Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Study of LoRa Transmission over Seawater

Low Power Wide Area Networks (LPWANs) are gaining attention in both academia and industry by offering the possibility of connecting a large number of nodes over extended distances. LoRa is one of the technologies used as a physical layer in such networks. This paper investigates the LoRa links over seawater in two typical scenarios: clear Line-of-Sight (LOS) and obstructed path in two different Industrial, Scientific and Medical (ISM) radio bands: 868MHz and 434MHz. We used three different LoRa devices in the experiments: the Own Developed LoRa Transceiver (ODT) and two commercial transceivers. Firstly we investigated transceivers’ Receive Signal Strength Indicator (RSSI) and Signal-to-Noise (SNR) measurement chain linearity and provided correction factors for RSSI to correlate it with actual signal levels received at transceivers’ inputs. Next, we carried out field experiments for three different LoRa Spreading Factors, SF∈[7,10,12], within a bandwidth of BW=125kHz and Coding Rate CR=4/6. The experiments showed that LoRa links are fully feasible over seawater at distances at least 22km long, using only low-cost off-the-shelf rubber duck antennas in LOS path condition in both ISM bands. In addition, we showed that LoRa links can be established over 28km obstructed LOS oversea path in ISM 434MHz band, but using costly, higher gain antennas. Furthermore, the laboratory experiments revealed that RSSI is linear in a wide range, up to −50dBm, whereas the SNR measurement chain goes into saturation for Received Signal Strength (RSS) values higher than −100dBm. These findings enabled accurate interpretation of the results obtained in field experiments.
Palpable Breast Lump Triage by Minimally Trained Operators in Mexico Using Computer-Assisted Diagnosis and Low-Cost Ultrasound

Purpose In low- to middle-income countries (LMICs), most breast cancers present as palpable lumps; however, most palpable lumps are benign. We have developed artificial intelligence–based computer-assisted diagnosis (CADx) for an existing low-cost portable ultrasound system to triage which lumps need further evaluation and which are clearly benign. This pilot study was conducted to demonstrate that this approach can be successfully used by minimally trained health care workers in an LMIC country. Patients and Methods We recruited and trained three nonradiologist health care workers to participate in an institutional review board–approved, Health Insurance Portability and Accountability Act–compliant pilot study in Jalisco, Mexico, to determine whether they could use portable ultrasound (GE Vscan Dual Probe) to acquire images of palpable breast lumps of adequate quality for accurate computer analysis. Images from 32 women with 32 breast masses were then analyzed with a triage-CADx system, generating an output of benign or suspicious (biopsy recommended). Triage-CADx outputs were compared with radiologist readings. Results The nonradiologists were able to acquire adequate images. Triage by the CADx software was as accurate as assessment by specialist radiologists, with two (100%) of two cancers considered suspicious and 30 (100%) of 30 benign lesions classified as benign. Conclusion A portable ultrasound system with CADx software can be successfully used by first-level health care workers to triage palpable breast lumps. These results open up the possibility of implementing practical, cost-effective triage of palpable breast lumps, ensuring that scarce resources can be dedicated to suspicious lesions requiring further workup.
Feasible Computationally Efficient Path Planning for UAV Collision Avoidance

This paper presents a robust computationally efficient real-time collision avoidance algorithm for Unmanned Aerial Vehicle (UAV), namely Memory-based Wall Following-Artificial Potential Field (MWF-APF) method. The new algorithm switches between Wall-Following Method (WFM) and Artificial Potential Field method (APF) with improved situation awareness capability. Historical trajectory is taken into account to avoid repetitive wrong decision. Furthermore, it can be effectively applied to platform with low computing capability. As an example, a quad-rotor equipped with limited number of Time-of-Flight (TOF) rangefinders is adopted to validate the effectiveness and efficiency of this algorithm. Both software simulation and physical flight test have been conducted to demonstrate the capability of the MWF-APF method in complex scenarios.
Analysis of Dynamics of Demand, Revenue and Ergonomic Aspects of Tourism

The results of statistical and econometric modeling of tourist expenditures are used to assess and forecast the development of tourism in different countries or regions. The main difficulty of statistical observation in tourism and in tourism statistics as a whole lies in the impossibility of classifying tourism in any particular sector in the economic and statistical sense of the word. The tourism industry has interrelations with various sectors and types of economic activity, the description of which requires the use of various statistical forms. Demand for tourist services in certain periods of time can grow even exponentially, depending on the number of tourists, the quality of the environment, the market price of recreation and its duration of the holiday season.
Artificial Neural Networks as a Tool for Recognition of Movements by Electroencephalograms

Recognition of human brain activity associated with imaginary or real movements is a complex task that requires an accurate and conscious choice of analysis approach. Recent researches revealed the great potential of machine learning algorithms for electroencephalography data analysis due to the ability of these methods to establish nonlinear and nonstationary correlations, and the most attention is focused on artificial neural networks (ANNs). Here, we introduce the ANN-based method for recognition and classification of patterns in electroencephalograms (EEGs) associated with imaginary and real movements of untrained volunteers. In order to get the fastest and the most accurate classification performance of multichannel motor imagery EEG-patterns, we propose our approach to selection of appropriate type, topology, learning algorithm and other parameters of neural network. We considered linear neural network, multilayer perceptron, radial basis function network and support vector machine. We revealed that appropriate quality of recognition can be obtained by using particular groups of electrodes according to extended international 10 −10 system. Besides, pre-processing of EEGs by low-pass filter can significantly increase the classification performance. Obtained results provide better insight on neural networks potential for integration in brain-computer interfaces that are based on EEG patterns recognition.
Seamless Database Evolution for Cloud Applications

We present DECA (Database Evolution in Cloud Applications), a framework that facilitates fast, robust, and agile database evolution in cloud applications. Successful business requires to react to changing wishes and requirements of customers instantly. In today’s realities, this often boils down to adding new features or fixing bugs in a software system. We focus on cloud application platforms that allow seamless development and deployment of applications by operating both the old and the new version in parallel for the time of development/deployment. This does not work if a common database is involved, since they cannot co-exist in multiple versions. To overcome this limitation, we apply current advances in the field of agile database evolution. DECA equips developers with an intuitive Database Evolution Language to create a new co-existing schema version for development and testing. Meanwhile, users can continuously use the old version. With the click of a button, we migrate the database to the new version and move all the users without unpredictable downtime and without the risk of corrupting our data. So, DECA speeds up the evolution of information systems to the pace of modern business.
Early Intervention in Mental Health for Adolescents and Young Adults: A Systematic Review

OBJECTIVE. The purpose of this systematic review was to describe the evidence for the effectiveness of early intervention to improve and maintain performance in occupations for youths with or at risk for serious mental illness (SMI). METHOD. Titles and abstracts of 670 articles were reviewed, 234 were retrieved for full review, and 30 met inclusion criteria. RESULTS. Moderate to strong evidence supports cognitive remediation (CR) and mixed evidence supports cognitive‐behavioral therapy (CBT) as an adjunct modality to improve general functioning. Moderate to strong evidence supports use of supported employment and supported education (SE/E) to improve social and occupational outcomes in employment and academics. Strong evidence supports family psychoeducation (FPE) to prevent relapse and rehospitalization and improve problem‐solving skills and general functioning. CONCLUSION. Occupational therapy practitioners should integrate CR, SE/E, and FPE into early intervention with youth with or at risk for SMI. In addition, CBT is an effective modality for use with this population. &NA; Cognitive remediation, supported employment and education, family psychoeducation, and cognitive‐behavioral therapy are effective interventions for occupational therapy practitioners to integrate into their practice if they work with young adults living with or at risk for SMI.
Framework for Analyzing User Behavior Using Big Data Technology

Advent of social media like Facebook and Twitter has played the role of the multiplier in adding the data on the web. Due to expeditious enlargement of the web, shopping and trading through e-commerce is on demand and accepted by people everywhere. Now a day's people use the internet to study and analyze the information about an article by reading various reviews about that article before purchasing it online. We can understand the customer behavior easily by observing and understanding social media data. After studying the behavior of user's country wise we can predict current trends that can be used in deciding usefulness of some tasks, product, and themes. But there are several challenges which create obstacles in analyzing the exact meaning of sentiments. We have addressed various significant challenges and a system has been designed to analyze sentiments with an aim to resolve many of listed issue. In this paper, sentiment analysis is performed on a large dataset of tweets using big data platform. By analyzing the user social data about a particular topic we have performed analysis to depict the user's behavior for a particular topic in certain country and city.
A Simplified Co-Simulation Model for Investigating Impacts of Cyber-Contingency on Power System Operations

In cyber-physical power systems, a contingency in the cyber may hamper secure operations of the physical system by changing the information flows. In order to investigate the degree to which a cyber-contingency affects power system operations, we first categorize the potential impacts on the information flow respecting the end-to-end information features in terms of delayed, disordered, dropped, and distorted data. A simplified co-simulation model is then put forth for analyzing the interdependencies between energy and information flows, and obtaining the quantitative relation between information features and power system operations. As the operational characteristics of a cyber system can be entirely captured by all end-to-end information flows, our developed model is hence termed as the information flow based co-simulation model. The information flow based model features in low complexity and covering potential cyber contingencies. Moreover, the quantitative relation can provide requirements for the planning and operation of cyber systems. To illustrate the effectiveness and advantages of the proposed model, a comparison with the commercial communication simulation software OPNET is made. Finally, a wide-area damping control application is taken as an example to demonstrate physical impacts of various cyber-contingencies.
3D Face Recognition on Point Cloud Data - An Approaching based on Curvature Map Projection using Low Resolution Devices

Facial recognition is the most natural and common form of biometrics, routinely used by humans and one of the most promising areas in biometrics research. The majority of traditional researches and commercial use of facial recognition systems are focused on methods that explores 2D (two-dimensional) images of human faces. All of them are based on features extraction that does not use any 3D shape information from the faces, especially with regard to depth. This paper presents a method based on Point Cloud and Curvature Map Projection to perform a 3D face recognition. The achieved results are presented and divided in two test scenarios, composed by a biometric evaluation analysis applying the Equal Error Rate score, Receiver Operating Characteristic and an accuracy comparison with other related works. The proposed work presents an accuracy of about 98.92%, allowing it to be applied for 3D face recognition tasks.
Endothelin-1 and angiotensin-II modulate urotensin-II vasoconstriction in rat aorta exposed to mercury.

OBJECTIVES The aim of this study was to evaluate the possible roles of endothelin-1 and angiotensin-II in urotensin-II vasoconstriction and in endothelial dysfunction induced by mercury. BACKGROUND Urotensin-II, the most potent vasoactive peptide, is entwined with the cardiovascular diseases and has been labelled as a new pathophysiological biomarker. METHODS Rat aortic rings were pre-incubated with sb-710411, bq-123, and captopril. Doses of human urotensin-II with increased concentrations were applied in all groups in the presence or absence of mercury chloride. In another set of the experiment, aortic rings were treated with a single dose of mercury chloride in the presence of each of the above blockers. RESULTS Angiotensin-II and endothelin-1 mediated the vascular responses to the peptide urotensin-II under conditions of both intact endothelium and endothelial impairments induced by mercury. Urotensin-II, angiotensin-II and endothelin-1 significantly participated in vascular responses to mercury chloride. CONCLUSION The novel finding was that urotensin-II is potentiated under the condition of endothelial dysfunction. Endothelin-1 and angiotensin-II pathways could be heavily exploited in modulating endothelial dysfunction impacts and peptide vascular actions (Tab. 1, Fig. 4, Ref. 30).
Compact Lattice Signatures

Lattice-based signature schemes have seen many improvements in the past few years with recent attempts (Güneysu et al., 2012; Ducas et al., 2013; Ducas et al., 2014; Lyubashevsky, 2016; Ducas et al., 2017) to bring lattice-based signature schemes at par with the traditional number-theoretic signature schemes. However, the trade-off between the signature size and the key size, time for a signature generation, and the practical and provable security is not necessarily the optimal. We propose a compact lattice-based signature scheme with key-size and signatures of order n, where n is the dimension of the lattice. The proposed signature scheme has faster algorithms for key generation, signing, and verification than the existing schemes. The proposed scheme is simple and is competitive with the other post-quantum signature schemes.
A Survey of Positioning Systems Using Visible LED Lights

As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.
Skeleton-Based Human Activity Recognition by Spatio-Temporal Representation and Convolutional Neural Networks with application to Cyber Physical Systems with Human in the Loop

The developments in Cyber-physical systems where smart computers that can sense and understand human behavior will have enormous societal and economic impact facilitating various services in critical infrastructure and everyday life. CPS with Human in the Loop is a system that takes human response into consideration and human presence and behavior are key parts of the system. So these architectures of smart devices will need to interpret human action in real-time and predict humans' immediate intention in complex, noisy and cluttered environments. One of the main goals of researchers will be the development of CPS that can understand complex human activities. In this paper we propose a novel skeleton-based approach utilizing spatio-temporal information and convolutional neural networks for classification of human activities.
Sex Differences in Spontaneous Brain Activity in Adolescents With Conduct Disorder

Purpose: Sex differences in conduct disorder (CD) pathophysiology have yet to be resolved. In this study, we applied the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) to compare spontaneous brain activity in male versus female adolescents diagnosed with CD in light of the gender paradox hypothesis. Materials and Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) examinations were conducted with 51 CD patients (35 males) and 53 age-matched healthy controls (HCs; 35 males). Pearson analysis was conducted to detect relationship between ALFF/fALFF values in gender-differentiated regions and clinical characteristics. Results: We observed that male CD patients showed significant increased ALFF in the bilateral superior temporal gyrus (STG)/insula, and significant decreased ALFF in the left anterior cingulate cortex (ACC), left middle frontal gyrus (BA8 andBA11), left middle temporal gyrus and left inferior/middle temporal gyrus relative to female CD patients. The fALFF in male CD patients was significantly increased in the right STG/insula, decreased in the right superior frontal gyrus, left middle frontal gyrus, right inferior frontal gyrus, and right postcentral gyrus relative to female CD patients. Considering the sex-by-diagnosis interactions in CD patients, the male CD patients had significantly higher fALFF in the left putamen, lower fALFF in the right postcentral gyrus relative to the female CD patients. Conclusion: The brain regions whose activity index values differed in relation to sex should be further explored in CD pathophysiology studies, particularly with respect to sex differences in clinical symptoms, emotional features, cognitive features, and prevalence rates in CD. The present findings are consistent with the gender paradox hypothesis.
Health Literacy and Use and Trust in Health Information

There is a need to investigate which health information sources are used and trusted by people with limited health literacy to help identify strategies for addressing knowledge gaps that can contribute to preventable illness. We examined whether health literacy was associated with people’s use of and trust in a range of potential health information sources. Six hundred participants from a GfK Internet survey panel completed an online survey. We assessed health literacy using the Newest Vital Sign, the sources participants used to get health information, and the extent to which participants trusted health information from these sources. We performed multivariable regressions, controlling for demographic characteristics. Lower health literacy was associated with lower odds of using medical websites for health information and with higher odds of using television, social media, and blogs or celebrity webpages. People with lower health literacy were less likely to trust health information from specialist doctors and dentists, but more likely to trust television, social media, blogs/celebrity webpages, friends, and pharmaceutical companies. People with limited health literacy had higher rates of using and trusting sources such as social media and blogs, which might contain lower quality health information compared to information from healthcare professionals. Thus, it might be necessary to enhance the public’s ability to evaluate the quality of health information sources. The results of this study could be used to improve the reach of high-quality health information among people with limited health literacy and thereby increase the effectiveness of health communication programs and campaigns.
Bellrock: Anonymous Proximity Beacons From Personal Devices

Proximity beacons provide simple, low-cost location data. However, beacon deployments remain rare. In this paper we introduce Bellrock, a framework that repurposes static personal devices (phones, laptops, etc.) as proximity beacons without revealing the location of the device owners, and provides conventional beacons with access control. This is done by using mutable pseudo-anonymous identifiers that can be unmasked by a cloud service.We develop Bellrock as a general framework, describing the repurposing scheme and the anonymisation techniques, before applying it to Bluetooth Low Energy beacons. We implement and demonstrate the scalability of the de-anonymisation server, which uses a series of heuristics. We implement a Bellrock client on Android and demonstrate negligible impact on battery lifetime. We evaluate Bellrock using extensive real-world office worker movements. We find that Bellrock was able to provide proximity locations for 8,542 of the 21,796 failed locations that would have occurred without it. We further find that office workers were in range of one or more of their co-workers over 90% of the time, indicating Bellrock can provide relative proximity information even in the absence of a conventional beacon deployment. Overall, we find that Bellrock is both feasible and practical, providing a beacon deployment where there was none, or supplementing existing deployments.