Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis

Background: Early screening and treatment of esophageal cancer (EC) is particularly important for the survival and prognosis of patients. However, early EC is difficult to diagnose by a routine endoscopic examination. Therefore, convolutional neural network (CNN)-based artificial intelligence (AI) has become a very promising method in the diagnosis of early EC using endoscopic images. The aim of this study was to evaluate the diagnostic performance of CNN-based AI for detecting early EC based on endoscopic images. Methods: A comprehensive search was performed to identify relevant English articles concerning CNN-based AI in the diagnosis of early EC based on endoscopic images (from the date of database establishment to April 2022). The pooled sensitivity (SEN), pooled specificity (SPE), positive likelihood ratio (LR+), negative likelihood ratio (LR−), diagnostic odds ratio (DOR) with 95% confidence interval (CI), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) for the accuracy of CNN-based AI in the diagnosis of early EC based on endoscopic images were calculated. We used the I2 test to assess heterogeneity and investigated the source of heterogeneity by performing meta-regression analysis. Publication bias was assessed using Deeks' funnel plot asymmetry test. Results: Seven studies met the eligibility criteria. The SEN and SPE were 0.90 (95% confidence interval [CI]: 0.82–0.94) and 0.91 (95% CI: 0.79–0.96), respectively. The LR+ of the malignant ultrasonic features was 9.8 (95% CI: 3.8–24.8) and the LR− was 0.11 (95% CI: 0.06–0.21), revealing that CNN-based AI exhibited an excellent ability to confirm or exclude early EC on endoscopic images. Additionally, SROC curves showed that the AUC of the CNN-based AI in the diagnosis of early EC based on endoscopic images was 0.95 (95% CI: 0.93–0.97), demonstrating that CNN-based AI has good diagnostic value for early EC based on endoscopic images. Conclusions: Based on our meta-analysis, CNN-based AI is an excellent diagnostic tool with high sensitivity, specificity, and AUC in the diagnosis of early EC based on endoscopic images.
Influence of Teachers' Self-Efficacy and Attitude towards the Integration of ICT into Teaching and Learning at the Basic School Level

Background: ICT has become crucial in our everyday life and for that matter teaching and learning. It is, therefore, imperative to examine its involvement in education. Purpose: The current study sought to explore the influence of teachers' self-efficacy and attitude towards the integration of ICT into teaching and learning at the basic school level. Method: The study was a descriptive survey with a sample size of 159 teachers representing 270 accessible population which was purposively located. Respondents responded to the questionnaire via an online survey in the quest for data collection. Data to answer research questions and test research hypotheses were analyzed using frequencies, percentages, means, standard deviations, and Pearson Product Moment Correlational Coefficient. Findings: The study revealed that most teachers have a high level of self-efficacy. The study also found that majority of the teachers have positive attitudes toward the integration of ICT in teaching and learning. However, it was revealed that there is a significant difference between male and female teachers’ attitudes towards the integration of ICT in teaching and learning. Finally, there is no significant relationship between self-efficacy and teachers’ attitudes toward the integration of ICT in teaching and learning. Recommendation: Based on the findings, it was, therefore, included in the recommendations that to successfully incorporate ICT into teaching and learning, policymakers must offer the essential teaching and learning tools to teachers.
Bee pollen supplementation in diets for rabbit does and growing rabbits

It was evaluated the effects of bee pollen (BP) on the doe and kits productivity and on the carcass and organs of the rabbits. Twenty White New Zealand does and their kits were used in a randomized block design, with four treatments and five blocks, in a factorial arrangement 2 x 2 with two supplementation levels for the doe and for the kits after the weaning. BP supplementation for the does did not influence (p > 0.05) the doe and kit productivity during the lactation, except by the kits survival rate (p 0.05) the rabbit performance from the weaning until the slaughter age, the slaughter weight, carcass characteristics, except by the spleen and small intestine weights, higher in supplemented rabbits. It was not recommended that BP supplementation for does and/or rabbits for not improving the rabbit productive performance.
Quantitative Estimation of Saline-Soil Amelioration Using Remote-Sensing Indices in Arid Land for Better Management

Soil salinity and sodicity are significant issues worldwide. In particular, they represent the most dominant types of degraded lands, especially in arid and semi-arid regions with minimal rainfall. Furthermore, in these areas, human activities mainly contribute to increasing the degree of soil salinity, especially in dry areas. This study developed a model for mapping soil salinity and sodicity using remote sensing and geographic information systems (GIS). It also provided salinity management techniques (leaching and gypsum requirements) to ameliorate soil and improve crop productivity. The model results showed a high correlation between the soil electrical conductivity (ECe) and remote-sensing spectral indices SIA, SI3, VSSI, and SI9 (R2 = 0.90, 0.89, 0.87, and 0.83), respectively. In contrast, it showed a low correlation between ECe and SI5 (R2 = 0.21). The salt-affected soils in the study area cover about 56% of cultivated land, of which the spatial distribution of different soil salinity levels ranged from low soil salinity of 44% of the salinized cultivated land, moderate soil salinity of 27% of salinized cultivated land, high soil salinity of 29% of the salinized cultivated land, and extreme soil salinity of 1% of the salinized cultivated land. The leaching water requirement (LR) depths ranged from 0.1 to 0.30 m ha−1, while the gypsum requirement (GR) ranged from 0.1 to 9 ton ha−1.
A 9 Gb/s/ch Transceiver With Reference-Less Data-Embedded Pseudo-Differential Clock Signaling for Graphics Memory Interfaces

A 9 Gb/s/ch transceiver with a reference-less data-embedded pseudo-differential clock signaling (RDCS) for graphics memory interfaces is proposed in a 65-nm CMOS technology. In the RDCS transceiver, the output data is embedded into differential clock signal by adopting a multi-level amplitude modulation in the transmitter (TX), and the data is recovered by extracting the data information from clock signal without reference clock in the receiver (RX) side. Because the data is synchronized with the clock at the TX, the received data can be recovered without DLL in the RX side. In addition, the additional pins required in the graphics memory interfaces can be removed by applying the proposed RDCS. The proposed design achieves less than 10−12 bit error rate with 9 Gb/s/ch data rate, and measured jitter in the recovered clock is 1.42 psRMS. In addition, the power efficiencies of the TX and RX are 2.33 and 1.03 pJ/bit, respectively.
Protocolo para transferência parcial de conhecimento e sua aplicação à verificação segura de marcas d’água

Seja y = f(x) para uma função one-way f. Apresentamos um algoritmo simples que permite exibir a terceiros apenas parte dos bits de x numa demonstração, por meio de um esquema de prova de conhecimento nulo, de que x pertence de fato à pré-imagem de y sob f. Como aplicação, mostramos que é possível exibir uma marca d'água embarcada em um artefato digital sem necessidade de revelar sua localização. O resultado é um protocolo seguro para verificação de marcas d'água de software que não aumenta a probabilidade de um atacante ser bem-sucedido em um ataque de remoção.
VENμS: Mission Characteristics, Final Evaluation of the First Phase and Data Production

VENμS (Vegetation and Environment New micro (μ) Satellite) is a micro satellite launched in 2017 by the Israeli Space Agency (ISA) and the French Centre National d’Etudes Spatiales (CNES). VENμS is a research satellite containing two very different devices: an electric Hall effect thruster and a multispectral optical camera. This paper focuses on the multispectral camera. The camera provides images at a resolution of 5 m, with a field of view of 27 km, and the orbit of the satellite was chosen to allow us to revisit of each observed site with constant angles every second day. In November 2020, VENμS ended the first phase of its mission. This phase, called VM01, allowed us to provide about 150 accurate time series over selected scientific sites over almost three years. Extensive work was conducted to calibrate the camera and assess the quality of the products. Not everything worked as planned before launch and a large amount of work was necessary to correct some defects of the camera or to improve the geometric registration of images. This article establishes the image quality VM01 final assessment including the presentation of radiometric and geometric calibration methods, the estimation of instrument performances and their associated temporal stabilities and the monitoring activities. In addition, it highlights the whole mechanism of data programming, reception and production. The end of VM01 phase is not the end of the VENμS mission, and a new phase started on a one-day repeat orbit.
UAV-Anwendungen im Bergbau - Modellversuche als Beitrag zur photogrammetrischen Forschung und Entwicklung

As basis for the ongoing digitization and automation in mining, the creation of visually appealing and highly accurate 3D models, as well as their automatic interpretation and processing, is of particular importance. Especially for surveying and inspection applications unmanned aerial systems equipped with cameras are now widely used and are state of the art. However the greatest future need for research certainly lies in data processing, where on the one hand the achievable model quality, and on the other hand the automated 19. GEOKINEMATISCHER TAG Freiberg 2018 31 data interpretation for special mining related issues such as the characterization of muckpiles or geological characterization is at the center of the work. In particular, due to the possibility of reproducibility, but also due to higher operability, model experiments are currently playing an important role in photogrammetric research at the Montanuniversität in addition to classical real-scale investigations in the field. In the present paper, examples from applied and fundamental research are given, highlighting the added value of laboratory experiments while critically contrasting them with reality.
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From broken habits to new intentions: how COVID-19 expands our knowledge on post-adoptive use behaviour of digital communication and collaboration

ABSTRACT The COVID-19 pandemic has caused an unprecedented surge in digital communication and collaboration. While a rich body of knowledge exists on IS use, our understanding of changes in post-adoptive use behaviour regarding communication and collaboration is comparatively limited. Existing models assume decreasing growth rates over time and are not designed to capture spikes in use behaviour such as the one observed during the pandemic. In this mixed methods study, we propose a hybrid model of sensemaking and post-adoptive communication and collaboration use that explains changes in use behaviour and outlines the influence of external trigger events. Based on real-world data from MS Teams, we show that individual feature use varies over time, with an increasing growth rate triggered by COVID-19. To understand drivers for the heterogeneous changes, we further conduct qualitative interviews. We find habits were deliberately altered during COVID-19 and replaced with new intentions through sensemaking. We derive propositions that may encourage further research into the subject. Extended knowledge of post-adoptive behaviour and its triggers assists practitioners in adjusting to the new normal or reacting to new situations beyond COVID-19.
Design and Realization of Circular Polarized SIW Slot Array Antenna for Cubesat Intersatellite Links

This paper presents the design and fabrication of an antenna based on Substrate Integrated Waveguide (SIW) for intersatellite crosslinks in C-band. The entire antenna consists of two elements of SIW slots array that is fed by a hybrid 3 dB directional coupler. The entire SIW slots array antenna is circularly polarized (CP), and each element has four longitudinal slots. The element’s effective field matching is realized with a microstrip to SIW transition. The antenna design has been evaluated with its return loss, gain plot, and radiation pattern characteristics to validate the fabricated antenna. The fabricated prototype antenna radiates a left-handed circularly polarized (LHCP) electromagnetic wave with the peak gain of 5 dB and offers approximately 2% AR bandwidth around 5 GHz.
Blockchain Implications for Marketing; A Review and an Empirical Analysis

Blockchain technology was initially implemented for cryptocurrencies in 2009, but it caught the undivided attention of multiple industries such as finance, supply chain management, healthcare, and governments. This research was set out to investigate and evaluate six benefits of blockchain for marketing: fostering disintermediation, combating click fraud, reinforcing trust and transparency, enhancing privacy protection, empowering digital marketing security, and enabling creative loyalty programs. An empirical study in the form of an online survey was conducted to examine the realization of benefits in practice. The research concluded that blockchain does indeed provide promising benefits for marketing, but that depends on whether marketers use public (permissioned) blockchain or private (permissioned) blockchain, and also the ability of the blockchain community to resolve fundamental challenges and pending issues such as scalability, speed, interoperability, and privacy, besides several many others.
Complexity in the Universe

Nontrivial, "complex" or " organized" states of a physical system may be characterized as those implausible save as the result of a long causal history or evolution. This notion, formalized by the tools of the theory of universal digital computers, is compared to other notions of complexity, and an attempt is made to sketch open problems in the computation theory and statistical physics whose resolution would lead to a better fundamental understanding of "self-organization" in the universe. The manifest complexity of many parts of the universe, especially living organisms and their byproducts, was formerly thought to be an expression of divine creativity, but is now widely believed to result from a general capacity of matter, implicit in known physical laws, to "self-organize" under certain conditions. As a rough illustration of the essential ideas of self-organization, consider a sealed aquarium illuminated by a light source and containing a dead mixture of prebiotic chemicals. After a long time this system will fluctuate or otherwise find its way into a "live" macrostate containing, for example, fish and green plants. This live state will be somewhat stabilized relative to dead states by the light, which enables the organisms to grow, reproduce and metabolically defend themselves against thermal and other degradation. In an aquarium of ordinary size, dead states would still be overwhelmingly more probable even in the presence of the light, because spontaneous biogenesis would probably be far less likely than a "gambler's ruin" ecological fluctuation in which, say, the fish died of starvation after eating all the plants. But the larger the aquarium, the less likely will be a simultaneous extinction everywhere within it, and the more likely a spontaneous biogenesis somewhere within it. Finally, if the aquarium were the size of the earth, it might spend most of its time alive, as suggested by paleontological evidence of one dead-to-live and no live-to-dead transitions so far. To elevate this kind of thinking from a truism that everyone agrees with but no one really understands, to a level of provable or refutable conjectures in statistical
Building a Neural Historical Art Classifier with GLCM Texture Features and Attention Feature Extraction

Evaluating historical art is expensive and time-consuming since it requires both contextualized objective information and subjective opinion from professionally-trained appraisers. We implement a novel fine-tuned deep learning model to predict art style from digital painting images. We address class imbalances and over-fitting in artwork data through multiple data augmentation strategies, account for intra-class variation using custom texture-based features, and leverage deep discriminative representation learning with attention mapping (DDRL-AM) to better learn inter-class dissimilarities. Our models use a fine-tuned ResNet50 with geometric image transformations, texture features computed with gray-level co-occurrence matrices (GLCMs), and DDRL-AM to improve accuracy by 6.6% compared to the best previously published benchmarks on this task. We also learn that attention mapping with Grad-CAM does not add significant predictive power in determining art style.
Dual Representation for Human-in-the-loop Robot Learning

What does it take to make reinforcement leaning (RL) work for real robots? One promising approach is to incorporate human guidance, in the forms of evaluation or preference, to speed up learning. In human-in-the-loop RL, understanding and representing the mental models of human trainers is a key challenge. In this work, we propose a dual representation framework for human-in-the-loop robot learning. The robot learner keeps two representations, one for learning the low-level control policy, and the other as a mental model of human trainers. We address the challenge of how to make these systems work together, while maintaining the unique advantage associated with each system. We show that in continuous control tasks, our proposed learning algorithms based on the dual representation hypothesis can lead to significant improvements in task performance and sample efficiency
Fatigue among Long-Term Breast Cancer Survivors

Simple Summary: The number of long-term breast cancer survivors is increasing. Earlier research showed that many breast cancer survivors suffer from fatigue during and shortly after treatment. Fatigue is distressing and can severely impact quality of life. In this research, we assessed whether the prevalence of fatigue is also elevated long after breast cancer treatment. We showed that even ten years after diagnosis, one in four breast cancer survivors experience fatigue. This is more than women of the same age without a history of cancer. In addition, we found that fatigue among long-term breast cancer survivors was associated with symptoms of depression and anxiety. Abstract: Background: Fatigue is the most common and persistent symptom among women in the ﬁrst ﬁve years after a breast cancer diagnosis. However, long-term prevalence of fatigue, among breast cancer survivors, needs further investigation. Aim: To compare fatigue experienced by long-term breast cancer survivors with that in a reference population and to evaluate the determinants of that fatigue. Design and Setting: A cross-sectional cohort study of 350 breast cancer survivors ≥ 5 years after diagnosis and a reference population of 350 women matched by age and general practitioner. Method: Fatigue was measured using the Multidimensional Fatigue Inventory (MFI-20), and a sum score of >60 (multidimensional fatigue) was the primary outcome. Logistic regression was applied to compare the prevalence of multidimensional fatigue between the survivor and reference populations, adjusted for body mass index (BMI) and for cardiovascular and psychological variables. Odds ratios (ORs) and 95% conﬁdence intervals (95%CIs) were estimated. Logistic regression was applied to evaluate the determinants of multidimensional fatigue among the survivors. Results: Breast cancer survivors (median 10 years after diagnosis), more often experienced multidimensional fatigue than the reference population (26.6% versus 15.4%; OR, 2.0 [95%CI, 1.4–2.9]), even after adjusting for confounders. The odds of multidimensional fatigue were also higher among survivors with symptoms of depression (32.2% versus 2.7%; OR, 17.0 [95%CI, 7.1–40.5]) or anxiety (41.9% versus 10.1%; OR, 6.4 [95%CI, 3.6–11.4]). Conclusion: One in four breast cancer survivors experience multidimensional fatigue and fatigue occurs more frequently than in women of the same age and general practitioner. This fatigue appears to be associated with symptoms of depression and anxiety.
Monocular depth estimation from single image

In this project, we tackle the problem of depth estimation from a single image. We reviewed several different techniques for depth estimation from monocular images. The first approach builds on top of a fully convolutional network upsampling part of the network [8]. The second approach uses encoder-decoder architecture using transfer learning, the encoder uses RGB images encoded into a feature vector using ResNet 34 [ [1]]. Both the techniques have been proven to be better than fully connected convolutional neural networks which we did not pursue in this project.
Financing the transition to a green economy: their word is their (green) bond?

Responding to climate change involves the implementation of initiatives that require significant upfront capital investment. At a time when bank lending is squeezed, green bonds offer an alternative financing for initiatives with an environmental goal. Lately, the Ile-de-France Region’s issuance of environmentally and socially responsible bonds on March 20 th 2012 demonstrates that an increasing number of players are taking interest in this tool. But green bonds are not, however, the panacea to access to finance issues that mainly depend on the bond issuer’s characteristics.