Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a generative model of natural motion

Understanding how animals move has fascinated scientists in many different disciplines since the ancient Greece. Today we have an unprecedented ability to study the control mechanisms of animal's functional activities, analyze the kinematic and dynamic properties of the locomotion, and build scientific models to recreate and predict a wide range of natural motions. The power that enables us to achieve what the scientists of the past could only hypothesize stems from three sources. First, we have more complete and integrated domain knowledge across biomechanics, robotics, kinesiology, and physics. Second, we have better computing tools to tackle the problems that were intractable in the past. Third, we have more accurate and efficient data acquisition methods that enable large datasets of motions in details. This thesis work introduces a physics-based model that makes the first step towards a generative model for synthesis of human natural motions. We design a model that produces both highly dynamic motions and low energy motions. Exploiting the dynamic properties of highly dynamic motions, we describe a simple but powerful algorithm to synthesize realistic motions such as jumping, diving, and gymnastics. For low energy motions that are not strictly determined by the dynamic rules, we introduce a physics-based representation of motion style based on several factors of locomotion derived from biomechanical hypotheses. We also introduce an automatic method for extracting style parameters from a single motion sequence and for synthesizing a new motion sequence in the same style but performing a different task. In practice, this generative model can be applied to producing realistic motions in real-time in a parameterized motion space. Furthermore, we can extend this model to creating complex motion with multiple characters.
Asset-Based Futures: A Sociology for the 21st Century

This article engages with the question of whether the COVID-19 pandemic can be understood as an event that is moving us towards a new era. Highlighting the paradox that this question has emerged in the context of the stoppages and shutdowns associated with the pandemic, we suggest that any assessment of social change and the futures such change might unfold must be situated on the terrain formatted by the temporal logics of capitalism. At our current juncture this requires that sociology as a discipline understand these logics as asset rather than commodity based. Drawing on state responses to the pandemic in Australia, we show how mundane payments play a critical role in these logics, operating as differentiating technologies of time. We suggest that for sociology to build a sociology of futures that is relevant for the 21st century, it must come to terms with the time machine of the asset economy.
Randomized Pricing with Deferred Acceptance for Revenue Maximization with Submodular Objectives

A lot of applications in web economics need to maximize the revenue under a budget for payments and also guarantee the truthfulness of users, so Budget-Feasible Mechanism (BFM) Design has aroused great interests during last decade. Most of the existing BFMs concentrate on maximizing a monotone submodular function subject to a knapsack constraint, which is insufficient for many applications with complex objectives or constraints. Observing this, the recent studies (e.g., [4, 5, 11]) have considered non-monotone submodular objectives or more complex constraints such as a k-system constraint. In this study, we follow this line of research and propose truthful BFMs with improved performance bounds for non-monotone submodular objectives with or without a k-system constraint. Our BFMs leverage the idea of providing random prices to users while deferring the decision on the final winning set, and are also based on a novel randomized algorithm for the canonical constrained submodular maximization problem achieving better performance bounds compared to the state-of-the-art. Finally, the effectiveness and efficiency of our approach are demonstrated by extensive experiments on several applications about social network marketing, crowdsourcing and personalized recommendation.
Polarization Charge Density in Strained Graphene

Graphene, the world’s first truly two-dimensional material, is unique for having an electronic structure described by an effective Lorentz invariant theory. One important consequence is that the ratio or Coulomb energy to kinetic energy is a constant, depending only on conditions within the lattice rather than on the average charge density as in a typical Galilean invariant material. Given this unusual property, a natural question would be how do phenomena, such as screening of a Coulomb impurity, happen in graphene? Moreover, how does the addition of uniaxial strain enhance or diminish this behavior? Here I discuss our work to calculate the charge density distribution in a lattice of strained graphene under the effect of an external Coulomb impurity. Graphene can have its band structure significantly altered by the application of uniaxial strain. Two cases are here explored: relatively weak strain at some finite chemical potential, and extreme strain with zero chemical potential. In the first system, the strain induces elliptic Dirac cones, engendering some inherent directionality to graphene’s electronic properties that did not exist before. This anisotropy manifests itself in the polarization function, and so too in the screening charge density. A finite chemical potential in this case is necessary for any screening to take place in graphene since, without it, there are no electron states near the Fermi level to polarize. Both in the strained and unstrained case, decaying oscillations known as Friedel oscillations are observed. The result of strain is a multifaceted anisotropy of the charge distribution: the amplitude, frequency, and the position of the first peak in the oscillations are each varied depending on the direction one observes. In the second system, extreme strain in graphene leads to a merging of Dirac cones, yielding a transition to a new energy spectrum. This band structure is unusual in that it becomes quadratic along the direction of strain while remaining linear along the perpendicular. We evaluate the screening response to a Coulomb impurity in this case at zero chemical potential, and yet long-range distribution tails are still observed. The result is a very exotic charge distribution, in which the radial distribution of charge and the angular distribution are highly coupled, and at various distances, both screening and anti-screening regions are observed around the impurity. The anti-screening regions are local, and the net induced charge density still satisfies the accepted model of screening.
Human-in-the-loop Regular Expression Extraction for Single Column Format Inconsistency

Format inconsistency is one of the most frequently appearing data quality issues encountered during data cleaning. Existing automated approaches commonly lack applicability and generalisability, while approaches with human inputs typically require specialized skills such as writing regular expressions. This paper proposes a novel hybrid human-machine system, namely “Data-Scanner-4C”, which leverages crowdsourcing to address syntactic format inconsistencies in a single column effectively. We first ask crowd workers to create examples from single-column data through “data selection” and “result validation” tasks. Then, we propose and use a novel rule-based learning algorithm to infer the regular expressions that propagate formats from created examples to the entire column. Our system integrates crowdsourcing and algorithmic format extraction techniques in a single workflow. Having human experts write regular expressions is no longer required, thereby reducing both the time as well as the opportunity for error. We conducted experiments through both synthetic and real-world datasets, and our results show how the proposed approach is applicable and effective across data types and formats.
Leveraging the Community Structure of a Social Network for Maximizing the Spread of Influence

We consider the problem of Influence Maximization (IM), the task of selecting k seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme. Our experiments on a real-world social network show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence.
A Method to Assess and Explain Disparate Impact in Online Retailing

This paper presents a method for assessing whether algorithmic decision making induces disparate impact in online retailing. The proposed method specifies a statistical design, a sampling algorithm, and a technological setup for data collection through web crawling. The statistical design reduces the dimensionality of the problem and ensures that the data collected are representative, variation-rich, and suitable for the investigation of the causes behind any observed disparities. Implementations of the method can collect data on algorithmic decisions, such as price, recommendations, and delivery fees that can be matched to website visitor demographic data from established sources such as censuses and large scale surveys. The combined data can be used to investigate the presence and causes of disparate impact, potentially helping online retailers audit their algorithms without collecting or holding the demographic data of their users. The proposed method is illustrated in the context of the automated pricing decisions of a leading retailer in the United States. A custom-built platform implemented the method to collect data for nearly 20,000 different grocery products at more than 3,000 randomly-selected zip codes. The data collected indicates that prices are higher for locations with high proportions of minority households. Although these price disparities can be partly attributed to algorithmic biases, they are mainly explained by local factors and therefore can be regarded as business necessities.
Underdetermined Blind Source Separation Algorithm Based on Normal Vector of Hyperplane

Discussion of blind signal separation problem under underdetermined case(i.e.,the case of less observed signals than sources)is presented.First,a formula to calculate the normal vector of any hyperplane is given and a mixing matrix recovery algorithm based on the normal vector of any hyperplane is proposed.Second,for audio signal,k-source intervals are introduced and a method to detect them is proposed.So,the algorithms under the k-SCA condition are extended to blind non-sparse signal separation.To reconstruct the sources,a new algorithm is proposed to simplify the l~1-norm solution.Several experiments demonstrate the performance of the proposed algorithm.
IoT System Vulnerability Analysis and Network Hardening with Shortest Attack Trace in a Weighted Attack Graph

In recent years, Internet of Things (IoT) devices have been extensively deployed in edge networks, including smart homes and offices. Despite the exciting opportunities afforded by the advancements in the IoT, it also introduces new attack vectors and vulnerabilities in the system. Existing studies have shown that the attack graph is an effective model for performing system-level analysis of IoT security. In this paper, we study IoT system vulnerability analysis and network hardening. We first extend the concept of attack graph to weighted attack graph and design a novel algorithm for computing a shortest attack trace in a weighted attack graph. We then formulate the network hardening problem. We prove that this problem is NP-hard, and then design an exact algorithm and a heuristic algorithm to solve it. Extensive experiments on 9 synthetic IoT systems and 2 real-world smart home IoT testbeds demonstrate that our shortest attack trace algorithm is robust and fast, and our heuristic network hardening algorithm is efficient in producing near optimal results compared to the exact algorithm.
Poster Abstract: Towards Distributed Machine Learning for Data Acquisition in Wireless Sensor Networks

Wireless sensor networks (WSNs) use low-cost sensors to monitor various environments, offering accurate and continuous surveillance. WSNs face a significant challenge in managing their limited energy resources due to communication overhead. To address this issue, we present a novel approach that leverages Neural Network (NN) models to predict data and reduce communication in WSNs. Our solution incorporates NN models on both the sensor and the cloud, enabling predictions to be made at the local level. The sensor sends data to the cloud only when the model is no longer able to predict accurately, cloud then fine-tunes the model based on the received data and sends updated weights of the NN to the sensor, reducing the need for communicating each sensed value to the cloud.
CMINet: a Graph Learning Framework for Content-aware Multi-channel Influence Diffusion

The phenomena of influence diffusion on social networks have received tremendous research interests in the past decade. While most prior works mainly focus on predicting the total influence spread on a single network, a marketing campaign that exploits influence diffusion often involves multiple channels with various information disseminated on different media. In this paper, we introduce a new influence estimation problem, namely Content-aware Multi-channel Influence Diffusion (CMID), and accordingly propose CMINet to predict newly influenced users, given a set of seed users with different multimedia contents. In CMINet, we first introduce DiffGNN to encode the influencing power of users (nodes) and Influence-aware Optimal Transport (IOT) to align the embeddings to address the distribution shift across different diffusion channels. Then, we transform CMID into a node classification problem and propose Social-based Multimedia Feature Extractor (SMFE) and Content-aware Multi-channel Influence Propagation (CMIP) to jointly learn the user preferences on multimedia contents and predict the susceptibility of users. Furthermore, we prove that CMINet preserves monotonicity and submodularity, thus enabling (1 − 1/e)-approximate solutions for influence maximization. Experimental results manifest that CMINet outperforms eleven baselines on three public datasets.
Demo Abstract: Collaborative Real-Time Scheduling (CRTS) Algorithm for AGV Transportation System within a CPS Architecture

The use of Autonomous Guided Vehicles (AGVs) is important in smart factories to enhance manufacturing operations. However, current AGV scheduling algorithms are inadequate and not connected to real-time data on manufacturing needs, leading to underutilized AGVs and missed delivery deadlines. The lack of algorithms that ensure timely delivery of materials results in idle time of AGVs and material shortages. We present a collaborative real-time scheduling (CRTS) algorithm for AGVs in smart factories. The algorithm not only ensures timely delivery of materials to processing units but also predicts the minimum number of AGVs required. The algorithm is designed to operate within a cyber-physical system architecture, where AGVs and processing units exchange data via a wireless network. The simulation results on the Node-Red platform show that the algorithm is efficient and adequate to meet real-time delivery requirements, with an average AGV utilization of over 92%.
Practical applicability of user experience capability/maturity models in the development processes of free and open-source software: a systematic literature review

ABSTRACT The popularity of User Experience (UX) is exponentially mounting because it predicts users’ engagement through satisfying needs. It further determines product success, revenues augmentation, employee gratifications, user errors, and vital supports. However, contemporary UX Capability Maturity Models (UXCMMs) do not adequately address Free and Open Source Software (FOSS) UX, a substantiated root of adoption misfortune. Improving UX may result from a precise appraisal of the as-is organisational status. Although Software Engineering endorses CMMI, ISO 15504/ISO/IECTS 33061 and UXCMMs for this judgement, none has processes for evaluating FOSSs’ UX. Moreover, the FOSS community has neither theories nor hypotheses to map UXCMMs’ formulation processes. It knows very little regarding their practical applicability, which leaves functional relevance uncertainties. In retorting to present uncertainties, this study accomplished the Systematic Literature Review and encountered eight pertinent articles. They show that most UXCMMs originated from recognised models. Moreover, neither developed nor validated experimentally, used similar methodologies with patched documents and are not exclusive for the FOSS community. They neither handle challenges properly nor reflect earlier studies’ endorsements. The study’s contributions to discovered gaps include proposing additional research areas and a FOSS-specific UXCMMs development checklist to identify UX metrics and standardise UXCMMs’ development processes.
X‐Ray Spectral and Timing Observations of AO Piscium

Hard X‐ray observations of AO Piscium acquired using the Proportional Counter Array detector on board the Rossi X‐Ray Timing Explorer (RXTE) satellite on 1997 July 11–15 are presented. The data are modeled using theoretical spectra from two‐temperature radiative shocks. Based on fits to the X‐ray continuum, we find best‐fit shock models for M* = 0.60 ± 0.05 M⊙, consistent with earlier works. Using our models, we next consider the result of Mukai et al., who found that the soft X‐ray emission‐line spectrum for AO Psc acquired by Chandra was better fitted by a photoionization model powered by a power‐law spectrum rather than by a cooling‐flow model. The power‐law spectral index found by Mukai et al. is not consistent with the RXTE data but could be produced by a shock generated by accretion onto a massive white dwarf, M*≳1.2 M⊙. Furthermore, a high‐mass white dwarf shock model, M* ≈ 1 M⊙, is consistent with the Fe lines at 6.7 and 6.97 keV found by Mukai et al. These lines are generally thought to be produced by thermal plasma. The Chandra result that the white dwarf in AO Psc is more massive than implied by earlier X‐ray observations is intriguing and warrants further study. Optical studies have found that the white dwarf in AO Psc is spinning up. We show that based on X‐ray data acquired from 1983 to 1997, the spin period had small P˙ consistent with zero.
USE OF HIGH‐ STRENGTH STEEL FOR SLENDER MEDIUM SPAN BRIDGES: TWO RECENT CASE STUDIES IN FRANCE

This paper presents two cases of existing bridges with prestressed concrete deck replaced by composite steel‐concrete decks. The new decks provide a larger carriage width with increased load carrying capacity according to current design requirements. Furthermore, they have less piers (and therefore larger spans) but still keep the same construction height of the previous solutions. This leads to very slender continuous decks which are an optimal field of application for high‐strength steel. The technical solution adopted is an evolution of the traditional technology of composite steel‐concrete deck, commonly used in the span range between 20m and 40m.
Analysis of antennal responses to motion stimuli in the honey bee by automated tracking using DeepLabCut

Considering recent developments in gene manipulation methods for honey bees, establishing simple, robust, and indoor assay systems which can analyze behavioral components in detail is important for the rise of honey bee behavioral genetics. We focused on the movements of antennae of the honey bee, which are used for not only multimodal sensory perception but also interactions between individuals. We developed an experimental system for analyzing the antennal responses (ARs) of the honey bee using DeepLabCut, a markerless posture-tracking tool using deep learning. The tracking of antennal movements during the presentation of vertical (downward and upward) motion stimuli using DeepLabCut successfully detected the ARs reported in the previous studies, where bees tilted their antennae in the direction opposite to the motion stimuli. In addition, we successfully detected ARs in response to horizontal (forward and backward) motion stimuli. An investigation of the developmental maturation of honey bee ARs showed that ARs to motion stimuli were not detected in bees immediately after emergence but became detectable through post-emergence development in an experience-independent manner. Furthermore, unsupervised clustering analysis using multidimensional data created by processing tracking data using DeepLabCut classified antennal movements into different clusters, suggesting that data-driven behavioral classification can apply to AR paradigms. These results reveal novel AR to visual stimuli and developmental maturation of ARs and suggest the efficacy of data-driven analysis for behavioral classification in behavioral studies of the honey bee. Summary statement Automated tracking using DeepLabCut was successfully applied to measure the antennal response to motion stimuli and unsupervised classification of antennal movements in honey bees.
Convergence of Multilevel MCMC methods on path spaces

Within this work, the efficiency of Markov Chain Monte Carlo methods on infinite dimensional spaces, such as function spaces, is analyzed. We study two aspects in this respect: The first aspect is a Multilevel Markov Chain Monte Carlo algorithm. It extends a Multilevel Monte Carlo method introduced by Giles to Markov Chains, and overcomes the need for a trade–off between discretization error and Monte Carlo error. We develop the Multilevel algorithm, state and prove its order of convergence and show results of numerical simulations. The second part of this work deals with the analysis of the speed of convergence of the Metropolis Adjusted Langevin Algorithm (MALA). Controlling the speed of convergence is an important tool for bounding the error of Markov Chain Monte Carlo methods. It is also a crucial ingredient for bounding the order of convergence of the Multilevel algorithm. We apply a method of Eberle to the Hilbert space case and obtain a subexponential bound on the distance of the distribution of the MALA–process to its invariant measure. Both aspects are illustrated by an application from molecular dynamics called Transition Path Sampling. In this example, Markov Chain Monte Carlo methods on path spaces are used to simulate the properties of transitions from one metastable state of a molecule to another. We present this application and apply the results on the Multilevel estimator and the MALA–process in this context.
A Direct Comparison of the Loudness of Pure Tones

The loudness of pure tones of frequencies 60 to 4000 cycles and for intensities from the threshold to 90 T.U. above the 700 cycle threshold. \char22{}The loudness of eleven pure tones was studied by adjusting the voltage applied to a telephone receiver to make these tones as loud as certain fixed levels of a 700 cycle tone. The average results of 22 observers, 11 men and 11 women, were arranged as contour lines of equal loudness through the normal auditory sensation area in terms of r.m.s. pressure in ear canal as a function of frequency. Frequencies from 60 to 4000 cycles were used and intensities from threshold of audibility to 90 T.U. above the 700 cycle threshold. It was found that if the amplitudes of pure tones are increased in equal ratios the loudness of low frequency tones increases much more rapidly than that of high frequency tones. For frequencies above 700 cycles the rate is nearly uniform.