Miracle-dz/UniFAR-FTUs huggingface dataset:
On BL-GARCH(1,2) Models

In this study, we focus on the class of BL-GARCH models, which is initially introduced by Storti & Vitale [11] in order to handle a leverage e¤ects and volatility clustering. First we illustrate some properties of BL-GARCH(1,2) model, like the positivity, stationarity and marginal distribution, then well study the statistical inference, applying the composite likelihood on panel of BL-GARCH(1,2) model. Keywords: Random coe¢ cient Autoregressive model; BLGARCH models; composite likelihood. 1 Introduction Classical modelling of time series is not usually appropriate for nancial data, such as ARMA models do not allow the variability in volatility over time, are not able to capture asymmetries in the conditional variance of nancial time series, and fail to generate the squared autocorrelations. In front of these monetry and nancial problems, Engle [1982] proposed a new class of Autoregressive conditionally heteroscedastic models (ARCH), followed by generalized ARCH or GARCH suggested by Bollerslev [1986]. Storti and Vitale [2003] proposed an innovative approach to modelling laverage e¤ects in nancial time series based on the Bilinear GARCH noted by BL GARCH models which are considered as a generalization of GARCH models. In this present paper we study the BL GARCH models, speci cally BL GARCH (1; 2) that is widely used and proved its prformance for the volatility analysis of nancial time series. We focus on studies of Storti & Vitale [2003] and Diongue &Guégan and Wol¤ [2009], which they have well discussed and treated this class of models.
When Too Good is Too Much: Social Incentives and Job Selection

We analyze the effects of substitutability of social incentives on the labor supply of gigworkers (N=944) in a natural field experiment. In our treatments, we vary the proportion of the worker's wage that is donated to a social cause. Our experimental design allows us to observe the decision to accept a job (extensive margin) and different dimensions of productivity (intensive margin). The results show that when the worker has to donate small or moderate parts to a prosocial organization, labor supply on the extensive margin remains unaffected, but productivity on the intensive margin increases; when workers have to give larger portions of their wages, labor supply and productivity decrease. When workers have to donate parts of their wages to an antisocial cause, labor supply on the extensive and intensive margin is negatively affected. We discuss the implications of these results for the understanding of social incentives and corporate social responsibility on labor supply.
Response of Asplenium nidus to Drought Stress and Identification of Drought Tolerance Genes

To explore how Asplenium nidus responds to drought stress, and to mine drought-responsive genes for Asplenium nidus, we conducted a pot experiment in the IOT smart greenhouse. We measured a series of physiological and biochemical indexes for drought-treated plants, and analyzed the expression of drought-responsive genes in Asplenium nidus by RT-qPCR. The results show that the Asplenium nidus is a species with drought tolerance. Asplenium nidus inhibits plant photosynthesis and reduces life activities by limiting stomatal opening to adapt to drought. To resist drought stress, the Asplenium nidus changes the osmotic potential by increasing the proline content to maintain normal metabolic activities and prevents the damage of active oxygen by increasing the enzyme activities of SOD and POD. Based on the analysis of the relative expression levels of genes, AVP1-2 and AVP1-4 may be drought-resistant genes in Asplenium nidus. This study lays the foundation for in-depth research on drought tolerance mechanisms and drought-resistant gene mining of Asplenium nidus.
The structural and functional roles of the flavin cofactor FAD in mammalian cryptochromes

The importance of circadian rhythms in human health and disease calls for a thorough understanding of the underlying molecular machinery, including its key components, the flavin adenine dinucleotide (FAD)-containing flavoproteins cryptochrome 1 and 2. Contrary to their Drosophila counterparts, mammalian cryptochromes are direct suppressors of circadian transcription and act independently of light. Light-independence poses the question regarding the role of the cofactor FAD in mammalian cryptochromes. The weak binding of the cofactor in vitro argues against its relevance and might be a functionless evolutionary remnant. From the other side, the FAD-binding pocket constitutes the part of mammalian cryptochromes directly related to their ubiquitylation by the ubiquitin ligase Fbxl3 and is the target for protein-stabilizing small molecules. Increased supplies of FAD stabilize cryptochromes in cell culture, and the depletion of the FAD precursor riboflavin with simultaneous knock-down of riboflavin kinase affects the expression of circadian genes in mice. This review presents the classical and more recent studies in the field, which help to comprehend the role of FAD for the stability and function of mammalian cryptochromes.
Sentiment Analysis of Scientific Citations between Lexical Methods and Statistical Methods

The exponential growth of available scientific articles provides researchers with many new challenges and opportunities. Researchers usually analyses divers’ scientific papers to find those pertinent to their research. Citation sentiment analysis is an important task as it can help researchers in identifying shortcomings and detecting problems in a particular approach, determining the quality of a paper for classifying citations that it is positive, negative or neutral in the weighting scheme and recognizing problems that have not been addressed as well as possible gaps in current research approaches. In this paper we present a hybrid approach related to the sentiments analysis generally and citation sentiments analysis specifically; Lexicon-based approaches and Corpus-based approaches (Machine learning and Deep learning). Then, a brief survey study is presented about the use of each approach. Finally a comparison of the used techniques and the ones based on machine learning.
Generative Adversarial Networks for Clustering Semiconductor Wafer Maps

Semiconductor manufacturing processes are characterized by a certain amount of process deviations. Automated detection of these production issues followed by an automated root cause analysis has a potential to increase the effectiveness of semiconductor production. Manufacturing defects exhibit typical patterns in measured wafer test data. Recognizing these patterns is an essential step for finding the root cause of production issues. This paper demonstrates that combining Information Maximizing Generative Adversarial Network (InfoGAN) and Wasserstein GAN (WGAN) is suitable for extracting the most characteristic features from large real-world sensory wafer test data and in various aspects outperforms traditional unsupervised dimensionality reduction techniques. These features are then used in subsequent clustering task to group wafers into clusters according to the patterns they exhibit. The main outcome of this work is a statistical model for recognizing spatial patterns given a wafer map. We experimentally evaluate the performance of the proposed approach over a real dataset.
Measuring Situational Anxiety Related to Information Retrieval on the Web Among English-Speaking Internet Users

Searching for information on the web is regarded as a complex problem-solving activity involving a range of cognitive and affective processes. Anxiety is a key affective factor. In this article, we describe the construction and initial validation stages of the Information Retrieval on the Web Anxiety Rate (IROWAR) scale. The final structure of this inventory was validated with a sample of 183 English-speaking Internet users. Reliability analyses indicated that the factors were internally consistent (Cronbach’s alpha: 0.92). When we checked divergent validity, we found negative correlations with both self-efficacy and positive attitude towards the Internet. There were no effects of either sex or age on the total IROWAR score, but the Internet search anxiety sum score decreased with the length of use. This scale will be useful in several domains, including research on the determinants of web anxiety, individuals’ experience of web anxiety and ways of supporting them and Internet learning.
Principles of Data Management (Abridged)

Variance is a popular and often necessary component of aggregation queries. It is typically used as a secondary measure to ascertain statistical properties of the result such as its error. Yet, it is more expensive to compute than primary measures such as SUM, MEAN, and COUNT. There exist numerous techniques to compute variance. While the definition of variance implies two passes over the data, other mathematical formulations lead to a singlepass computation. Some single-pass formulations, however, can suffer from severe precision loss, especially for large datasets. In this paper, we study variance implementations in various real-world systems and find that major database systems such as PostgreSQL and most likely System X, a major commercial closed-source database, use a representation that is efficient, but suffers from floating point precision loss resulting from catastrophic cancellation. We review literature over the past five decades on variance calculation in both the statistics and database communities, and summarize recommendations on implementing variance functions in various settings, such as approximate query processing and large-scale distributed aggregation. Interestingly, we recommend using the mathematical formula for computing variance if two passes over the data are acceptable due to its precision, parallelizability, and surprisingly computation speed.
Unsteady flow in a turbocharger centrifugal compressor

Experimental investigations on a single stage centrifugal compressor with radial inlet duct showed that measured alternating strains of the rotating blades depend considerably on the circumferential position of the diffuser ring to the volute tongue. By modeling of the entire turbocharger compressor stage with volute and inducer casing bleed system included, 3D unsteady flow simulations provided comprehensive insight into the excitation mechanism. A part load operating point was investigated experimentally and numerically. For operating conditions due to resonance transient CFD was employed, since only then a meaningful prediction of the blade excitation, induced by the unsteady air flow, is expected. The CFD results show primarily the interaction between the volute tongue and the two different vaned diffuser ring
Degree, Closeness, and Betweenness: Application of group centrality measurements to explore macro-disciplinary evolution diachronically

Three group centrality measures--degree, closeness, and betweenness--are utilized in this paper to explore the role of disciplines in two journal co-citation networks by using 677 journals from 40 disciplines categorized by Web of Knowledge. The result shows that social science disciplines play a more central role in knowledge communication and interaction among disciplines than science discipline. Some science disciplines, such as Computer Science, have become more dominant in terms of the three centrality measures over time. The use of group centrality measures provides a novel way of exploring role of disciplines in science mapping and provides evidence that a more comprehensive result can be obtained when all three of these measurements are utilized
A Comparative Measurement Study of Web Tracking on Mobile and Desktop Environments

Abstract Web measurement is a powerful approach to studying various tracking practices that may compromise the privacy of millions of users. Researchers have built several measurement frameworks and performed a few studies to measure web tracking on the desktop environment. However, little is known about web tracking on the mobile environment, and no tool is readily available for performing a comparative measurement study on mobile and desktop environments. In this work, we built a framework called WTPatrol that allows us and other researchers to perform web tracking measurement on both mobile and desktop environments. Using WTPatrol, we performed the first comparative measurement study of web tracking on 23,310 websites that have both mobile version and desktop version web-pages. We conducted an in-depth comparison of the web tracking practices of those websites between mobile and desktop environments from two perspectives: web tracking based on JavaScript APIs and web tracking based on HTTP cookies. Overall, we found that mobile web tracking has its unique characteristics especially due to mobile-specific trackers, and it has become increasingly as prevalent as desktop web tracking. However, the potential impact of mobile web tracking is more severe than that of desktop web tracking because a user may use a mobile device frequently in different places and be continuously tracked. We further gave some suggestions to web users, developers, and researchers to defend against web tracking.
Dynamic Audio-Visual Client Recognition Modeling

This paper contains a report on an Visual Client Recognition System using Matlab software which identifies five clients and can be improved to identify as many clients as possible depending on the number of clients it is trained to identify which was successfully implemented. The implementation was accomplished first by visual recognition system implemented using The Principal Component Analysis, Linear Discriminant Analysis and Nearest Neighbor Classifier. A successful implementation of second part was achieved by audio recognition using MelFrequency Cepstrum Coefficient, Linear Discriminant Analysis and Nearest Neighbor Classifier the system was tested using images and sounds that have not been trained to the system to see whether it can detect an intruder which lead us to a very successful result with précised response to intruder and also explored another means implementing the visual recognition section using a Neural Network The work on visual recognition system was converted into a simulink block set which was then implemented in a Signal wave.
Identification of phosphate-solubilizing microorganisms and determination of their phosphate-solubilizing activity and growth-promoting capability

Phosphate-solubilizing microorganisms have been considered as a novel alternative approach to provide phosphate fertilizers that promote plant growth. In this study, three strains were isolated and identified as Penicillium oxalicum FJG21, Penicillium oxalicum FJQ5, and Bacillus subtilis BPM12, with a relatively high phosphate-solubilizing activity. Various phosphate sources were investigated, and Ca3(PO4)2 was identified as the effective phosphate source. Factors governing the phosphate-solubilizing activity of the strains included carbon and nitrogen sources, initial pH, and fermentation time. A high soluble phosphorus content was achieved with 529.0 μg·mL-1, 514.0 μg·mL-1, and 330.7 μg·mL-1 for Penicillium oxalicum FJG21, Penicillium oxalicum FJQ5, and Bacillus subtilis BPM12, respectively. An inverse correlation of the quantity of soluble phosphorus content and the pH value of the medium was observed. In addition, Bacillus subtilis BPM12 displayed a prominent capability of producing indole acetic acid. Penicillium oxalicum FJG21 and Penicillium oxalicum FJQ5 exhibited high cellulase activities. These phosphate-solubilizing microorganisms with good phosphate-solubilizing capability and growth-promoting ability are the promising strains for agricultural utilization.
Face Anti-Spoofing Detection Using Least Square Weight Fusion of Channel-Based Feature Classifiers

Current face biometric systems are vulnerable to spoofing attack. In order to deal with various spoofing attacks, such as photo attack and video replay attack, a stable anti-spoofing algorithm should be designed. In this paper, we proposed a novel face anti-spoofing detection algorithm using least square weight fusion of channel-based feature classifiers. To this end, we first fuse the color and texture features through information entropy, the spatial and frequency features are then filtered and fused by SVM-RFE feature selection method. In addition, the fusion features of two convolutional neural networks are constructed by autoencoder. Second, for the generated three kinds of fusion features, we adpot AdaBoost, SVM and Randomforest to accomplish the robust classification, respectively. The final goal of the proposed methodis to utilize the least square method to adjust the optimal weights of the obtained three kinds of classification results, by this means, the stable and efficient face anti-spoofing detection result can be achieved. Experimental results conducted on two of the most challenging anti-spoofing datasets, including CASIA FASD and Replay-Attack, demonstrate the effectiveness of the proposed method.
,A KNOWLEDGE-BASED SYSTEM FOR UNSUPERVISED CLASSIFICATION OF HIGH RESOLUTION MULTISPECTRAL SATELLITE IMAGES

A knowledge-based, hierarchical, unsupervised classification scheme for high resolution multispectral satellite images is proposed. This scheme, which finds its conceptual bases in the work of Nagao and Matsuyama (1980) for structural analysis of aerial photographs, introduces a new filtering algorithm which is able to preserve fine linear structures of the image. The classification products are: 1) a raster image where detected output classes are characterized by some specific information content and/or by spectral separability (by cluster analysis); 2) property tables describing each elementary region in terms of descriptive and geometric attributes; 3) geometry of output elementary regions converted to vector format. An example of the application of this classification scheme to a Landsat TM multispectral image is presented.
A MUILTISENSOR DATA FUSION APPROACH FOR SIMULTANEOUS LOCALIZATION AND MAPPING

Simultaneous localization and mapping (SLAM) has been an emerging research topic in the fields of robotics, autonomous driving, and unmanned aerial vehicles over the last 30 years. Unfortunately, SLAM research is generally inaccessible for student researchers due to expensive hardware and painful software setup. By introducing a loosely coupled, modular multi-sensor data fusion architecture, we present an autonomous driving research platform that can be adapted to computing platforms with various computational constraints and serve multiple applications and educational purposes. Our goal is to create an easily accessible SLAM module with cost-friendly hardware dependencies and minimal software setup for SLAM researchers, teachers, and learners.
DCT and Simulink Based Realtime Robust Image Watermarking

Ownership of digital content has become a serious matter, due to the exponential raise in the global repository of digital multimedia content, like images are to be considered in this paper. The validated proof as an imperceptible and robust watermark is needed to be embedded in the digital images. This paper proposes a simulation of DCT with Fuzzy Logic based HVS model for Realtime Robust Image Watermarking technique using Simulink.
Steps towards effective eGovernacne Framework in India

The benefits of virtualization are typically considered to be server consolidation, increased availability, isolation, ease of operating system deployment and simplified disaster recovery. The basic use of virtualization is the optimization of technical resources, improved service delivery with valuable reduction of the cost. Server consolidation based on virtualization technology will simplify system administration, reduce the cost of power and physical infrastructure, and improve utilization in today’s internet-service-oriented enterprise data centers. As the size and complexity of modern computing systems keep increasing to meet the demanding requirements of Performance applications, manageability is becoming an important concern to achieve both performance and productivity computing. Here I am presenting analytical framework for utility computing for e-Governance using Virtual Machines to achieve testing and R & D, new effortless deployments, basic system administration, adaptability, scalability, new models of service delivery, efficiency, enhancement, user convenience, sustainability, and leverage of shared government infrastructure. However to deliver the maximum throughput requires careful attention toward system details for the minimal loss of CPU performance and I/O efficiency.
Poster: How to Homomorphically Compute on Data Encrypted under Different Keys?

Homomorphic encryption (HE) supports computations on encrypted data without decryption. This capability can enable secure computation outsourced to a semi-honest cloud without giving up the privacy of user data. One of the demanding applications in cloud computing is secure collaborative computing in which data is contributed by multiple participants and is encrypted using different keys. Current solutions for homomorphic computation on ciphertexts encrypted under multiple keys are inefficient. More specifically, existing works either require key setup before any computation or incur large ciphertext size (at best, grow linearly to the number of involved keys). In this paper, we give an overview of our new approach that leverages the advantages of threshold and multi-key HE to support computations on ciphertexts under different keys while achieving lower complexity. In our collaborative setting, we reduce the ciphertext size from (N + 1) in the state-of-the-art approaches to 2.
Video Commercial Image Preference Study Through The Web Analytical Tool

In the web Kansei image survey, one single perspective view, instead of multiple views for detailed product information, is often used for evaluation. Moreover, on-line video commercials are rarely seen in marketing survey. To solve the insufficient product information problem, this study integrates the eye tracking system to offer more precise data in addition to the multiple perspective views in the 2-dimensioal web analytical system. For commercial image test, video files are added in the web analytical system The web technique and quantitative analytical method are expected to help enterprises in their consumer attitude and image evaluation through video commercials. Through the web analytical system, the image and target user group preference analysis can be conducted for the references of marketing. In this study, four motorbike brands, SYM, KYMCO, YAMAHA, PGO, are used for the image preference survey. Through such a web analytical system, the coordinate values for the video commercials can be obtained for product positioning analysis in real time to select a proper video commercial with better image perceptions for further development. The survey verifies the suitability and feasibility of integrating video files with multiple views in the web analytical system. This will upgrade the total function and service of the web analytical system in new product design and marketing commercial development.
Are Banking Crises Really an Equal Opportunity Menace?

This paper investigates exposure to banking crises. Based on a long-term perspective and descriptive statistics, Reinhart and Rogoff (2008, 2009) and Qian et al. (2011) concluded that banking crises are equal opportunity menaces in the sense that both advanced and developing economies face the same exposure. This paper confirms this result, relying on the hazard function of a duration model. Moreover, we extend the concept that banking crises are an equal opportunity menace in two directions. First, we show that graduation from inflation, currency, or debt crises does not reduce the exposure to banking crises. Second, we indicate that top banking centers do not have a higher exposure to banking crises.