Miracle-dz/UniFAR-FTUs huggingface dataset:
TwiBot-22: Towards Graph-Based Twitter Bot Detection

Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect. However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from limited dataset scale, incomplete graph structure, as well as low annotation quality. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches. In this paper, we propose TwiBot-22, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets. In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress. To facilitate further research, we consolidate all implemented codes and datasets into the TwiBot-22 evaluation framework, where researchers could consistently evaluate new models and datasets. The TwiBot-22 Twitter bot detection benchmark and evaluation framework are publicly available at https://twibot22.github. io/
Gaussian process model for the local stellar velocity field from Gaia data release 2

We model the local stellar velocity field using position and velocity measurements for 4M stars from the second data release of Gaia. We determine the components of the mean or bulk velocity in ∼27 000 spatially-defined bins. Our assumption is that these quantities constitute a Gaussian process where the correlation between the bulk velocity at different locations is described by a simple covariance function or kernel. We use a sparse Gaussian process algorithm based on inducing points to construct a non-parametric, smooth, and differentiable model for the underlying mean stellar velocity field. We estimate the Oort constants A, B, C, and K and find values in excellent agreement with previous results. Maps of the velocity field within 2 kpc of the Sun reveal complicated substructures, which provide clear evidence that the local disk is in a state of disequilibrium. We present the first 3D map of the divergence of the stellar velocity field and identify regions of the disk that may be undergoing compression and rarefaction.
AdS/BCFT correspondence and Lovelock theory in the presence of canonical scalar field

Oleksii Sokoliuk1,2, Fabiano F. Santos3, and Alexander Baransky2 1 Main Astronomical Observatory of the NAS of Ukraine (MAO NASU), Kyiv, 03143, Ukraine 2 Astronomical Observatory, Taras Shevchenko National University of Kyiv, 3 Observatorna St., 04053 Kyiv, Ukraine 3 Institute of Theoretical Physics, Beijing University of Technology, Beijing 100124, ChinaInstituto de F́ısica,Universidade Federal do Rio de Janeiro, Caixa Postal 68528, Rio de Janeiro-RJ, 21941-972 – Brazil
Factuality Enhanced Language Models for Open-Ended Text Generation

Pretrained language models (LMs) are susceptible to generate text with nonfactual information. In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation. We design the FactualityPrompts test set and metrics to measure the factuality of LM generations. Based on that, we study the factual accuracy of LMs with parameter sizes ranging from 126M to 530B. Interestingly, we find that larger LMs are more factual than smaller ones, although a previous study suggests that larger LMs can be less truthful in terms of misconceptions. In addition, popular sampling algorithms (e.g., top-p) in open-ended text generation can harm the factuality due to the ''uniform randomness'' introduced at every sampling step. We propose the factual-nucleus sampling algorithm that dynamically adapts the randomness to improve the factuality of generation while maintaining quality. Furthermore, we analyze the inefficiencies of the standard training method in learning correct associations between entities from factual text corpus (e.g., Wikipedia). We propose a factuality-enhanced training method that uses TopicPrefix for better awareness of facts and sentence completion as the training objective, which can vastly reduce the factual errors. We release our code and FactualityPrompts benchmark at: https://github.com/nayeon7lee/FactualityPrompt.
When Traceability Goes Awry: an Industrial Experience Report

The concept of traceability between artifacts is considered an enabler for software project success. This concept has received plenty of attention from the research community and is by many perceived to always be available in an industrial setting. In this industry-academia collaborative project, a team of researchers, supported by testing practitioners from a large telecommunication company, sought to investigate the partner company's issues related to software quality. However, it was soon identified that the fundamental traceability links between requirements and test cases were missing. This lack of traceability impeded the implementation of a solution to help the company deal with its quality issues. In this experience report, we discuss lessons learned about the practical value of creating and maintaining traceability links in complex industrial settings and provide a cautionary tale for researchers.
Corrections to scaling in the 3D Ising model: A comparison between MC and MCRG results

Corrections to scaling in the 3D Ising model are studied based on Monte Carlo (MC) simulation results for very large lattices with linear lattice sizes up to L=3456. Our estimated values of the correction-to-scaling exponent omega tend to decrease below the usually accepted value about 0.83 when the smallest lattice sizes are discarded from the fits. This behavior apparently confirms some of the known estimates of the Monte Carlo renormalization group (MCRG) method, i.e., omega about 0.7 and omega = 0.75(5). We discuss the possibilities that omega is either really smaller than usually expected or these values of omega describe some transient behavior. We propose refining MCRG simulations and analysis to resolve this issue. In distinction from omega, our actual MC estimations of the critical exponents eta and nu provide stable values eta=0.03632(13) and nu=0.63017(31), which well agree with those of the conformal bootstrap method, i.e., eta=0.0362978(20) and nu=0.6299709(40).
Incremental Recursive Ranking Grouping for Large-Scale Global Optimization

Real-world optimization problems may have a different underlying structure. In black-box optimization, the dependencies between decision variables remain unknown. However, some techniques can discover such interactions accurately. In large-scale global optimization (LSGO), problems are high dimensional. It was shown effective to decompose LSGO problems into subproblems and optimize them separately. The effectiveness of such approaches may be highly dependent on the accuracy of problem decomposition. Many state-of-the-art decomposition strategies are derived from differential grouping (DG). However, if a given problem consists of nonadditively separable subproblems, DG-based strategies may discover many nonexisting interactions. On the other hand, monotonicity checking strategies proposed so far do not report nonexisting interactions for any separable subproblems but may miss discovering many of the existing ones. Therefore, we propose incremental recursive ranking grouping (IRRG) that suffers from none of these flaws. IRRG consumes more fitness function evaluations than the recent DG-based propositions, e.g., recursive DG 3 (RDG3). Nevertheless, the effectiveness of the considered cooperative co-evolution frameworks after embedding IRRG or RDG3 was similar for problems with additively separable subproblems that are suitable for RDG3. After replacing the additive separability with nonadditive, embedding IRRG leads to results of significantly higher quality.
Excitations and spectra from equilibrium real-time Green's functions

The real-time contour formalism for Green’s functions provides time-dependent information of quantum many-body systems. In practice, the long-time simulation of systems with a wide range of energy scales is challenging due to both the storage requirements of the discretized Green’s function and the computational cost of solving the Dyson equation. In this manuscript, we apply a real-time discretization based on a piece-wise high-order orthogonal-polynomial expansion to address these issues. We present a superconvergent algorithm for solving the real-time equilibrium Dyson equation using the Legendre spectral method and the recursive algorithm for Legendre convolution. We show that the compact high order discretization in combination with our Dyson solver enables long-time simulations using far fewer discretization points than needed in conventional multistep methods. As a proof of concept, we compute the molecular spectral functions of H 2 , LiH, He 2 and C 6 H 4 O 2 using self-consistent second-order perturbation theory and compare the results with standard quantum chemistry methods as well as the auxiliary second-order Green’s function perturbation theory method.
DogTouch: CNN-based Recognition of Surface Textures by Quadruped Robot with High Density Tactile Sensors

The ability to perform locomotion in various terrains is critical for legged robots. However, the robot has to have a better understanding of the surface it is walking on to perform robust locomotion on different terrains. Animals and humans are able to recognize the surface with the help of the tactile sensation on their feet. Although, the foot tactile sensation for legged robots has not been much explored. This paper presents research on a novel quadruped robot DogTouch with tactile sensing feet (TSF). TSF allows the recognition of different surface textures utilizing a tactile sensor and a convolutional neural network (CNN). The experimental results show a sufficient validation accuracy of 74.37% for our trained CNN-based model, with the highest recognition for line patterns of 90%. In the future, we plan to improve the prediction model by presenting surface samples with the various depths of patterns and applying advanced Deep Learning and Shallow learning models for surface recognition.Additionally, we propose a novel approach to navigation of quadruped and legged robots. We can arrange the tactile paving textured surface (similar that used for blind or visually impaired people). Thus, DogTouch will be capable of locomotion in unknown environment by just recognizing the specific tactile patterns which will indicate the straight path, left or right turn, pedestrian crossing, road, and etc. That will allow robust navigation regardless of lighting condition. Future quadruped robots equipped with visual and tactile perception system will be able to safely and intelligently navigate and interact in the unstructured indoor and outdoor environment.
ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion

Knowledge graphs are inherently incomplete. Therefore substantial research has been directed toward knowledge graph completion (KGC), i.e., predicting missing triples from the information represented in the knowledge graph (KG). KG embedding models (KGEs) have yielded promising results for KGC, yet any current KGE is incapable of: (1) fully capturing vital inference patterns (e.g., composition), (2) capturing prominent patterns jointly (e.g., hierarchy and composition), and (3) providing an intuitive interpretation of captured patterns. In this work, we propose ExpressivE, a fully expressive spatio-functional KGE that solves all these challenges simultaneously. ExpressivE embeds pairs of entities as points and relations as hyper-parallelograms in the virtual triple space $\mathbb{R}^{2d}$. This model design allows ExpressivE not only to capture a rich set of inference patterns jointly but additionally to display any supported inference pattern through the spatial relation of hyper-parallelograms, offering an intuitive and consistent geometric interpretation of ExpressivE embeddings and their captured patterns. Experimental results on standard KGC benchmarks reveal that ExpressivE is competitive with state-of-the-art KGEs and even significantly outperforms them on WN18RR.
Simplifying Polylogarithms with Machine Learning

Polylogrithmic functions, such as the logarithm or dilogarithm, satisfy a number of algebraic identities. For the logarithm, all the identities follow from the product rule. For the dilogarithm and higher-weight classical polylogarithms, the identities can involve five functions or more. In many calculations relevant to particle physics, complicated combinations of polylogarithms often arise from Feynman integrals. Although the initial expressions resulting from the integration usually simplify, it is often difficult to know which identities to apply and in what order. To address this bottleneck, we explore to what extent machine learning methods can help. We consider both a reinforcement learning approach, where the identities are analogous to moves in a game, and a transformer network approach, where the problem is viewed analogously to a language-translation task. While both methods are effective, the transformer network appears more powerful and holds promise for practical use in symbolic manipulation tasks in mathematical physics.
The Missing Link: Finding label relations across datasets

Computer vision is driven by the many datasets available for training or evaluating novel methods. However, each dataset has a different set of class labels, visual definition of classes, images following a specific distribution, annotation protocols, etc. In this paper we explore the automatic discovery of visual-semantic relations between labels across datasets. We aim to understand how instances of a certain class in a dataset relate to the instances of another class in another dataset. Are they in an identity, parent/child, overlap relation? Or is there no link between them at all? To find relations between labels across datasets, we propose methods based on language, on vision, and on their combination. We show that we can effectively discover label relations across datasets, as well as their type. We apply our method to four applications: understand label relations, identify missing aspects, increase label specificity, and predict transfer learning gains. We conclude that label relations cannot be established by looking at the names of classes alone, as they depend strongly on how each of the datasets was constructed.
Cycle Text2face: Cycle Text-to-face Gan via Transformers

Text-to-face is a subset of text-to-image that require more complex architecture due to their more detailed production. In this paper, we present an encoder-decoder model called Cycle Text2Face. Cycle Text2Face is a new initiative in the encoder part, it uses a sentence transformer and GAN to generate the image described by the text. The Cycle is completed by reproducing the text of the face in the decoder part of the model. Evaluating the model using the CelebA dataset, leads to better results than previous GAN-based models. In measuring the quality of the generate face, in addition to satisfying the human audience, we obtain an FID score of 3.458. This model, with high-speed processing, provides quality face images in the short time.
Reconstruct Face from Features Using GAN Generator as a Distribution Constraint

Face recognition based on the deep convolutional neural networks (CNN) shows superior accuracy performance attributed to the high discriminative features extracted. Yet, the security and privacy of the extracted features from deep learning models (deep features) have been often overlooked. This paper proposes the reconstruction of face images from deep features without accessing the CNN network configurations as a constrained optimization problem. Such optimization minimizes the distance between the features extracted from the original face image and the reconstructed face image. Instead of directly solving the optimization problem in the image space, we innovatively reformulate the problem by looking for a latent vector of a GAN generator, then use it to generate the face image. The GAN generator serves as a dual role in this novel framework, i.e., face distribution constraint of the optimization goal and a face generator. On top of the novel optimization task, we also propose an attack pipeline to impersonate the target user based on the generated face image. Our results show that the generated face images can achieve a state-of-the-art successful attack rate of 98.0\% on LFW under type-I attack @ FAR of 0.1\%. Our work sheds light on the biometric deployment to meet the privacy-preserving and security policies.
A Practical Technique for Privacy-Preserving Profile Base Rank-Search Engine

Content in Internet are growing rapidly, the search provider users demand accurate, search results as per their need. Personalized web search (PWS) are representing for improving the quality of various search services on the internet. But one option is available to users is PWS means personalize web search that is specially used for the personal data of user provided to the search provider. Even if evidences show that user reluctance to disclose their private information during search has become a major barrier for the wide proliferation of personalize web search. In this paper models preference of users as hierarchical profiles of system. It proposes a framework which is known as UPS and it generalizes profile at the same time, it is use for the maintaining the privacy requirements by user [1]
Evidence of jet induced optical microvariability in radio-loud Narrow Line Seyfert 1 Galaxies

To quantify the role of radio jets for Intra-Night Optical Variability (INOV) in Radio-Loud Narrow-Line Seyfert 1 (RLNLSy1) galaxies, we report the first systematic comparative INOV study of 23 RLNLSy1 galaxies, with 15 RLNLSy1s having confirmed detection of jets (jetted) and the remaining 8 RLNLSy1s having no detection of jets (non-jetted) based on their Very Long Baseline Array observations. We have monitored these two samples, respectively, in 37 and 16 sessions of a minimum 3-hour duration each. Based upon Fη-test at 99% confidence level with a typical INOV amplitude (ψ) detection threshold of > 3%, we find the INOV duty cycles of 12% for the sample of jetted RLNLSy1s, however, none of the sources showed INOV in the sample of non-jetted RLNLSy1s. Among the jetted RLNLSy1s, we find that the Duty Cycle (DC) for jetted γ-ray detected (γ-ray) RLNLSy1s is found to be 34% in contrast to null INOV detection in the case of non-γ-ray RLNLSy1s. It suggests that instead of the mere presence of a jet, relativistic beaming plays a significant role for INOV in the case of low-luminous high accreting AGNs such as NLSy1s in which dilution of the AGN’s non-thermal optical emission by the (much steadier) optical emission contributed by the nuclear accretion disc is quite likely. Our study of jetted γ-ray RLNLSy1s shows more frequent INOV detection for sources with higher apparent jet speed. Further, our results also suggest that among the NLSy1s, only jetted γ-ray RNLSy1 galaxies DC approaches blazar like DC.
Weighing the Darkness. III. How Gaia Could, but Probably Will Not, Astrometrically Detect Free-floating Black Holes

The gravitational pull of an unseen companion to a luminous star is well known to cause deviations to the parallax and proper motion of a star. In a previous paper in this series, we argue that the astrometric mission Gaia can identify long-period binaries by precisely measuring these arcs. An arc in a star’s path can also be caused by a flyby: a hyperbolic encounter with another massive object. We quantify the apparent acceleration over time induced by a companion star as a function of the impact parameter, velocity of interaction, and companion mass. In principle, Gaia could be used to astrometrically identify the contribution of massive compact halo objects to the local dark matter potential of the Milky Way. However, after quantifying their rate and Gaia’s sensitivity, we find that flybys are so rare that Gaia will probably never observe one. Therefore, every star in the Gaia database exhibiting astrometric acceleration is likely in a long-period binary with another object. Nevertheless, we show how intermediate-mass black holes, if they exist in the local stellar neighborhood, can induce anomalously large accelerations on stars.
Klein-Like Tunneling of Sound via Negative Index Metamaterials

Klein tunneling is a counterintuitive quantum-mechanical phenomenon, predicting perfect transmission of relativistic particles through higher energy barriers. This phenomenon was shown to be supported at normal incidence in graphene due to pseudospin conservation. Here I show that Klein tunneling analogue can occur in classical systems, and remarkably, not relying on mimicking graphene’s spinor wavefunction structure. Instead, the mechanism requires a particular form of constitutive parameters of the penetrated medium, yielding transmission properties identical to the quantum tunneling in graphene. I demonstrate this result by simulating tunneling of sound in a two-dimensional acoustic metamaterial. More strikingly, I show that by introducing a certain form of anisotropy, the tunneling can be made unimpeded for any incidence angle, while keeping most of its original Klein dispersion properties. This phenomenon may be denoted by the omnidirectional Klein-like tunneling. The new tunneling mechanism and its omnidirectional variant may be useful for applications requiring lossless and direction-independent transmission of classical waves.
Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video

Cow lameness is a severe condition that affects the life cycle and life quality of dairy cows and results in considerable economic losses. Early lameness detection helps farmers address illnesses early and avoid negative effects caused by the degeneration of cows' condition. We collected a dataset of short clips of cows passing through a hallway exiting a milking station and annotated the degree of lameness of the cows. This paper explores the resulting dataset and provides a detailed description of the data collection process. Additionally, we proposed a lameness detection method that leverages pre-trained neural networks to extract discriminative features from videos and assign a binary score to each cow indicating its condition:"healthy"or"lame. "We improve this approach by forcing the model to focus on the structure of the cow, which we achieve by substituting the RGB videos with binary segmentation masks predicted with a trained segmentation model. This work aims to encourage research and provide insights into the applicability of computer vision models for cow lameness detection on farms.