Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Interoception and Autonomic System Reactivity during Synchronization Behavior

Background: Within the social interoception field, little is known about the impact of interoception on autonomic system reactivity during synchronization tasks. The impact of social framing manipulation and Interoceptive Attentiveness (IA; defined as concentrated attention on the breath for a specific time interval) on autonomic responses during interpersonal synchronization was investigated in this research. Methods: Under two experimental interoceptive conditions—the concentration and no focus on the breath condition—participants completed two synchronization tasks. A social framing was given to participants by informing them that they needed to complete the tasks in unison to improve their collaboration abilities. Autonomic responses (electrodermal activity and cardiovascular indices) were collected throughout task performance. Results: Two orders of results were observed: high cognitive engagement was detected during the focus on the breath condition and for the social frame. This effect was specifically observed for the motor compared to the linguistic synchronization task. Meanwhile, a potential lack of emotional control was observed in the no focus on the breath condition when the synchronization tasks were not socially framed. Conclusions: Such results encourage the use of the hyperscanning paradigm to deepen the impact of IA in real-time and ecological interpersonal synchronization dynamics.
A Systematic Literature Review on Survey Data Collection System

Data are substantial in the process of decision making. One of the important steps to provide data is data collection. Currently, there are several methods of data collection system that have been used. Each method has features and technology characteristics that can differ from one another. The study conducts a systematic literature review (SLR) with the objective to understand the feature, technology, method for data collection system in the previous research. This understanding can be used as a base for developing a data collection system platform. The first step is determining the research question (RQ), then searching the previous research on several database journals e.g. IEEE Xplore, Scopus, SpringerLink, ScienceDirect dan ACM. After reviewing 23 chosen articles, the study conclude that there are several features in data collection system that can be divided into categories of preparation, collection, transmission and security; Cloud, portable devices (mobile phone) and GPS are some of the technologies that are widely used to support data collection system; PAPI, CATI, CAPI, and CAWI currently exist method for data collection system; CAPI is the most widely used choice in previous research; The integration of several technologies or methods increase the functionality on the implementation.
ICTV Virus Taxonomy Profile: Filoviridae

Members of the family Filoviridae produce variously shaped, often filamentous, enveloped virions containing linear non-segmented, negative-sense RNA genomes of 15–19 kb. Several filoviruses (e.g., Ebola virus) are pathogenic for humans and are highly virulent. Several filoviruses infect bats (e.g., Marburg virus), whereas the hosts of most other filoviruses are unknown. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on Filoviridae, which is available at www.ictv. global/report/filoviridae.
An Improved Belief Propagation Iterative Algorithm Based on Dynamic Scheduling

In this paper, on the basis of analysing existing schedule message passing mechanism of Belief Propagation (BP) iterative algorithm, we propose a Belief Propagation iterative algorithm based on Dynamic Schedule Row Message Passing (DS-RMP). This algorithm optimizes the update mode of the check node message in the iteration, reduce the imbalance of check nodes' confidence degree in the original algorithm, and promote the efficiency of message passing. The simulation results indicate that, compared with the existing Belief Propagation algorithm, the Belief Propagation iterative algorithm not only reduces the average number of iterations, but also improves the bit error rate performance effectively.
Real Estate Value Prediction Using Linear Regression

The real estate market is a standout amongst the most focused regarding pricing and keeps fluctuating. It is one of the prime fields to apply the ideas of machine learning on how to enhance and foresee the costs with high accuracy. There are three factors that influence the price of a house which includes physical conditions, concepts and location. The current framework includes estimating the price of houses without any expectations of market prices and cost increment. The objective of the paper is prediction of residential prices for the customers considering their financial plans and needs. By breaking down past market patterns and value ranges, and coming advancements future costs will be anticipated. This examination means to predict house prices in Mumbai city with Linear Regression. It will help clients to put resources into a bequest without moving toward a broker. The result from this research proved linear regression gives minimum prediction error which is 0.3713.
Efficient and QoS-Aware Drone Coordination for Simultaneous Environment Coverage

Simultaneous coverage represents an important problem for various drone applications where objects must be covered by simultaneously captured images from different viewpoints that may impose additional requirements on geometry, resolution, and depth error. In order to solve simultaneous coverage, the drones must plan their movements such that the required viewpoints are concurrently reached while minimizing the overall mission time and satisfying the quality requirements. In this paper, we introduce a market-based and QoS-aware coordination method for assigning drones to viewpoints during the mission. In our simulation study, we compare three algorithm variants based on the achieved mission time and the required communication effort.
Multi-source heterogeneous data fusion of cross-border e-commerce platform based on the neural network model

: At present, the fusion representation for multi-source texts is relatively simple, the difference between long and short texts is not considered, and the representation accuracy needs to be improved; in addition, when performing heterogeneous data fusion, the deep learning proposed in recent years can map each structural data to the same shared space. However, few studies have focused on user-generated content in e-commerce platforms. Therefore, we did a study on multi-source heterogeneous data fusion and representation strategies for user-generated content on e-commerce platforms. The convolutional neural network model is used to realize the fusion representation of heterogeneous data so that the various modalities and information of user-generated data can be considered when the product feature representation is performed, especially when the product text data is small, the integrated heterogeneous data may be it plays a better role in feature expansion, and further improves the accuracy and robustness of product feature representation in e-commerce platforms. A user preference estimation algorithm based on RBM is constructed in combination with the category attributes of the product itself; based on the existing explicit preference combined with the user's implicit preference, joint learning is performed to complete the user personalized recommendation based on collaborative filtering; the proposed algorithm is applied to multiple Amazon sub-datasets to verify the superiority of the proposed algorithm and the feasibility and accuracy of user-generated multi-source heterogeneous data fusion. The results show that the fusion of user-generated multi-source heterogeneous data can effectively improve the overall performance of the recommendation algorithm.
More than the Sum of the Respiratory Events: Personalized Medicine Approaches for Obstructive Sleep Apnea.

Traditionally, the presence and severity of obstructive sleep apnea (OSA) has been defined by the apnea-hypopnea index (AHI). Continuous positive airway pressure (CPAP) is generally first line therapy despite low adherence as it reliably reduces the AHI when used, and the response to other therapies is variable. However, there is growing appreciation that the underlying etiologies (i.e. endotype) and clinical manifestations (i.e. phenotype) of OSA in an individual are not well described by the AHI. We define and review the important progress made on understanding and measuring physiological mechanisms (or endotypes) that help define subtypes of OSA and identify the potential use of genetics to further refine disease classification. This more detailed understanding of OSA pathogenesis should influence clinical treatment decisions as well as help inform research priorities and clinical study design. In short, treatments could be individualized based on the underlying cause of OSA; patients could better understand which symptoms and outcomes will respond to OSA treatment, and by how much; and researchers could select populations most likely to benefit from specific treatment approaches for OSA.
Optimization of Emergency Supply and Distribution of Fresh Agricultural Products Under Public Health Emergencies

COVID-19 is a major public health emergency, the Chinese government adopts pandemic prevention and control measures such as home quarantine in order to block the strong contagiousness of the virus. In order to solve the problem of distribution difficulties due to the limited transportation resources brought by the pandemic and to guarantee the demand of fresh agricultural products for the isolated residents, this study takes the emergency supply as the main objective, and designs three distribution modes, namely, single distribution center, multi-vehicle model based on the classification of temperature zones, and multi-distribution center for isolated communities of different sizes, with the objectives of minimizing the number of vehicles in use, minimizing the average response time, and minimizing the risk of viral infections. A multi-objective optimization model was constructed as the objectives. Then the genetic algorithm is improved by adding destruction operator, repair operator and greedy operation, and the effectiveness of the algorithm is verified through empirical research, and the advantages of the algorithm in convergence speed are compared and analyzed. Finally, different distribution methods are suggested for the number of communities of different sizes. This study can provide a scientific basis for government departments to formulate an optimized decision-making plan for the emergency supply and distribution of fresh agricultural products under public health emergencies.
A New Non-Isolated Multi-Input DC-DC Converter with High Voltage gain and Low Average of Normalized Peak Inverse Voltage

In this paper, a novel non-isolated multi-input DC-DC converter (MIC) with high voltage gain has been presented. The presented topology comprises of simple high gain units. The main merits of the proposed topology are continuous current of input sources and possibility of using input sources of different voltage-current characteristics. Duty cycle of each unit can be specified independently and the amount of desired output voltage can be achieved. This topology has a simple switching pattern and the switching frequency can be selected high as well. The number of input units can be increased thus the stress of the switches is decreased as the duty ratio can be selected lower for each unit. The proposed topology is also appropriate for renewable energy sources’ applications and in order to validate the performance of the aforementioned merits, two units of multi-input DC-DC converter topology has been simulated in PSCAD/EMTDC software. Simulation results confirm the valid performance of the proposed topology.
A Survey on Haptic Feedback through Sensory Illusions in Interactive Systems

A growing body of work in human-computer interaction (HCI), particularly work on haptic feedback and haptic displays, relies on sensory illusions, which is a phenomenon investigated in perception research. However, an overview of which illusions are prevalent in HCI for generating haptic feedback in computing systems and which remain underrepresented, as well as the rationales and possible undiscovered potentials therein, have not yet been provided. Existing surveys on human-computer interfaces using sensory illusions are not only outdated but, more importantly, they do not consider literature across disciplines, namely perception research and HCI. This paper provides a systematic literature review (SLR) of haptic feedback generated by sensory illusions. By reporting and discussing the findings of 90 publications, we provide an overview of how sensory illusions can be used and adapted to produce haptic feedback and how they are implemented and evaluated in HCI. We moreover identify current trends and research gaps and discuss ideas for possible research directions worth investigating.
Duty-Cycle Measurement and Correction Technique in 90-nm CMOS

A concept of pulsewidth modification is used to vary the duty cycle. Analog technique with feedback loop is applied to correct the duty cycle of any circuit without using more power. This technique works at the frequencies where most of the digital techniques does not work. Additional noise and degradation in sensitivity in a receiver rises due to non 50% duty cycle and even order harmonics which is eliminated. Pulsewidth modification cell is used for expansion and contraction of pulsewidth. Push pull inverter is used as an amplifier to amplify and bias the analog valtage.
Contact Graph Based Anonymization for Geosocial Network Datasets

With the development of mobile devices which are integrated position sensors, users share their locations by checking in on geo-social network systems, such as Foursquare and Facebook Places. There are various purposes for the location-rich social network could be of research interest. However, such datasets may face the privacy concerns, like reidentification and privacy violation if they are not adequately anonymized. In this paper, we study the problem of anonamizing the geo-social network dataset, based on adversarial knowledge of location information of its users. We propose a novel approach for preserving privacy in geo-social network data publishing based on location contact graph, which considers the relationship of locations and “co-location” pattern. Based on this graph, we propose a graph similarity based anonymization (GSA) algorithm, a k-anonymity-based method to guarantee anonymity. We also evaluate the algorithm using two real datasets, Gowalla and Brightkite, and experiment results show that the performance of the method is convincing.
Electrophysiological correlates of prediction formation in anticipation of reward‐ and punishment‐related feedback signals

Abstract Feedback processing during decision making involves comparing anticipated and actual outcome. Although effects on ERPs of valence, magnitude, expectancy, and context during feedback processing have been extensively investigated, the electrophysiological processes underlying prediction formation in anticipation of feedback signals have received little attention. The aim of the present study was to explore these processes of prediction formation and their influence on subsequent feedback signals. Twenty healthy, right‐handed volunteers performed a forced‐choice task in which they had to indicate which of two presented objects was more expensive. After the volunteer's choice, an expert cue, which was accurate in 80% of trials, was presented to manipulate prediction formation about future reward and punishment. ERPs were recorded during presentation of the expert cue and during feedback. Results revealed that prediction formation of future rewards and punishments is accompanied by differences in the P2 component and a subsequent delay period. During feedback processing, the prediction‐related P2 was associated with the processing of valence reflected in the feedback‐related P2. Furthermore, the prediction‐related difference in the delay period was associated with error processing in feedback‐related medial frontal negativity. These findings suggest that prediction signals prior to feedback contain information about whether a prediction is correct or wrong (expectancy) and if the outcome will be a reward or punishment (valence).
Plant Growth and Development in Relation to Phosphorus: A review

Phosphorus (P) deficiency is a primal limiting factor for crop yields, particularly during the early developmental phase. This nutritious element plays a key role in several physiological mechanisms such as seed and fruit formation, respiration, photosynthesis, energy storage, and transfer, cell division, cell enlargement, and many other processes in the plant. Part of P applied to soil as fertilizer is could be into insoluble forms, rendering it unavailable to plants and causing environmental pollution. Therefore, increasing p absorption ability by plants appears to be critical for increasing agricultural productivity and decreasing pollution. Improving the crops P use efficiency (PUE) could be an effective method to increase P uptake. The maximum PUE is observed where nutrient supply via chemical fertilizer is the lowest level. This review discussed the significance of plant growth, yield quality, and PUE.
Detection and Prevention of Phishing Websites Using Machine Learning Approach

Phishing costs Internet user's lots of dollars per year. It refers to exploiting weakness on the user side, which is vulnerable to such attacks. The phishing problem is huge and there does not exist only one solution to minimize all vulnerabilities effectively, thus multiple techniques are implemented. In this paper, we discuss three approaches for detecting phishing websites. First is by analyzing various features of URL, second is by checking legitimacy of website by knowing where the website is being hosted and who are managing it, the third approach uses visual appearance based analysis for checking genuineness of website. We make use of Machine Learning techniques and algorithms for evaluation of these different features of URL and websites. In this paper, an overview about these approaches is presented.
Expert Profile Identification From Community Detection on Author-Publication-Keyword Graph With Keyword Extraction

Expert profiling aims to discover the expertise of an author. This task is useful for identifying the research groups existing within an organization as well as measuring the similarities between authors’ expertise. Thus, identifying areas of expertise becomes a critical part of this task, especially in cases where the publications are unannotated. Commonly used topic modeling methods such as Latent Dirichlet Allocation still fall short in determining the number of topics automatically and discovering the hierarchical relationships between topics. To solve these issues, we adopted a graph-based approach which constructs a graph from publication features such as authors and keywords (Silva et al., 2018). We applied the Louvain algorithm repeatedly to discover the topics with hierarchical order automatically. We utilize keyword extraction methods to generate keywords for each respective publication to handle the missing values. We perform experiments to determine the optimum HPMI value. Results showed that graphs constructed from default and SIFRank keywords with transformation weights of $\alpha =0.5$ and $\beta =1.0$ produce topics with the best HPMI score. We evaluate the profiles from this method (CDT) with ATM as the baseline. It is shown that CDT produces better MAP, MRR, and nDCG scores than ATM. The work in this manuscript shows how community detection and keyword extraction could be utilized in expert profiling tasks. Our observation shows that the Louvain algorithm used only cluster publications into one topic, and thus still has limitations in classifying multidisciplinary publications. Further development could be done to handle such publications and increase the quality of keywords.
An Enhanced Lane Detection Technique based on Active Learning

The lane detecting algorithm plays a major role in advanced driver assistance systems and autonomous driving systems. In recent years, deep learning-based lane detection techniques have shown encouraging results; nonetheless, the quality and size of the training data set have a signi ﬁ cant impact on how effective these techniques are. Active learning is a technique that can improve the capacity of deep learning-based lane identi ﬁ cation systems to repeatedly choose and classify valuable samples from a large body of unlabeled data. In this research, a novel 1-dimensional deep learning approach is used to present an augmented Active Learning based Lane Detection Algorithm (ALDA) that picks informative samples based on diversity-and uncertainty-based criteria. Several benchmark datasets, including the CUlane, have been used to assess the suggested technique, In terms of accuracy and robustness, the suggested method ALDA performs better than four cutting-edge lane-detecting algorithms. The ﬁ ndings show that active learning can signi ﬁ cantly reduce the quantity of labeled data required for training while preserving good performance. The suggested method may improve the dependability and security of advanced driver assistance systems and autonomous driving systems. When compared with other distinct Deep Learning approaches, the proposed ALDA obtains an accuracy of 98.01 %, Precision of 98.5173 %, Recall of 95.2296 %, F1 score of 96.845 %, mAP of 92.7 %, and MSE of 0.0097.
Genome of ‘Charleston Gray’, the principal American watermelon cultivar, and genetic characterization of 1,365 accessions in the U.S. National Plant Germplasm System watermelon collection

Summary Years of selection for desirable fruit quality traits in dessert watermelon (Citrullus lanatus) has resulted in a narrow genetic base in modern cultivars. Development of novel genomic and genetic resources offers great potential to expand genetic diversity and improve important traits in watermelon. Here, we report a high‐quality genome sequence of watermelon cultivar ‘Charleston Gray’, a principal American dessert watermelon, to complement the existing reference genome from ‘97103’, an East Asian cultivar. Comparative analyses between genomes of ‘Charleston Gray’ and ‘97103’ revealed genomic variants that may underlie phenotypic differences between the two cultivars. We then genotyped 1365 watermelon plant introduction (PI) lines maintained at the U.S. National Plant Germplasm System using genotyping‐by‐sequencing (GBS). These PI lines were collected throughout the world and belong to three Citrullus species, C. lanatus, C. mucosospermus and C. amarus. Approximately 25 000 high‐quality single nucleotide polymorphisms (SNPs) were derived from the GBS data using the ‘Charleston Gray’ genome as the reference. Population genomic analyses using these SNPs discovered a close relationship between C. lanatus and C. mucosospermus and identified four major groups in these two species correlated to their geographic locations. Citrullus amarus was found to have a distinct genetic makeup compared to C. lanatus and C. mucosospermus. The SNPs also enabled identification of genomic regions associated with important fruit quality and disease resistance traits through genome‐wide association studies. The high‐quality ‘Charleston Gray’ genome and the genotyping data of this large collection of watermelon accessions provide valuable resources for facilitating watermelon research, breeding and improvement.