Miracle-dz/UniFAR-FTUs huggingface dataset:
Ditransitive complementation per clause type in academic research

Transitivity plays an important role in shaping academic writers’ identities and allowing them to voice their presence in their research community. Transitivity, as a system of choices including intensive complementation, intransitive complementation, monotransitive complementation, ditransitive complementation and complex-transitive complementation, allows writers to make different selections according to the generic features of the corresponding culture (Halliday & Mathiessen, 2014; Mathiessen, 2004). Considering the gap in the literature of genre analysis of ditransitivity (Mukherjee, 2005), the present research attempts to focus on writers’ lexico-grammatical choices of ditransitives according to clause type and to find out how these choices are determined by the generic features of the Research Article and the characteristics of Medical Science and Sociology. In order to explore academic writers’ preferences, the different types of clauses including ditransitives have been annotated via the UAM CorpusTool, one computational software. Thanks to this software, quantitative and qualitative analyses are carried out. The present research has shown that ditransitive clauses are not equally distributed in the corpus as writers more frequently opt for ditransitive subordinate clauses than ditransitive main ones. On the one hand, this is explained by academic writers’ preferences for a concise packaging of information when it comes to main clauses as the latter serve as guiding statements in a sentence. On the other hand, ditransitives in subordinate clauses allow writers to describe various types of relations and contribute to the coherence and cohesion of the text. Across disciplines, the present study has proven that the distribution of the three types of ditransitive subordinate clauses, i.e. ditransitive nominal clauses, ditransitive adjectival clauses and ditransitive adverbial clauses, is determined by disciplinary norms since Medical Science and Sociology, as belonging to different domains of knowledge, have different research methods.
BIOCHEMICAL STUDY ON THE REGENERATIVE EFFECT OF BEE VENOM ON EXPERIMENTALLY INDUCED DIABETES

In the present study, the potential therapeutic effect of bee venom administration diabetic rats was evaluated. Thirty male albino rats were divided into three equal groups of 10 rats each. Group Ι:(Control group): received no drugs. Group II:(Diabetic rats group): rats received a single dose of Streptozotocin (STZ) (50mg/kg-b.wt i.p). Group III:(Diabetic rats + bee venom treated group): rats are treated with + bee venom 0.5 mg /kg body weight /day, i.p) for 21 days after diabetes induction. Blood samples and pancreatic tissue were collected at the 22th day from the onset of bee venom administration. The obtained results showed that, STZ-induced diabetic rats exhibited a significant increase in serum glucose, triglycerides, total cholesterol, LDL-cholesterol, malondialdehyde (MDA), advanced glycation end products (AGEs) and glycated hemoglobin (HbA1c) with marked decrease in HDL-cholesterol, insulin levels, Catalase (CAT), glutathione peroxidase (GSH-px), superoxide dismutase (SOD). Treatment with bee venom was able to mitigate diabetic abnormalities through decreasing serum glucose, triacylglycerols, total cholesterol, LDLcholesterol, malondialdehyde (MDA), advanced glycation end products (AGEs) and glycated hemoglobin (HbA1c) and increasing HDL-cholesterol, insulin levels, Catalase (CAT), glutathione peroxidease (GSH-px) and superoxide dismutase (SOD), These results suggest that bee venom are effective in increasing insulin sensitivity and secretion in diabetic rats and improving other biochemical blood parameters.
Adversarial Signboard against Object Detector

Object detector is an indispensable component in many computer vision and artiﬁcial intelligence systems, such as autonomous robot and image analyzer for proﬁling social media users. Analyzing its vulnerabilities is essential for detecting and preventing attacks and minimizing potential loss. Researchers have proposed a number of adversarial examples to evaluate the robustness of object detectors. All these adversarial examples change pixels inside target objects to carry out attacks but only some of them are suitable for physical attacks. According to the best knowledge of the authors, no published work successfully attacks object detector without changing pixels inside the target object. In an unpublished work, the authors designed an adversarial border which tightly surrounds target object and successfully misleads Faster R-CNN and YOLOv3 digitally and physically. Adversarial border does not change pixels inside target object but makes it look weird. In this paper, a new adversarial example named adversarial signboard, which looks like a signboard, is proposed. By putting it below a target object, it can mislead the state-of-the-art object detectors. Using stop sign as a target object, adversarial sign-board is evaluated on 48 videos with totally 5416 frames. The experimental results show that adversarial signboard derived from Faster R-CNN with ResNet-101 as a backbone network can mislead Faster R-CNN with a different backbone network, Mask R-CNN, YOLOv3 and R-FCN digitally and physically.
Foreground-background decomposition in complex auditory scenes

Tremendous insight into auditory scene analysis has been gained by using two-tone sequences in auditory streaming paradigms as a simplified model scenario. Since natural acoustic scenes usually contain more than two potential sound sources, recent efforts strive towards studying scene analysis with more complex source configurations. Our current work along these lines focuses on the question of how perceptual foreground and background are decomposed. In a subjective-reporting procedure, we show that listeners spontaneously report hearing more than one sound source in the perceptual foreground at once. The perceptual background is addressed by combining behavioral measures with electroencephalography (EEG). Our results suggest that integrated and segregated representations of the perceptual background are held in parallel. We discuss these findings in the light of current theories and outline some methodological challenges. To face one of these challenges, namely the considerable effort in training and instructions in subjective-reporting procedures, we merged a visual multistability procedure with an auditory multistable stimulus and collected eye-tracking data alongside the auditory reports. Results show a good agreement between reported auditory perception and visual foreground formation as determined via eye movements. We will discuss whether eye-tracking is an adequate en passant method for continuous measurements in auditory perception.
Successful Nonsurgical Treatment for Synchronous Acute Cholecystitis and Acute Appendicitis: A Case Report and Review of the Literatures

Acute appendicitis and acute cholecystitis are commonly seen in acute abdominal disease. However, it is rarely described that synchronous acute cholecystitis and acute appendicitis presented. Here, we present a case of 78-year-old male suffered from cholelithiasis with acute cholecystitis synchronized with acute appendicitis treated with nonsurgical management successfully.
Building quality indicators for medication use in people aging with intellectual disabilities and behaviour disorders

Objectives: The quality of healthcare provided to people with intellectual disabilities, is under scrutiny. The aim of this project was to build quality indicators (QIs) that could be used to measure the quality of the medication use process for people with intellectual disabilities and behaviour disorders. Methods: Candidate Qls were developed using literature and guideline review and expert evaluation. The candidate Qls were submitted to a 32-member panel of multidisciplinary experts, from Ireland and the UK. The modified Delphi panel included psychiatrists, pharmacists, nursing personnel, speech and language therapists, geriatricians, psychologists and representation from academia, regulation and administration. Over 2 rounds the QIs were rated for importance, scientific soundness and feasibility on a 9 point likert scale. Consensus criteria were preset. Results: A total of 38 candidate QIs underwent Delphi scoring. QIs addressed patient experience, access to care, continuity of care, equity, patient safety, effectiveness, appropriateness and assessment. Following round one, 23 QIs were accepted, none were rejected and 15 were equivocal. A total of 30 candidate QIs were accepted following round 2. Selection criteria were raised to differentiate hierarchy of final list. Conclusions: This project was the first attempt to develop QIs for the medication use process in people with intellectual disabilities and behaviour disorders. They infer a judgement about the quality of care provided in the medication use process. They may become important definitions of quality care for people with intellectual disabilities and behaviour disorders and be useful for quality improvement in this vulnerable population.
Unifying Evolutionary Algorithms and Improving Differential Evolution

In past, only a few attempts have been made in adopting a unified outlook towards different paradigms in Evolutionary Computation. The underlying motivation of these studies has been in gaining better understanding of evolutionary methods, both at the level of theory as well as application, in order to design efficient evolutionary algorithms for solving wide-range complex problems. One such attempt is made in this paper, where we reinstate ‘Unified Theory Of Evolutionary Computation’, drawn from past studies, and investigate four steps – Initialization, Selection, Generation and Replacement, which are sufficient to describe common Evolutionary Optimization Systems such as Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming, Particle Swarm Optimization and Differential Evolution. As a next step we consider Differential Evolution, a relatively new evolutionary paradigm, and discover its inability to efficiently solve unimodal problems when compared against a benchmark Genetic Algorithm. Targeted towards enhancing DE’s performance, several modifications are successfully proposed and validated through simulation results. The Unified Approach is found helpful in understanding the role and re-modeling of DE steps to efficiently solve unimodal problems.
Demand Shocks and Real GDP Measurement

This paper investigates the implications of demand shocks for real GDP measurement. Traditionally, index number theory proceeds under the assumption of fixed preferences. However, historical data on alternative quantity indexes for US real GDP suggest large deviations from the standard theoretical predictions. This paper suggests explanations for this puzzle based on the role of demand shocks in generating economic fluctuations, and investigates the implications of demand shocks for utility-based interpretations of real GDP.
Social Exclusion and Social Preferences: Evidence from Colombia’s Leper Colony

This paper explores the intergenerational consequences of social exclusion on prosociality. A lab-in-the-field approach in the historical region of Colombia’s leper colony reveals that descendants of socially excluded individuals are locally altruistic and extend such altruism to outsiders who have undergone similar circumstances. These individuals also display mistrust toward those who have, historically, been exclusionary—in this case, doctors. The content of historical narratives shared by ancestors who were excluded, which emphasize the endured mistreatment and doctors’ historical misinformation, is one mechanism that partially explains the intergenerational patterns. (JEL D64, H51, I12, I18, N36, N96, Z13)
A Video Descriptor Based on Multivariate Gaussians for Human Action Recognition

Human action recognition is a research field in Computer Vision which studies ways to describe human actions using visual video content. This area has several applications, such as surveillance systems, video indexing and human-computer interfaces. In this work we present a global motion descriptor based on multivariate Gaussian distributions estimated from three-dimensional histograms of oriented gradients extracted from videos. As the multivariate Gaussian distributions space is not linear, strategies based on Lie Algebra were used to embed the Gaussian space into a linear space aiming to use Euclidean operations. Finally, we evaluated our descriptor for the KTH, MuHAVi and SKIG datasets using a support vector machine classifier.
ASSESSMENT OF THE DYEING PROPERTIES OF MAPLE VENEER TREATED BY DICHLOROTRIAZINE REACTIVE DYE BASED ON FUZZY COMPREHENSIVE EVALUATION

Effects of dye concentration, dyeing temperature and time, volume ratio, and dyeing assistant and fixing agent, fixing time on the dye-uptake and K/S were investigated with orthogonal experiments. The major factors on dye-uptake and K/S were identified using visual and variance analysis. Dye-uptake and K/S were synthetically evaluated and optimally selected with fuzzy comprehensive evaluation. Results showed that dye concentration and temperature were significantly affected by all the seven factors during dyeing process on dye-uptake and K/S. The optimal parameters for the dye effect of maple veneer were: temperature 65.0oC, dye concentration 3.0 %, dyeing assistant 40.0 g.L-1, dyeing time 3.0 hours, fixing agent 20.0 g.L-1, fixing time 10.0 minutes, volume ratio 1:20. Additionally, attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX) results indicated that reactive dyes were combined with maple fiber and diffused into the wood lumen and wood ray. X-ray diffraction (XRD) results indicated the intensive of crystallinity in the dyed wood was declined due to the dichlorotriazine reactive dye. Furthermore, thermogravimetric analysis (TGA) demonstrated that the residue of dyed veneer was higher than undyed, and the temperature at maximum degradation rate of dyed and undyed veneer was different.
An instrumental account of deception and reaction to deceit in bargaining

In the Current paper we present an instrumental approach to deception. This approach incorporates the notion that bargainers (a) Will use deception as a means to reach their goals in bargaining but (b) will refrain from using deception when they have alternative means to reach their goals. We demonstrate that different goals can lead to differences in the use of deception (Experiment 1). Furthermore, we demonstrate that reactions to deceit can also be understood from an instrumental perspective (Experiment 2).
Assessment of Climate Change Impacts on Irrigation Water Requirement and Rice Yield : A Case Study from Ngamoeyeik Irrigation Project in Myanmar

This study examines the impacts of climate change on irrigation water requirement (IWR) and yield for rainfed rice and irrigated paddy at Ngamoeyeik Irrigation Project (NIP) in Myanmar. The future climate of the NIP area was projected using the outputs of two General Circulation Models (GCMs) namely ECHAM5 (scenario A2 and A1B) and HadCM3 (scenarios A2 and B2) for future time period of 2020s, 2050s and 2080s. The climate change scenarios were downscaled to basin level by using Statistical DownScaling Model (SDSM). Calibrated and validated AquaCrop v4.0 model was used to forecast the rainfed (MayOctober) rice yield and irrigation water requirement for irrigated paddy (November-April) under future climate. The analysis shows a decreasing trend in maximum temperature (-0.8 to +0.1°C) for the three scenarios and three time windows considered; however, an increasing trend is observed for minimum temperature (+0.2 to +0.4°C) for all cases. The analysis on precipitation also suggests that rainfall in wet season is expected to vary largely from -29% (2080s; A1B) to +21.9% (2080s; B2) relative to the average rainfall of the baseline period. A higher variation is observed for the rainfall in dry season ranging from -42% for 2080s, B2; and +96% in case of 2020s, A2 scenario. A decreasing trend of irrigation water requirement is observed for irrigated paddy in the study area under the three scenarios indicating that small irrigation schemes are suitable to meet the requirements. An increasing trend in the yield of rainfed paddy was estimated under climate change demonstrating the increased food security in the region.
Venture Capital and High Growth Firms : developing a predictive model .

The purpose of this paper is to establish what balance-sheet ratios enable us to predict which firms are more candidate to an high-growth path. Following the intuition behind credit scoring model (i.e. z-score model), we consider idea that balance sheet at prior time the high growth affects balance sheet at time of the exceptional growth. To this end, we used a quantile regression and TOBIT analysis that discriminates on the financial data of two groups of firms, for a defined threshold (High growth and Non High Growth firms), selected from a population of approximately 22,000. The results of the analysis pointing out a relevant of firm size, firm’s age, more important, of internal cash flows (in spite of bank loans) to the growth and success of a firm.
TwitterMancer: Predicting User Interactions on Twitter

This work investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions. We have crawled an extensive dataset of Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. Using a machine learning framework, we find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain “higher-dimensional” triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior.Our code and data are available at https://github.com/twittermancer/.
Influence of Basic Colour Parameters on Colour Memory

We frequently need to compare two or more colours, and we can rely only on the colour impression from our memory. Colours are not stored in our memories in their actual state and they can gradually be erased. This paper addresses the subject of short-term colour memory. The approach is based on an experiment where subjects observed a given colour for a certain period of time. The purpose of the research was to determine the relation between the reference colour, time delay and the accuracy in recalling of the colour from the subject’s memory. The colours studied in the research were presented with no association to bodies, shapes or textures. The main variables in the observing conditions were the basic colour parameters which defi ne the colour, i.e. hue, saturation and brightness. The analysis of the fi nal results showed that colour is not stored in our memories correctly and that it loses its basic parameters after 10 s. As the time delay increases, the accuracy of the colour impression in our memory diminishes. Colour is stored in our memory as clearer and more saturated. Bright colours are remembered as even brighter, while dark colours are stored as darker. The sensation of hue is generally stored very precisely, while the deviation in hue depends on the observed colour.
Misinformation in Social Media: Definition, Manipulation, and Detection

The widespread dissemination of misinformation in social media has recently received a lot of attention in academia. While the problem of misinformation in social media has been intensively studied, there are seemingly different definitions for the same problem, and inconsistent results in different studies. In this survey, we aim to consolidate the observations, and investigate how an optimal method can be selected given specific conditions and contexts. To this end, we first introduce a definition for misinformation in social media and we examine the difference between misinformation detection and classic supervised learning. Second, we describe the diffusion of misinformation and introduce how spreaders propagate misinformation in social networks. Third, we explain characteristics of individual methods of misinformation detection, and provide commentary on their advantages and pitfalls. By reflecting applicability of different methods, we hope to enable the intensive research in this area to be conveniently reused in real-world applications and open up potential directions for future studies.