Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacotherapy of Anxiety Disorders: Current and Emerging Treatment Options

Anxiety disorders are the most prevalent psychiatric disorders and a leading cause of disability. While there continues to be expansive research in posttraumatic stress disorder (PTSD), depression and schizophrenia, there is a relative dearth of novel medications under investigation for anxiety disorders. This review's first aim is to summarize current pharmacological treatments (both approved and off-label) for panic disorder (PD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), and specific phobias (SP), including selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), azapirones (e.g., buspirone), mixed antidepressants (e.g., mirtazapine), antipsychotics, antihistamines (e.g., hydroxyzine), alpha- and beta-adrenergic medications (e.g., propranolol, clonidine), and GABAergic medications (benzodiazepines, pregabalin, and gabapentin). Posttraumatic stress disorder and obsessive-compulsive disorder are excluded from this review. Second, we will review novel pharmacotherapeutic agents under investigation for the treatment of anxiety disorders in adults. The pathways and neurotransmitters reviewed include serotonergic agents, glutamate modulators, GABAergic medications, neuropeptides, neurosteroids, alpha- and beta-adrenergic agents, cannabinoids, and natural remedies. The outcome of the review reveals a lack of randomized double-blind placebo- controlled trials for anxiety disorders and few studies comparing novel treatments to existing anxiolytic agents. Although there are some recent randomized controlled trials for novel agents including neuropeptides, glutamatergic agents (such as ketamine and d-cycloserine), and cannabinoids (including cannabidiol) primarily in GAD or SAD, these trials have largely been negative, with only some promise for kava and PH94B (an inhaled neurosteroid). Overall, the progression of current and future psychopharmacology research in anxiety disorders suggests that there needs to be further expansion in research of these novel pathways and larger-scale studies of promising agents with positive results from smaller trials.
The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.
Griffin: A Storage Service for Granular Edge Applications

The emerging edge computing paradigm is enabling a series of real-time, location-aware applications. The data produced by applications like autonomous driving, collaborative machine learning, and real-time video processing is often only usable in a limited time- and space-window. Data placement, sharing, and computation need to happen with low latency and location awareness, using a heterogeneous infrastructure that is not on par with datacenter hardware. In this work we plan to design and implement Griffin, a distributed edge storage service that is aware of mobility, latency and consistency requirements of applications, and that can support storage function programmability for low-latency responses.
Métodos de Machine Learning Aplicados no Cenário da Educaçáo a Distância Brasileira (Machine Learning Techniques Applied to the Brazilian Distance Education)

Tackling student evasion has been a major challenge for the Brazilian Educational System. In the last few years, there has been an increasing interest in Distance Education to address such an important issue. This new paradigm facilitates the attendance of students who have difficulties in attending classes in person due to work, geographical or socioeconomic reasons. However, Distance Education has also shown a growing number of evading students. To tackle Distance Education evasion, we propose the use of Data Mining and Machine Learning techniques to predict the number of students at risk of evasion. Such an approach might help Education Institutions to quantify, plan and develop solutions to this problem. Our work employs Decision Trees, Bootstrap Aggregating Ensemble, Multilayer Perceptron and Support Vector Machines to produce accurate estimates of evading students in Higher Education Institutions that have Distance Education programs. Our experiments showed that our approach could deliver good generalization performance. Resumo—Combater a evasão de alunos tem sido um grande desafio para o Sistema Educacional Brasileiro. Nos últimos anos, tem havido um interesse crescente pela Educação a Distância para lidar com tal problema. Esse novo paradigma facilita o atendimento a alunos que têm dificuldade em frequentar as aulas presencialmente por motivos laborais, geográficos ou socioeconômicos. No entanto, a Educação a Distância também tem mostrado um número crescente de alunos evadidos. Para enfrentar a evasão na Educação a Distância, propomos o uso de técnicas de Data Mining e Machine Learning para prever o número de alunos em risco de evasão. Tal abordagem pode ajudar as instituições de ensino a quantificar, planejar e desenvolver soluções para este problema. Nosso trabalho emprega Árvores de Decisão, Bootstrap Aggregating Ensemble, Multilayer Perceptron e Support Vector Machines para produzir estimativas precisas de evasão de alunos em instituições de ensino superior com programas de Educação a Distância. Nossos experimentos mostraram que nossa abordagem pode fornecer um bom desempenho de generalização para a predição de evasão.
Can COVID-19 Change the Big5 Personality Traits of Healthcare Workers?

To curb the spread of the coronavirus, authorities around the world implemented lockdown measures for months. In these locked down days, people as well as healthcare workers (HWs) are increasingly relying on social media platforms to socialize among themselves and get the latest updates of this pandemic situation. As a result, social media users’ interaction data have become a key resource in analyzing their behaviors. COVID-19 is the first global pandemic in the global scale after the appearance of social media. HWs such as physicians, nurses, technicians, and hospital support staff provide essential services to the patients with COVID-19 even after having lack of sufficient equipment, poor preparation, and risking their own lives. These HWs may experience sudden change in their personality traits due to pandemic, which are not studied in the existing literature. In this paper, we investigate how the pandemic influences Big5 personality traits of HWs using social media data. Our analysis shows that HWs’ neuroticism, conscientiousness, and extroversion have increased significantly due to pandemic. In contrast, we observe a decrease of HWs’ agreeableness and openness dimensions of Big5 personality.
An Optimized Decision Tree based Android Malware Detection Approach using Machine Learning

The growing trend of attacking Android smart phones using malicious app has started posing significant threats for the users. Many approaches have been introduced for protecting the users against such malware. However, those solutions tend to use many features to get better accuracy in detecting Android malware which eventually results in higher complexity in creating machine learning based models. Hence, an effective model is required to find the most significant features resulting in a faster model yet having better accuracy. In this paper, we have proposed a robust approach to detect android malwares using only selective features that are extracted using Ranker search method and Gain Ratio attribute evaluator. We have used machine learning algorithms which include J48 Decision Tree, Random Forest and Random Tree to classify the preprocessed dataset into malware and benign. We have produced faster results using Random Tree algorithm and obtained higher accuracy using Random Forest algorithm. Further, we have measured and compared various performance metrics with respect to different numbers of attributes and different classifiers. Our proposed detection method can help users distinguish malicious applications from benign ones in a faster yet precise manner.
Solving The Maze of Diagnosing Parkinson’s Disease based on Portable EEG Sensing to be Adaptable to Go In-The-Wild

Parkinson’s disease is a common and highly threatening neurodegenerative disease, which has no confirmed well-adopted diagnosis method to date. All research efforts in this regard focus on diagnosing in controlled environment such as laboratories. Going beyond this common limitation, in this paper, we exploit the Electroencephalogram (EEG) signal to effectively come up with a portable Parkinson’s disease diagnosis system that can go in-the-wild. To do so, we collect EEG signals from both Parkinson’s patients and healthy people in different emotional states. Our statistical analysis finds a substantial differences in the signals in several emotional states for Parkinson’s patients and healthy people. Accordingly, we develop a machine learning based model to diagnose Parkinson’s disease that results in 97% accuracy.
Controlled accuracy for discrete Chebyshev polynomials

An algorithm is proposed for stable determination of the normalized discrete Chebyshev polynomials. This is achieved by adaptively restricting the range over which the recurrence relation or difference equation is applied. The adaptation of the range is controlled by a factor indicating how much the normalized basis functions are allowed to deviate from unit norm.
Electric Vehicles Driving Cost and Competitiveness

On the brink of climate changes, many world leaders started talking about transition towards greener technologies. The electric vehicles seem to be the favorite in the transport field. The paper presents a comparative analysis of the current and expected future driving costs and competitiveness of electric vehicles compared to internal combustion engine ones. Our aim is to find out if electric vehicles are indeed able to take over the market from a practical perspective. Their main drawbacks are presented together with available technologies which could overcome them.
A Closed-Form Estimator for Bearings-Only Fusion of Heterogeneous Passive Sensors

Bearings-only target motion analysis with fusion of heterogeneous passive sensors with and without propagation delay attempts to estimate a target trajectory by fusing bearings from two sensors: a sensor whose measurements have negligible propagation delay and a sensor with measurements that have non-negligible propagation delay. This problem is highly nonlinear and challenging, particularly due to the fact that the propagation delay is unknown and is highly coupled with the unknown target states. This paper proposes a novel closed-form instrumental variable based batch estimator for this problem that is computationally fast. The estimator is derived by writing pseudolinear type equations for the error vector—difference between the true target state and a nominal solution. A theoretical bias and mean square error analysis of the proposed estimator is carried out which provides insight into the performance of this estimator. Results indicate that this is a computationally efficient algorithm that is able to produce comparable performance to the maximum likelihood estimator and achieves the Cramer Rao lower bound.
Modeling of Complexly Structured Reporting Forms and Requests in the Tasks of Automated Provision of Public Services

The introduction of electronic state and municipal services is an important component of improving management efficiency, developing the economy and social sphere, and forming a digital economy. The platform will help to perform most of the management functions not with the help of authorities, but based on platform solutions. The transition from one form of execution to another is possible on the basis of the following key principles: constant accumulation of objective data about control objects, control through the use of big data technologies, the Internet of things, artificial intelligence, transfer of the right to make routine decisions to information systems, constant audit of systems and assistance in preventing crashes. It is noted that an unresolved task in the administration of the sphere of state (municipal) services is to improve their quality and availability when processing complexly structured reporting forms and requests. The solution of these problems and the improvement of the quality of the services provided by public services can be achieved by describing the meaning (semantics) of the content of complexly structured reporting forms and implementing work with them by using knowledge representation models, for example, such as semantic networks, frames and ontologies. When creating a model of complexly structured reporting forms, the user’s preferences were used at the time of initialization of the request (user contexts).
A Design for Remote Center of Motion Control Using a Software Computing Approach

The remote center of motion (RCM) control allows the surgical tools to keep rotating around the trocar insertion point of minimally invasive surgery (MIS) during the robot-assisted movement. In this paper, we propose a method to achieve RCM control for a general 6-DOF industrial manipulator to through software programming. This method can be employed by any robotic manipulator to obtain the coordinate transform of the tool center point (TCP) of the manipulator according to a simple RCM formula. The respective inverse kinematics of the manipulator calculate the joint commands of the manipulator to complete the RCM control. The RCM algorithm has been implemented on the 6-DOF HIWIN RA605 robot. The experimental result show that the average error of RCM point is 0. 08mm, and the trajectory tracking error is about 0. 92mm.
A Hyper-ellipsoidal Support Vector Data Description Incremental Learning Method for Bearing Fault Diagnosis

This paper proposes a novel method based on hyper-ellipsoidal Support Vector Data Description (SVDD) for bearing fault diagnosis. First, features of bearing fault data are selected based on integrated indicators to solve the overlapping problems of features from different bearing faults. Second, considering that multiple fault data of bearings in practical applications cannot be obtained at one time in a short time, the incremental learning model is established by creating high-dimensional spatial feature hyper-ellipsoids with the concept of SVDD. Finally, we conducted experiments by two laboratory data sets to validate the effectiveness of the proposed method.
Hardware Architecture for Ultra-Wideband Channel Impulse Response Measurements Using Compressed Sensing

We propose a compact hardware architecture for measuring sparse channel impulse responses (IR) by extending the M-Sequence ultra-wideband (UWB) measurement principle with the concept of compressed sensing. A channel is excited with a periodic M-sequence and its response signal is observed using a Random Demodulator (RD), which observes pseudo-random linear combinations of the response signal at a rate significantly lower than the measurement bandwidth. The excitation signal and the RD mixing signal are generated from compactly implementable Linear Feedback Shift registers (LFSR) and operated from a common clock. A linear model is derived that allows retrieving an IR from a set of observations using Sparse-Signal-Recovery (SSR). A Matrix-free model implementation is possible due to the choice of synchronous LFSRs as signal generators, resulting in low computational complexity. For validation, real measurement data of a time-variant channel containing multipath components is processed by simulation models of our proposed architecture and the classic M-Sequence method. We show successful IR recovery using our architecture and SSR, outperforming the classic method significantly in terms of IR measurement rate. Compared to the classic method, the proposed architecture allows faster measurements of sparse time-varying channels, resulting in higher Doppler tolerance without increasing hardware or data stream complexity.
Inclusion of Instantaneous Influences in the Spectral Decomposition of Causality: Application to the Control Mechanisms of Heart Rate Variability

Heart rate variability is the result of several physiological regulation mechanisms, including cardiovascular and cardiorespiratory interactions. Since instantaneous influences occurring within the same cardiac beat are commonplace in this regulation, their inclusion is mandatory to get a realistic model of physiological causal interactions. Here we exploit a recently proposed framework for the spectral decomposition of causal influences between autoregressive processes [2] and generalize it by introducing instantaneous couplings in the vector autoregressive model (VAR). We show the effectiveness of the proposed approach on a toy model, and on real data consisting of heart period (RR), systolic pressure (SAP) and respiration (RESP) variability series measured in healthy subjects in baseline and head up tilt conditions. In particular, we show that our framework allows one to highlight patterns of frequency domain causality that are consistent with well-interpretable physiological interaction mechanisms like the weakening of respiratory sinus arrhythmia at high frequencies and the activation of the baroreflex control at lower frequencies, in response to postural stress.
DEAR: Distributed Evaluation of Alerting Rules

Cloud computing passed the hype cycle long ago and firmly established itself as a future technology since then. However, to utilize the cloud as cost-efficiently as possible, a continuous monitoring is key to prevent an over- or under-commissioning of resources. In large-scaled scenarios, several challenges for cloud monitoring, such as high network traffic volume, low accuracy of monitoring data, and high time-to-insight, require new approaches in IT Operations while considering administrative complexity. To handle these challenges, we present DEAR, the Distributed Evaluation of Alerting Rules. DEAR is a plugin for monitoring systems which automatically distributes alerting rules to the monitored resources to solve the trade-off between high accuracy and low network traffic volume without administrative overhead. We evaluate our approach against requirements of today's IT monitoring and compare it to conventional agent-based monitoring approaches.
Blind Bandwidth Extension of Speech based on LPCNet

A blind bandwidth extension is presented which improves the perceived quality of 4 kHz speech by artificially extending the speech’s frequency range to 8 kHz. Based on the source-filter model of the human speech production, the speech signal is decomposed into spectral envelope and excitation signal and each of them is extrapolated separately. With this decomposition, good perceptual quality can be achieved while keeping the computational complexity low. The focus of this work is in the generation of an excitation signal with and autoregressive model that calculates a distribution for each audio sample conditioned on previous samples. This is achieved with a deep neural network following the architecture of LPCNet [1]. A listening test shows that it significantly improves the perceived quality of bandlimited speech. The system has an algorithmic delay of 30 ms and can be applied in state-of-the-art speech and audio codecs.
Stressful Situations in the Work of the Airport Dispatcher

Airport dispatchers work very often under time pressure and their decisions can significantly affect the safety of air traffic at the airport. However, there is rather little attention paid to the issue. Therefore, this article focuses on the evaluation of stress factors that may have a negative impact on the work of the airport dispatcher. For the purpose, a questionnaire was created, the aim of which was to identify potential risk factors for stress situations when performing the tasks of an airport dispatcher. All dispatchers at Bratislava Airport took part in the questionnaire survey. The results showed a negative impact of such factors as working conditions, salary rate and contact with supervisors on the work performance of the airport dispatcher.
TensorExpress: In-Network Communication Scheduling for Distributed Deep Learning

TensorExpress provides in-network communication scheduling for distributed deep learning (DDL). In cloud-based DDL, parameter communication over a network is a key bottleneck. Previous studies proposed tensor packet reordering approaches to reduce network blocking time. However, network contention still exists in DDL. TensorExpress mitigates network contention and reduces overall training time. It schedules tensor packets in-network using P4, a switch programming language. TensorExpress improves latency and network blocking time up to 2.5 and 2.44 times, respectively.
Plant Trait Segmentation for Plant Growth Monitoring

3D point cloud segmentation is an important step for plant phenotyping applications. The segmentation should be able to separate the various plant components such as leaves and stem robustly to enable traits to be measured. Also, it is important for the segmentation method to work on range of plant architectures with good accuracy and computation time. In this paper, we propose a segmentation method using Euclidean distance to segment the point cloud generated using a structure-from-motion algorithm. The proposed algorithm requires no prior information about the point cloud. Experimental results illustrate that our proposed method can effectively segment the plant point cloud irrespective of its architecture and growth stage. The proposed method has outperformed the standard methods in terms of computation time and segmentation quality.
Equilibrium studies for acid dye adsorption onto chitosan

The ability of chitosan as an adsorbent for the removal of acid dyestuff, namely, acid green 25, acid orange 10, acid orange 12, acid red 18, and acid red 73, from aqueous solution has been studied. The experimental equilibrium data for the single component dye−chitosan systems have been analyzed using the linearized forms of Langmuir, Freundlich, and Redlich−Peterson isotherms. The Langmuir isotherm was found to provide the best theoretical correlation of the experimental data for the adsorption of all five acid dyes. On the basis of the Langmuir analysis, the monolayer adsorption (saturation) capacities were determined to be 645.1, 922.9, 973.3, 693.2, and 728.2 mg of dye per gram of chitosan for acid green 25, acid orange 10, acid orange 12, acid red 18, and acid red 73, respectively. The differences in adsorption capacities may be due to the effect of molecular size and the number of sulfonate groups of each dye. The results demonstrated that monovalent and/or smaller dye molecules have superior adsor.