Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromechanical System Design for Self-Balancing Robot

Self-balancing robot is based on the principle of Inverted pendulum, which is a two-wheel vehicle balances itself up in the vertical position with reference to the ground. It consists of both hardware and software implementation. Mechanical model based on the state space design of the cart, pendulum system. To find its stable inverted position, we used a generic feedback controller (i.e., PID controller). According to the situation we have to control both angel of pendulum and position of cart. Mechanical design consists of two dc gear motor with encoder, one Arduino microcontroller, IMU (inertial mass unit) sensor and motor driver as a basic need. IMU sensor which consists of accelerometer and gyroscope gives the reference acceleration and angle with respect to ground (vertical), When encoder which is attached with the motor gives the speed of the motor. These parameters are taken as the system parameter and determine the external force needed to balance the robot up. It will be prevented from falling by giving acceleration to the wheels according to its inclination from the vertical. If the bot tilts by an angle, then in the frame of the wheels; the center of mass of the bot will experience a pseudo force which will apply a torque opposite to the direction of tilt. We used a HC05 Bluetooth module to control the robot via our smartphone.
Diver Interest via Pointing in Three Dimensions: 3D Pointing Reconstruction for Diver-AUV Communication

This paper presents Diver Interest via Pointing in Three Dimensions (DIP-3D), a method to relay an object of interest from a diver to an autonomous underwater vehicle (AUV) by pointing that includes three-dimensional distance information to discriminate between multiple objects in the AUV's camera image. Traditional dense stereo vision for distance estimation underwater is challenging because of the relative lack of saliency of scene features and degraded lighting conditions. Yet, including distance information is necessary for robotic perception of diver pointing when multiple objects appear within the robot's image plane. We subvert the challenges of underwater distance estimation by using sparse reconstruction of keypoints to perform pose estimation on both the left and right images from the robot's stereo camera. Triangulated pose keypoints, along with a classical object detection method, enable DIP-3D to infer the location of an object of interest when multiple objects are in the AUV's field of view. By allowing the scuba diver to point at an arbitrary object of interest and enabling the AUV to autonomously decide which object the diver is pointing to, this method will permit more natural interaction between AUVs and human scuba divers in underwater-human robot collaborative tasks.
Enhancing Spoofing Speech Detection Using Rhythm Information

Current spoofing speech detection systems need more convincing evidence. In this paper, the flaws of rhythm information inherent in the TTS-generated speech are analyzed to increase the reliability of detection systems. TTS models take text as input and utilize acoustic models to predict rhythm information, which introduces artifacts in the rhythm information. By filtering out vocal tract response, the remaining glottal flow with rhythm information retains detection ability for TTS-generated speech. Based on these analyses, a rhythm perturbation module is proposed to enhance the copy-synthesis data augmentation method. Fake utterances generated by the proposed method force the detecting model to pay attention to the artifacts in rhythm information and effectively improve the ability to detect TTS-generated speech of the anti-spoofing countermeasures.
Automatic News Summerization

Natural Language Processing is booming with its applications in the real world, one of which is Text Summarization for large texts including news articles. This research paper provides an extensive comparative evaluation of extractive and abstractive approaches for news text summarization, with an emphasis on the ROUGE score analysis. The study employs the CNN-Daily Mail dataset, which consists of news articles and human-generated reference summaries. The evaluation employs ROUGE scores to assess the efficacy and quality of generated summaries. After Evaluation, we integrate the best-performing models on a web application to assess their real-world capabilities and user experience.
Do We Run Large-scale Multi-Robot Systems on the Edge? More Evidence for Two-Phase Performance in System Size Scaling

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the robot-robot communication. Despite applying such strategies, any multi-robot system breaks above a certain critical system size (i.e., number of robots) as too many robots share a resource (e.g., space, communication channel). We provide additional evidence based on simulations, that at these critical system sizes, the system performance separates into two phases: nearly optimal and minimal performance. We speculate that in real-world applications that are configured for optimal system size, the supposedly high-performing system may actually live on borrowed time as it is on a transient to breakdown. We provide two modeling options (based on queueing theory and a population model) that may help to support this reasoning.
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection

Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly. As a result, various automatic methods have been proposed to construct CSKG with larger semantic coverage. However, these unsupervised approaches introduce spurious noise that can lower the quality of the resulting CSKG, which cannot be tackled easily by existing denoising algorithms due to the unique characteristics of nodes and structures in CSKGs. To address this issue, we propose Gold (Global and Local-aware Denoising), a denoising framework for CSKGs that incorporates entity semantic information, global rules, and local structural information from the CSKG. Experiment results demonstrate that Gold outperforms all baseline methods in noise detection tasks on synthetic noisy CSKG benchmarks. Furthermore, we show that denoising a real-world CSKG is effective and even benefits the downstream zero-shot commonsense question-answering task.
GPT-4 as an interface between researchers and computational software: improving usability and reproducibility

Large language models (LLMs) are playing an increasingly important role in science and engineering. For example, their ability to parse and understand human and computer languages makes them powerful interpreters and their use in applications like code generation are well-documented. We explore the ability of the GPT-4 LLM to ameliorate two major challenges in computational materials science: i) the high barriers for adoption of scientific software associated with the use of custom input languages, and ii) the poor reproducibility of published results due to insufficient details in the description of simulation methods. We focus on a widely used software for molecular dynamics simulations, the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), and quantify the usefulness of input files generated by GPT-4 from task descriptions in English and its ability to generate detailed descriptions of computational tasks from input files. We find that GPT-4 can generate correct and ready-to-use input files for relatively simple tasks and useful starting points for more complex, multi-step simulations. In addition, GPT-4's description of computational tasks from input files can be tuned from a detailed set of step-by-step instructions to a summary description appropriate for publications. Our results show that GPT-4 can reduce the number of routine tasks performed by researchers, accelerate the training of new users, and enhance reproducibility.
Robust-MBFD: A Robust Deep Learning System for Motor Bearing Faults Detection Using Multiple Deep Learning Training Strategies and A Novel Double Loss Function

This paper presents a comprehensive analysis of motor bearing fault detection (MBFD), which involves the task of identifying faults in a motor bearing based on its vibration. To this end, we first propose and evaluate various machine learning based systems for the MBFD task. Furthermore, we propose three deep learning based systems for the MBFD task, each of which explores one of the following training strategies: supervised learning, semi-supervised learning, and unsupervised learning. The proposed machine learning based systems and deep learning based systems are evaluated, compared, and then they are used to identify the best model for the MBFD task. We conducted extensive experiments on various benchmark datasets of motor bearing faults, including those from the American Society for Mechanical Failure Prevention Technology (MFPT), Case Western Reserve University Bearing Center (CWRU), and the Condition Monitoring of Bearing Damage in Electromechanical Drive Systems from Paderborn University (PU). The experimental results on different datasets highlight two main contributions of this study. First, we prove that deep learning based systems are more effective than machine learning based systems for the MBFD task. Second, we achieve a robust and general deep learning based system with a novel loss function for the MBFD task on several benchmark datasets, demonstrating its potential for real-life MBFD applications.
Take the aTrain. Introducing an Interface for the Accessible Transcription of Interviews

aTrain is an open-source and offline tool for transcribing audio data in multiple languages with CPU and NVIDIA GPU support. It is specifically designed for researchers using qualitative data generated from various forms of speech interactions with research participants. aTrain requires no programming skills, runs on most computers, does not require an internet connection, and was verified not to upload data to any server. aTrain combines OpenAI's Whisper model with speaker recognition to provide output that integrates with the popular qualitative data analysis software tools MAXQDA and ATLAS.ti. It has an easy-to-use graphical interface and is provided as a Windows-App through the Microsoft Store allowing for simple installation by researchers. The source code is freely available on GitHub. Having developed aTrain with a focus on speed on local computers, we show that the transcription time on current mobile CPUs is around 2 to 3 times the duration of the audio file using the highest-accuracy transcription models. If an entry-level graphics card is available, the transcription speed increases to 20% of the audio duration.
Novel techniques for alpha/beta pulse shape discrimination in Borexino

Borexino could efficiently distinguish between alpha and beta radiation in its liquid scintillator by the characteristic time profile of their scintillation pulse. This alpha/beta discrimination, first demonstrated at the tonne scale in the Counting Test Facility prototype, was used throughout the lifetime of the experiment between 2007 and 2021. With this method, alpha events are identified and subtracted from the beta-like solar neutrino events. This is particularly important in liquid scintillator as alpha scintillation is quenched many-fold. In Borexino, the prominent Po-210 decay peak was a background in the energy range of electrons scattered from Be-7 solar neutrinos. Optimal alpha-beta discrimination was achieved with a"multi-layer perceptron neural network", which its higher ability to leverage the timing information of the scintillation photons detected by the photomultiplier tubes. An event-by-event, high efficiency, stable, and uniform pulse shape discrimination was essential in characterising the spatial distribution of background in the detector. This benefited most Borexino measurements, including solar neutrinos in the \pp chain and the first direct observation of the CNO cycle in the Sun. This paper presents the key milestones in alpha/beta discrimination in Borexino as a term of comparison for current and future large liquid scintillator detectors
Single-centre experience with autosomal recessive limb-girdle muscular dystrophy: case series and literature review

Abstract Limb-girdle muscular dystrophy (LGMD) is a group of myopathies that lead to progressive muscle weakness, predominantly involving the shoulder and pelvic girdles; it has a heterogeneous genetic etiology, with variation in the prevalence of subtypes according to the ethnic backgrounds and geographic origins of the populations. The aim of the present study was to analyze a series of patients with autosomal recessive LGMD (LGMD-R) to contribute to a better characterization of the disease and to find the relative proportion of the different subtypes in a Southern Brazil cohort. The sample population consisted of 36 patients with LGMD-R. A 9-gene targeted next-generation sequencing panel revealed variants in 23 patients with LGMD (64%), and it identified calpainopathy (LGMD-R1) in 26%, dysferlinopathy (LGMD-R2) in 26%, sarcoglycanopathies (LGMD-R3–R5) in 13%, telethoninopathy (LGMD-R7) in 18%, dystroglicanopathy (LGMD-R9) in 13%, and anoctaminopathy (LGMD-R12) in 4% of the patients. In these 23 patients with LGMD, there were 27 different disease-related variants in the ANO5 , CAPN3 , DYSF , FKRP , SGCA , SGCB , SGCG , and TCAP genes. There were different causal variants in different exons of these genes, except for the TCAP gene, for which all patients carried the p.Gln53* variant, and the FKRP gene, which showed recurrence of the p.Leu276Ile variant. We analyzed the phenotypic, genotypic and muscle immunohistochemical features of this Southern Brazilian cohort.
Sustained Effect of Clinical Decision Support for Heart Failure: A Natural Experiment Using Implementation Science

Abstract Objectives In a randomized controlled trial, we found that applying implementation science (IS) methods and best practices in clinical decision support (CDS) design to create a locally customized, “enhanced” CDS significantly improved evidence-based prescribing of β blockers (BB) for heart failure compared with an unmodified commercially available CDS. At trial conclusion, the enhanced CDS was expanded to all sites. The purpose of this study was to evaluate the real-world sustained effect of the enhanced CDS compared with the commercial CDS. Methods In this natural experiment of 28 primary care clinics, we compared clinics exposed to the commercial CDS (preperiod) to clinics exposed to the enhanced CDS (both periods). The primary effectiveness outcome was the proportion of alerts resulting in a BB prescription. Secondary outcomes included patient reach and clinician adoption (dismissals). Results There were 367 alerts for 183 unique patients and 171 unique clinicians (pre: March 2019–August 2019; post: October 2019–March 2020). The enhanced CDS increased prescribing by 26.1% compared with the commercial (95% confidence interval [CI]: 17.0–35.1%), which is consistent with the 24% increase in the previous study. The odds of adopting the enhanced CDS was 81% compared with 29% with the commercial (odds ratio: 4.17, 95% CI: 1.96–8.85). The enhanced CDS adoption and effectiveness rates were 62 and 14% in the preperiod and 92 and 10% in the postperiod. Conclusion Applying IS methods with CDS best practices was associated with improved and sustained clinician adoption and effectiveness compared with a commercially available CDS tool.
Differences between hemispheres and in saccade latency regarding volleyball athletes and non-athletes during saccadic eye movements: an analysis using EEG

Abstract Background The saccadic eye movement is responsible for providing focus to a visual object of interest to the retina. In sports like volleyball, identifying relevant targets quickly is essential to a masterful performance. The training improves cortical regions underlying saccadic action, enabling more automated processing in athletes. Objective We investigated changes in the latency during the saccadic eye movement and the absolute theta power on the frontal and prefrontal cortices during the execution of the saccadic eye movement task in volleyball athletes and non-athletes. We hypothesized that the saccade latency and theta power would be lower due to training and perceptual-cognitive enhancement in volleyball players. Methods We recruited 30 healthy volunteers: 15 volleyball athletes (11 men and 4 women; mean age: 15.08 ± 1.06 years) and 15 non-athletes (5 men and 10 women; mean age: 18.00 ± 1.46 years). All tasks were performed simultaneously with electroencephalography signal recording. Results The latency of the saccadic eye movement presented a significant difference between the groups; a shorter time was observed among the athletes, associated with the players' superiority in terms of attention level. During the experiment, the athletes observed a decrease in absolute theta power compared to non-athletes on the electrodes of each frontal and prefrontal area. Conclusion In the present study, we observed the behavior of reaction time and absolute theta power in athletes and non-athletes during a saccadic movement task. Our findings corroborate the premise of cognitive improvement, mainly due to the reduction of saccadic latency and lower beta power, validating the neural efficiency hypothesis.
Decompressive Craniectomy in Extensive Ischemic Stroke. An Experience in a Single Institution

Abstract Background Decompressive craniectomy (DC) is a valuable treatment for reducing early lethality in malignant intracranial hypertension (IH); however, it has been shown that the decision to implement DC in patients with extensive ischemic stroke should not be based solely on the detection of IH with the use of intracranial pressure (ICP) devices. Objective To establish the usefulness of DC in patients with extensive ischemic stroke who came to the emergency room during the period between May 2018 and March 2019. Methods This was an analytical, prospective, and longitudinal study whose population corresponded to all patients with a diagnosis of extensive ischemic stroke. Results The sample consisted of 5 patients, of which 3 were female and 2 males, the average age was 62.2 years old (minimum 49 years old, maximum 77 years old). Of all the patients who underwent DC, it was found that 80% of the patients did not present an increase in intracranial pressure. Decompressive craniectomy was not performed in a case that responded adequately to medical treatment. The mean values of ICP were 25 mmHg with a minimum value of 20 mmHg and a maximum value of 25 mmHg; in patients with a moderate value, the ICP averages were < 20 mmHg. The mortality was of 40% (RANKIN of 6 points). Conclusions Decompressive craniectomy is useful in extensive ischemic stroke. The decision to implement DC in patients with extensive stroke rests on clinicoradiological parameters. The monitoring of the IPC was not particularly useful in the early detection of the neurological deterioration of the patients studied.
Weltweite Leitlinie zur Sturzprävention

ZUSAMMENFASSUNG Die Inzidenz von Stürzen bei älteren Menschen ist weiter steigend. Diese gehen mit einer erheblichen Morbidität und Mortalität sowie einer Belastung des Gesundheitssystems einher. Bei den Betroffenen führen Stürze häufig zu einem Verlust der Selbstständigkeit. Um bislang bestehende Leitlinien zu dieser Thematik zu vereinheitlichen und aktuelle wissenschaftliche Entwicklungen miteinzubeziehen sind im September 2022 die „World Guidelines for Falls Prevention and Management: a Global Initiative“ entstanden. Diese wurden durch ein internationales und multidisziplinäres Leitlinienkomitee entworfen und sollen medizinischen Fachkräften eine Richtlinie zur Risikostratifizierung und zum Management von sturzgefährdeten Personen bieten. Hierbei sollen die Erwartungen der jeweiligen Person sowie die lokal verfügbaren Ressourcen berücksichtigt werden. Es erfolgt eine Unterteilung in niedriges, intermediäres und hohes Risiko, woraus sich entsprechende Handlungsempfehlungen ableiten.
Two slow surface waves across North America

Surface shear waves (Lg) with initial period about 1/2 to 6 sec. with sharp commencements and amplitudes larger than any conventional phase have been recorded for continental paths at distances up to 6,000 km. These waves have a group velocity of 3.51±. 07 km/sec. and for distances greater than 20° they have reverse dispersion. For distances less than about 10° the periods shorten and Lg merges into the recognized near-earthquake phase Sg. An additional large amplitude phase in which the orbital motion of the particle is retrograde elliptical and the velocity is 3.05±. 07 km/sec. has also been observed for continental paths. It is believed that these phases are propagated through a wave guide formed by a superficial sialic layer. The problem of explaining the propagation of these surface waves is that of finding a crustal structure which is consistent with the other data of geology and geophysics and which will provide a suitable wave guide for the new phases. A possible nature of the wave guide is described.
Reliabilities of Eye Fixations for Assessing Short-term Memory on Forward Span Tasks

The use of eye-tracking systems for early-stage dementia diagnosis has generated increasing interest. However, the stability of the eye-tracking markers across different cognitive tasks, particularly in terms of their reliabilities, remains unclear. This study aimed to address this gap by investigating the reliabilities of one eye-tracking marker, eye fixations, during memory tasks. Specifically, we examined the reliabilities of six eye fixation events on short-term memory tasks across 120 trials and three sessions – namely, the internal consistency and the repeatability. Our findings indicate that all eye fixation events showed acceptable reliability scores (above 0.6) for the memory tasks used. These results contribute insights into using eye-tracking assessments for early-stage dementia diagnosis in real-world applications.
Predicting Autism from Head Movement Patterns during Naturalistic Social Interactions

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized in part by difficulties in verbal and nonverbal social communication. Evidence indicates that autistic people, compared to neurotypical peers, exhibit differences in head movements, a key form of nonverbal communication. Despite the crucial role of head movements in social communication, research on this nonverbal cue is relatively scarce compared to other forms of nonverbal communication, such as facial expressions and gestures. There is a need for scalable, reliable, and accurate instruments for measuring head movements directly within the context of social interactions. In this study, we used computer vision and machine learning to examine the head movement patterns of neurotypical and autistic individuals during naturalistic, face–to–face conversations, at both the individual (monadic) and interpersonal (dyadic) levels. Our model predicts diagnostic status using dyadic head movement data with an accuracy of , highlighting the value of head movement as a marker of social communication. The monadic data pipeline had lower accuracy () compared to the dyadic approach, emphasizing the importance of studying back-and-forth social communication cues within a true social context. The proposed classifier is not intended for diagnostic purposes, and future research should replicate our findings in larger, more representative samples.