of 10 patients with portal hypertension (7 with liver cirrhosis and 3 without cirrhosis), complicated by gastric varices (GV) with bleeding. All cases were treated successfully with EUS‐guided coiling leading to variceal eradication. There were 10 occurrences of minimal self‐limited bleeding at the puncture site during the procedure, and only one occurrence of major bleeding that necessitated cyanoacrylate glue injection for homeostasis. There were no other adverse events within a mean follow‐up time of 9.7 months (range, 1‐28 months). Conclusion: In our series, EUS‐guided angiotherapy was effective for GV eradication with a high safety profile.
Area under the curve analysis of blood pressure reveals increased spontaneous locomotor activity in SPAK knock‐in mice: relevance for hypotension induced by SPAK inhibition?

SPAK (Ste20/SPS1‐related proline/alanine‐rich kinase) has been recently identified as a protein kinase which targets the electroneutral cation‐coupled chloride cotransporters and it stands out as a target for inhibition in novel anti‐hypertensive agents. From this prospective, any information about physiological consequences of SPAK inhibition would be useful to better understand the efficacy and potential adverse effects of the SPAK‐based anti‐hypertensive therapy. Radiotelemetry was employed to continuously measure the parameters of blood pressure (mean arterial blood pressure, systolic blood pressure, and diastolic blood pressure), heart rate, and physical activity in SPAK knock‐in (KI) mice and littermate controls for 24 h. For each parameter, the area under the curve (AUC) was calculated and compared between the SPAK KI mice and wild type mice. There was no statistically significant difference in the AUC of blood pressure parameters between SPAK KI and littermate mice. When mice were physically inactive, the AUCs for blood pressures were significantly lower in SPAK KI than in littermates. When physically active, blood pressures were significantly higher in SPAK KI than in littermates. The heart rate followed a similar pattern. The AUC of physical activity was significantly increased in SPAK KI mice when compared to littermates and the SPAK KI mice spent significantly less time in an inactive state and significantly more time in active states. Comparison between SPAK KI mice and unrelated wild type mice yielded similar results to the comparison between SPAK KI mice and littermates. We conclude that SPAK inhibition increases spontaneous locomotor activity, which has a significant effect on blood pressure.
Activity Recognition for Ergonomics Assessment of Industrial Tasks With Automatic Feature Selection

In industry, ergonomic assessment is currently performed manually based on the identification of postures and actions by experts. We aim at proposing a system for automatic ergonomic assessment based on activity recognition. In this letter, we define a taxonomy of activities, composed of four levels, compatible with items evaluated in standard ergonomic worksheets. The proposed taxonomy is applied to learn activity recognition models based on Hidden Markov Models. We also identify dedicated sets of features to be used as input of the recognition models so as to maximize the recognition performance for each level of our taxonomy. We compare three feature selection methods to obtain these subsets. Data from 13 participants performing a series of tasks mimicking industrial tasks are collected to train and test the recognition module. Results show that the selected subsets allow us to successfully infer ergonomically relevant postures and actions.
LRU-2 vs 2-LRU: An Analytical Study

Hierarchical caching enables users to obtain content from one of (possibly) many caches between an edge router cache and an origin server, reducing latency/overall network traffic. This can be used in many network architectures, including P2P networks and Content Distribution Networks. Of particular interest are Information Centric Networks (ICNs), which decouple content identifiers from specific network hosts and explicitly consider universal caching (collaboration between network routers) as a desirable feature.Performance analysis of a large-scale hierarchy of caches requires accurate mathematical models for various cache replacement algorithms. There is no previous study that models LRU-k. This cache replacement algorithm is important since its principle is the basis of recent algorithms such as k-LRU that outperform LRU in many situations. We first model LRU-2 using Che's approximation, as a specific case of LRU-k for $k=2$. We also extend our model to a hierarchical network of LRU-2 caches. The model is validated analytically and with simulation. The experiments show that the proposed model approximates LRU-2 accurately. LRU-2 and 2-LRU are also compared analytically and with simulations. The comparison between the two algorithms illustrates that 2-LRU outperforms LRU-2.
Interaction Force Estimation Using Extended State Observers: An Application to Impedance-Based Assistive and Rehabilitation Robotics

This letter presents a force observer that estimates the external interaction forces from the measured joint position and joint actuation for a class of robotic manipulators. This is done without an explicit (physical) force or torque sensor, through an extended state observer (ESO) assuming a known model of the dynamics of the manipulator. It is designed to be capable of estimating relatively fast time-varying external forces. The main result shows that the proposed ESO is theoretically able to estimate unknown time-varying external forces to an arbitrary accuracy for any initial condition starting in a given compact set. Simulation results demonstrate the effectiveness of the proposed ESO. Moreover, first experimental evaluations with a healthy subject and a poststroke patient on a rehabilitation robotic platform show the feasibility of the approach in this context and highlight the importance of the quality of the dynamic model.