Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Detection in Blockchain Networks: A Comprehensive Survey

Over the past decade, blockchain technology has attracted a huge attention from both industry and academia because it can be integrated with a large number of everyday applications of modern information and communication technologies (ICT). Peer-to-peer (P2P) architecture of blockchain enhances these applications by providing strong security and trust-oriented guarantees, such as immutability, verifiability, and decentralization. Despite these incredible features that blockchain technology brings to these ICT applications, recent research has indicated that the strong guarantees are not sufficient enough and blockchain networks may still be prone to various security, privacy, and reliability issues. In order to overcome these issues, it is important to identify the anomalous behaviour within the actionable time frame. In this article, we provide an in-depth survey regarding integration of anomaly detection models in blockchain technology. For this, we first discuss how anomaly detection can aid in ensuring security of blockchain based applications. Then, we demonstrate certain fundamental evaluation metrics and key requirements that can play a critical role while developing anomaly detection models for blockchain. Afterwards, we present a thorough survey of various anomaly detection models from the perspective of each layer of blockchain. Finally, we conclude the article by highlighting certain important challenges alongside discussing how they can serve as future research directions for new researchers in the field.
Magnifying Networks for Images with Billions of Pixels

The shift towards end–to–end deep learning has brought unprecedented advances in many areas of computer vision. However, deep neural networks are trained on images with resolutions that rarely exceed 1 , 000 × 1 , 000 pixels. The growing use of scanners that create images with extremely high resolutions (average can be 100 , 000 × 100 , 000 pixels) thereby presents novel challenges to the field. Most of the published methods preprocess high–resolution images into a set of smaller patches, imposing an a priori belief on the best properties of the extracted patches (magnification, field of view, location, etc. ). Herein, we introduce Magnifying Networks (MagNets) as an alternative deep learning solution for gigapixel image analysis that does not rely on a preprocessing stage nor requires the processing of billions of pixels. MagNets can learn to dynamically retrieve any part of a gigapixel image, at any magnification level and field of view, in an end—to—end fashion with minimal ground truth (a single “global”, slide–level label). Our results on the publicly available Camelyon16 and Camelyon17 datasets corroborate to the effectiveness and efficiency of MagNets and the proposed optimization framework for whole slide image classification. Importantly, MagNets process far less patches from each slide than any of the existing approaches ( 10 to 300 times less).
Parallel Batch-Dynamic kd-Trees

kd-trees are widely used in parallel databases to support efficient neighborhood/similarity queries. Supporting parallel updates to kd-trees is therefore an important operation. In this paper, we present BDL-tree, a parallel, batch-dynamic implementation of a kd-tree that allows for efficient parallel k-NN queries over dynamically changing point sets. BDL-trees consist of a log-structured set of kd-trees which can be used to efficiently insert or delete batches of points in parallel with polylogarithmic depth. Specifically, given a BDL-tree with n points, each batch of B updates takes O(B log (n+B)) amortized work and O(log(n + B) log log (n+B)) depth (parallel time). We provide an optimized multicore implementation of BDLtrees. Our optimizations include parallel cache-oblivious kd-tree construction and parallel bloom filter construction. Our experiments on a 36-core machine with two-way hyperthreading using a variety of synthetic and real-world datasets show that our implementation of BDL-tree achieves a selfrelative speedup of up to 34.8× (28.4× on average) for batch insertions, up to 35.5× (27.2× on average) for batch deletions, and up to 46.1× (40.0× on average) for k-nearest neighbor queries. In addition, it achieves throughputs of up to 14.5 million updates/second for batch-parallel updates and 6.7 million queries/second for k-NN queries. We compare to two baseline kdtree implementations and demonstrate that BDL-trees achieve a good tradeoff between the two baseline options for implementing batch updates.
A Comparative Study on Robust Graph Neural Networks to Structural Noises

Graph neural networks (GNNs) learn node representations by passing and aggregating messages between neighboring nodes. GNNs have been applied successfully in several application domains and achieved promising performance. However, GNNs could be vulnerable to structural noise because of the message passing mechanism where noise may be propagated through the entire graph. Although a series of robust GNNs have been proposed, they are evaluated with different structural noises, and it lacks a systematic comparison with consistent settings. In this work, we conduct a comprehensive and systematical comparative study on different types of robust GNNs under consistent structural noise settings. From the noise aspect, we design three different levels of structural noises, i.e., local, community, and global noises. From the model aspect, we select some representative models from sample-based, revision-based, and construction-based robust GNNs. Based on the empirical results, we provide some practical suggestions for robust GNNs selection.
Object Wake-up: 3D Object Reconstruction and Animation from a Single Image

Given a single chair image, could we extract its 3D shape and animate its plausible articulations and motions? This is an interesting new question that may have numerous downstream augmented reality and virtual reality applications. In this paper, we propose an automated approach to tackle the entire process of reconstruct such 3-D generic objects from single images, rigging and animation. Comparing with previous efforts on object manipulation, our work goes beyond 2D manipulation. Moreover, we endow plausible human-like or animal-like deformations to the otherwise rigid-body objects such as chairs; this leads to greater ﬂex-ibility in terms of feasible object motions. Empirically our approach is demonstrated with satisfactory performance on public datasets as well as our in-house dataset; when compared on the related tasks of 3D reconstruction and skeleton prediction, our results surpass those of the State-of-the-arts by a noticeable margin. Our implementation and dataset are to be made publicly available upon paper acceptance.
Domain Adaptive Semantic Segmentation without Source Data: Align, Teach and Propagate

Beneﬁting from considerable pixel-level annotations collected from a speciﬁc situation (source), the trained semantic segmentation model performs quite well but fails in a new situation (target) due to the large domain shift. To mitigate the domain gap, previous cross-domain semantic segmentation methods always assume the co-existence of source data and target data during domain alignment. However, the accessing to source data in the real scenario may raise privacy concerns and violate intellectual property. To tackle this problem, we focus on an interesting and challenging cross-domain semantic segmentation task where only the trained source model is provided to the target domain. Speciﬁcally, we propose a uniﬁed framework called ATP , which consists of three schemes, i.e . , feature A lignment, bidirectional T eaching, and information P ropagation. First, we devise a curriculum-style entropy minimization objective to implicitly align the target features with unseen source features via the provided source model. Second, besides positive pseudo labels in vanilla self-training, we are among the ﬁrst ones to introduce negative pseudo labels to this ﬁeld and develop a bidirectional self-training strategy to enhance the representation learning in the target domain. Finally, the information propagation scheme is employed to further reduce the intra-domain discrepancy within the target domain via pseudo semi-supervised learning. Extensive results on synthesis-to-real and cross-city driving datasets validate ATP yields state-of-the-art
Rotation invariant seal imprint verification method

This paper proposes a rotation invariant seal imprint verification method. First, the magnitude of Zernike moments is calculated after calculation of the center of the normalized seal imprint. Second, a Wiener filter is used to improve the Zernike moments of the seal imprint contaminated with noise. Third, the discrete cosine transform (DCT) of the autocorrelation of the output sequence of the Wiener filter is computed. Then, the seal imprint can be verified using the discrete K-L expansion of the DCT. Finally the verification experiments are given to show the effectiveness of our method.
he pph ] 1 2 D ec 2 02 1 Electromagnetic form factors of neutron , Λ and Σ 0 in the oscillating view of point

An-Xin Dai, 2 Zhong-Yi Li, 2 Lei Chang, ∗ and Ju-Jun Xie 2, 4, † Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China School of Physics, Nankai University, Tianjin 300071, China School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan 450001, China ( Dated: December 14, 2021)
Empirical Correlations between Cumulative Absolute Velocity and Amplitude-Based Ground Motion Intensity Measures

Empirical correlation equations are developed between cumulative absolute velocity (CAV) and other common ground motion intensity measures, namely, peak ground acceleration (PGA), peak ground velocity (PGV), 5% damped pseudo spectral acceleration (SA), acceleration spectrum intensity (ASI), spectrum intensity (SI), and displacement spectrum intensity (DSI). It is found that, for a given earthquake rupture, CAV has the strongest correlation with high and moderate frequency intensity measures (IMs), that is, ASI, PGA, PGV and high-frequency SA, and to a lesser extent with low frequency IMs (DSI and low-frequency SA). The largest positive correlations of approximately 0.7 however are not high in an absolute sense, a result of the cumulative nature of CAV. The equations allow estimation of the joint distribution of these intensity measures for a given earthquake rupture, enabling the inclusion of CAV, and its benefit as a cumulative intensity measure, in seismic hazard analysis, ground motion selection, and seismic response analysis.
Overall Level of Service of Urban Walking Environment and Its Influence on Pedestrian Route Choice Behavior

The objective of this study was to understand and quantify better the influence of the overall level of service (LOS) of the urban walking environment on pedestrian route choice behavior. A methodology for estimating the overall LOS of pedestrian walkways and crosswalks was developed on the basis of the concept of total utility value, which comes from a stated preference survey. Each sidewalk and crosswalk link was assigned an overall LOS according to its operational and geometric characteristics determined from field measurements. For analysis of pedestrian behavior, this study used data from a revealed preference survey on individual route choice behavior. A geographic information system network database was used to store the characteristics of the routes that pedestrians used. Network analysis with the ArcGIS program was used to analyze the routes, which included determination of the shortest-path routes and the optimized-LOS-path routes between origin-destination pairs. A comparative analysis of the actual routes and the estimated alternative routes was performed. On the basis of the results, a multinomial logit model was developed to express the route choice behaviors of pedestrians quantitatively. The model results indicate that pedestrians choose routes not only for distance but also for the overall LOS of sidewalks and crosswalks. On longer travel paths, pedestrians divert from the shortest-path route and are found to use sidewalks and crosswalks with high LOS. On shorter routes, pedestrians tend not to avoid sidewalks or crosswalks with low LOS. This analysis suggests that efforts to accommodate pedestrians in urban areas should focus on improving the walking environment of the road network.
Seasonal differences in escape behaviour in the urban hooded crow, Corvus cornix

Abstract. This study examined escape behaviour of hooded crows, Corvus cornix, in an urban environment, in Belgrade, Serbia, with the goal of assessing the effect of breeding season on components of escape behaviour; flight initiation distance (FID), distance fled (DF), and escape method (flying vs. running/walking). Using GLMMs, it was shown that FID was longer during the breeding season, and increased with alert distance. DF was significantly influenced by escape method, with birds escaping for distances greater than 10 m more often when flying, whereas escape method itself was significantly influenced by FID, with birds escaping more often by flying at greater distances from an approaching predator. The majority of individuals (78%) fled for distances < 10 m after initiating escape, and escaped by walking or running (67%). Our results show that hooded crows, although adapted to living in highly urbanized environments, may be more risk-averse during the breeding season, which may allow them to more successfully take care of their young.
A Novel Locally c-di-GMP-Controlled Exopolysaccharide Synthase Required for Bacteriophage N4 Infection of Escherichia coli

Key findings in model organisms led to the concept of “local” signaling, challenging the dogma of a gradually increasing global intracellular c-di-GMP concentration driving the motile-sessile transition in bacteria. In our current model, bacteria dynamically combine both global and local signaling modes, in which specific DGCs and/or PDEs team up with effector/target systems in multiprotein complexes. ABSTRACT A major target of c-di-GMP signaling is the production of biofilm-associated extracellular polymeric substances (EPS), which in Escherichia coli K-12 include amyloid curli fibers, phosphoethanolamine-modified cellulose, and poly-N-acetylglucosamine. However, the characterized c-di-GMP-binding effector systems are largely outnumbered by the 12 diguanylate cyclases (DGCs) and 13 phosphodiesterases (PDEs), which synthetize and degrade c-di-GMP, respectively. E. coli possesses a single protein with a potentially c-di-GMP-binding MshEN domain, NfrB, which—together with the outer membrane protein NfrA—is known to serve as a receptor system for phage N4. Here, we show that NfrB not only binds c-di-GMP with high affinity but, as a novel c-di-GMP-controlled glycosyltransferase, synthesizes a secreted EPS, which can impede motility and is required as an initial receptor for phage N4 infection. In addition, a systematic screening of the 12 DGCs of E. coli K-12 revealed that specifically DgcJ is required for the infection with phage N4 and interacts directly with NfrB. This is in line with local signaling models, where specific DGCs and/or PDEs form protein complexes with particular c-di-GMP effector/target systems. Our findings thus provide further evidence that intracellular signaling pathways, which all use the same diffusible second messenger, can act in parallel in a highly specific manner. IMPORTANCE Key findings in model organisms led to the concept of “local” signaling, challenging the dogma of a gradually increasing global intracellular c-di-GMP concentration driving the motile-sessile transition in bacteria. In our current model, bacteria dynamically combine both global and local signaling modes, in which specific DGCs and/or PDEs team up with effector/target systems in multiprotein complexes. The present study highlights a novel example of how specificity in c-di-GMP signaling can be achieved by showing NfrB as a novel c-di-GMP binding effector in E. coli, which is controlled in a local manner specifically by DgcJ. We further show that NfrB (which was initially found as a part of a receptor system for phage N4) is involved in the production of a novel exopolysaccharide. Finally, our data shine new light on host interaction of phage N4, which uses this exopolysaccharide as an initial receptor for adsorption.
Design of A Sustainable Building: A Conceptual Framework for Implementing Sustainability in the Building Sector

This paper presents a conceptual framework aimed at implementing sustainability principles in the building industry. The proposed framework based on the sustainable triple bottom line principle, includes resource conservation, cost efficiency and design for human adaptation. Following a thorough literature review, each principle involving strategies and methods to be applied during the life cycle of building projects is explained and a few case studies are presented for clarity on the methods. The framework will allow design teams to have an appropriate balance between economic, social and environmental issues, changing the way construction practitioners think about the information they use when assessing building projects, thereby facilitating the sustainability of building industry.
Laser-Assisted Field Evaporation of ${\rm RB}{\rm a}_ 2{\rm C}{\rm u}_ 3{\rm O}_{ 7-{\rm \delta }}$ (R = Gd, Sm) High-Temperature Superconducting Coated Conductors

Abstract In the field of high-temperature superconductors, atom probe tomography is a relatively new instrument, with the ability to provide a new perspective on the 3D nanoscale microstructure. However, field evaporation of nonmetallic materials is fraught with unique challenges that matter little in the world of metallic evaporation. In this study, we review the laser absorption, correlated evaporation, molecular dissociation, and the crystallographic effects on the field evaporation of 800-m ${\rm RB}{\rm a}_ 2{\rm C}{\rm u}_ 3{\rm O}_{ 7-{\rm \delta }}$ (R = Gd, Sm) coated conductor tapes deposited by Reactive Co-Evaporation Cyclic Deposition and Reaction (RCE-CDR). Ultraviolet 355 nm laser pulsing was found to have a substantial beneficial effect on minimizing the fracture probability compared with 532 nm illumination, especially when evaporating insulating oxide precipitates. This, in turn, allows for the 3D compositional analysis of defects such as flux pinning centers introduced by precipitation and doping. As a result, evidence for the precipitation of nanoscale ${\rm G}{\rm d}_ 2{\rm C}{\rm u}_ 2{\rm O}_ 5$ is discussed. The effect of crystallographic orientation is studied, where [001] aligned evaporation is found to develop compositional aberrations.
Not the great equalizers: Covid-19, 1918–20 influenza, and the need for a paradigm shift in pandemic preparedness

Despite common perceptions to the contrary, pandemic diseases do not affect populations indiscriminately. In this paper, we review literature produced by demographers, historians, epidemiologists, and other researchers on disparities during the 1918–20 influenza pandemic and the Covid-19 pandemic. Evidence from these studies demonstrates that lower socio-economic status and minority/stigmatized race or ethnicity are associated with higher morbidity and mortality. However, such research often lacks theoretical frameworks or appropriate data to explain the mechanisms underlying these disparities fully. We suggest using a framework that considers proximal and distal factors contributing to differential exposure, susceptibility, and consequences as one way to move this research forward. Further, current pandemic preparedness plans emphasize medically defined risk groups and epidemiological approaches. Therefore, we conclude by arguing in favour of a transdisciplinary paradigm that recognizes socially defined risk groups, includes input from the social sciences and humanities and other diverse perspectives, and contributes to the reduction of health disparities before a pandemic hits.
The Association of Career Outcome Expectations and STEM Career Choice Among Hispanic First-Generation Students: A Unique Pattern

ABSTRACT Prior research examined career interests of first-generation and of Hispanic students in science, technology, engineering, and mathematics (STEM), but less is known about how career outcome expectations (COEs) motivate career interests of students at the intersection: Hispanic first-generation college students. Surveying 15,725 students from 119 U.S. universities in a nationally representative stratified random sample, we found two COEs—being innovative and communal—were strongly associated with STEM career interest; however, these effects were attenuated among Hispanic first-generation college students. Our findings suggested that the COEs that typically influence STEM interest may not apply to Hispanic first-generation college students—a growing population in higher education and STEM.
Talking to Trolls - How Users Respond to a Coordinated Information Operation and Why They're So Supportive

This research explored how users interacted with inauthentic social media accounts with the goal of gaining insight into tactics employed by state-backed disinformation efforts. We combine hand coding with natural-language processing to measure the ways in which users talked with and about the accounts employed by the Russian-afﬁliated Internet Research Agency in the month before the 2016 U.S. Election. We ﬁnd that user mentions were overwhelming supportive of the IRA accounts, belying the standard characterization of these personas as “trolls. ” This pattern is particularly strong for the more ideological troll types, suggesting that a strategy of building homophilic connections with like-minded people was central to the IRA campaign. This strategy seems to work—on days that the personas’ mentions were more supportive, they received more engagement. behavior of some state affiliated, professional social media disinformation campaigns. In this study, we examined how social-media users engaged with inauthentic Twitter accounts created by the Russian IRA. Below, we will show, through an initial qualitative phase of research, that when users engaged with the ideologically oriented IRA persona, their messaging was largely supportive in nature. Building from this qualitative stage of research we conducted quantitative analysis on a larger corpus of data. Through this analysis, we found a positive relationship between the level of supportiveness that IRA accounts received for their messaging and both the level of engagement they received as measured by retweets and the growth of the IRA accounts as measured by followers. These findings illustrate the important role positive interactions with users can play in coordinated information operation tactics on social media.