Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Approach for Measuring Similarity between Government Documents of China

In China, the government publishes hundreds of thousands of government documents every year. The civil servants in China are struggling to find relevant government documents while doing their office works, such as writing documents, analyzing government policy, explaining the policy to the public. Furthermore, the public also finds it difficult to find the exact government documents since the most popular search engines in China such as Baidu, Sogou are not specialized in the field of government document searching and indexing. Determining the similarity between documents is critical to applications such as search and recommendation. Currently, most kinds of literatures focus on semantic similarity between words and paragraph fragments. As for government documents in China, they are written under standards so that they can be considered as semi-structured data after data cleansing. In this paper, we propose a hybrid approach for measuring the document-level similarity between government documents of China. We represent government documents as the publisher of the document, the document domain, the document type, the publishing time and other contents. By calculating the similarity between the elements, the government documents similarity is formed by the weighted sum of the similarity between the elements. Experiment results show that the proposed hybrid method outperforms the classic methods like LDA and doc2vec.
Sexual dichromatism in the neotropical genus Mannophryne (Anura: Aromobatidae)

Recent reviews on sexual dichromatism in frogs included Mannophryne trinitatis as the only example they could find of dynamic dichromatism (males turn black when calling) within the family Aromobatidae and found no example of ontogenetic dichromatism in this group. We demonstrate ontogenetic dichromatism in M. trinitatis by rearing post-metamorphic froglets to near maturity: the throats of all individuals started as grey coloured; at around seven weeks, the throat became pale yellow in some, and more strongly yellow as development proceeded; the throats of adults are grey in males and variably bright yellow in females, backed by a dark collar. We demonstrated the degree of throat colour variability by analysing a large sample of females. The red: green (R:G) ratio ranged from ~1.1 to 1.4, reflecting variation from yellow to yellow/orange, and there was also variation in the tone and width of the dark collar, and in the extent to which the yellow colouration occurred posterior to the collar. Female M. trinitatis are known to be territorial in behaviour. We show a positive relationship between throat colour (R:G ratio) and escape performance, as a proxy for quality. Our field observations on Tobago’s M. olmonae showed variability in female throat colour and confirmed that males in this species also turn black when calling. Our literature review of the 20 Mannophryne species so far named showed that all females have yellow throats with dark collars, and that male colour change to black when calling has been reported in eight species; in the remaining 12 species, descriptions of males calling are usually lacking so far. We predict that both dynamic and ontogenetic sexual dichromatism are universal in this genus and provide discussion of the ecological role of dichromatism in this genus of predominantly diurnal, non-toxic frogs, with strong paternal care of offspring.
Dynamic Update Upper Bounds Influence Maximization Algorithm

In the field of social network analysis, influence maximization is an important research area. The goal is to find K nodes that maximize the final range of influence based on a given spread model. In the study of influence maximization, the research method based on greedy algorithm mainly focuses on how to further reduce Monte-Carlo simulations. In this paper, based on the dynamic update of the upper bound of marginal benefit of node, we propose an Upper Bound Update Greedy (UBUG) algorithm and an Upper Bound Update Heuristic (UBUH) algorithm. Experimental results show that the UBUG algorithm can reduce more MC simulations than the CELF [1] and UBLF [2] algorithms without losing the accuracy of the results. And compared to other heuristic algorithms, the UBUH not only meets the requirements of high calculation speed but also has a good accuracy.
Application Research of Accelerated Deep Convolutional Neural Network in Cross-Media Retrieval

Deep learning methods are increasingly used in cross-media retrieval research because of their powerful performance. However, due to the large number of parameters and the large amount of calculation of the neural network model, the speed of cross-media retrieval is limited accordingly. In view of the above problems, this paper applies the compressed convolutional neural network VGG-16 to cross-media retrieval, and obtains a better retrieval speed. The specific method is to extract image features using channel-pruned deep convolutional neural network VGG-16 and extract text features with LDA, and then use the multinomial logistic regression to further train and predict the extracted image and text features. At the same time, the high-level semantics of the text is used to regularize the high-level semantics of the image. Finally, the Centred Correlation metric algorithm is used to calculate the similarity between the image and the text, so as to achieve the bidirectional retrieval of the image-text. Experiments show that the proposed method can effectively improve the speed of cross-media retrieval, and the retrieval speed can be increased nearly four times without using GPU acceleration.
A Light Autoencoder Networks for Face Swapping

Face swapping in images is a process where an input identity is transformed into a target identity while preserving the former's facial expression, pose and lighting. In this paper, a new face swap method using autoencoder networks is investigated, which can capture the two different image sets to be trained. The images from two sets are unstructured collection of people's photographs. This approach is enabled by framing the face swapping problem in terms of style transfer, where the goal is to render an image in the style of another one. We design a new autoencoder network that enables the processing to produce photorealistic results. Moreover, in low resolution video, the proposed method can achieve face swap by combining autoencoder network with pre- and post-processing steps.
The Design of Analgesic Clinical Trials

In this text specialists in the area of analgesic trial design present a guide to the clinical evaluation of agents and methods for acute and chronic pain control. The material in the book deals with topics such as chronic drug studies, tailoring designs to patients with specific diseases, probing neural mechanisms of pain, integrating recent advances in pharmacokinetics and statistics, and studying the effectiveness of efforts to improve clinicians' analgesic practice. For investigators studying the properties and clinical potential of analgesic compounds, the book reviews the regulatory requirements, describes the most efficient ways to screen for efficacy and toxicity, and explains how to plan a series of controlled trials, choosing from many standard clinical "pain models". For clinicians seeking improved strategies for pain relief, the book presents analgesic trials for specific types of pain such as postoperative pain, neuropathic pain syndromes, cancer pain, low back pain, osteoarthritis, sickle cell disease, sports injuries, orofacial pain, headache and dysmenorrhea. Also included are discussions of new methodologies in analgesic studies - such as multiple dose studies; comparing spinal, transdermal, and other novel routes to standard oral or systemic administration; patient-controlled analgesia paradigms; evaluation of analgesic prescribing practices; and advances in pharmacokinetics and biostatistics. Following many of the chapters are commentaries by experts in analgesic research that highlight unresolved issues and ongoing controversies.
Artificial Intelligence Techniques on Real-time Strategy Games

Real-time strategy (RTS) games can be seen as simulating real and complex dynamic environments in a limited and small world, posing important challenges for the development of artificial intelligence. Existing applications of artificial intelligence technology in RTS games are not yet able to compete with professional human players. But there are already ways to control the macro of RTS games, and they can compete with amateur human players. RTS games are an excellent platform for testing artificial intelligence technology, and more and more smarter methods are being used in the overall control of it. The purpose of this paper is to systematically review the artificial intelligence technologies used in RTS games in recent years, including the definition of RTS games, the challenges faced, the platform for research problems, and the artificial intelligence methods for problem solving. Finally, we propose the future research direction of real-time strategy games. This article provides a quick start guide for the researchers, the theoretical framework of the system and possible research directions.
Understanding Readers: Conducting Sentiment Analysis of Instagram Captions

The advent of media transition highlights the importance of user-generated content on social media. Amongst the methods of analysis of user-generated content, sentiment analysis is widely used. Nevertheless, few studies use sentiment analysis to investigate user-generated content on Instagram in the context of public libraries. Therefore, this study aims to fill this research gap by conducting sentiment analysis of two million captions on Instagram. Supervised machine learning algorithms were employed to create the classifier. Three opinion polarities and six emotions were ultimately identified via these captions. These polarities provide new insights for understanding readers, thus helping libraries to deliver better services.
Research on influencing factors of learners' intention of online learning behaviour in open education based on UTAUT model

The paper systematically compiled relevant research results in our country and abroad. Based on UTAUT reference model, we identified six factors that influence adult learners' online learning behavior intention: performance expectation, effort expectation, self-efficacy, social influence, technological facilitating conditions and resource facilitating conditions. On these basis, we proposed our research hypotheses and designed survey questionnaire, we carried out the analysis for the subjective data collected from over1000 learners through questionnaires, and the result of regression analysis, in combination with interview analysis, showed that five factors(except social influence)had positive effects on adult learners' online learning behavior intention. We also analyzed the objective data collected from over 5000 online learners from Shanghai Open University through online learning platform, and the results showed that there were significant differences in online learning behavior for adult learners of different ages and different genders.
Repeated Sprint Ability and Muscular Responses According to the Age Category in Elite Youth Soccer Players

The aim of this study was to analyse the influence of age category on the performance and muscle response after a Repeated Sprint Ability (RSA) test in elite youth soccer players. 62 soccer players from three different age categories (Under 14 [n = 21], Under 16 [n = 20], and Under 18 [n = 21]) were selected to participate in this study. Players completed an RSA test (7 × 30 m) with a 20-s recovery between sprints. The muscular response to an electrical stimulus before and after the test of both the biceps femoris (BF) and the rectus femoris (RF) were evaluated using tensiomyography. A two-way ANOVA was used to analyse the differences in RSA parameters in each of the four distance-intervals (0–5; 5–25; 25–30; 0–30 m) between sprint and age category. The U14 age category (5.30 ± 0.30 s) showed higher mean sprint times than U16 (4.62 ± 0.20 s) and U18 (4.46 ± 0.17 s) throughout the entire test (p < 0.01). U16 players revealed a worse best sprints time (RSABEST) than U18 players (+0.12 s, CI95%: to 0.01 to 0.24; ES: 1.09, p = 0.03). The muscular contractile properties were similar in the three age categories analyzed (p > 0.05), although the delay time (Td) of the muscle was significantly lower after the RSA test in U16 players (−1.53 ms, CI95%: −2.607 to −0.452; ES: 0.38) and U18 players (−1.11 ms, CI95%: −2.10 to −0.12; ES: 0.22). In conclusion, this study revealed an increase in physical performance and muscle response variability after a repeated sprint ability test in the U16's and over. The fatigue induced by the RSA test did not show differences depending on the age of the players, although muscle mechanical properties were altered after the RSA test in U16 and U18 soccer players. Physical performance and muscle response can be complementary variables in managing fatigue according to the age category in soccer players.
Combining depth-estimation-based multi-spectral photometric stereo and SLAM for real-time dense 3D reconstruction

Obtaining dense 3D reconstruction with low computational cost is one of the important goals in the field of Simultaneous Localization and Mapping (SLAM). In this paper we propose a dense 3D reconstruction framework from monocular multi-spectral video sequences using jointly semi-dense SLAM and depth-estimation-based Multi-spectral Photometric Stereo approaches. Starting from multi-spectral video, we use SALM to reconstruct a semi-dense 3D shape that will be densified. Then the depth maps estimated via conditional Generative Adversarial Nets (cGAN) are fed as priors into optimization-based multi-spectral photometric stereo for dense surface normal recovery. Finally, we use camera poses for view conversion in fusion procedure where we combine the relative sparse point cloud with the dense surface normal to get a dense point cloud. Experiments show that our method can effectively obtain denser 3D reconstruction.
Hegel on Purpose

Abstract In this paper we propose a new interpretation of Hegel's views on action and responsibility, defending it against its most plausible exegetical competitors.1 Any exposition of Hegel will face both terminological and substantive challenges, and so we place, from the outset, some interpretative constraints. The paper divides into two parts. In part one, we point out that Hegel makes a number of distinctions which any sensible account of responsibility should indeed make. Our aim here is to show that Hegel at least has the materials for a sensible and nuanced account, whatever the precise details of how they hang together. Part two then turns to a hard question concerning the relation of two different aspects of our deeds to responsibility. We consider five alternate ways of relieving the tension in Hegel's text, before putting forth our own preferred solution.
An Extension of MIC for Multivariate Correlation Analysis Based on Interaction Information

Maximal Information Coefficient (MIC) is a novel bivariate correlation analysis method that attracts a lot of attention from various research fields, due to its generality and equitability. So on the basis of interaction information, we propose to extend MIC into the context of multivariate correlation analysis. From the results of simulative experiments, our propose method can detect a wide range of associations by assigning 1 to noiseless functional data sets and 0 to statistically independent data sets, verifying its generality, equitability and robustness.
MSGP: the first database of the protein components of the mammalian stress granules

Abstract In response to different stress stimuli, cells transiently form stress granules (SGs) in order to protect themselves and re-establish homeostasis. Besides these important cellular functions, SGs are now being implicated in different human diseases, such as neurodegenerative disorders and cancer. SGs are ribonucleoprotein granules, constituted by a variety of different types of proteins, RNAs, factors involved in translation and signaling molecules, being capable of regulating mRNA translation to facilitate stress response. However, until now a complete list of the SG components has not been available. Therefore, we aimer at identifying and linting in an open access database all the proteins described so far as components of SGs. The identification was made through an exhaustive search of studies listed in PubMed and double checked. Moreover, for each identified protein several details were also gathered from public databases, such as the molecular function, the cell types in which they were detected, the type of stress stimuli used to induce SG formation and the reference of the study describing the recruitment of the component to SGs. Expression levels in the context of different neurodegenerative diseases were also obtained and are also described in the database. The Mammalian Stress Granules Proteome is available at https://msgp.pt/, being a new and unique open access online database, the first to list all the protein components of the SGs identified so far. The database constitutes an important and valuable tool for researchers in this research area of growing interest.
An Efficient Malicious Code Detection System Based on Convolutional Neural Networks

With the rapid development of the Internet, people have variety in their life style. While the Internet is developing rapidly, some malware has also been generated, and the number of malicious codes grows exponentially, which seriously affects the security of the Internet. In the prevention of malicious code, the accurate classification of malicious code is the most critical components. At present, researchers mainly focus on static detection and dynamic detection to study malicious code variant detection schemes. In the study of malicious code classification, this paper first analyzes the limitations of static detection and dynamic detection, then employs convolution neural networks to classify and identify malicious code, and finally implements an efficient malicious code detection system based on convolutional neural networks. Our malware detection model realizes the idea of automatic detection of malicious code variants. Experimental results demonstrated that the proposed malicious code detection system is efficient.
Are the Processes Underlying Discrimination the Same for Women and Men? A Critical Review of Congruity Models of Gender Discrimination

Although classic congruity models of gender discrimination (e.g., role congruity theory, lack of fit) predict negative outcomes for both women and men in gender-incongruent domains, the literature has focused almost exclusively on discrimination against women. A number of recent studies have begun to address the question of whether and under what circumstances men can also be the targets of gender discrimination. However, the results of these studies have so far been mixed. Therefore, the question of whether men, like women, also suffer discrimination when in gender incongruent roles and domains remains unclear. The goal of the present paper is to integrate and critically examine the burgeoning literature on gender discrimination against men in order to assess whether the symmetrical predictions of congruity models are supported. Through this close analysis and integration of the literature, I aim to identify remaining gaps in the research on gender discrimination. In particular, I propose that researchers of gender discrimination would benefit from expanding their scope beyond that of paid work.
On designing a cybersecurity educational program for higher education

Cybersecurity education is critical for preparing current and future IT professionals to deal with the multitude of security threats that occur worldwide on an ever-increasing scale. We believe that this issue can be addressed most effectively at the level of higher education, which provides the best balance of existing skills and available resources as needed for such highly-technical topics. In this paper we present first of all a methodology for designing a cybersecurity educational program, so that it becomes easier for all interested parties to design such programs, which helps extending the global scale of cybersecurity education. Secondly, we describe in detail how we applied our methodology to design an appropriate cybersecurity program for the case of higher education in Japan, followed by a discussion of the current state of our endeavor.
The Rpv3-3 Haplotype and Stilbenoid Induction Mediate Downy Mildew Resistance in a Grapevine Interspecific Population

The development of new resistant varieties to the oomycete Plasmopara viticola (Berk.& Curt) is a promising way to combat downy mildew (DM), one of the major diseases threatening the cultivated grapevine (Vitis vinifera L.). Taking advantage of a segregating population derived from “Merzling” (a mid-resistant hybrid) and “Teroldego” (a susceptible landrace), 136 F1 individuals were characterized by combining genetic, phenotypic, and gene expression data to elucidate the genetic basis of DM resistance and polyphenol biosynthesis upon P. viticola infection. An improved consensus linkage map was obtained by scoring 192 microsatellite markers. The progeny were screened for DM resistance and production of 42 polyphenols. QTL mapping showed that DM resistance is associated with the herein named Rpv3-3 specific haplotype and it identified 46 novel metabolic QTLs linked to 30 phenolics-related parameters. A list of the 95 most relevant candidate genes was generated by specifically exploring the stilbenoid-associated QTLs. Expression analysis of 11 genes in Rpv3-3+/− genotypes displaying disparity in DM resistance level and stilbenoid accumulation revealed significant new candidates for the genetic control of stilbenoid biosynthesis and oligomerization. These overall findings emphasized that DM resistance is likely mediated by the major Rpv3-3 haplotype and stilbenoid induction.
Assessing the function of geophagy in a Malagasy rain forest lemur

Geophagy has been observed in nearly every long‐term study of folivorous primates. Yet despite frequent observations of this behavior, conclusive explanations for soil consumption remain ambiguous. This study tests the most frequently proposed hypotheses for geophagy using data collected on the geophagic behavior of the Milne‐Edwards’ sifaka (Propithecus edwardsi) living in two forests with varying levels of disturbance within Ranomafana National Park, Madagascar. Field data were collected from December 2002 to November 2003, during which time soil samples were collected for mineral analyses from 102 sites selected for geophagy and 42 control sites at which no geophagy was recorded. Results indicate that control soils differ significantly between the two study sites and that this difference is primarily attributable to varying levels of habitat disturbance. The frequency of soil consumption also does not vary significantly by sex or between logged and unlogged habitats. Soil consumption significantly correlated with fruit/seed consumption overall, but to a lesser degree in logged compared with unlogged sites. Clay minerals found in soils likely prevent absorption of dietary toxins in the gut and/or may mediate gut pH. This provides strong evidence for the protection hypothesis for geophagy, which may be especially important in areas where logging, or other forms of habitat disturbance, has been experienced.