Miracle-dz/UniFAR-FTUs huggingface dataset:
Can You Identify These Celebrities? A Network Analysis on Differences between Word and Face Recognition

Face recognition is located in the fusiform gyrus, which is also related to other tasks such word recognition. Although these two processes have several similarities, there are remarkable differences that include a vast range of approaches, which results from different groups of participants. This research aims to examine how the word-processing system processes faces at different moments and vice versa. Two experiments were carried out. Experiment 1 allowed us to examine the classical discrimination task, while Experiment 2 allowed us to examine very early moments of discrimination. In the first experiment, 20 Spanish University students volunteered to participate. Secondly, a sample of 60 participants from different nationalities volunteered to take part in Experiment 2. Furthermore, the role of sex and place of origin were considered in Experiment 1. No differences between men and women were found in Experiment 1, nor between conditions. However, Experiment 2 depicted shorter latencies for faces than word names, as well as a higher masked repetition priming effect for word identities and word names preceded by faces. Emerging methodologies in the field might help us to better understand the relationship among these two processes. For this reason, a network analysis approach was carried out, depicting sub-communities of nodes related to face or word name recognition, which were replicated across different groups of participants. Bootstrap inferences are proposed to account for variability in estimating the probabilities in the current samples. This supports that both processes are related to early moments of recognition, and rather than being independent, they might be bilaterally distributed with some expert specializations or preferences.
Arrhythmia Classification using 2D Convolutional Neural Network

Arrhythmia is an abnormal situation of heartbeat rate that may cause a critical condition to our body and this condition gets more dangerous as our cardiovascular system gets more vulnerable as we grow older. To diagnose this abnormality, the arrhythmia expert or cardiologist uses an electrocardiogram (ECG) by analyzing the pattern. ECG is a heartbeat signal that is produced by a tool called an electrocardiograph sensor that records the electrical impulses produced by the heart. Convolutional Neural Networks (CNN) is often used by researchers to classify ECG signals to Arryhtmia classes. The state-of-the-art research had applied CNN 2D (CNN 2D) with accuracy up to 99% with 128x128 image size obtained by transforming the ECG signal. In this paper, authors try to classify arrhythmia disorder with a different approach by creating simpler image classifier using CNN 2D with a smaller variety of input size that is smaller than state-the-art input and group the classes based on transformed ECG signal from MIT-BIH Arrhythmia database with the purpose to know what the most optimum input and the best accuracy to classify ECG signal image. The result of this research had produced an accuracy of up to 98.91% for 2 Classes, 98.10% for 7 Classes dan 98.45% for 8 Classes.
The Impact of Plastic Money Use on VAT Compliance: Evidence from EU Countries

This paper examines the impact of credit and debit card usage on VAT compliance using annual national level data for 26 European Union countries from 2000 to 2016. Exploiting spatio temporal variation in plastic money use along with an instrumental variables approach, we find that a 1% increase in card payments reduces VAT gaps 0.51 percentage points whereas a 1% increase in cash withdrawals increases VAT gaps by 0.6 percentage points. Our contribution lies in using more adequate measures of VAT compliance gap and in accounting for potential confounders such as the ex-ante enforcement capacity of tax administrations.
A Method to Detect and Avoid Hardware Trojan for Network-on-Chip Architecture based on Error Correction Code and Junction Router (ECCJR)

Modern technologies has changed our life, such as everywhere computing communication and internet. Number of transistors increasing in a system day by day and this trend will continue further. The wire connection is easily breakable and not a reliable technology in field of networks. In conventional network dedicated wired path is used among the intellectual property (IP) core for the purpose of communication and due to this wired connection network is not reliable and not scalable. Network-on-Chip Architecture was introduced to solve these problems and gave notable improvements over conventional bus and crossbar communication architectures. Many companies prefer third party vendors for the development of their design in order to reduce the cost. It gives advantage but due to the access of design anyone can do changes at any stage of development cycle. This type of malicious modification of hardware during design or fabrication process is known as Hardware Trojan (HT). It can change the functional behavior of a system or may leak the secret information of critical application which results in degradation of system performance. The proposed research is based on combination of Error Correcting Code and Junction router to detect and avoid HT which can be used for reliable communication in NoC architecture. Simulation results showed good performance of proposed algorithm in term of Packet Latency and Reliability.
Hyaluronic Acid Hydrogel Modified with Nogo-66 Receptor Antibody and Poly(L-Lysine) Enhancement of Adherence and Survival of Primary Hippocampal Neurons

Hyaluronic acid (HA) hydrogel was modified with poly(L-lysine) (PLL) and Nogo-66 Receptor antibody (antiNgR) to enhance the repair of central nervous system (CNS) injuries. The immobilization of PLL was characterized by X-ray photoelectron spectroscopy (XPS) and the immobilization of antiNgR was studied by immunofluorescence. The cytocompatibility of this modified hydrogel was analyzed by culturing primary hippocampal neurons. The quantity and morphology of the neurons were influenced by different modifications; the primary hippocampal neurons cultured with modified HA hydrogel exhibited multipolar and bipolar morphology were compared with unmodified hydrogel cultures. The number of neurons obtained by culturing with HA hydrogel modified with both PLL and antiNgR was almost twice the number of neurons cultured with HA modified with only PLL or antiNgR. This phenomenon was attributed to the collaborative effect of PLL and antiNgR on the neurons. The characteristics of this new hydrogel system, including pore structure, water absorption, hydrolysis degradation did not change much when compared with the hydrogel modified with PLL or antiNgR, respectively. It is expected that this modified HA hydrogel has potential as a CNS tissue engineering material.
Changes in Disulfide and Sulfhydryl Contents and Electrophoretic Patterns of Extruded Wheat Flour Proteins

ABSTRACT Twin-screw extrusion of wheat flour and the effects on the flour proteins were studied using flour samples containing 9, 20, and 30% protein. Vital gluten containing 70% protein was used to achieve the flour protein levels. The three flour samples were extruded with a twin-screw extruder at a combination of processing parameters (exit die temperatures of 120, 140, and 160°C, and screw speeds of 240, 320, and 400 rpm). Increasing extruder exit die temperatures resulted in increased sulfhydryl content of the 9 and 20% protein content flour samples, but appeared to have little or no effect on the 30% protein content flour sample. Similarly, disulfide content decreased, albeit disproportionately, following the same trend. Both sulfhydryl and disulfide contents of extruded samples were lower than those of the nonextruded samples and could imply denaturation of protein, aggregation through intermolecular disulfide bonds, or oxidation during extrusion processing. Total cysteine content of extruded sampl.
Two-Stage Human Activity Recognition Using 2D-ConvNet

is built depending on these features. Here some of the most frequently used feature detectors for human activity recognition have been discussed, which include Histogram of optical flow (HOF), Spatial-Temporal Interest Points (STIP), Histogram of Oriented Gradients (HOG), and dense trajectories [43] etc. However, the extraction of these features is a really difficult and time consuming process as well as it is challenging to know which kind of feature is relevant to the problem because feature selection varies from problem to problem in real time. Therefore a deep learning based model has been proposed and discussed in below section to attend the demand for handcrafted features and reduce the complexity of this process. At a recent time, deep learning has arisen as a group of deep architecture based learning models that render high-level abstraction of data. A deep learning model is a systematic presentation of multiple
The One‐Migrant‐per‐Generation Rule in Conservation and Management

In the face of continuing habitat fragmentation and isolation, the optimal level of connectivity be- tween populations has become a central issue in conservation biology. A common rule of thumb holds that one migrant per generation into a subpopulation is sufficient to minimize the loss of polymorphism and het- erozygosity within subpopulations while allowing for divergence in allele frequencies among subpopulations. The one-migrant-per-generation rule is based on numerous simplifying assumptions that may not hold in nat- ural populations. We examine the conceptual and theoretical basis of the rule and consider both genetic and nongenetic factors that influence the desired level qf connectivity among subpopulations. We conclude that one migrant per generation is a desirable minimum, but it may be inadequaite for many natural popula- tions. We suggest that a minimum of I and a maximum of 10( migrants per generation would be an appro- priate general rule of thumb for genetic purposes, bearing in mind that fctctors other than genetics maly fur- ther influence the ideal level of connectivity.
Seasonal population changes of myxomycetes and associated organisms in four woodland soils

The number of myxomycete plasmodium-forming units (PFUs) and associated microorganisms in soils from four woodland sites in the West of England were estimated at approximately monthly intervals over the course of a year, and other characteristics of the soils were determined. The numbers of PFUs were low except in an unusual high-phosphate woodland soil. This soil contained the fewest dictyostelid slime moulds. Conversely, the woodland soil with most dictyostelids contained the fewest PFUs. Naked soil amoebae were the most abundant phagotrophs. Myxomycetes were a comparatively small component of the phagotrophic community of the three typical woodland soils. Changes in the populations of naked amoebae, ciliates, myxomycetes and dictyostelids were sometimes synchronized with changes in the bacterial populations, which showed 9–35-fold seasonal variations in abundance at the four sites.
Congruity between mood and brand involvement enhances the effectiveness of message appeals: Dual processing model perspective

ABSTRACT This research examined how mood, brand involvement, and message appeals interacted with each other; and how this interaction influenced consumers’ evaluation of advertisements. Results showed that people with higher brand involvement processed information systematically, and the presence of a negative mood encouraged them to process information systematically to a greater extent. Hence, people with higher brand involvement in a negative mood evaluated functional ads more favorably. In contrast, people with lower brand involvement processed information heuristically, and the presence of a positive mood enhanced heuristic processing. Therefore, people with lower brand involvement in a positive mood evaluated experiential ads more positively. Theoretical and practical implications are also discussed.
A cultural criminology of “new” jihad: Insights from propaganda magazines

The backgrounds and modus operandi of more recent jihadi terrorists tend to share factors and characteristics more typically associated with non-political violence such as mass-killings and gang violence. Their attacks, moreover, seem to have been precipitated not by the direct instructions of a formal hierarchy but by the encouragement of propaganda produced and disseminated by networked, media-savvy terrorist groups. It is necessary to explain how these “recruitment” efforts work. Cultural criminology, with its understanding of the relationship between mediated meaning and individual experience, can provide such an analysis. The article presents a qualitative document analysis of 32 propaganda magazines produced by the Islamic State and Al-Qaeda. It demonstrates that they contain significantly more than religious rhetoric and military strategy. Rather, they are part of a process that crystalizes a jihadi subculture that appeals to disaffected and/or marginalized, excitement-seeking youths. The magazines cultivate violence by constructing a militarized style that celebrates outlaw status, where violence is eroticized and aestheticized. They idealize the notion of a jihadi terrorist that is tough and willing to commit brutal violence. The lifestyle portrayed offers the possibility of heroism, excitement, belonging and imminent fame, themes often espoused by conventional, Western consumer culture. The magazines occasionally draw on street jargon, urban music, fashion, films, and video games. The subcultural model of jihadi propaganda we explicate provides a novel way of understanding terrorist recruiting tactics and motivations that are not necessarily in opposition to contemporary conventional criminal and “mainstream” cultures, but in resonance with them.
Estrutura genética espacial em populações naturais de Dimorphandra mollis (Fabaceae) na região norte de Minas Gerais, Brasil

O conhecimento e o entendimento da estruturacao genetica intrapopulacional sao importantes para o manejo e conservacao dos recursos geneticos florestais, bem como para avaliar os impactos da exploracao e fragmentacao e estabelecer estrategias de amostragem em populacoes naturais. O objetivo deste estudo foi determinar a estrutura genetica espacial (EGE) dentro das populacoes de Dimorphandra mollis Benth. , visando gerar informacoes para a conservacao genetica in situ das populacoes naturais da especie. Dez locos aloenzimaticos foram utilizados para estimar as frequencias de 20 alelos referentes a 180 individuos, distribuidos em tres populacoes naturais (Campina Verde, Pau de Fruta e Vargem da Cruz) no norte de Minas Gerais, Brasil. A diversidade genetica media (Ĥe) para a especie foi de 0,463, considerada alta e todos os locos foram polimorficos, com media de 2,0 alelos por loco. Os genotipos de D. mollis nas populacoes Vargem da Cruz e Pau de Fruta encontram-se distribuidos espacialmente de maneira aleatoria (blog = -0,007, P = 0,171; blog = -0,004, P = 0,772, respectivamente). Por outro lado, os individuos da populacao Campina Verde apresentaram EGE positiva na menor classe de distância (F(300,m) = 0,05, P < 0,001), indicando agrupamentos de individuos aparentados. A EGE significativa nessa populacao foi confirmada pela estatistica Sp, com valor de 0,047 (P < 0,001). Fatores como a dispersao de polen e sementes restritas e o historico de perturbacao antropica dessa populacao podem ter contribuido para esse padrao de estrutura familiar dos genotipos. Os niveis de estruturacao genetica detectados nas populacoes estudadas devem ser considerados para estrategias mais eficientes de amostragem visando a conservacao genetica in situ.
Building truths in AI: Making predictive algorithms doable in healthcare

ABSTRACT Increasingly, artificial intelligent (AI) algorithms are being applied to automatically assist or automate decisions. Such statistical models have been criticized in the existing literature especially for producing cultural biases and for challenging our notions of knowledge. However, few studies have contributed to an essential understanding of the way in which algorithms are designed with particular truths to enable systematic decision-making. Drawing on an ethnographic study in a Scandinavian AI company, this article analyzes how truth is built through layered interpretative practices in applied AI for healthcare, and critically assesses how such practices shed light on the pragmatic notion of truth(s) in AI. The study identifies five practices that all show difficulty in modeling fuzzy patient conditions into one firm truth. The key contribution is that truth goes from being a process of discovering a more ‘right’ truth to become a process of reinventing the existing truth and healthcare practice. These findings suggest that truth in applied AI is a key devise for making predictive algorithms a viable business, and that developers are in a favorable position to make not only AI doable but also the very truth they intend to find and model. The study in this way shows how change is an inherent part of making AI systems, and that centralizing truth practices is a fruitful way of analyzing such changes and developers’ agency. We argue for analytical awareness of how AI truth practices may prompt a world that is fit to algorithms rather than a world to which algorithms are fit.
The Multidimensional Prognostic Index (MPI) for the prognostic stratification of hospitalized older patients with COVID-19: a prospective multicenter observational cohort study. Objectives, study design and expected outcomes (MPI_COVID-19)

The emergent coronavirus-19 disease (COVID-19) pandemic posed and still poses serious issues in the management of the inpatients and in the resource allocation, in particular for those patients requiring Intensive Care Unit (ICU) management. Epidemiological data clearly suggest that multimorbid older patients have the poorest prognosis. However, it is conceivable that age and number of comorbidities alone do not reflect the real condition and the expected prognosis of the patients affected by COVID-19. A different approach based on comprehensive geriatric assessment (CGA) could help to better identify older patients more at risk of dismal outcomes and who, at some point of their clinical course, will need the ICU admission. The Multidimensional Prognostic Index (MPI) is a well-accepted tool derived from a standard CGA which allows to measure prognosis of older patients in different clinical settings including hospital. Therefore, we designed a multicenter, prospective, observational study to evaluate the role of MPI in predicting risk of ICU admission and in-hospital mortality among 500 COVID-19-positive older subjects admitted to geriatric and internal medicine wards. In addition, risk of re-hospitalization, institutionalization and death after 3 months from discharge will be assessed. The MPI yields a straightforward value from 0 to 1 and might be able to adequately stratify complex, vulnerable COVID-19 patients for best possible decision-making and treatment allocation.
Multi-Technical Flexibility Retrofit Planning of Thermal Power Units Considering High Penetration Variable Renewable Energy: The Case of China

High penetration variable renewable energy introduces flexibility issues to the power system. For countries with coal as their main energy source, retrofitting existing thermal power units is one of the most realistic and feasible measures to improve power system flexibility. Multiple retrofit options will almost certainly be available for each individual power plant—all with distinct investment costs and performance implications. Therefore, this paper develops a multi-technical flexibility retrofit planning model to inform investment decisions of thermal power units in the short term. The model is formulated as a mix linear programming, with the goal of minimizing the systems overall investment and operational costs. In particular, a linear formulation is proposed to solve the coupling problem of retrofitting and operating, and take account of the changes in various units’ operational parameters after retrofit. The correctness and effectiveness of the proposed models are verified by a case study through a modified IEEE-30 bus system. The results demonstrate that it is necessary to consider the complementariness of multiple technologies between units. Besides, the proposed model could minimize the overall system investment and operational costs, and provide advice to planners and power generation companies.
The Failure of Gender Equality Initiatives in Academia: Exploring Defensive Institutional Work in Flemish Universities

Although a large number of studies have explored the main causes of gender inequality in academia, less attention has been given to the processes underlying the failure of gender equality initiatives to enhance gender representation, especially at the professorial level. We offer a critical discourse analysis of recently promulgated gender policy documents of the five Flemish universities, and demonstrate that defensive institutional work is a fundamental process underlying resistance to gender equality in the academic profession. That is, powerful organizational actors resist gender change by (un)intentionally deploying a combination of discursive strategies that legitimate what we describe as non–time-bound gender equality initiatives: The expected outcomes are undetermined in time, and they delegitimate concrete, time-bound measures that define specific outcomes against well-defined deadlines. By explicitly bringing a temporal dimension into our analysis, we argue that defensive institutional work deflects questions regarding what ought to be achieved when, and contributes to the slow pace of gender change in academia.
Immunocytochemistry on scrape cellblock: An aid in the diagnosis of metastatic neoplasm with unknown primary: A series of four cases

Scrape cellblock (SCB) is a novel technique to suggest possible primary site in fine-needle aspiration cytology (FNAC) smears from the liver, lung, and lymph nodes which are the common sites of metastasis of many primary tumors. Immunocytochemistry (ICC) on SCB averts the need of more invasive diagnostic procedures and gives a conclusive diagnosis. We present a series of four cases with unknown primary site, in which ICC was done on SCB to suggest possible primary site. Three of them were liver space-occupying lesions (SOL) and one from the periportal lymph node. In all four cases, wet-fixed smear for hematoxylin and eosin stain was prepared as routine procedure. FNAC was reported as metastatic adenocarcinoma in two and metastatic spindle cell neoplasm in one liver SOL. Periportal node was reported metastatic adenocarcinoma. Two hematoxylin and eosin-stained slides from each case with higher cellularity were used to scrape off the material to prepare SCB. ICC was put which gave conclusive diagnosis in all the cases. On ICC, two cases of metastatic carcinoma in the liver were diagnosed as metastatic neuroendocrine neoplasm from Gastrointestinal Tract and metastatic adenocarcinoma from the stomach. Spindle cell neoplasm of the liver was diagnosed as gastrointestinal stromal tumor from the stomach. Pancreatic head mass in metastatic periportal node was confirmed later by radiologic examination. SCB is a useful technique to make the best use of available material where reaspiration is difficult. ICC on SCB is of maximum utility to suggest possible primary sites in metastatic cases with unknown primary or where biopsy of the lesion is not possible.