Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring and Disentangling Ambiguity and Confidence in the Lab

In this paper we present a novel experimental procedure aimed at better understanding the interaction between confidence and ambiguity attitudes in individual decision making. Different ambiguity settings not only can be determined by the lack of information in possible scenarios completely “external” to the decision-maker, but can also be a consequence of the decision maker’s ignorance about her own characteristics or performance and, thus, deals with confidence. We design a multistage experiment where subjects face different sources of ambiguity and where we are able to control for self-assessed levels of competence. By means of a Principal Component Analysis, we obtain a set of measures of “internal” and “external” ambiguity aversion. Our regressions show that the two measures are significantly correlated at the subject level, that the subjects’ “internal” ambiguity aversion increases in performance in the high-competence task and that “external” ambiguity aversion moderately increases in earnings. Self-selection does not play any role.
Social Work Education; the international journal - student essay competition*

ABSTRACT This paper explores a Social Work focus upon intersecting mental health and recovery-oriented practices. A discussion of historical and current biomedical approach limitations to mental health and pertinent ethical considerations for social workers examines policy and practice incongruences and suggests current recovery-focused policy in Australia is available for to Social Work to capitalise on for better outcomes of service provision. Written as a course assessment from the perspective of a mature aged Social Work student, personal aspects of reflective practice in relation to new social workers participating as agents of biomedical discourse is briefly discussed. Overall intentions are to reflect on opportunities for Social Work as a practice body and social workers new and experienced to orient toward a less submissive role where operating within current biomedical paradigms.
A switching capacitor based multi‐port bidirectional DC–DC converter

This paper introduces a novel bidirectional multi-port DC–DC converter recommended for electric vehicle applications. The proposed structure has three ports with bidirectional features which can be operated in both step-up and step-down modes. This converter can supply the load from two independent voltage sources and can obtain a high voltage conversion ratio in both step-up and step-down operations. The low voltage stress throughout the power switches, high efﬁciency, and low components count are the characteristics of the proposed topology. For demonstrating the proposed structure’s performance, the principle of operation modes, steady-state analysis, and comparison results with other similar works are provided. Finally, an experimental prototype is built with a 120 W power level at 50 kHz operating, which validates the suggested converter’s effectiveness and bidirectional trait. The suggested converter’s efﬁciency in boost and buck mode is obtained about 94.2% and 94.37%, respectively that makes this converter to be used in renewable energy systems and electric vehicles with hybrid energy sources.
The impact of magnetic fields on cold streams feeding galaxies

High-redshift, massive haloes are observed to have sustained high star formation rates, which require that the amount of cold gas in the halo is continuously replenished. The cooling time-scale for the hot virialized halo gas is too long to provide the source of cold gas. Supersonic, cold streams have been invoked as a mechanism for feeding massive haloes at high redshift and delivering the cold gas required for continued star formation at the rates observed. This mechanism for replenishing the cold gas reservoir is motivated by some cosmological simulations. However, the cold streams are likely to be subject to the supersonic version of the Kelvin–Helmholtz instability (KHI), which eventually leads to stream disruption. Cosmological simulations have yet to obtain the spatial resolution required for understanding the detailed stability properties of cold streams. In this paper, we consider instead an idealized model of magnetized cold streams that we spatially resolve. Using linear theory, we show how magnetic fields with dynamically important field strengths do not inhibit the KHI but rather enhance its growth rate. We perform non-linear simulations of magnetized stream disruption and find that magnetic fields can nevertheless increase stream survival times by suppressing the mixing rate of cold gas with the circumgalactic medium. We find that magnetic fields can allow streams to survive ∼2–8 times longer and, consequently, that streams ∼2–8 times thinner can reach the central galaxy if the magnetic field strength is $\sim 0.3\rm {-}0.8 \, \mu$G.
Algorithm of Weed Detection in Crops by Computational Vision

This research has been based on the use of precision agriculture tools for the management of weeds in crops. It has focused on the creation of an image-processing algorithm to detect the existence of weeds in a specific site of crops. The main objective has been to obtain a formula so that a weed detection system can be developed through binary classifications. The initial step of image processing is the detection of green plants in order to eliminate all the soil in the image, reducing information that is not necessary. Then, it has focused on the vegetation by segmentation and eliminating unwanted information through medium and morphological filters. Finally, a labeling of objects has been made in the image so that weed detection can be done using a threshold based on the area of detection. This algorithm establishes an accurate monitoring of weeds and can be implemented in automated systems for the eradication of weeds in crops, either through the use of automated sprayers for specific site or a weed-cutting mechanism. In addition, it increases the performance of operational processes in crop management, reducing the time spent searching for weeds throughout a plot of land and focusing weed removal tasks on specific sites for effective control.
A Training-Free, One-Shot Detection Framework for Geospatial Objects in Remote Sensing Images

Deep learning based object detection has achieved great success. However, these supervised learning methods are data-hungry and time-consuming. This restriction makes them unsuitable for limited data and urgent tasks, especially in the applications of remote sensing. Inspired by the ability of humans to quickly learn new visual concepts from very few examples, we propose a training-free, one-shot geospatial object detection framework for remote sensing images. It consists of (1) a feature extractor with remote sensing domain knowledge, (2) a multi-level feature fusion method, (3) a novel similarity metric method, and (4) a 2-stage object detection pipeline. Experiments on sewage treatment plant and airport detections show that proposed method has achieved a certain effect. Our method can serve as a baseline for training-free, one-shot geospatial object detection.
Super Cooling Point Phenotypes and Cold Resistance in Hyles euphorbiae Hawk Moths from Different Climate Zones

The spurge hawkmoth Hyles euphorbiae L. (Sphingidae) comprises a remarkable species complex with still not fully resolved taxonomy. Its extensive natural distribution range covers diverse climatic zones. This predestinates particular populations to cope with different local seasonally unfavorable environmental conditions. The ability of the pupae to overcome outer frosty conditions is well known. However, the differences between two main ecotypes (‘euphorbiae’ and ‘tithymali’) in terms of the inherent degree of frost tolerance, its corresponding survival strategy, and underlying mechanism have not been studied in detail so far. The main aim of our study was to test the phenotypic exhibition of pupae (as the relevant life cycle stadia to outlast unfavorable conditions) in response to combined effects of exogenous stimuli, such as daylight length and cooling regime. Namely, we tested the turnout of subitan (with fast development, unadapted to unfavorable conditions) or diapause (paused development, adapted to unfavorable external influences and increased resistance) pupae under different conditions, as well as their mortality, and we measured the super cooling point (SCP) of whole pupae (in vivo) and pupal hemolymph (in vitro) as phenotypic indicators of cold acclimation. Our results show higher cold sensitivity in ‘tithymali’ populations, exhibiting rather opportunistic and short-termed cold hardiness, while ‘euphorbiae’ produces a phenotype of seasonal cold-hardy diapause pupae under a combined effect of short daylight length and continuous cold treatment. Further differences include the variability in duration and mortality of diapause pupae. This suggests different pre-adaptations to seasonal environmental conditions in each ecotype and may indicate a state of incipient speciation within the H. euphorbiae complex.
Exact Properties of Some Heteroscedastic TOST Alternatives for Bioequivalence

ABSTRACT The two one-sided tests (TOST) procedure is the common method for assessing bioequivalence between two different drugs or formulations of the same drug. Several solutions to Behrens-Fisher problem have been extended to establish statistical equivalence under variance heterogeneity. Unlike other existing studies that rely exclusively on simulation results, the analytical formulas of power and Type I error rate are derived. Exact numerical investigations were conducted to evaluate the behavior of the Welch–Satterthwaite, Cochran–Cox, and Banerjee–McCullough TOST methods. The results show that the Type I error rates of the Cochran–Cox and Banerjee–McCullough tests never exceed the nominal significance level. The Banerjee–McCullough technique is actually more conservative than the Cochran–Cox procedure. The extended Welch–Satterthwaite approach maintains closest to the preassigned significance level and is almost equal to the target error probability. The exact power levels of the Cochran–Cox and Banerjee–McCullough tests are completely or nearly identical, and are always less than that of the Welch–Satterthwaite method. On the basis of excellent control of Type I error and greater power performance, the extended Welch–Satterthwaite procedure is recommended over the other two heteroscedastic TOST methods. These findings provide an update of previous studies that endorse the somehow inferior Banerjee–McCullough method.
Understanding Leadership Challenges and Responses in Data-driven Transformations

The purpose of this study is to better understand challenges and typical responses of leadership in data-driven transformations. Increasingly organizations aspire to practices of data-driven decision making. In this context the transformational aspects transcend traditional change management processes and pose new and different challenges to leadership. To explore these challenges and typical responses we performed four in-depth case studies of organizations that are more mature in terms of their transformation towards a data-driven organization. Propositions derived from change management and digital transformation literature guide our exploration. Our findings help understand the key role of leadership in a data-driven transformation, particularly through (1) continuously communicating and explaining the value of being data-driven, (2) securing and managing critical resources, including data and analysts, and (3) creating a data-driven culture. Our study contributes to literature by combining insights from change management and data-driven transformations to better understand the dynamics of leadership in this context.
Let's Get in Touch - Decision Making about Enterprise Architecture Using 3D Visualization in Augmented Reality

Making informed decisions about historically grown and often complex business and Information Technology (IT) landscapes can be particularly difficult. Enterprise Architecture Management (EAM) addresses this issue by enabling stakeholders to base their decisions on relevant information about the organization’s current and future Enterprise Architectures (EAs). However, visualization of EA is often confronted with low usefulness perceptions. Informed by the cognitive fit theory (CFT), we argue that decision-makers benefit from interacting with EA visualizations using Augmented Reality (AR), because it enables a consistent task-related mental representation based on the natural use of decision-makers’ visual-spatial abilities. The goal of this paper is to demonstrate ARs suitability for EA-related decision-making. We follow the design science research (DSR) approach to develop and evaluate an AR head-mounted display (HMD) prototype, using the Microsoft HoloLens. Our results suggest that EA-related decision-making can profit from applying AR, but users find the handling of the HMD device cumbersome.
Self-Regulation of Motivation and Confinement by COVID-19: A Study in Spanish University Students

(1) Background: confinement by COVID-19 in 2020 meant that face-to-face teaching changed to virtual teaching. The goal of this study was to test how confinement affected to self-regulation of motivation (SRM); (2) Methods: a longitudinal design was used to obtain information from 75 university students in three moments: before confinement, 20 days after confinement, and 40 days after confinement. The SRM Strategies Questionnaire and the Pittsburgh Sleep Index were used to evaluate the study variables; (3) Results: the SRM decreased as confinement progressed. Moreover, those students who had higher levels of SRM before confinement, showed worse sleep quality 20 days after. There was no interaction effect between moments and gender and between moments and work situation on any of the dimensions of the SRM. There were gender differences (women scored higher) in almost all of the SRM dimensions in the three moments, but there were no differences dependent on the work situation (i.e., only study or study and work); (4) Conclusions: the change from face-to-face classes to virtual learning decreased the SRM of university students and was related to worse sleep quality. Women showed higher levels of SRM than men, but these levels were reduced to the same extent in both genders.
Purchase Prediction Based on a Non-parametric Bayesian Method

Predicting customer’s next purchase is of paramount importance for online retailers. In this paper, we present a new purchase prediction method to predict customer behavior based on nonparametric Bayesian framework. The proposed method is inspired by topic modeling for text mining. Unlike the conventional methods, we regard c ustomer’s purchase as the result of motivations and automatically determine the number of user purchase motivations. Given customer’s purchase history, we show that customer’s next purchase can be predicted by non-parametric Bayesian model. We apply the model to real-world dataset from Amazon.com and prove it outperforms the traditional methods. Besides that, the proposed method can also determine the number of the motivations owned by users automatically, rendering it a promising approach with a good scalability.
Two-Sided Value-Based Music Artist Recommendation in Streaming Music Services

Most work on music recommendations has focused on the consumer side not the provider side. We develop a two-sided value-based approach to music artist recommendation for a streaming music scenario. It combines the value yielded for the music industry and consumers in an integrated model. For the industry, the approach aims to increase the conversion rate of potential listeners to adopters, which produces new revenue. For consumers, it aims to improve their utility related to recommendations they receive. We use one year of listening records for 15,000+ Last.fm users to train and test the proposed recommendation model on 143 artists. Compared to collaborative filtering, the results show some improvement in recommendation performance by considering both sides’ value in conjunction with other factors, including time, location, external information and listening behavior.
Creating Task-Generic Features for Fake News Detection

Information spreads at a pace never seen before on online platforms, even when this information is fake. Fake news can have substantial impact, for instance when it concern politics and influences the results of legislations or elections. Finding a methodology to verify if some piece of news is true or false is hence essential. In this work, we propose a methodology to create task-generic features that are paired with textual features in order to detect fake news. Task-generic features are created by elaborating on metadata attached to answers from Google’s search engine, and by using crowdsourcing for missing values. We experimentally validate our method on a dataset for fake news detection based on the PolitiFact website. Our results show an improvement in F1-Score of 3% over the state of the art, which is significant for a 6-class task.
Using Social Media to Explore Mental Health-Related Behaviors and Discussions among Young Adults

There have been recurring reports of online harassment and abuse among adolescents and young adults through Anonymous Social Networking websites (ASNs). We explored discussions related to social and mental health behaviors among college students, including cyberbullying on the popular ASN, Yik Yak. From April 6, 2016, to May 7, 2016, we collected anonymous conversations posted on Yik Yak at 19 universities in four different states. We found that prosocial messages were approximately five times as prevalent as bullying messages. Frequency of cyberbullying messages was positively associated with messages seeking emotional help. We found significant geographic variation in the frequency of messages offering supportive versus bullying messages. Across campuses bullying and political discussion were positively associated. Results suggest that ASN sites can be mined for real-time data about students’ mental health-related attitudes and behaviors. We discuss the implications for using this information in education and healthcare services.
Reply on RC2

[1] RC2: This study presents a new integrated modelling of snowpack and soil water/energy transfers, called STEMMUS-UEB, presenting three levels of soil transfer complexities. The model is evaluated on one site equipped with soil temperature, moisture and energy fluxes sensors. The performances of the 3 options of the model are discussed. This is an interesting paper but quite difficult to follow and some questions need to be addressed before further consideration for publication.
Effect of different modified atmosphere packaging and storage time on bioactive compounds in fresh-cut Tropic Beauty peaches

Modified atmosphere packaging (MAP) was evaluated for respiration rate, color, total phenolic content (TPC), antioxidant activity DPPH, flavonoids and pigments of fresh-cut Tropic Beauty peach. Modified atmospheres (21% O2 [Control], 5% CO2 [MAP1], 8% CO2 [MAP2] and 0% O2 [MAP3]) were evaluated for storing fresh-cut peaches for up to 10 days in temperatures of 5oC. MAP significantly (p<0.05) affected the proprieties investigated as compared to control. Peaches with MAP3 have higher respiration(p<0.05) respiration rate than the peaches with the other treatments. Fresh-cut peaches in MAP2 showed more stable (p<0.05) carotenoid and anthocyanin contents, better attributes in the bioactive compounds. MAP1 and MAP2 exhibited better antioxidant proprieties at low storage temperature (5oC) for up to 10 days storage and similar result was verified by principal component analysis used where modified atmosphere was observed as major factor.
Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.
Preference Neural Network

This paper proposes a novel label ranker network to learn the relationship between labels to solve ranking and classification problems. The Preference Neural Network (<italic>PNN</italic>) uses <italic>spearman</italic> correlation gradient ascent and two new activation functions, positive smooth staircase (<italic>PSS</italic>), and smooth staircase (<italic>SS</italic>) that accelerate the ranking by creating almost deterministic preference values. <italic>PNN</italic> is proposed in two forms, fully connected simple Three layers and Preference Net (<italic>PN</italic>), where the latter is the deep ranking form of <italic>PNN</italic> to learning feature selection using ranking to solve images classification problem. <italic>PN</italic> uses a new type of ranker kernel to generate a feature map. <italic>PNN</italic> outperforms five previously proposed methods for label ranking, obtaining state-of-the-art results on label ranking, and <italic>PN</italic> achieves promising results on <italic>CFAR-100</italic> with high computational efficiency.