Miracle-dz/UniFAR-FTUs huggingface dataset:
Recordism: A social-scientific prospect of blockchain from social, legal, financial, and technological perspectives

Blockchain technology has the potential to revolutionize the architecture of cyberspace by transforming the way information is stored, circulated, and exchanged in cyberspace through decentralization, transparency, and de-identification. This means that ordinary participants can simultaneously become traders, miners, retailers, and customers, thus breaking down barriers, reducing the information gap between participants in the community, and contributing to the futuristic metaverse with an open, progressive, and equal ideology. The impact of this information transformation empowered by blockchain extends to our understanding of methodology, legal governance in cyberspace, and financial and technological development. This study asks: what are the implications of the blockchain-driven information revolution for society and social sciences? In order to answer this main question, the paper focuses on four key perspectives: methodological, legal, financial, and technical. Through the analysis of these four perspectives, the paper provides a comprehensive understanding of the impact of blockchain on society, the social sciences, and technology, making a contribution to current scholarship. It finds that blockchain is not only an innovative cognition method, but also a community representative, serving as a source of trust, a governance watchdog, an enforcer of cyber laws, and an incubator for future technologies. Despite some challenges in integrating blockchain with existing social structures, this paper concludes that blockchain has the potential to play a significant role in shaping the future.
Continuous Jumping for Legged Robots on Stepping Stones via Trajectory Optimization and Model Predictive Control

Performing highly agile dynamic motions, such as jumping or running on uneven stepping stones has remained a challenging problem in legged robot locomotion. This paper presents a framework that combines trajectory optimization and model predictive control to perform robust and consecutive jumping on stepping stones. In our approach, we first utilize trajectory optimization based on full-nonlinear dynamics of the robot to generate periodic jumping trajectories for various jumping distances. A jumping controller based on a model predictive control is then designed for realizing smooth jumping transitions, enabling the robot to achieve continuous jumps on stepping stones. Thanks to the incorporation of MPC as a real-time feedback controller, the proposed framework is also validated to be robust to uneven platforms with unknown height perturbations and model uncertainty on the robot dynamics.
IMOT: General-Purpose, Fast and Robust Estimation for Spatial Perception Problems with Outliers

Spatial perception problems are the fundamental building blocks of robotics and computer vision. However, in many real-world situations, they inevitably suffer from the issue of outliers, which hinders traditional solvers from making correct estimates. In this paper, we present a novel, general-purpose robust estimator IMOT (Iterative Multi-layered Otsu's Thresholding) using standard non-minimal solvers to rapidly reject outliers for spatial perception problems. First, we propose a new outlier-robust iterative optimizing framework where in each iteration all the measurement data are separated into two groups according to the residual errors and only the group with lower residual errors can be preserved for estimation in the next iteration. Second, we introduce and employ the well-known Otsu's method (from image processing) to conduct thresholding on the residual errors so as to obtain the best separation (grouping) statistically which maximizes the between-class variance. Third, to enhance robustness, we design a multi-layered Otsu's thresholding approach in combination with our framework to sift out the true inliers from outliers that might even occupy the majority of measurements. We test our robust estimator IMOT on 5 different spatial perception problems including: rotation averaging, rotation search, point cloud registration, category-level registration, and SLAM. Experiments show that IMOT is robust against 70%--90% of outliers and can typically converge in only 3--10 iterations, being 3--125 times faster than existing robust estimators: GNC and ADAPT. Moreover, IMOT is able to return robust results even without noise bound information.
Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Context: Processing Software Requirement Specifications (SRS) manually takes a much longer time for requirement analysts in software engineering. Researchers have been working on making an automatic approach to ease this task. Most of the existing approaches require some intervention from an analyst or are challenging to use. Some automatic and semi-automatic approaches were developed based on heuristic rules or machine learning algorithms. However, there are various constraints to the existing approaches to UML generation, such as restrictions on ambiguity, length or structure, anaphora, incompleteness, atomicity of input text, requirements of domain ontology, etc. Objective: This study aims to better understand the effectiveness of existing systems and provide a conceptual framework with further improvement guidelines. Method: We performed a systematic literature review (SLR). We conducted our study selection into two phases and selected 70 papers. We conducted quantitative and qualitative analyses by manually extracting information, cross-checking, and validating our findings. Result: We described the existing approaches and revealed the issues observed in these works. We identified and clustered both the limitations and benefits of selected articles. Conclusion: This research upholds the necessity of a common dataset and evaluation framework to extend the research consistently. It also describes the significance of natural language processing obstacles researchers face. In addition, it creates a path forward for future research.
Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics

The ideological asymmetries have been recently observed in contested online spaces, where conservative voices seem to be relatively more pronounced even though liberals are known to have the population advantage on digital platforms. Most prior research, however, focused on either one single platform or one single political topic. Whether an ideological group garners more attention across platforms and/or topics, and how the attention dynamics evolve over time, have not been explored. In this work, we present a quantitative study that links collective attention across two social platforms -- YouTube and Twitter, centered on online activities surrounding popular videos of three controversial political topics including Abortion, Gun control, and Black Lives Matter over 16 months. We propose several sets of video-centric metrics to characterize how online attention is accumulated for different ideological groups. We find that neither side is on a winning streak: left-leaning videos are overall more viewed, more engaging, but less tweeted than right-leaning videos. The attention time series unfold quicker for left-leaning videos, but span a longer time for right-leaning videos. Network analysis on the early adopters and tweet cascades show that the information diffusion for left-leaning videos tends to involve centralized actors; while that for right-leaning videos starts earlier in the attention lifecycle. In sum, our findings go beyond the static picture of ideological asymmetries in digital spaces and provide a set of methods to quantify attention dynamics across different social platforms.
Optical modelling of accommodative light field display system and prediction of human eye responses

The spatio-angular resolution of a light field (LF) display is a crucial factor for delivering adequate spatial image quality and eliciting an accommodation response. Previous studies have modelled retinal image formation with an LF display and evaluated whether accommodation would be evoked correctly. The models were mostly based on ray-tracing and a schematic eye model, which pose computational complexity and inaccurately represent the human eye population's behaviour. We propose an efficient wave-optics-based framework to model the human eye and a general LF display. With the model, we simulated the retinal point spread function (PSF) of a point rendered by an LF display at various depths to characterise the retinal image quality. Additionally, accommodation responses to the rendered point were estimated by computing the visual Strehl ratio based on the optical transfer function (VSOTF) from the PSFs. We assumed an ideal LF display that had an infinite spatial resolution and was free from optical aberrations in the simulation. We tested points rendered at 0-4 dioptres of depths having angular resolutions of up to 4x4 viewpoints within a pupil. The simulation predicted small and constant accommodation errors, which contradict the findings of previous studies. An evaluation of the optical resolution on the retina suggested a trade-off between the maximum achievable resolution and the depth range of a rendered point where in-focus resolution is kept high. The proposed framework can be used to evaluate the upper bound of the optical performance of an LF display for realistically aberrated eyes, which may help to find an optimal spatio-angular resolution required to render a high quality 3D scene.
Inventory of high-quality flat-band van der Waals materials

More is left to do in the ﬁeld of ﬂat bands besides proposing theoretical models. One unexplored area is the ﬂat bands featured in the van der Waals (vdW) materials. Exploring more ﬂat-band material candidates and moving the promising materials toward applications have been well recognized as the cornerstones for the next-generation high-eﬃciency devices. Here, we utilize a powerful high-throughput tool to screen desired vdW materials based on the Inorganic Crystal Structure Database. Through layers of ﬁltration, we obtained 861 potential monolayers from 4997 vdW materials. Signiﬁcantly, it is the ﬁrst example to introduce ﬂat-band electronic properties in the vdW materials and propose three families of representative ﬂat-band materials by mapping two-dimensional (2D) ﬂat-band lattice models. Unlike existing screening schemes, a simple, universal rule, i.e. , 2D ﬂat-band score criterion, is ﬁrst proposed to eﬃciently identify 229 high-quality ﬂat-band candidates, and guidance is provided to diagnose the quality of 2D ﬂat bands. All these eﬀorts to screen experimental available ﬂat-band candidates will certainly motivate continuing exploration towards the realization of this class of special materials and their applications in material science.
A Formal Privacy Framework for Partially Private Data

Formal privacy, in particular diﬀerential privacy (DP) and its variants, is a useful framework for mathematically provable privacy guarantees in a transparent manner. Despite its many useful theoretical properties, DP has one substantial blind spot: any release that non-trivially depends on conﬁdential data without additional privacy-preserving randomization fails to satisfy DP. Such a restriction is rarely met in practice, as most data releases under DP are only ”partially private” (borrowing the language from “partially synthetic” data), due to legal or practical requirements, for example. This poses a signiﬁcant barrier to accounting for privacy risk and utility holistically under logistical constraints frequently imposed on data curators, especially for those working with survey data, administrative data, or other oﬃcial statistics. In this paper, we propose a privacy deﬁnition, (cid:15) -DPZ, that extends Puﬀerﬁsh privacy to accommodate public information. We prove that it maintains similar desirable privacy properties as standard (cid:15) -DP. Next, we propose a generic release mechanism for PPD, the Wasserstein Exponential Mechanism, with a new sensitivity based on optimal transport measures of distributional distance. We prove that this new mechanism satisﬁes (cid:15) -DPZ and provide example implementations of this mechanism. In particular, we demonstrate the importance of base measure choice as it pertains to congeniality of the resulting statistic with public information, proving results on inferential improvements using these congenial base measures. We also derive algorithms for valid statistical inference on mixtures of private and public information, and demonstrate their improved performance over methods based on post-processing more commonly used in the DP literature. Finally, we explore and empirically demonstrate all these ideas in a case study by generating PPD from US Census and CDC to investigate private COVID-19 infection rates. In doing so, we show how data curating agencies can use our framework to overcome barriers to operationalizing formal privacy while providing more transparency and accountability to data subjects and users in negotiating between privacy and utility.
Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring

The goal of dynamic scene deblurring is to remove the motion blur in a given image. Typical learning-based approaches implement their solutions by minimizing the L1 or L2 distance between the output and the reference sharp image. Recent attempts adopt visual recognition features in training to improve the perceptual quality. However, those features are primarily designed to capture high-level contexts rather than low-level structures such as blurriness. Instead, we propose a more direct way to make images sharper by exploiting the inverse task of deblurring, namely, reblurring. Reblurring amplifies the remaining blur to rebuild the original blur, however, a well-deblurred clean image with zero-magnitude blur is hard to reblur. Thus, we design two types of reblurring loss functions for better deblurring. The supervised reblurring loss at training stage compares the amplified blur between the deblurred and the sharp images. The self-supervised reblurring loss at inference stage inspects if there noticeable blur remains in the deblurred. Our experimental results on large-scale benchmarks and real images demonstrate the effectiveness of the reblurring losses in improving the perceptual quality of the deblurred images in terms of NIQE and LPIPS scores as well as visual sharpness.
Improvement of the construction project of tuna purse seine vessels through a cost and term analysis

ABSTRACT The shipbuilding of large fishing vessels industry has been traditionally reticent to major changes. Nonetheless, in a globalised competitive world, significant changes need to be introduced in the tuna purse seine vessel new construction projects. The present research proposes consequently an approach concerning cost and term analyses, in order to facilitate the decisions-taking process. Concretely, a series of tools and diagrams to examine the impact of the improvements proposed are presented, comparing the needed resources for their implementation and their results in cost and term. These analyses will contemplate as the direct results associated with the project object of the study, as its transversal impact with other organisational projects. The implications for project managers and final clients are clear, explaining them through different diagrams and graphics. These analyses are proposed to enhance programs management and their corresponding marketing strategy, obtaining therefore a superior competitiveness.
On incorporating social speaker characteristics in synthetic speech

In our previous work, we derived the acoustic features, that contribute to the perception of warmth and competence in synthetic speech. As an extension, in our current work, we investigate the impact of the derived vocal features in the generation of the desired characteristics. The acoustic features, spectral flux, F1 mean and F2 mean and their convex combinations were explored for the generation of higher warmth in female speech. The voiced slope, spectral flux, and their convex combinations were investigated for the generation of higher competence in female speech. We have employed a feature quantization approach in the traditional end-to-end tacotron based speech synthesis model. The listening tests have shown that the convex combination of acoustic features displays higher Mean Opinion Scores of warmth and competence when compared to that of individual features.
LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments

In argumentative discourse, persuasion is often achieved by refuting or attacking others’ arguments. Attacking an argument is not always straightforward and often consists of complex rhetorical moves in which arguers may agree with a logic of an argument while attacking another logic. Furthermore, an arguer may neither deny nor agree with any logics of an argument, instead ignore them and attack the main stance of the argument by providing new logics and presupposing that the new logics have more value or importance than the logics presented in the attacked argument. However, there are no studies in computational argumentation that capture such complex rhetorical moves in attacks or the presuppositions or value judgments in them. To address this gap, we introduce LPAttack, a novel annotation scheme that captures the common modes and complex rhetorical moves in attacks along with the implicit presuppositions and value judgments. Our annotation study shows moderate inter-annotator agreement, indicating that human annotation for the proposed scheme is feasible. We publicly release our annotated corpus and the annotation guidelines.
Learning-Based Approaches for Graph Problems: A Survey

Over the years, many graph problems speciﬁcally those in NP-complete are studied by a wide range of researchers. Some famous examples include graph colouring, travelling salesman problem and subgraph isomorphism. Most of these problems are typically addressed by exact algorithms, approximate algorithms and heuristics. There are however some drawback for each of these methods. Recent studies have employed learning-based frameworks such as machine learning techniques in solving these problems, given that they are useful in discovering new patterns in structured data that can be represented using graphs. This research direction has successfully attracted a considerable amount of attention. In this survey, we provide a systematic review mainly on classic graph problems in which learning-based approaches have been proposed in addressing the problems. We discuss the overview of each framework, and provide analyses based on the design and performance of the framework. Some potential research questions are also suggested. Ultimately, this survey gives a clearer insight and can be used as a stepping stone to the research community in studying problems in this ﬁeld.
Matrix Completion with Sparse Noisy Rows

Exact matrix completion and low rank matrix estimation problems has been studied in different underlying conditions. In this work we study exact low-rank completion under non-degenerate noise model. Non-degenerate random noise model has been previously studied by many researchers under given condition that the noise is sparse and existing in some of the columns. In this paper, we assume that each row can receive random noise instead of columns and propose an interactive algorithm that is robust to this noise. We show that we use a parametrization technique to give a condition when the underlying matrix could be recoverable and suggest an algorithm which recovers the underlying matrix.
Structural randomised selection

An important problem in the analysis of high-dimensional omics data is to identify subsets of molecular variables that are associated with a phenotype of interest. This requires addressing the challenges of high dimensionality, strong multicollinearity and model uncertainty. We propose a new ensemble learning approach for improving the performance of sparse penalised regression methods, called STructural RANDomised Selection (STRANDS). The approach, that builds and improves upon the Random Lasso method, consists of two steps. In both steps, we reduce dimensionality by repeated subsampling of variables. We apply a penalised regression method to each subsampled dataset and average the results. In the ﬁrst step, subsampling is informed by variable correlation structure, and in the second step, by variable importance measures from the ﬁrst step. STRANDS can be used with any sparse penalised regression approach as the “base learner”. Using synthetic data and real biological datasets, we demonstrate that STRANDS typically improves upon its base learner, and that taking account of the correlation structure in the ﬁrst step can help to improve the eﬃciency with which the model space may be explored.
Simultaneous Evidence of Edge Collapse and Hub-filament Configurations: A Rare Case Study of a Giant Molecular Filament, G45.3+0.1

We study multiwavelength and multiscale data to investigate the kinematics of molecular gas associated with the star-forming complexes G045.49+00.04 (G45E) and G045.14+00.14 (G45W) in the Aquila constellation. An analysis of the FUGIN 13CO(1–0) line data unveils the presence of a giant molecular filament (GMF G45.3+0.1; length ∼75 pc, mass ∼1.1 × 106 M ⊙) having a coherent velocity structure at [53, 63] km s−1. The GMF G45.3+0.1 hosts G45E and G45W complexes at its opposite ends. We find large-scale velocity oscillations along GMF G45.3+0.1, which also reveals the linear velocity gradients of −0.064 and +0.032 km s−1 pc−1 at its edges. The photometric analysis of point-like sources shows the clustering of young stellar object (YSO) candidate sources at the filament’s edges where the presence of dense gas and H ii regions are also spatially observed. The Herschel continuum maps along with the CHIMPS 13CO(3–2) line data unravel the presence of parsec scale hub-filament systems (HFSs) in both sites, G45E and G45W. Our study suggests that the global collapse of GMF G45.3+0.1 is end dominated, with the addition to the signature of global nonisotropic collapse at the edges. Overall, GMF G45.3+0.1 is the first observational sample of filament where the edge-collapse and the hub-filament configurations are simultaneously investigated. These observations open the new possibility of massive star formation, including the formation of HFSs.
Phonon heat capacity and self-heating normal domains in NbTiN nanostrips

Self-heating normal domains in thin superconducting NbTiN nanostrips with the granular structure were characterized via steady-state hysteretic current–voltage characteristics measured at different substrate temperatures. The temperature dependence and the magnitude of the current, which sustains a domain in equilibrium at different voltages, can only be explained with a phonon heat capacity noticeably less than expected for 3D Debye phonons. This reduced heat capacity coincides with the value obtained earlier from magnetoconductance and photoresponse studies of the same films. The rate of heat flow from electrons at a temperature Te to phonons in the substrate at a temperature TB is proportional to (Tep−TBp) with the exponent p ≈ 3, which differs from the exponents for heat flows mediated by the electron–phonon interaction or by escaping of 3D Debye phonons via the film/substrate interface. We attribute both findings to the effect of grains on the phonon spectrum of thin NbTiN films. Our findings are significant for understanding the thermal transport in superconducting devices exploiting thin granular films.