Miracle-dz/UniFAR-FTUs huggingface dataset:
A Formal Specification and Verification Framework for Timed Security Protocols

Nowadays, protocols often use time to provide better security. For instance, critical credentials are often associated with expiry dates in system designs. However, using time correctly in protocol design is challenging, due to the lack of time related formal specification and verification techniques. Thus, we propose a comprehensive analysis framework to formally specify as well as automatically verify timed security protocols. A parameterized method is introduced in our framework to handle timing parameters whose values cannot be decided in the protocol design stage. In this work, we first propose <italic>timed applied <inline-formula><tex-math notation="LaTeX">$\pi$ </tex-math><alternatives><inline-graphic xlink:href="li-ieq1-2712621.gif"/></alternatives></inline-formula>-calculus </italic> as a formal language for specifying timed security protocols. It supports modeling of continuous time as well as application of cryptographic functions. Then, we define its formal semantics based on <italic>timed logic rules </italic>, which facilitates efficient verification against various authentication and secrecy properties. Given a parameterized security protocol, our method either produces a constraint on the timing parameters which guarantees the security property satisfied by the protocol, or reports an attack that works for any parameter value. The correctness of our verification algorithm has been formally proved. We evaluate our framework with multiple timed and untimed security protocols and successfully find a previously unknown timing attack in Kerberos V.
Design, Development and Calibration of a Lightweight, Compliant Six-Axis Optical Force/Torque Sensor

This paper introduces the fabrication of a six degree-of-freedom force and torque sensor based on fiber-optic sensing technology and its novel calibration methodology. The sensor is cost effective, lightweight, and flexible with a large force and torque measurement range suitable for biomechanics and rehabilitation systems particularly when a wearable sensing system is desired. Six fiber-optic sensing elements are used to detect three main forces <italic>Fx</italic>, <italic>Fy</italic>, and <italic>Fz</italic>, and three main torques <italic>Tx</italic>, <italic>Ty</italic>, and <italic>Tz</italic>. Sensor data were collected by applying dynamic forces and torques with various magnitudes, directions, and frequencies and compared with measurements obtained from a standard force and torque reference. The proposed calibration procedure is intended to reduce errors stemmed from a nonlinear force-deformation relationship and to increase the estimation speed by splitting the calibration into two estimation models: a linear model, based on a standard least squares method (<italic>LSM</italic>) to estimate the linear portion, and a nonlinear decision trees’ model (<italic>DT</italic>) to estimate the residuals. Both the models work simultaneously as a single calibration system named least squares decision trees <italic>LSDT</italic>. Using <italic>LSDT</italic>, the estimation speed increased by 55.17%, and the root mean square errors (<italic>RMSEs</italic>) reduced to 0.53%. In comparison, each model separately had a RMSEs of 1.26% and 4.70% for the <italic>DT</italic> and the <italic>LSM</italic>, respectively.
Effectiveness of an Evidence-Based Practice educational intervention with second-year nursing students 1

ABSTRACT Objectives: to evaluate the effectiveness of an educational intervention on the knowledge, skills and attitudes of evidence-based practice among second-year nursing students. Method: a quasi-experimental before-and-after study. The study population consisted of 120 students enrolled in the Nursing Care in Healthcare Processes course. The educational intervention was based on theoretical and practical classes about the evidence-based practice process and the use of the critical incident technique during the clinical clerkship. Effectiveness was measured with the Evidence-Based Practice Competence Questionnaire in three paired measures using repeated-measures analysis of variance. Results: the mean scores of the Evidence-Based Practice Competence Questionnaire were 79.83 (CI 95% 78.63-81.03) for the basal measurement, 84.53 (CI 95% 83.23-85.83) for the intermediate measurement, and 84.91 (CI 95% 83.26-86.55) for the final measurement, with a statistically significant difference among the three paired measurements (p<0.001). There were statistically significant differences in Attitudes (p = 0.034) and Knowledge (p <0.001) but not in Skills (p = 0.137). Conclusion: this educational intervention based on theoretical and practical classes about the evidence-based practice process and the use of the critical incident technique during the clinical clerkship enhances evidence-based practice competence among second-year nursing degree students.
Should patients with diabetes be encouraged to integrate social media into their care plan?

Aim: To evaluate the use of social media of individuals with diabetes mellitus (DM). Materials & methods: Both web-based and in-clinic surveys were collected from individuals with DM. Descriptive and correlation analyses were employed to evaluate respondents’ diabetes-specific social networking site behaviors. Results: Forty-five patients with DM completed the web-based survey and 167, the clinic-based survey, of whom only 40 visited diabetes-specific social networking sites. Analysis of online survey data indicated that self-reported adherence to lifestyle recommendations was significantly correlated (p < 0.01) with visiting the sites. Clinic-based survey data found that patients who reported using DM-specific web sites monitored home glucose values more often and had better compliance with insulin administration (both p < 0.05) compared with nonusers. Conclusion: This study provides insight into why individuals visit DM-specific social networking sites. Certain self-management behaviors may improve as a result of visiting these sites. Further work is needed to explore how to leverage social media technology to assist patients with the management of DM.
Two-Channel Bilateral Control of Delta Robots with Gravity Compensation

This paper presents three-dimensional bilateral delta robots based on a novel transfer-function-based two-channel bilateral control system. This configuration has proven to provide better control perfomance while only requiring two channels of communication, for position information exchange, as opposed to the traditional four-channel bilateral control method. The proposed three-dimensional bilateral Delta robot architecture proves to have good performance, however it is affected by gravity, and therefore, in order to ensure smooth operation of the delta robots we introduce a compensation for gravity on the bilateral control system, based on the analysis of the kinematics of the delta configuration. In this paper, we discuss the application of transfer-function-based two-channel bilateral control to three-dimensional delta robots, and the accuracy improvement obtained by the application of gravity compensation to the two-channel bilateral control.
Proxy Oriented Approach for Evaluating Usability of a Resilient Life-Critical Interactive Systems

Resilience deals with the system capacity to adjust itself to face unexpected events by ensuring four activities that are: anticipation, monitoring, response and adaptation. In this work, we focus especially on how monitoring is a potential strategy to anticipate unexpected situations for enhancing resilience. We propose a solution for monitoring human-system interaction based on continuous, automatic and real-time model-based usability evaluation. The proposed monitoring proxy tool is able to log user activities, using Petri Net language, while interacting with the human-machine system. The identification of usability issues is carried out through detecting any deviation from what task model assumes comparing to user activities. This approach has been defined and applied using a simple case study: an automated speed control car system.
Sentiment Analysis of Sub-Events Extracted Out of an Event Using Word2vec

Word2vec is an assortment of related models specially employed to yield word embeddings. By its application to a relatively large dataset that corresponds to a given event coming about at a given point of time at a given location, we can break down the event into sub-events, and study them further. Investigating sub-events in the right direction can help us in countless ways. It can enable us to decipher their local yet inevitable impacts which might otherwise have gone missing in the sea of the whole event altogether. In our paper, we have broken down the event (of the happenings of 'Kashmir') into sub-events and pulled out a few randomly. We have then applied sentiment-analysis to each one of them instead of applying it on to the whole event all at once. The rise and fall of the sentiment with respect to each sub-event is plotted and the variation is visualised in the end. The procedure is not just limited to our domain of interest but can be adopted to study any event.
Afuronto: A Six Hop Peer-to-Peer Network of Zillions of Nodes

We present, in this paper, a new overlay network design that is suited for large scale peer-to-peer networks requiring fast message transmission. We particularly address the problem of designing an overlay communication network that spans across a set of data centres located in multiple geographic locations with the necessity of requiring locality properties for applications. Our design for the overlay network is motivated by the social network structures and we use different operators as found in evolutionary computing to emulate a semi-structured network. We present the network structure and the various algorithms required for basic communication and maintenance. We also study the proposed network by simulation and compare with existing overlay networks. The simulated results show that on an average there are at most six hops required for a message originated at any node in the network to be transferred to any other node in the network. The network so designed is scalable, and resilient to failure and, can include node of arbitrary in processing capacity, network bandwidth and storage.
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos

We propose an end-to-end learning framework for segmenting generic objects in both images and videos. Given a novel image or video, our approach produces a pixel-level mask for all “object-like” regions—even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning an object/background label to each pixel, implemented using a deep fully convolutional network. When applied to a video, our model further incorporates a motion stream, and the network learns to combine both appearance and motion and attempts to extract all prominent objects whether they are moving or not. Beyond the core model, a second contribution of our approach is how it leverages varying strengths of training annotations. Pixel-level annotations are quite difficult to obtain, yet crucial for training a deep network approach for segmentation. Thus we propose ways to exploit weakly labeled data for learning dense foreground segmentation. For images, we show the value in mixing object category examples with image-level labels together with relatively few images with boundary-level annotations. For video, we show how to bootstrap weakly annotated videos together with the network trained for image segmentation. Through experiments on multiple challenging image and video segmentation benchmarks, our method offers consistently strong results and improves the state-of-the-art for fully automatic segmentation of generic (unseen) objects. In addition, we demonstrate how our approach benefits image retrieval and image retargeting, both of which flourish when given our high-quality foreground maps. Code, models, and videos are at: http://vision.cs. utexas.edu/projects/pixelobjectness/.
An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter

As one of the key functions in lithium-ion battery management system, the state-of-health (SOH) estimation is of great significance to ensure the safe and reliable operation and reduce the maintenance cost of the battery energy storage system. Unscented particle filter (UPF) algorithm is becoming a promising method for battery state estimation since it combines the latest measurement information to give the proposal distribution which is closer to the true posterior distribution. At the same time, UPF algorithm is able to represent the uncertainty involved in the estimation results, which makes great significance for battery SOH estimation. On the other hand, it is difficult to measure the battery actual capacity in practice despite the capacity is a direct indicator of battery SOH. In this paper, an on-line health indicator (HI) is extracted from the measurable parameters while battery is working. The mapping model between the extracted HI and battery SOH is established and applied as the observation in the state-space model. An on-line estimator based on UPF algorithm is developed for battery SOH assessment. The maximum estimation error based on battery cycling test data is less than 5%. This indicates that the proposed method has a good adaptability for lithium-ion battery degradation with non-linear and non-Gaussian characteristics. Additionally, the experiments on different types of lithium-ion battery show the good robustness and applicability of this approach.
Prevalence of Helicobacter pylori infection in upper gastrointestinal tract disorders (dyspepsia) patients visiting outpatient department of a hospital of North India

Background: Helicobacter pylori infection is a major health ailment in developing countries. The infection is related with high morbidity and mortality ranging from chronic gastritis to gastric malignancies. The prevalence of H. pylori infection greatly varies from country to country and region to region. The aim is to study the prevalence of H. pylori infection in upper gastrointestinal (GI) tract disorders (dyspepsia) patients. Methods: The study was carried out in an outpatient department, India. Patients presenting with dyspeptic symptoms were subjected to serological investigation and upper GI endoscopy, histopathological examination, and rapid urease test of specimen. Diagnosis of H. pylori infection was made if one or both diagnostic test results were positive. Results: H. pylori infection was diagnosed in 85% of patients. There was no significant difference in sex- and age-related distribution (<50 years' age group and >50 years' age group) of H. pylori infection. However, association of H. pylori infection was positive in 83.3% (45) of patients with endoscopic abnormalities (54). Conclusion: The prevalence of H. pylori infection is high in the study population. Early detection and prompt treatment are essential for prevention of serious complications.
Current natural therapies in the treatment against glioblastoma

Glioblastoma (GBM) is the most common and aggressive brain tumor, which causes the highest number of deaths worldwide. It is a highly vascularized tumor, infiltrative, and its tumorigenic capacity is exacerbated. All these hallmarks are therapeutic targets in GBM treatment, including surgical removal followed by radiotherapy and chemotherapy. Current therapies have not been sufficient for the effective patient's management, so the classic therapies have had to expand and incorporate new alternative treatments, including natural compounds. This review summarizes natural products and their physiological effects in in vitro and in vivo models of GBM, specifically by modulating signaling pathways involved in angiogenesis, cell migration/invasion, cell viability, apoptosis, and chemoresistance. The most important aspects of natural products and their derivatives were described in relation to its antitumoral effects. As a final result, it can be obtained that within the compounds with more evidence that supports or suggests its clinical use are the cannabinoids, terpenes, and curcumin, because many have been shown to have a significant effect in decreasing the progress of GBM through known mechanisms, such as chemo‐sensitization or decrease migration and cell invasion. Natural compounds emerge as promising therapies to attack the progress of GBM.
Climate-woodland effects on population genetics for two congeneric lichens with contrasting reproductive strategies

Genetic variation is expected to be influenced by the interaction between reproductive mode and dispersal traits on the one hand and environmental and habitat setting affecting establishment success on the other. We evaluated how environmental/habitat setting affects population genetic variation (i.e. variation in genetic diversity and structure) when regulated by contrasting dispersal traits. We used fungus-specific microsatellite markers to examine genetic diversity and structure of two closely related epiphytic lichen fungi that differ in their primary reproductive mode: Nephroma laevigatum (sexually reproducing, n = 191, 10 microsatellites) and N. parile (asexually reproducing, n = 182, 12 microsatellites), along a steep climatic gradient in Scotland. Despite their reproductive differences, we found a high proportion of clones in both species and a background pattern of genetic structure related to climatic gradients. We also demonstrated that woodland connectivity, rather than geographic distance, explained genetic diversity in both species. Environmental/habitat setting, modulated by the reproductive mode of the species, affects genetic diversity and structure, but the putative dissimilarity in their reproductive mode is less important than has been previously assumed. We reinforce the importance of protecting highly connected populations, positioned along a gradient capturing the segregation of gene pool differences in response to climatic variation.
Denoising Sparse Autoencoder-Based Ictal EEG Classification

Automatic seizure detection technology can automatically mark the EEG by using the epileptic detection algorithm, which is helpful to the diagnosis and treatment of epileptic diseases. This paper presents an EEG classification framework based on the denoising sparse autoencoder. The denoising sparse autoencoder (DSAE) is an improved unsupervised deep neural network over sparse autoencoder and denoising autoencoder, which can learn the closest representation of the data. The sparsity constraint applied in the hidden layer of the network makes the expression of data as sparse as possible so as to obtain a more efficient representation of EEG signals. In addition, corrupting operation used in input data help to enhance the robustness of the system and make it suitable for the analysis of non-stationary epileptic EEG signals. In this paper, we first imported the pre-processed training data to the DSAE network and trained the network. A logistic regression classifier was connected to the top of the DSAE. Then, put the test data into the system for classification. Finally, the output results of the overall network were post-processed to obtain the final epilepsy detection results. In the two-class (nonseizure and seizure EEGs) problem, the system has achieved effective results with the average sensitivity of 100%, specificity of 100%, and recognition of 100%, showing that the proposed framework can be efficient for the classification of epileptic EEGs.
Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters

In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s2) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.
LED-Based Portable Optical Biosensor for Measurement of Serum Urea Levels Using Urease Immobilized Agarose-Guar Gum Composite Film

A simple, reliable, low cost, and portable biosensor has been developed for direct measurement of urea levels in serum samples. It is based on hydrolysis of urea by immobilized urease, generating ammonia that is estimated by modified Berthelot method using simple optical detection system. The immobilization of urease was achieved in agarose guar-gum composite film for the first time which offered an excellent retention of enzyme activity (90%–95%) with negligible leaching and good operational stability. The optical detection system involving LED light, optical fiber, and phototransistor provides simple and portable measurement system. The sensor shows linear response in the wide range of urea concentration from 0.025 to 10 mM with reusability for 10 cycles and superior storage stability of 180 days. The fabricated biosensor was successfully employed for measurement of normal and abnormal urea levels in serum samples with accuracy of 96% using very small sample volume (<inline-formula> <tex-math notation="LaTeX">$15~\mu \text{l}$ </tex-math></inline-formula>). It showed an excellent correlation (<inline-formula> <tex-math notation="LaTeX">$r^{2}=0.99$ </tex-math></inline-formula>) for determination of urea levels with that of using Siemens autoanalyzer.