Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlations between Colour Lightness and the Sweetness of Stingless Bee Honey with Its Minerals Content

The colour and the sweetness of honey are the main characteristics refers by consumers when buying honey for their consumption. Consumers always believe that the darker the colour and the sweeter the taste, the better the quality of honey. This study aims to investigate the correlation of colour lightness and the sweetness of stingless bee honey with its mineral contents. Honey samples were collected from seven beehives around Terengganu, Malaysia. All samples have different colours and tastes. The colour of honey was measured based on CIELAB colour system using a reflectance spectrometry and the sweetness of honey is measured using a refractometer. ICP-OES is used to analyze mineral elements present in the honey samples. We found that the lighter the colour of honey, the less sweet it is (r= -0.77). The results obtained also shows that the lighter the colour of honey, the higher the amount of potassium (r= -0.94) and phosphorus (r= -0.94) in honey. Meanwhile, the sweeter the honey, the higher the amount of potassium (r= 0.87), sodium (r= 0.85) and phosphorus (r= 0.82). Our findings prove that the colour and sweetness of honey can only be used to estimate the certain nutritive value of honey. Even though these findings still cannot fully uncover the myth of honey; consumers still can depend on these characteristics to choose the best honey for their own consumption.
Supervised Metric Learning For Music Structure Features

Music structure analysis (MSA) methods traditionally search for musically meaningful patterns in audio: homogeneity, repetition, novelty, and segment-length regularity. Hand-crafted audio features such as MFCCs or chromagrams are often used to elicit these patterns. However, with more annotations of section labels (e.g., verse, chorus, and bridge) becoming available, one can use supervised feature learning to make these patterns even clearer and improve MSA performance. To this end, we take a supervised metric learning approach: we train a deep neural network to output embeddings that are near each other for two spectrogram inputs if both have the same section type (according to an annotation), and otherwise far apart. We propose a batch sampling scheme to ensure the labels in a training pair are interpreted meaningfully. The trained model extracts features that can be used in existing MSA algorithms. In evaluations with three datasets (HarmonixSet, SALAMI, and RWC), we demonstrate that using the proposed features can improve a traditional MSA algorithm significantly in both intra- and cross-dataset scenarios.
A Secure Healthcare System for Privacy-Preserving based on Blockchain Technology

In recent years, Internet of Things (IoT) technology is recognized as a technological revolution used in different sectors, especially those with the emerged automation concept. It has many applications in various fields, for instance, smart cities, retail, healthcare, etc. However, many issues are yet to be fully addressed, such as scalability, connectivity, privacy and security. Blockchain technology has emerged as a promising solution for privacy and security challenges. It uses a decentralized distributed ledger, which records digital assets’ information and keeps these records immutable and then reduces fraud risk. This paper proposes a Blockchainbased medical data protection system that enables users to control over their sensitive data collected from wearable sensors. Patients can upload medical records and healthcare providers can retrieve data while preserving sensitive health information against potential threats. We present a prototype implementation based on Quorum Blockchain and evaluate its memory and CPU time overheads using quorum profiling tool. The empirical results show that the integration of Blockchain technology with existing IoT systems is feasible and provides effective performance and security.
Text summarization using transfer learning

A summary of a long text document enables people to easily grasp the information of the topic without having the need to read the whole document. This thesis aims to automate text summarization by using two approaches: extractive and abstractive. The former approach utilizes submodular functions and the language representation model BERT, while the latter uses the language model GPT-2. We operate on two types of datasets: CNN/DailyMail, a benchmarked news article dataset and Podcast, a dataset comprised of podcast episode transcripts. The results obtained using the GPT-2 on the CNN/DailyMail dataset are competitive to state-of-the-art. Besides the quantitative evaluation, we also perform a qualitative investigation in the form of a human evaluation, along with inspection of the trained model that demonstrates that it learns reasonable abstractions.
Infant EEG Band Power Analysis at 6 Months and 18 Months

Neural development of infants has drawn increasing research interests from the community. In this paper, we investigated the frequency band power of 112 infants who participated in an auditory oddball experiment, and the visual expectation (VE) score of 177 infants who went through a visual expectation paradigm test. Analysis found that the frequency band power decreases in the delta and theta bands, and increases in the alpha and beta bands when the infants grow up from 6 months old to 18 months old. We also proposed a sustainability index to measure the capability of a subject to maintain their band power in the auditory oddball experiment when infants grow up from 6 months old to 18 months old. Analysis shows that the sustainability index increased significantly in the alpha and beta band, decreased in the delta and theta bands. Correlation between the VE score and frequency band power was investigated on 47 infants who participated in both auditory oddball experiment and visual expectation paradigm test. Analysis shows that the reaction speed to stimulus have statistical a significant correlation with the changes of band power and sustainability index in posterior and temporal section, and in the higher frequency bands.
The contribution of state web portals to the transparency of the vaccination process against COVID-19 in Brazil

The article aimed to characterize the contribution of Brazilian state web portals to the transparency of the vaccination process against COVID-19. This research is descriptive, undertaken through a documentary study with a quali-quantitative approach. The purpose of the investigation comprises the web portals of the 26 Brazilian states and the Federal District. The study was guided by the application of the COVID-19 Vaccination Transparency Index (ITVC-19). The study data were obtained in six surveys carried out from the analysis of the portals, using content analysis as the processing technique. The constancy of some states at higher and lower levels (i.e., the extremes) and the evolution of others going from lower to higher levels was observed. Although the vast majority of web portals of Brazilian states contribute to the transparency of the vaccination against COVID-19, there are still states the portal of which, for being at opaque, low, or intermediary levels, seem to exist more due to a dominant technological imperative and less to favor the transparency of government actions. The study has a direct theoretical implication when it enables the development of an index that contributes to analyzing the transparency in the vaccination process against COVID-19. It is assumed that, in the future, the index may also be used for new studies on vaccination campaigns, not just this one restricted to the pandemic context. Consequently, it contributes to bridging the gap in the literature, notably the national literature. The practical contribution is also demonstrated by the provision of a diagnosis that, albeit specific, may be used by public managers interested in advancing vaccination transparency.
Remission of catatonia after intravenous propofol infusion for unrelated reasons

In the aftermath of a cerebrovascular accident, a 66-year-old male patient with a recurrent depressive disorder displayed prolonged stupor, mutism, negativism, immobility, catalepsia and posturing. The abrupt improvement of his symptoms directly after administering oral lorazepam confirmed the diagnosis of catatonia. Subsequent titration of lorazepam reduced the catatonic symptoms, but did not completely resolve them. Propofol infusion given for an unrelated reason led to an abrupt, remarkable and complete remission of the catatonic symptoms. This article contains a case report, description of the possible pathophysiological explanation for the beneficial effect of propofol on catatonia and a review of literature.
Acceptance of Digital Voice Assistants for Grocery Shopping

: Following the technology acceptance model, the present study analyzes the acceptance of digital voice assistants. We specifically address the use of these assistants for habitual purchases in the case of grocery shopping. Beyond extending insights into digital shopping behavior, this study contributes to the literature by including privacy concerns and technology anxiety in this context. We are particularly interested in how privacy concerns affect consumers’ decision process. The empirical findings suggest that privacy concerns directly influence consumers' behavioral intention. Further, this effect is predominantly triggered by concerns regarding the (unknown) data collection process and the (unknown) data storage situation whereas the device and, surprisingly, the secondary use of data are two dimensions that have weaker effects in this situation. Additionally, the results demonstrate that technology anxiety also negatively anchors consumers' perceptions of digital voice assistants.
The Effective Length and Bracing Requirements for Out of Plane Buckling of Timber Rafters in Compression

SYNOPSIS The designers effective length and the bracing required to prevent the out of place buckling of timber rafters in compression was investigated. Tests on several portions of full-scale roofs were conducted. It was found that the strength and stiffness of the bracing system may be treated almost completely separately; the strength of the bracing does not influence the design, while the stiffness is of utmost importance. The causes of lateral forces, the design of bracing and the influence of bracing on rafter design are discussed in detail.
Overview of Septentrio's Galileo Receiver Development Strategy

ABSTRACT This paper contains an overview of our current and future Galileo-related projects, architecture and design choices for our line of Galileo receivers. Septentrio is developing Galileo receivers for various European projects: receivers intended for the testing of GSTB-V2, test user receivers intended for the in-orbit validation of all the Galileo services, and professional user receivers designed for high-precision RTK-based applications. The paper begins with a general overview of Galileo signal components and a list of Galileo user observables to be generated by future receivers. Because some signal components are supposed to be tracked co-operatively, the list of user observables is significantly shorter than the list of signal components. Further to this, we describe Septentrio’s Galileo-related receiver design projects, which include 3 major milestones: (i) GETR receiver, intended for in-orbit validation of Galileo signals transmitted by GSTB-V2 satellites; (ii) test user receiver (TUR), which shall be used to validate user requirements to the actual Galileo system; (iii) commercial Galileo/GPS receiver, intended for high-precision RTK applications. We pay particular attention to the GETR receiver (GETR stands for Galileo Experimental Test Receiver), already available at the time of this writing. The layout and the functionality of the new receiver are described and some early performance results are presented.
Deeper Convolutional Neural Networks and Broad Augmentation Policies Improve Performance in Musical Key Estimation

In recent years, complex convolutional neural network architectures such as the Inception architecture have been shown to offer signiﬁcant improvements over previous architectures in image classiﬁcation. So far, little work has been done applying these architectures to music information retrieval tasks, with most models still relying on sequential neural network architectures. In this paper, we adapt the Inception architecture to the speciﬁc needs of harmonic music analysis and use it to create a model (In-ceptionKeyNet) for the task of key estimation. We then show that the resulting model can signiﬁcantly outperform state-of-the-art single-task models when trained on the same datasets. Additionally, we evaluate a broad range of augmentation methods and ﬁnd that extending augmentation policies to include a more diverse set of methods further improves accuracy. Finally, we train both the proposed and state-of-the-art single-task models on differently sized training datasets and different augmentation policies and compare the differences in generalization performance.
Magnetic Flux Leakage Sensing-Based Steel Cable NDE Technique Incorporated on a Cable Climbing Robot for Bridge Structures

In this study, an automated cable monitoring system using a NDE technique and a cable climbing robot is proposed. MFL (Magnetic Flux Leakage- based inspection system was applied to monitor the condition of cables. This inspection system measures magnetic flux to detect the local faults (LF) of steel cable. To verify the feasibility of the proposed damage detection technique, an 8-channel MFL sensor head prototype was designed and fabricated. A steel cable bunch specimen with several types of damage was fabricated and scanned by the MFL sensor head to measure the magnetic flux density of the specimen. To interpret the condition of the steel cable, magnetic flux signals were used to determine the locations of the flaws and the level of damage. Measured signals from the damaged specimen were compared with thresholds set for objective decision making. In addition, the measured magnetic flux signal was visualized into a 3D MFL map for convenient cable monitoring. Finally, the results were compared with information on actual inflicted damages to confirm the accuracy and effectiveness of the proposed cable monitoring method.
VOCANO: A note transcription framework for singing voice in polyphonic music

High variability of singing voice and insufﬁciency of note event annotation present a huge bottleneck in singing voice transcription (SVT). In this paper, we present VO-CANO, an open-source VOCAl NOte transcription framework built upon robust neural networks with multi-task and semi-supervised learning. Based on a state-of-the-art SVT method, we further consider virtual adversarial training (VAT), a semi-supervised learning (SSL) method for SVT on both clean and accompanied singing voice data, the latter being pre-processed using the singing voice separation (SVS) technique. The proposed framework outperforms the state of the arts on public benchmarks over a wide variety of evaluation metrics. The effects of the types of training models and the sizes of the unlabeled datasets on the performance of SVT are also discussed.
Epilepsy detection from EEG data using 2D Convolutional Neural Network

An epileptic seizure, a disorder in brain functionality, happens when electrical bursts spread across the brain, causing the person to lose control or consciousness. Predicting epileptic seizures before they occur is useful for seizure prevention with medicine or for neural pre-surgical planning. Machine learning and computational methods are used to predict epileptic seizures from electroencephalogram (EEG) recordings. However, noise removal and feature extraction in EEG data are two important challenges that have a negative impact on the effectiveness of both time and true positive prediction rate. We offer a model in this paper that provides a reliable strategy for both preprocessing and feature extraction. Our model is based on a two-dimensional Convolution Neural Network (CNN), with EEG input provided to the CNN in the form of two-dimensional images. On the University of Bonn data set’s normal vs intericatl vs ictal instance, our proposed system achieved 97.8 percent accuracy and over 97 percent for the other parameters, which include precision, recall, F1-score, and ROC-AUC. Our findings are reproducible using the code available on github [1].
Comparing the efficacy of dexmedetomidine versus propofol for sedating mechanically ventilated patients after cardiovascular surgeries: Our experience in a tertiary hospital

Many cardiovascular surgeries are fast-tracked to extubation and require short-term sedation. Dexmedetomidine and propofol have very different mechanisms of action and pharmacokinetic profiles that make them attractive sedative agents in this patient population. Recently, there has been increased use of dexmedetomidine in the intensive care unit (ICU), but few studies exist or have been published directly comparing both agents in this setting. We conducted a retrospective cohort study with patients admitted to the ICU after cardiovascular surgery from January 2021 through June 2019 at Apple hospital, Tanuku. Adult patients who underwent coronary artery bypass and/or cardiac valve surgery received either dexmedetomidine or propofol continuous infusion for short-term sedation after cardiovascular surgery. The primary end point was time (hours) on mechanical ventilation after surgery. Secondary end points included ICU length of stay (LOS), hospital LOS, incidence of delirium, and requirement of a second sedative agent. A total of 352 patients met study inclusion criteria, with 33 enrolled in the dexmedetomidine group and 319 in the propofol group. Time on mechanical ventilation was shorter in the dexmedetomidine group (7.4 hours vs. 12.9 hours, P = .042). No difference was seen in ICU or hospital LOS. The need for a second sedative agent to achieve optimal sedation (24% vs. 27%, P = .737) and incidence of delirium (9% vs. 7.5%, P = .747) were similar between both groups. Sedation with dexmedetomidine resulted in a significant reduction in time on mechanical ventilation. However, no difference was seen in ICU or hospital LOS, incidence of delirium, or mortality.
Analysis of Dynamic Changes in Customer Sentiment on Product Features After the Outbreak of COVID-19 Based on Online Reviews

Sudden changes in life and work patterns due to COVID-19 have affected customer requirements for the products. Under these circumstances, companies can achieve high profitability and customer satisfaction when they can efficiently identify and respond quickly to changing customer preferences caused by COVID-19. This article presents empirical research on dynamic changes in customer responses for product features caused by the spread of COVID-19 through sentiment analysis based on online reviews. A case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment before/during COVID-19. The importance of the result is shown by comparing it to the actual market data.
Wind pressure and buckling of grouped steel tanks

Wind tunnel experiments on small scale groups of tanks are reported in the paper, with the aim of evaluating the pressure patterns due to group effects. A real tank configuration is studied in detail because one tank buckled during a hurricane category 3. Three configurations are studied in a wind tunnel, two with several tanks and different wind directions, and a third one with just one blocking tank. The pressures were measured in the cylindrical part and in the roof of the tank, in order to obtain pressure coefficients. Next, computational buckling analyses were carried out for the three configurations to evaluate the buckling pressure of the target structure. Finally, imperfection-sensitivity was investigated for one of the configurations, and moderate sensitivity was found, with reductions in the maximum load of the order of 25%. The results help to explain the buckling of the tank for the levels of wind experienced during the hurricane.
An Interaction-Aware Approach for Online Cut-in Behavior Prediction and Risk Assessment for Autonomous Driving

The development of autonomous driving has become one of the biggest trends of the 21st century’s technology. However, the promotion and the mass production of autonomous vehicles are still at the beginning stage. The human-driven vehicles will still predominate the traffic. Therefore, understanding the interaction and decision logic between human-driven vehicles, and utilizing it to predict their driving behavior are the keys to the development of autonomous driving techniques. Cut-in behavior is one of the top priorities due to its high risks. Rear-end collisions happen a lot when the lag vehicles cannot predict this abnormal lane change behavior of the front vehicles and response in time. However, related studies on cut-in event prediction and risk assessment have rarely been presented in autonomous driving field. A phase-based design framework is proposed in this work to realize online prediction and risk estimation of the cut-in behavior considering interactions between the involved vehicles. After preprocessing and analyzing of a naturalistic driving dataset, a cut-in behavior predictor and a risk estimator are devised based on Gaussian mixture model and Gaussian mixture regression method. Compared with baseline approaches, both the predictor and estimator designed following the proposed framework achieve enhanced results, which can further improve the driving safety of autonomous vehicles when cut-in behavior occurs.
A SPATIAL ANALYSIS OF THE FRIDA KAHLO MUSEUM IN THE CONTEXT OF INFORMATION SHARING BEHAVIOR VIA SOCIAL MEDIA

Since internet has become widely available, various mediums have emerged that people could produce their content on. Through these mediums, an opportunity rose that was to communicate independently from the space and thus, information sharing behavior has been observed among colleagues, students, communities, and institutions. It was possible to observe that this behavior increased during the pandemic process. This study is mainly aimed to discuss how space and art became instrumental in these shares and to discuss the relationship between spatial configuration and shares through museums. Another aim of the study can be described as to reveal the differences of spatial elements shared on social media at the end of the real and virtual museum experience with space syntax analysis. In this context, data were obtained from the Frida Kahlo Museum. The data collected were listed according to the frequency of sharing, and spots were identified in the museum plan. Integration values of these points in the Space Syntax 2D software were determined. The relationship between spatial configuration and frequency of sharing has been revealed. As a result, it has been determined that the sharing status of the Frida Kahlo Museum's elements is associated to the spatial configuration in sharing contents of the real experience. Besides, it has been determined that the semantic features of the space are associated. In contrast, during the virtual experience, it has been observed that the spatial configuration is not directly associated to the sharing frequency. It was observed that objects of art and daily life directed more sharing behavior. In this study, which examines the behaviors of virtual and real museum visitors, the effort to reveal the connection of space with information sharing is the original idea of the study.