Miracle-dz/UniFAR-FTUs huggingface dataset:
What Our Cities Can Offer After Natural Disasters

The importance of open green spaces in cities is increasing every day. These areas are the air supply of cities and citizens and have an important place in the urban fabric due to the roles they assume after disasters. The earthquake studies in Turkey have accelerated after the Marmara earthquake in 1999 due to which 18373 people had lost their lives. After the earthquake, the importance of open green spaces that can be used for sheltering, housing and evacuation purposes has become clear and their deficiency has attracted attention. The study aims to reveal ana analyze the functions that will be assumed by open green spaces after an earthquake in the central district of Bingol, a city in the first-degree seismic zone in Turkey, the carrying capacity and locations of the green spaces and their adequacy using different analysis methods. The study firstly examines the importance and adequacy of the open urban green spaces with respect to rapid urbanization-induced intense housing. In the second stage, we discussed the concept of earthquake and to what degree implications of a disaster are taken into consideration when planning green spaces in the urban fabric. Lastly, the central district of Bingol was selected as an exemplary case and the open green spaces that will be used after an earthquake were determined and their adequacy was analyzed.
Article A Review of Blockchain Architecture and Consensus Protocols: Use Cases, Challenges, and Solutions

Over the last decade, blockchain technology has emerged to provide solutions to thecomplexity and privacy challenges of using distributed databases. It reduces cost for customers byeliminating intermediaries and builds trust in peer-to-peer communications. Over this time, theconcept of blockchain has shifted greatly due to its potential in business growth for enterprisesand the rapidly evolving applications in a collaborative smart-city ecosystem, healthcare, andgovernance. Many platforms, with different architectures and consensus protocols, have beenintroduced. Consequently, it becomes challenging for an application developer to choose the rightplatform. Furthermore, blockchain has misaligned with the goals for an efficient green collaborativedigital ecosystem. Therefore, it becomes critical to address this gap and to build new frameworks toalign blockchain with those goals. In this paper, we discuss the evolution of blockchain architectureand consensus protocols, bringing a retrospective analysis and discussing the rationale of theevolution of the various architectures and protocols, as well as capturing the assumptions conduciveto their development and contributions to building collaborative applications. We introduce aclassification of those architectures helping developers to choose a suitable platform for applicationsand providing insights for future research directions in the field to build new frameworks.
LINEAR INDOCTRINATION FOR NUMEROUS SUBSTANTIATION STRATUMS USING IMAGE FUSION

− In this research paper where Image Processing is one of the emerging areas that are catching up, and NASA has predicted it to attain main stream adoption. The research process works on the global image processing adoption clearly shows that 51% of the firms are sceptical about Object tracking and close to 35% state that technology is still immature to track ground vehicle traffic. This research tries to address on clear object registration which is the process of alignment images and to determine both a stitched image and weighting mask functions of multiple input images for image blending. The target is then modelled by extracting spectral and spatial features for accurate multidimensional images. Here multiple objects can be tracked simultaneously with user initialized starting points. Establishing the transformation process in the input image has been adopted by Scale Invariant Feature Transforms. The Object tracking is done by Linear programming in continuous video frame for efficient search. Hence, an improved Object tracking detection has been developed by viola-Jones object detection framework by calculating Bhattacharya distance for spectral featuring.
Factors on Conspicuous Consumption of Consumers in Emerging Markets: A Social Identify Theory Perspective

Drawing on social identify theory, this conceptual study proposed that conspicuous consumption behaviors of consumers in emerging markets were a function of their interdependent self-concept, face consciousness, and cultural capital. This study further proposed that consumer innovativeness moderated the relationships between individual traits (such as interdependent self-concept, face consciousness, and cultural capital) and conspicuous consumption behaviors of consumers in emerging markets. Implications and directions for future research were also discussed.
Analysis on Semantic level Information Retrieval and Query Processing

Query processing and Information Retrieval plays important application of Natural Language Processing (NLP) and Data Mining. It aims to retrieve relevant documents for natural language queries. Nowadays large amounts of unstructured data are scattered across the web. So Information Retrieval from these large volumes of unstructured data using natural languages become more crucial and challenging task. The relevant Information Retrieval from such a large amount of unstructured data needs knowledge about the semantic information or contextual information. The semantic information retrieval from unstructured data uses the methods from Data Analytics, Natural Language Processing and Machine Learning etc. Here we propose a survey on diﬀerent models for Information Retrieval, Information Retrieval using Natural Languages and emphasis on semantic level Information Retrieval. And also perform the comparison and analysis of various models.
A New Approach for Real-time Iris Liveness Detection Based on Eye Movement’s Features

This research introduces a new approach for iris liveness detection based on eye movement’s features. Given 50 valid eye frames from real-time video stream, this research identify them as the images that were taken whether directly from humans’ eyes or not. Our method ensures the selected images for further processing are eye ones without user’s cooperation during the sampling process. In addition, it also controls the application timeout by identifying either fake iris or low quality eye images. In my study, this Research used two features to model eye movements: (1) the width/height ratio of the eye outline and (2) the distance from the pupil center to the eye centroid. In order to model the acquired data distribution, this research used a bag of two-component Gaussian Mixing Models (GMMs). The class label of the acquired data depends on the GMMs which it belongs. The average accuracy of my proposed method on my and IriTech, Inc.’s databases was 94.704% and 100%, respectively. This result indicated that my method can make real-time iris-based systems work automatically with high security.
Higher-level descriptors in molecular connectivity

The optimal modeling of the lattice enthalpy of metal halides, of the boiling points of the mixed class of alcohols plus amines and of two activities of chlorofluorocarbons, the rates of hydrogen abstraction and the minimum anesthetic concentrations, has been achieved thanks to the introduction of three new types of higher-order terms. The first type of term, the mixed molecular connectivity-pseudoconnectivity term, Z = f(X, Y), is function of the molecular connectivity term, X, and of the molecular pseudoconnectivity term, Y, only. Terms X and Y are function of a basis index β that can be either a molecular connectivity index, Χ, or a molecular pseudoconnectivity index, ψ. The Z term can be found by trying different operational combinations of X and Y terms. The second and third type of mixed higher-order terms, Z' = f (Z, β) and Z' = f (X, Y, β), can be found by the aid of a trial-and-error search procedure in which Z or X and Y terms are held constant throughout the search. Modeled properties show these three types of higher-order mixed terms at work: the two different properties of chlorofluorocarbons are modeled by the aid of a Z = f (X, Y) term, the lattice enthalpy of metal halides is instead modeled by a Z' = f(Z, β) term, and the boiling point of the mixed class alcohols plus amines is modeled by a Z'= f(X, Y, β) term.
Identity Preserving Face Completion with Landmark based Generative Adversarial Network

Given an occluded or partial face image, face completion concentrates on ﬁlling the missing area with semantical-aware pixels. With the rapid development of deep learning, the learning-based method becomes a trend in the inpainting ﬁeld. Though improve dramatically, the results still fail to capture the identity consistency with the original image. Hence, they are limited to actual scenarios. For the sake of tackling the aforementioned issue, we are dedicated to ﬁrstly taking a review of the existing relevant approaches and then introducing a novel identity preserving loss to generate visually pleasing and plausible face images on the premise of identity consistency. Our plan can not only be applied to face editing tasks, but also facilitate the accuracy of occlusion face recognition.
UCL Centre for Blockchain Technologies

With a focus on the guarantees of origin certificates market, we describe the financial aspects of originating Asset-Backed Tokens in efectively mobilizing funds to sustainable infrastructure investments. As economic disintermediation challenges energy regulation, it is of great significance to propose a blockchain-based market design whereby savings kept in decentralized e-wallets can be directly linked to energy consumption from renewable sources via IOU cryptocurrencies while producers raise capital at lower cost. These ABTs resemble to perpetual zero-coupon bonds issued below par. Yet, they are convertible to liquid money due to an embedded American put (soft) option readily settle originator's energy bills at a discount compared to the economy's functional (traditional fiat) currency.
Running head: FRAGILE FAMILIES Fragile Families Nine-Year Core and More Survey: Parental Education and Paternal Incarceration as Indicators of Child Vocabulary Proficiency

The Fragile Families and Child Wellbeing Study (2016) administered a comprehensive, longitudinal survey following over 4,000 children for a period of two years. This study focused on factors typical of poverty and family fragility. Our study explored parental levels of education and the incarceration status of the father to see if those factors have an impact on the literacy development in children, specifically their vocabulary. The following research questions were addressed: Is there a relationship between parents’ level of education and academic performance in literacy? Does children’s academic performance in literacy of differ as a function of fathers’ incarceration status? Using a correlational design, data pertaining to level of education of mother and father, incarceration status of father, and child literacy development as defined by performance on the Peabody Picture Vocabulary Test (PPVT-4) was analyzed through statistical applications. Findings include significant relationships between parent level of education and child’s vocabulary performance as well as a relationship between incarceration status of father and academic performance in literacy of the child. Implications for future research are discussed.
Integration : Challenges , Strategies and Prospects of Students ’ Integration in Educational Environment and Society

The study presents arguments on multiculturalism and integration and aims to look into the challenges, strategies, and prospects of the students’ integration in the educational environment and society. Reasonable literature exists on its different aspects and the assumed relationship of multiculturalism and academic integration into the educational environment in different countries. The existing literature was identified to review and discuss the concerning arguments on the issue. Different strategies have been reviewed to acculturate with the main emphasis on academic integration before focusing on the interplay of the processes of multiculturalism and integration and the probability for students’ integration in the academic culture. It was found that multiculturalism and integration interplay in varying ways, and there is a likelihood that students can integrate into a multicultural environment of an educational institute where culturally diverse groups of the students’ respect diversity and intercultural strategies supported by the host institute which helps them integrate.
MQTT-SN Acceleration using P4

MQTT is a major communication protocol for the IoT. In MQTT, all communication is done through a server called a "broker". Therefore, there is a concern that the broker is overloaded. In this study, we focus on MQTT-SN which is based on UDP and propose a method to offload the processing of the broker into the network using P4. P4 is a language for data plane programming, and the proposed method reduces the load on the broker by delivering publish messages that satisfy certain conditions directly from the network switch to the corresponding subscribers. We implemented the proposed method in BMv2, a reference implementation of P4, and conducted evaluation experiments. The experimental results show that the proposed method can reduce the number of packets that reach the broker. In addition, we confirmed that the proposed method reduces the delay time by more than 40% when sending a 100 Byte publish message
Migration and Emission Characteristics of Trace Elements in a 660 MW Coal-Fired Power Plant of China

Trace elements (TEs) emitted from coal-fired power plants have caused widespread concern. The onsite investigation of TEs emission from a Chinese 660 MW pulverized coal (PC) boiler equipped with selective catalytic reduction (SCR), electric submersible pump (ESP), wet flue gas desulfurization (WFGD), and wet electrostatic precipitator (WESP) was conducted by using U.S. Environmental Protection Agency Method 29. Simultaneous sampling of the coal, bottom ash, ESP ash, flue gas, and by-products from WFGD and WESP processes was performed. Results show that TEs mass balance rates for the entire system, furnace and each air pollution control device (APCD), are in the acceptable range of 70–130%, which confirms the validity and reliability of the field test data. The studied TEs are mainly distributed in bottom and ESP ash with the ratios of 2.11–12.15% and 87.83–97.83%, respectively, while little amounts of them exist in WFGD, WESP, and the stack. Coal combustion by-products such as bottom ash and gypsum have l.
Components of the State ’ s Inefficiency in Iran ( 2005-2013 )

This paper takes the three main indicators of Non-development-oriented states to be: the lack of an efficient bureaucratic system; rising consumption costs; and the lack of an effective relationship with the international community. Accordingly, the paper has looked into instances of such indicators in the ninth and tenth administrations which were in office from 2005 to 2013 in Iran. The existence of an unpredictable and unsystematic mechanism in the state is a sign of bureaucratic inefficiency. A rise in public funding in proportion to infrastructure and development funding will hamper the process of development. Given the high level of global communications, one prerequisite for development is to have an effective relationship with the international community in line with national interests. Based on surveys done for the research, it can be concluded that the 9 and 10 state in Iran could be categorized among Non-Developmental States.
Combining GCN and Transformer for Chinese Grammatical Error Detection

This paper describes our system at a task: Chinese Grammatical Error Diagnosis (CGED). The task is held by the Natural Language Processing Techniques for Educational Applications (NLP-TEA) to encourage the development of automatic grammatical error diagnosis in Chinese learning since 2014. The goal of CGED is to diagnose four types of grammatical errors: word selection (S), redundant words (R), missing words (M), and disordered words (W). The automatic CGED system contains two parts including error detection and error correction and our system is designed to solve the error detection problem. Our system is built on three models: 1) a BERT-based model leveraging syntactic information; 2) a BERT-based model leveraging contextual embeddings; 3) a lexicon-based graph neural network leveraging lexical information. We also design an ensemble mechanism to improve the single model’s performance. Finally, our system achieves the highest F1 scores at detection level and identification level among all teams participating in the CGED 2020 task.
Localization, Convexity, and Star Aggregation

Offset Rademacher complexities have been shown to provide tight upper bounds for the square loss in a broad class of problems including improper statistical learning and online learning. We show that the offset complexity can be generalized to any loss that satisfies a certain general convexity condition. Further, we show that this condition is closely related to both exponential concavity and self-concordance, unifying apparently disparate results. By a novel geometric argument, many of our bounds translate to improper learning in a non-convex class with Audibert's star algorithm. Thus, the offset complexity provides a versatile analytic tool that covers both convex empirical risk minimization and improper learning under entropy conditions. Applying the method, we recover the optimal rates for proper and improper learning with the $p$-loss for $1<p<\infty$, and show that improper variants of empirical risk minimization can attain fast rates for logistic regression and other generalized linear models.
THz Kerr nematic response in the superconducting state of Ba1−xKxFe2As2

Romain Grasset, Kota Katsumi, Pierre Massat, Hai-Hu Wen, Xian-Hui Chen, Yann Gallais, and Ryo Shimano 1 Cryogenic Research Center, The University of Tokyo, Tokyo, 113-0032, Japan Department of Physics, The University of Tokyo, Tokyo, 113-0033, Japan Laboratoire des Solides Irradiés, Ecole Polytechnique, CNRS, CEA, Institut Polytechnique de Paris, F-91128 Palaiseau, France Laboratoire Matériaux et Phénomènes Quantiques, UMR 7162 CNRS, Université de Paris, Bât. Condorcet 75205 Paris Cedex 13, France National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, China Hefei National Laboratory for Physical Sciences at Microscale and Department of Physics, and CAS Key Laboratory of Strongly-coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, Anhui 230026, China (Dated: May 20, 2021)
Local Aggressive Adversarial Attacks on 3D Point Cloud

Deep neural networks are found to be prone to adversarial examples which could deliberately fool the model to make mistakes. Recently, a few of works expand this task from 2D image to 3D point cloud by using global point cloud optimization. However, the perturbations of global point are not effective for misleading the victim model. First, not all points are important in optimization toward misleading. Abundant points account considerable distortion budget but contribute trivially to attack. Second, the multi-label optimization is suboptimal for adversarial attack, since it consumes extra energy in finding multi-label victim model collapse and causes instance transformation to be dissimilar to any particular instance. Third, the independent adversarial and perceptibility losses, caring misclassification and dissimilarity separately, treat the updating of each point equally without a focus. Therefore, once perceptibility loss approaches its budget threshold, all points would be stock in the surface of hypersphere and attack would be locked in local optimality. Therefore, we propose a local aggressive adversarial attacks (L3A) to solve above issues. Technically, we select a bunch of salient points, the high-score subset of point cloud according to gradient, to perturb. Then a flow of aggressive optimization strategies are developed to reinforce the unperceptive generation of adversarial examples toward misleading victim models. Extensive experiments on PointNet, PointNet++ and DGCNN demonstrate the state-of-the-art performance of our method against existing adversarial attack methods.
Gamma-Ray and Neutrino Signals from Accretion Disk Coronae of Active Galactic Nuclei

To explain the X-ray spectra of active galactic nuclei (AGN), non-thermal activity in AGN coronae such as pair cascade models has been extensively discussed in the past literature. Although X-ray and gamma-ray observations in the 1990s disfavored such pair cascade models, recent millimeter-wave observations of nearby Seyferts have established the existence of weak non-thermal coronal activity. In addition, the IceCube collaboration reported NGC 1068, a nearby Seyfert, as the hottest spot in their 10 year survey. These pieces of evidence are enough to investigate the non-thermal perspective of AGN coronae in depth again. This article summarizes our current observational understanding of AGN coronae and describes how AGN coronae generate high-energy particles. We also provide ways to test the AGN corona model with radio, X-ray, MeV gamma ray, and high-energy neutrino observations.
A non-intrusive reduced-order modelling for uncertainty propagation of time-dependent problems using a B-splines Bézier elements-based method and Proper Orthogonal Decomposition: application to dam-break flows

A proper orthogonal decomposition-based B-splines B\'ezier elements method (POD-BSBEM) is proposed as a non-intrusive reduced-order model for uncertainty propagation analysis for stochastic time-dependent problems. The method uses a two-step proper orthogonal decomposition (POD) technique to extract the reduced basis from a collection of high-fidelity solutions called snapshots. A third POD level is then applied on the data of the projection coefficients associated with the reduced basis to separate the time-dependent modes from the stochastic parametrized coefficients. These are approximated in the stochastic parameter space using B-splines basis functions defined in the corresponding B\'ezier element. The accuracy and the efficiency of the proposed method are assessed using benchmark steady-state and time-dependent problems and compared to the reduced order model-based artificial neural network (POD-ANN) and to the full-order model-based polynomial chaos expansion (Full-PCE). The POD-BSBEM is then applied to analyze the uncertainty propagation through a flood wave flow stemming from a hypothetical dam-break in a river with a complex bathymetry. The results confirm the ability of the POD-BSBEM to accurately predict the statistical moments of the output quantities of interest with a substantial speed-up for both offline and online stages compared to other techniques.
A Novel lightweight Convolutional Neural Network, ExquisiteNetV2

In the paper of ExquisiteNetV1, the ability of classification of ExquisiteNetV1 is worse than DenseNet. In this article, we propose a faster and better model ExquisiteNetV2. We conduct many experiments to evaluate its performance. We test ExquisiteNetV2, ExquisiteNetV1 and other 9 well-known models on 15 credible datasets under the same condition. According to the experimental results, ExquisiteNetV2 gets the highest classification accuracy over half of the datasets. Important of all, ExquisiteNetV2 has fewest amounts of parameters. Besides, in most instances, ExquisiteNetV2 has fastest computing speed.