Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of a Peer-Led Pain Management Program in Relieving Chronic Pain and Enhancing Pain Self-Efficacy Among Older Adults: A Clustered Randomized Controlled Trial

Chronic pain is common in nursing home residents, who may have difficulty seeking out pain management strategies. Peer support model show promise as a strategy for managing chronic conditions. This was a clustered randomized controlled trial. A peer-led pain management program was provided for the experimental group. Pain situation, depression, quality of life, non-drug strategies used, and pain knowledge were measured. A total of 262 participants joined the study (146 were allocated as experimental group and 116 as control group). Before our intervention, the mean pain score reported was as high as 6.36 on a 10-point Likert Scale. The high intensity of their pain very much interfered with the daily activities of the participants. Pain interference was high and the participants had poor coping as indicated by the low pain self-efficacy. Depression and a low quality of life score was found. Upon completion of our PAP, there was a significant increase in pain self-efficacy, pain interference as well as quality of life for the participants in the experimental group and not in the control group, and this improvement sustained in 3-month follow up. The present study used a peer support models and proven to be effective in managing pain and pain related situations for nursing home residents with chronic pain. The peer volunteers involved in the pain management program taught relevant pain knowledge and pain management strategies to help our participants. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT03823495, NCT03823495.
Audio Information Camouflage Detection for Social Networks

Sending camouflaged audio information for fraud in social networks has become a new means of social networks attack. The hidden acoustic events in the audio scene play an important role in the detection of camouflaged audio information. Therefore, the application of machine learning methods to represent hidden information in audio streams has become a hot issue in the field of network security detection. This study proposes a heuristic mask for empirical mode decomposition (HM-EMD) method for extracting hidden features from audio streams. The method consists of two parts: First, it constructs heuristic mask signals related to the signal’s structure to solve the modal mixing problem in intrinsic mode function (IMF) and obtains a pure IMF related to the signal’s structure. Second, a series of hidden features in environment-oriented audio streams is constructed on the basis of the IMF. A machine learning method and hidden information features are subsequently used for audio stream scene classification. Experimental results show that the hidden information features of audio streams based on HM-EMD are better than the classical mel cepstrum coefficients (MFCC) under different classifiers. Moreover, the classification accuracy achieved with HM-EMD increases by 17.4 percentage points under the three-layer perceptron and by 1.3% under the depth model of TridentResNet. The hidden information features extracted by HM-EMD from audio streams revealed that the proposed method could effectively detect camouflaged audio information in social networks, which provides a new research idea for improving the security of social networks.
West Pacific Earthquake Forecasting Using NOAA Electron Bursts With Independent L-Shells and Ground-Based Magnetic Correlations

Recent advances in statistical correlations between strong earthquakes and several non-seismic phenomena have opened the possibility of formulating warnings within days or even hours. The retrieved correlations have been discovered for those ionospheric physical observations which lasted a long time and realized using the same instruments, including multi-satellite recordings. One of those regarded the electron burst phenomena detected by NOAA, for which the conditional probability of a seismic event was calculated. Then an earthquake probability greater than its frequency was assigned when a satellite realized such a phenomenological observation. This approach refers to the correlations obtained between high-energy electrons detected using the NOAA POES and strong Indonesian and Philippine earthquakes. It is reformulated here to realize a test of earthquake forecasting. The fundamental step is obtained by using a unique electron L-shell interval of 1.21 ≤ L ≤ 1.31 , which decouples the electron parameters from the earthquake parameters. Then, the optimized correlation was recalculated to be 1.5–3.5 h early, between electron bursts and an increased number of seismic events with M ≥ 6 , therein improving the significance too. Moreover, this methodology is reconnected to the frequency theory, and to Molchan’s error diagram, by the probability gain, where a comparison among the significances of various methods is given. The previously proposed physical link between the crust and the ionosphere through magnetic interaction, presumably operating 4–6 h before strong earthquakes, is examined quantitatively on the basis of recent magnetic pulse measurements. Consequently, the probability gain of earthquake forecasting is hypothetically calculated for both the dependent measurements of electron bursts using NOAA satellites and possible ground-based magnetic pulse detection. This method of combining probability gains for earthquake forecasting is general enough that it can be applied to any pair of observables from space and the ground.
The Relationship Between Perceived Stress and Subjective Cognitive Decline During the COVID-19 Epidemic

During the outbreak of the COVID-19 epidemic, fear of disease and its consequences, recommended lifestyle changes, and severe restrictions set by governments acted as stressors and affected people’s mood, emotions, mental health, and wellbeing. Many studies conducted during this crisis focused on affective and physiological responses to stress, but few studies examined how the crisis affected cognition. The present cross-sectional study examined the relationship between physiological, affective, and cognitive responses to the epidemic. In an online survey conducted at the height of the first wave of the epidemic in Slovenia (April 15–25, 2020), 830 Slovenian residents aged 18–85 years reported the effects of stressors (confinement, problems at home, problems at work, lack of necessities, and increased workload), experienced emotions, generalized anxiety, perceived stress, changes in health, fatigue and sleep quality, and perceived changes in cognition during the epidemic. Risk factors for stress (neuroticism, vulnerability, general health, gender, and age) were also recorded. We hypothesized that stressors and stress risk factors will be related to subjective cognitive decline, with negative emotions, generalized anxiety, perceived stress, and physical symptoms acting as mediator variables. On average, the results showed a mild subjective cognitive decline during the epidemic. In structural equation modeling, 34% of its variance was predicted by the mediator variables, with negative emotions and physical symptoms having the largest contribution. Stress risk factors were predictably related to the four mediator variables. Among the stressors, confinement showed the strongest effect on the four mediator variables, implying the importance of thoughtful communication about necessary restrictive measures during emergency circumstances. The results of this study indicate that the possibility of altered cognitive function should be considered when planning work and study activities during the epidemic.
An Advanced Finite Element Modeling for the Failure of Notched Ceramic Matrix Composite With TFP Patch Reinforcement

An advanced modeling strategy for notched ceramic matrix composite coupons with patch reinforcement was proposed to investigate the failure mechanisms. This model considered the tailored fiber–placed (TFP) yarn details obtained from the design phase and the embedded element concept which was used to successfully overcome the meshing difficulties. Inter-ply “glue” layers were simulated using the surface-based contact cohesive element method, so the delamination due to interfacial material discontinuity and damage can be well reproduced and analyzed. For composite ply, the energy-based composite progressive damage model that is independent of the mesh size was applied. Virtual test campaign was performed with a variety of geometrical and material parameters, and the damage and failure mechanisms based on the stress analysis can be revealed to support the design optimization of patch reinforcement.
End-to-End Automatic Speech Recognition for Gujarati

We present a novel approach for improving the performance of an End-to-End speech recognition system for the Gujarati language. We follow a deep learning based approach which includes Convolutional Neural Network (CNN), Bi-directional Long Short Term Memory (BiLSTM) layers, Dense layers, and Connectionist Temporal Classification (CTC) as a loss function. In order to improve the performance of the system with the limited size of the dataset, we present a combined language model (WLM and CLM) based prefix decoding technique and Bidirectional Encoder Rep-resentations from Transformers (BERT) based post-processing technique. To gain key insights from our Automatic Speech Recognition (ASR) system, we proposed different analysis methods. These insights help to understand our ASR system based on a particular language (Gujarati) as well as can govern ASR systems’ to improve the performance for low resource languages. We have trained the model on the Microsoft Speech Corpus, and we observe a 5.11% decrease in Word Error Rate (WER) with respect to base-model WER.
High power electromagnetic pulse applicators for evaluation of biological effects induced by electromagnetic radiation waves

The effects of electromagnetic radiation waves on health is one of the major public concerns. These waves are mainly produced at a large scale but it is important to evaluate these effects on biological samples at the laboratory scale. Here we developed a set of micro applicators, which allow evaluating the effect of electromagnetic fields on biological samples with volumes in the microliter range. The applicators can be coupled to an optical microscope and allow a real-time observation of potential structural and functional alterations of the tested sample induced by different waveforms. New design approaches are suggested to simultaneously achieve maximized electric field coupling effect and optimized electric field homogeneity in the tested sample, while minimizing the return loss when the applicators are loaded with the biological samples. These applicators allow studying the biological effect of a variety of different signals, due to their wide frequency bandwidth (beyond 1.5 GHz) and their high permissible power. In addition, different electromagnetic parameters such as the electromagnetic field magnitude, pulse repetitive factor, number of bursts or delay between bursts may be set. The efficacy of the applicators was addressed for three different signals: two types of electromagnetic waves – a damped sinusoid centered at 200 MHz (wide band signal), a radar-like signal at 1.5 GHz (the ultra-narrow band signal) and a train of millisecond square-wave monopolar electric field pulses (causing electroporation). The biological effects were thus assessed (at the microscopic scale) on two different biological models, the giant unilamellar vesicles, and tumor and normal human cells, as well as being compared to results obtained (at full scale) with signals generated by antennas.
Precise Blood Glucose Sensing by Nitrogen-Doped Graphene Quantum Dots for Tight Control of Diabetes

Graphene quantum dots (GQD) are novel fluorescent carbon nanomaterials based on a graphite structure. Thanks to extraordinary properties such as high surface area and enhanced prevalent optical properties, they have received more interest for special applications. Glucose sensing is a critical factor for the diagnosis, and treatment of diabetes plays an important role and could contribute to the monitoring of diabetes and other related parameters, which has been effectively underscoring the health society. Detecting glucose has been cultivated through different systems, for example, electrochemical or optical techniques. Novel transducers made with GQD that fluorescent coordinate methods have considered the improvement of cutting-edge glucose sensors with prevalent affectability and accommodation. Currently, detection of glucose by nitrogen-doped GQD frameworks concerning the determined objectives has been considerably considered. Here, we explored the properties of fluorescent nitrogen-doped GQD as an excellent and effective index that significantly could promote nitrogen-doped GQDs and make them an appropriate candidate for detecting glucose.
Self Attended Stack-Pointer Networks for Learning Long Term Dependencies

We propose a novel deep neural architecture for dependency parsing, which is built upon a Transformer Encoder (Vaswani et al., 2017) and a Stack Pointer Network (Ma et al., 2018). We ﬁrst encode each sentence using a Transformer Network and then the dependency graph is generated by a Stack Pointer Network by selecting the head of each word in the sentence through a head selection process. We evaluate our model on Turkish and English treebanks. The results show that our trasformer-based model learns long term dependencies efﬁciently compared to sequential models such as recurrent neural networks. Our self attended stack pointer network improves UAS score around 6% upon the LSTM based stack pointer (Ma et al., 2018) for Turkish sentences with a length of more than 20 words.
Microbial Biosensors as Pesticide Detector: An Overview

Farmers are highly dependent upon agrochemicals to boost crop production through soil fertilization and and insect pests, pathogens, parasites, and weeds management . However, contentious application of agrochemicals on the farm has aggravated residual accumulation and has become problematic for environmental safety besides causing disease to humans and other animals. Thus, the analysis of chemical residues from the environment is vital for policymakers and communities. Mostly, chemists were devoted to analyzing the existing contaminants from different sources by using highly sophisticated chromatographic equipment, although it is time taking, laborious, costly, and that required well-trained professionals. However, biosensors are more important to analyze chemical contaminants from different samples using various bioreporters integrated with electrochemical and optical transducers. Microbes are metabolically diverse, amenable for genetic engineering, cost effective in culturing, and tolerant to diverse conditions. Thus, microbial biosensor is capturing attention and becoming more effective for environmental monitoring. Therefore, this review assessed the implication of microbial biosensors for pesticide detection and the role of genetic engineering for strain improvement.
D-Coref: A Fast and Lightweight Coreference Resolution Model using DistilBERT

Smart devices are often deployed in some edge-devices, which require quality solutions in limited amount of memory usage. In most of the user-interaction based smart devices, coreference resolution is often required. Keeping this in view, we have developed a fast and lightweight coreference resolution model which meets the minimum memory requirement and converges faster. In order to generate the embeddings for solving the task of coreference resolution, DistilBERT, a light weight BERT module is utilized. DistilBERT consumes less memory (only 60% of memory in comparison to BERT-based heavy model) and it is suitable for deployment in edge devices. DistilBERT embedding helps in 60% faster convergence with an accuracy compromise of 2.59%, and 6.49% with respect to its base model and current state-of-the-art, respectively.
SUKHAN: Corpus of Hindi Shayaris annotated with Sentiment Polarity Information

Shayari is a form of poetry mainly popular in the Indian subcontinent, in which the poet expresses his emotions and feelings in a very poetic manner. It is one of the best ways to express our thoughts and opinions. Therefore, it is of prime importance to have an annotated corpus of Hindi shayaris for the task of sentiment analysis. In this paper, we introduce SUKHAN, a dataset consisting of Hindi sha-yaris along with sentiment polarity labels. To the best of our knowledge, this is the ﬁrst corpus of Hindi shayaris annotated with sentiment polarity information. This corpus contains a total of 733 Hindi shayaris of various genres. Also, this dataset is of utmost value as all the annotation is done manually by ﬁve annotators and this makes it a very rich dataset for training purposes. This annotated corpus is also used to build baseline sentiment classiﬁcation models using machine learning techniques.
USB-IDS-1: a Public Multilayer Dataset of Labeled Network Flows for IDS Evaluation

The scarceness of publicly available data from real-life operational networks is a long-standing problem for the security research community. Many public intrusion detection datasets have spread in the literature; however, they may lack relevant details concerning the victim servers and applications. This paper describes USB-IDS-1, a novel public intrusion detection dataset developed at the University of Sannio at Benevento, Italy. The dataset considers both network traffic and application-level facets, such as performance measurements, configuration and defense modules of the victim server under attack. The paper describes the collection environment, provides key insights into traffic data and demonstrates the impact of the attacks adopted against the server in hand.
Control of Rotary Double Inverted Pendulum using Sliding Mode Controller

Rotary Double Inverted Pendulum is a single-input multi-output, non-linear, under-actuated, coupled and nonminimum phase system. This paper aims on development of sliding mode controller for rotary double inverted pendulum system. The sliding hyper-plane has been developed using Regular Form with Linear-quadratic minimization. Also, the design has been compared with Genetic Algorithm based LQR. The paper also shows the comparison of performances for these controllers on nominal as well as perturbed system.
A Voltage Fault Detection Method Enabled by A Recurrent Neural Network and Residual Threshold Monitor for Lithium-ion Batteries

In the interest of improving the safety and reliability of lithium-ion (Li-ion) batteries on a cell and pack level, this study introduces a data-driven approach for detecting voltage faults. Due to the severity of short circuits or current leaks in Li-ion batteries, the proposed method uses a residual threshold to identify faults in the fast-acting voltage signal. Its data-driven element bypasses the need for an in-depth understanding of battery physics which makes it easier to implement. The approach uses the long short-term memory (LSTM) neural network (NN) together with the walk-forward testing technique to estimate voltage signals in non-faulty conditions. Known faulty data is then evaluated in a normalized receiver operating characteristic (NROC) optimizer to carefully select a residual threshold which is used to flag voltage faults that could result in safety and reliability issues. The experimental results demonstrate the ability of the NN to attain high accuracy in non-faulty voltage estimations. The residual monitor also shows great performance in its ability to detect voltage faults quickly and accurately as they occur. The method is further shown to be adaptive due to its ability to be trained online as the battery ages.
An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company.
Predicting Actions in Videos and Action-Based Segmentation Using Deep Learning

In this paper, we propose a technique to recognize multiple actions in a video using deep learning. The proposed approach is concerned with interpreting the overall context of a video and transforming it into one or more appropriate actions. In order to cope with multiple actions in a video, our proposed technique first determines the individual segments/shots in a video using intersections of color histograms. The segmented parts are then fed to the action recognition system comprising a combination of a Convolution Neural Network (CNN) and a Long-Short-Term Memory (LSTM) network trained on our action vocabulary. The segments are then labeled according to their predicted actions and a compact set of distinct actions is produced. Using the corpus generated by the shot detection phase, which includes the location of keyframes in shots, and start/end timestamps of a shot, we can also perform video segmentation based on an action query. Hence, the proposed technique can be used for a number of tasks such as content censoring, on-demand scene retrieval, video summarizing, and query-based scene/video retrieval, to name a few. The proposed technique also stands apart from the existing approaches which either do not take into account the motion information for action prediction or do not perform action-based video segmentation. The experimental results presented in this paper show that the proposed technique not only finds the complete set of actions present in the video but can also find all the relevant parts in a video based on an action query.
Use of 3D Printing in Support of Personnel Protection during Covid Pandemic

The paper describes the development and the manufacture of surgical mask straps using 3D printing technology, the experimental verification of ergonomics and the analysis of the evaluation of the best strap. Twelve variants of the straps were used including the variants available on the Internet and those produced on three kinds of 3D printers. 150 straps were experimentally tested in long-term operation by the staff of the Children’s Hospital of the University Hospital in Brno. The evaluation in order to choose the best strap variant was conducted using the basic evaluation method and the multi-criteria decision analysis (MCDA). The article also gives recommendations towards the future developments.