Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task

PurposeTo understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-tracking values to optimize the attention mechanism in deep learning models. But these studies lack to explain the rationality of this approach. Whether the attention mechanism possesses this feature of human reading needs to be explored.Design/methodology/approachThe authors conducted experiments on a sentiment classification task. Firstly, they obtained eye-tracking values from two open-source eye-tracking corpora to describe the feature of human reading. Then, the machine attention values of each sentence were learned from a sentiment classification model. Finally, a comparison was conducted to analyze machine attention values and eye-tracking values.FindingsThrough experiments, the authors found the attention mechanism can focus on important words, such as adjectives, adverbs and sentiment words, which are valuable for judging the sentiment of sentences on the sentiment classification task. It possesses the feature of human reading, focusing on important words in sentences when reading. Due to the insufficient learning of the attention mechanism, some words are wrongly focused. The eye-tracking values can help the attention mechanism correct this error and improve the model performance.Originality/valueOur research not only provides a reasonable explanation for the study of using eye-tracking values to optimize the attention mechanism but also provides new inspiration for the interpretability of attention mechanism.
Young Adults’ Distress About Police Brutality Following the Death of George Floyd

Following the homicide of George Floyd on May 25, 2020, young adults participated widely in national protests against racism and police brutality. This study examined temporal trends and correlates of self-reported distress about police brutality from May 18 to August 3, 2020 among a cohort of young adults (ages 19–22; N = 2,080) from Los Angeles, CA. Bivariate and adjusted linear regression models estimated the association between survey completion date and distress about police brutality. Distress about police brutality increased substantially in the 2-weeks after Floyd’s death (43% increase over baseline), but gradually declined over the ensuing 8 weeks to a 19% increase over baseline. Results suggest that police violence is contributing to stress and worry among young adults.
Performance of fault classification on Photovoltaic modules using Thermographic images

In this paper, presents thermal image analysis on Fault Classification (FDC) of Photovoltaic (PV) Module. The traditional manual approach of PV inspection is generally more time-consuming, more dangerous, and less accurate than the modern approach of PV inspection using Thermography Images (TI). The benefit in using (TI) images is that it can be used to quickly establish problematic areas in PV Module and provide various measurement details. Thermal image analysis conducted in this research will contribute to inspect PV module by providing a more accurate and cost-efficient diagnosis of PV faults. To maintain the long-term reliability of solar modules and maximize the power output, faults in modules need to be diagnosed at an early stage. In this research, thermographic images were used to detect faults in PV Module using traditional methods and Deep learning methods are mainly used to identify and classify the type of faults that can happen in PV Module. This method will present and discuss on the fault classification and its performance parameters. The fault detection stage determined whether the PV module has an abnormal condition. In this research, performance metrics of fault classification using Deep Neural Networks (DNNs) models is analyzed, which offers high accuracy for detecting abnormalities in image classification tasks.
Shifted Models Cannot be Used for Predicting Responses of Biodiversity to Global Change: The African Elephant as an Example

Human activities have affected animals’ behaviour, distribution and population structure and this effect is predicted to increase in the future. Considerable effort is therefore being focussed on understanding and predicting such future changes in response to anthropogenic pressures, this to better conserve and restore populations and species. However, there is a risk that scientists and practitioners fail to recognise the extent of past human effects on biodiversity and use the situation they are familiar with as a baseline and a measure against which to assess current or future biodiversity changes. This failure to recognise past changes is known as the shifting baseline syndrome (Pauly 1995). We propose that when models (descriptive, quantitative or conceptual) describing niche features, such as distribution, habitat use, dietary resources or behaviour, are developed using data collected in a system (communities, species or populations) that has already undergone shifted baselines, such models should be referred to as ‘shifted models’. Here we define such shifted models and demonstrate associated shortcomings, focusing on the particular example of shifted environmental niche models in a recently published study on African elephants Loxodonta africana (Dejene et al. 2021).
Edgewrite: a versatile design for text entry and control

This dissertation presents a versatile design for text entry and control called EdgeWrite. EdgeWrite was designed to provide accessible text entry on a variety of platforms to people with motor impairments and to able-bodied users of small devices. The EdgeWrite design includes a square input area, four corner sensors, corner-sequence recognition, physical edges, goal crossing, and unistroke segmentation. Advanced EdgeWrite features include continuous recognition feedback, non-recognition retry, slip detection, and word-level stroking---concepts not before realized in a text entry method. The EdgeWrite alphabet, which is the same for all EdgeWrite versions, was designed for maximum guessability and learnability through participatory design. A key result is that EdgeWrite is about as guessable as Palm OS Graffiti, a method lauded for its immediate usability. Multiple versions of EdgeWrite were built and user-tested, including Stylus EdgeWrite for the Palm OS, Joystick EdgeWrite for game controllers and power wheelchairs, Touchpad EdgeWrite, Trackball EdgeWrite, Isometric Joystick EdgeWrite for mobile phones, and EdgeWrite on four keys or sensors. For each version, empirical results from formal user studies were obtained. Of particular importance for users with motor impairments are the results for Stylus and Trackball EdgeWrite, which show marked improvements over existing techniques. In addition, five versions of EdgeWrite built by other researchers further highlight EdgeWrite's versatility. As part of EdgeWrite's ongoing evaluations, a new character-level error analysis for text entry input streams was developed. This error analysis and the algorithms that automate it constitute a methodological and theoretical contribution to the field of text entry evaluation and measurement. The thesis is: A versatile design for text entry and control called "Edge Write," which uses physical edges, goal crossing, and a minimized need for sensing, is effective on handhelds and desktops for people with motor and situational impairments.
MULTI-STAGE AUV-BASED LOCALIZATION FOR UNDERWATER WIRELESS SENSOR NETWORK

Underwater Sensor Networks (USN) are used for harsh oceanographic missions where human operation is dangerous or impossible. Localization is essential for USNs. It is required for data tagging, node tracking and position-based routing algorithms. Localization is challenging because Global Positioning System (GPS) is not available in underwater; at the same time, existing GPS-less schemes based on fixed landmarks have high communication cost. Such cost is critical in Mobile Underwater Sensor Networks (MUSN), since sensor nodes drift with the ocean currents, thus requiring continuous refresh. In this paper, we present a multi-stage AUV-aided localization scheme for UWSNs. The proposed method combines the flexibility and localization accuracy of an AUV-aided localization, the energy efficiency of "silent localization" and improved localization coverage with k-stage localization based on sensor nodes. The proposed localization scheme for underwater acoustic sensor networks (UWSN) does not require a priori infra-structure or synchronization between nodes. An Autonomous Underwater Vehicle (AUV) aids in localizing the sensor nodes while roaming across the underwater sensor field. We evaluate the performance of the proposed scheme in terms of the localization coverage, accuracy and communication costs using simulations. We show that while improved performance with multiple stages is traded off with higher communication costs in general, the latter can be minimized while maintaining good performance with an appropriate choice of the acoustic communication range.
Forensic Electrochemistry: Electrochemical Analysis of Trace Methamphetamine Residues on Household Surfaces

Illicit methamphetamine is the most widely manufactured amphetamine-type of stimulants worldwide. A relatively simple manufacturing procedure had resulted in the creation of manufacturing sites in a variety of premises and structures, requiring forensic investigation. This study was aimed at developing an electrochemical detection technique to determine residual methamphetamine contamination on various surfaces. Carbon materials, namely graphene oxide, graphene nanoplatelets, graphene ink, and glassy carbon microsphere, modified glassy carbon electrode were used in detecting methamphetamine by differential pulse voltammetry (DPV). The surface morphology modified electrode was characterized and its electrochemical behavior was determined. Under optimal conditions, the calibration curve showed bilinear in the concentration range of 1−40 and 40−120 μM with good precision and accuracy. The limit of detection was determined to be 0.3 μM. Graphene oxide modified electrode coupled with DPV successfully detected the residual methamphetamine on four household surfaces, including glass, stainless steel, plastic, and varnished wood with recoveries greater than 82%. To conclude, a novel electrochemical detection technique for the presence of methamphetamine was successfully developed to be applied in clandestine laboratory settings. It would be beneficial to assist the forensic investigation, especially for those forensic evidence recovered from suspicious and suspected clandestine laboratories.
A Deep 1-D CNN and Bidirectional LSTM Ensemble Model With Arbitration Mechanism for LDDoS Attack Detection

Low-rate Distributed Denial of Service (LDDoS) attacks are complex and common security issues, which disrupt the essential services of the varied and emerging networks. However, the traditional detection models are normally in a dilemma condition since LDDoS attacks have the characteristics of concealment and persistence in space and time dimensions. In this paper, we propose a novel, practical and fast ensemble model (FastCBLA-EM) to detect the LDDoS attacks. In the FastCBLA-EM, a dilated padding-based dual sliding method is worked out for producing fixed-length chronological order samples from flows per well-tuned packet step. Next, a competitive network composed of 1-D Convolutional Neural Network (1-D CNN) and a bidirectional Long Short Term Memory (bi-LSTM) network is employed to learn both spatial and temporal representations of samples, parallelly. Finally, an arbitration mechanism based on the enhanced attention method is used to assemble and weigh these representations for detecting LDDoS attacks. We compare the performances of the FastCBLA-EM with other models by obtaining a group of well-tuned hyperparameters in the revised ISCX-2016-SlowDos flow-based dataset. Experimental results demonstrate that the detection accuracy of the FastCBLA-EM can be up to 99.7% and the time complexity is $O(n)$.
Mathematical Models of Single Stage Monod Model of Landfill Degradation using Laplace Transform and Homotopy Perturbation Method

A mathematical model to predict waste degradation and landfill gas production is discussed. The model is based on two reaction-diffusion equations containing a non-linear term related to Michaelis–Menten kinetics of the enzymatic reaction. In this paper, we present an approximate analytical solution of the non-linear differential equations that describe the diffusion coupled with a Michaelis–Menten kinetics law. Approximate analytical expressions for substrate and biomass concentrations have been derived for all values of parameter using Laplace transform method and homotopy analysis method. These results are compared with the numerical result and satisfactory is noted. The obtained results are valid for the whole solution domain.
Comparison of approaches to air spring height control

: The paper deals with the comparison of control approaches to the height of the bellows air spring. These approaches are PI control, a deep reinforcement learning algorithm, and a combination of both control techniques, the neuro-PI controller. In the case of a neuro-PI controller, it is essentially control by a reinforcement learning algorithm, but its control neural network contains only two weights that are gained in the learning process. The advantage of the neuro-PI controller created in this way is that its functionality can be formally verified in the same way as a classic PI controller. However, such a procedure is only possible when controlling relatively simple systems, such as the described application for controlling an air spring, which is a dynamic system with one degree of freedom. All the above approaches are applied to a mathematical model of an air spring created in the Simulink environment; a deep reinforcement learning algorithm is created in MATLAB.
AARGNN: An Attentive Attributed Recurrent Graph Neural Network for Traffic Flow Prediction Considering Multiple Dynamic Factors

Traffic flow prediction is a fundamental part of ITS (Intelligent Transportation System). Since the correlations of traffic data are complicated and are affected by various factors, traffic flow prediction is a challenging task. Existing traffic flow prediction methods generally take limited static factors (e.g., the distance between sensors and road network topological structure) into consideration and model the correlations of the traffic data separately to predict the future traffic. In this paper, we propose AARGNN (Attentive Attributed Recurrent Graph Neural Network), a GNN (graph neural network) based method considering multiple dynamic factors to predict short-term traffic flow. With multi-source urban data (e.g., POI, road network, incident, weather, etc.), AARGNN considers both static factors and dynamic factors (e.g., spatial distance, semantic distance, road characteristic, road situation, and global context) to predict the short-term traffic flow. Specifically, AARGNN constructs an attributed graph and encodes various factors into the attributes. The correlations of the traffic data are modeled by utilizing the GNN combined with LSTM (long short-term memory). In addition, AARGNN specifies the contributions of each factor based on attention mechanism. Experiments on real-world datasets show that the proposed method outperforms all baseline methods.
Management of Pheochromocytomas and Paragangliomas: A Case-Based Review of Clinical Aspects and Perspectives

Paraganglioma and pheochromocytoma are rare medical conditions. Thus, there are still a small number of studies, clinical trials, and evidence-based data in this field. This makes clinical decisions more difficult. In this study, we present a case report enriched with a short review of available essential clinical data, indicating the need for constant metoxycatecholamine level observation and a proper diagnostic imaging approach, especially in terms of ongoing pandemics. Our research also provides a summary of the molecular background of these diseases, indicating their future role in clinical management. We analyzed the ClinicalTrials.gov dataset in order to show future perspectives. In this paper, the use of the PET-CT before MRI or CT is proposed in specific cases during diagnosis processes contrary to the guidelines. PET-CT may be as effective as standard procedures and may provide a faster diagnosis, which is important in periods with more difficult access to health care, such as during the COVID-19 pandemic.
Teacher antagonism: reducing students’ sustained attention through decreased affect toward instructors and diminished motivation to learn

ABSTRACT This study was conducted to model how teacher misbehaviors associate with reductions in students’ sustained attention. Participants (N = 423 college students) responded to measures of their perceptions of teacher antagonism, affect for their instructor, intrinsic motivation to learn, and sustained attention throughout the semester. Results of a path analysis indicated that teacher antagonism did not impact students’ sustained attention directly or indirectly through their affect for the instructor or intrinsic motivation (individually, as simple indirect effects). Rather, results from a serial multiple mediator model demonstrated that the effect of teacher antagonism on students’ sustained attention occurred through a reduction in affect for their instructor and, in turn, a reduction in intrinsic motivation to learn. Results are discussed with regard to the potential impact on student learning and best practices in the classroom.
Liquid Nitrogen Cryotherapy in the Management of Hemangioma of the Tongue

Recurrent bleeding due to mild trauma, tongue biting, poor oral hygiene, and expansion of the lesions causing difficulty in chewing, swallowing, or speaking is common sequelae of vascular lesions of the tongue. Cryosurgery is a technique for destroying lesions by the rapid freezing method. The necrotic tissue that results is allowed to slough naturally after the lesion is frozen. Cryosurgery has been utilized in the medical and dentistry field to treat many lesions as it is successful and easy to perform. It has a number of advantages, including ease of use, minimal infection rate, and no bleeding intraoperatively. This report presents a case of a 15-year-old female patient with a hemangiomatous lesion on the tongue that was successfully treated by cryosurgery using a liquid nitrogen spray. The patient demonstrated complete resolution of the lesion with good wound healing during the one-year follow-up period.
Development of Low-Cost and Portable Pulse Oximeter Device with Improved Accuracy and Accessibility

Purpose In a clinical setting, blood oxygen saturation is one of the most important vital sign indicators. A pulse oximeter is a device that measures the blood oxygen saturation and pulse rate of patients with various disorders. However, due to ethical concerns, commercially available pulse oximeters are limited in terms of calibration on critically sick patients, resulting in a significant error rate for measurement in the critical oxygen saturation range. The device’s accessibility in developing countries’ healthcare settings is also limited due to portability, cost implications, and a lack of recognized need. The purpose of this study was to develop a reliable, low-cost, and portable pulse oximeter device with improved accuracy in the critical oxygen saturation range. Methods The proposed device measures oxygen saturation and heart rate using the reflectance approach. The rechargeable battery and power supply from the smartphone were taken into account, and the calibration in critical oxygen saturation values was performed using Prosim 8 vital sign simulator, and by comparing with a standard pulse oximeter device over fifteen iterations. Results The device’s prototype was successfully developed and tested. Oxygen saturation and heart rate readings were both accurate to 97.74% and 97.37%, respectively, compared with the simulator, and an accuracy of 98.54% for the measurement of blood oxygen saturation was obtained compared with the standard device. Conclusion The accuracy of oxygen measurement attained in this study is significant for measuring oxygen saturation for patients in critical care, anesthesia, pre-operative and post-operative surgery, and COVID-19 patients. The advancements made in this research have the potential to increase the accessibility of pulse oximeter in resource limited areas.
The Posterior Predictive Null

Bayesian model criticism is an important part of the practice of Bayesian statistics. Traditionally, model criticism methods have been based on the predictive check, an adaptation of goodness-of-ﬁt testing to Bayesian modeling and an eﬀective method to understand how well a model captures the distribution of the data. In modern practice, however, researchers iteratively build and develop many models, exploring a space of models to help solve the problem at hand. While classical predictive checks can help assess each one, they cannot help the researcher understand how the models relate to each other. This paper introduces the posterior predictive null check (PPN), a method for Bayesian model criticism that helps characterize the relationships between models. The idea behind the PPN is to check whether data from one model’s predictive distribution can pass a predictive check designed for another model. This form of criticism complements the classical predictive check by providing a comparative tool. A collection of PPNs, which we call a PPN study, can help us understand which models are equivalent and which models provide diﬀerent perspectives on the data. With mixture models, we demonstrate how a PPN study, along with traditional predictive checks, can help select the number of components by the principle of parsimony. With probabilistic factor models, we demonstrate how a PPN study can help understand relationships between diﬀerent classes of models, such as linear models and models based on neural networks. Finally, we analyze data from the literature on predictive checks to show how a PPN study can improve the practice of Bayesian model criticism. Code to replicate the results in this paper is available at https://github.com/gemoran/ppn-code .
A sociophonetic approach to the acquisition of Spanish rhotics in a bilingual community

This study explores Spanish-English bilingual children’s acquisition of Spanish rhotics. The children’s productions are compared to a group of Spanish-English bilingual adults who represent the Spanish spoken in the same community: Albuquerque, New Mexico. In their narrations of a wordless picture-book, both children and adults produce more non-canonical than canonical variants. Binary logistic regressions run on 817 rhotics produced by 21 children, ages 3-9-years-old, and 6 adults demonstrate that non-canonical variants, instead of canonical variants are more likely to occur as Spanish use increases and in word medial position. The results point towards the propagation of a working change in Spanish rhotics that sees both articulatory and sociolinguistic motivations. Implications for research on phonetic-phonological development are also discussed