Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphical models for nonstationary time series

We propose NonStGM, a general nonparametric graphical modeling framework for studying dynamic associations among the components of a nonstationary multivariate time series. It builds on the framework of Gaussian Graphical Models (GGM) and stationary time series Gaussian Graphical model (StGM), and complements existing works on parametric graphical models based on change point vector autoregressions (VAR). Analogous to StGM, the proposed framework captures conditional noncorrelations (both intertemporal and contemporaneous) in the form of an undirected graph. In addition, to describe the more nuanced nonstationary relationships among the components of the time series, we introduce the new notion of conditional nonstationarity/stationarity and incorporate it within the graph architecture. This allows one to distinguish between direct and indirect nonstationary relationships among system components, and can be used to search for small subnetworks that serve as the"source"of nonstationarity in a large system. Together, the two concepts of conditional noncorrelation and nonstationarity/stationarity provide a parsimonious description of the dependence structure of the time series.
Dynamic Gesture Recognition

The Human-Machine Interaction (HMI) research field is an important topic in machine learning that has been deeply investigated thanks to the rise of computing power in the last years. The first time, it is possible to use machine learning to classify images and/or videos instead of the traditional computer vision algorithms. The aim of this paper is to build a symbiosis between a convolutional neural network (CNN) and a recurrent neural network (RNN) to recognize cultural/anthropological Italian sign language gestures from videos. The CNN extracts important features that later are used by the RNN. With RNNs we are able to store temporal information inside the model to provide contextual information from previous frames to enhance the prediction accuracy. Our novel approach uses different data augmentation techniques and regularization methods from only RGB frames to avoid overfitting and provide a small generalization error.
Unsupervised View-Invariant Human Posture Representation

Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features. However, acquiring 3D skeleton data can be cumbersome, if not impractical, in in-the-wild scenarios. To overcome this problem, we present a novel unsupervised approach that learns to extract view-invariant 3D human pose representation from a 2D image without using 3D joint data. Our model is trained by exploiting the intrinsic view-invariant properties of human pose between simultaneous frames from different viewpoints and their equivariant properties between augmented frames from the same viewpoint. We evaluate the learned view-invariant pose representations for two downstream tasks. We perform comparative experiments that show improvements on the state-of-the-art unsupervised cross-view action classification accuracy on NTU RGB+D by a significant margin, on both RGB and depth images. We also show the efficiency of transferring the learned representations from NTU RGB+D to obtain the first ever unsupervised cross-view and cross-subject rank correlation results on the multi-view human movement quality dataset, QMAR, and marginally improve on the-state-of-the-art supervised results for this dataset. We also carry out ablation studies to examine the contributions of the different components of our proposed network.
Recommender Systems Based on Graph Embedding Techniques: A Review

As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user’s preferred items from millions of candidates by analyzing observed user-item relations. As for alleviating the sparsity and cold start problems encountered by recommender systems, researchers generally resort to employing side information or knowledge in recommendation as a strategy for uncovering hidden (indirect) user-item relations, aiming to enrich observed information (or data) for recommendation. However, in the face of the high complexity and large scale of side information and knowledge, this strategy largely relies for efficient implementation on the scalability of recommendation models. Not until after the prevalence of machine learning did graph embedding techniques be a recent concentration, which can efficiently utilize complex and large-scale data. In light of that, equipping recommender systems with graph embedding techniques has been widely studied these years, appearing to outperform conventional recommendation implemented directly based on graph topological analysis (or resolution). As the focus, this article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs and knowledge graphs, and proposes a general design pipeline of that. In addition, after comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models on simulations, this article manifests that the conventional models can still overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the comparative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and conventional recommendation in different tasks, and puts forward some open questions.
Surface activity of cancer cells: The fusion of two cell aggregates

A key feature that distinguishes cancer cells from all other cells is the capability to spread throughout the body. Although a good comprehension of how cancer cells collectively migrate by following molecular rules which influence the state of cell-cell adhesion contacts has been generated, the impact of collective migration on cellular rearrangement from subcellular to supracellular level remains less understood. Thus, considering collective cell migration (CCM) of cancer mesenchymal cells on one side and healthy epithelial cells on the other during the fusion of two cell aggregates could result in a powerful tool in order to address the contribution of structural changes at subcellular level which influence the cellular rearrangements and help to understand this important, but still controversial topic. While healthy epithelial cells undergo volumetric cell rearrangement driven by the tissue surface tension, which results in a collision of opposite directed velocity front near the contact point between two cell aggregates, mesenchymal cells follow quite different scenario. These cells are capable of reducing the surface tension and undergo surface cell rearrangement. The main goal of this contribution is to discuss the origin of surface activity of cancer cells by accounting for the crosstalk between cell-cell and cell-ECM adhesion contacts influenced by the cell contractility.
Change of human mobility during COVID-19: A United States case study

With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.
A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration

In this letter, we address the problem of autonomous exploration of unknown environments with an aerial robot equipped with a sensory set that produces large point clouds, such as LiDARs. The main goal is to gradually explore an area while planning paths and calculating information gain in short computation time, suitable for implementation on an on-board computer. To this end, we present a planner that randomly samples viewpoints in the environment map. It relies on a novel and efficient gain calculation based on the Recursive Shadowcasting algorithm. To determine the Next-Best-View (NBV), our planner uses a cuboid-based evaluation method that results in an enviably short computation time. To reduce the overall exploration time, we also use a dead end resolving strategy that allows us to quickly recover from dead ends in a challenging environment. Comparative experiments in simulation have shown that our approach outperforms the current state-of-the-art in terms of computational efficiency and total exploration time. The video of our approach can be found at https://www.youtube. com/playlist? list =PLC0C6uwoEQ8ZDhny1VdmFXLeTQOSBibQl.
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer

Unsupervised domain adaptation for semantic segmentation aims to make models trained on synthetic data (source domain) adapt to real images (target domain). Previous feature-level adversarial learning methods only consider adapting models on the high-level semantic features. However, the large domain gap between source and target domains in the high-level semantic features makes accurate adaptation difficult. In this paper, we present the first attempt at explicitly using low-level edge information, which has a small inter-domain gap, to guide the transfer of semantic information. To this end, a semantic-edge domain adaptation architecture is proposed, which uses an independent edge stream to process edge information, thereby generating high-quality semantic boundaries over the target domain. Then, an edge consistency loss is presented to align target semantic predictions with produced semantic boundaries. Moreover, we further propose two entropy reweighting methods for semantic adversarial learning and self-supervised learning, respectively, which can further enhance the adaptation performance of our architecture. Comprehensive experiments on two UDA benchmark datasets demonstrate the superiority of our architecture compared with state-of-the-art methods.
Energy absorbency and phase stability during NaCl solution icing

Deeper insight into the functionality of salt solute on tuning the energy absorbency R and the freezing temperature TN during phase transition is pivotal to many subject areas. Here we show that NaCl solvation turns the fS = nhC portion molecules into the hydrating supersolid phase by ionic polarization and leaves the rest fO = 1 – nhC portion ordinary. The C is the solute molar concentration and nh the number of molecules saturating the hydration per pair of Na + + Cl solute. Polarization shortens and stiffens the H–O bond and does the O:H nonbond contrastingly in the supersolid. Water absorbs energy by H–O cooling contraction in the quasisolid phase during the process of Liquid– Quasisolid–Ice transition. The solution R drops with the fO loss till zero corresponding to C = 0.1 and nh = 10 that saturates the solvation per solute at least. The polarization-weakening of the O:H nonbonds lowers the TN < 253 K of the supersolid phase. 1 College of Geology and Environment, Xi'an University of Science and Technology, Xi'an 710054, China (shenyj@xust.edu.cn; 19204053043@stu.xust.edu.cn) 2 Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China (yongzhi_wang@tongji.edu.cn) EBEAM, School of Materials Science and Engineering, Yangtze Normal University, Chongqing 408100, China (Lilei@yznu.edu.cn) 4 Key Laboratory of Low-dimensional Materials and Application Technology and School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China (Huangyongli@xtu.edu.cn) 5 School of EEE, Nanyang Technological University, Singapore 639798, Singapore (ecqsun@ntu.edu.sg; corresponding author)
Incremental Learning Techniques for Online Human Activity Recognition

Unobtrusive and smart recognition of human activities using smartphones inertial sensors is an interesting topic in the field of artificial intelligence acquired tremendous popularity among researchers, especially in recent years. A considerable challenge that needs more attention is the realtime detection of physical activities, since for many realworld applications such as health monitoring and elderly care, it is required to recognize users’ activities immediately to prevent severe damages to individuals’ wellness. In this paper, we propose a human activity recognition (HAR) approach for the online prediction of physical movements, benefiting from the capabilities of incremental learning algorithms. We develop a HAR system containing monitoring software and a mobile application that collects accelerometer and gyroscope data and send them to a remote server via the Internet for classification and recognition operations. Six incremental learning algorithms are employed and evaluated in this work and compared with several batch learning algorithms commonly used for developing offline HAR systems. The Final results indicated that considering all performance evaluation metrics, Incremental K-Nearest Neighbors and Incremental Naïve Bayesian outperformed other algorithms, exceeding a recognition accuracy of 95% in real-time.
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation

Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g. their gender or ethnicity. Style transfer is an effective way of transforming texts in order to remove any information that enables author profiling. However, for a number of current state-of-the-art approaches the improved privacy is accompanied by an undesirable drop in the down-stream utility of the transformed data. In this paper, we propose a simple, zero-shot way to effectively lower the risk of author profiling through multilingual back-translation using off-the-shelf translation models. We compare our models with five representative text style transfer models on three datasets across different domains. Results from both an automatic and a human evaluation show that our approach achieves the best overall performance while requiring no training data. We are able to lower the adversarial prediction of gender and race by up to 22% while retaining 95% of the original utility on downstream tasks.
A Bibliometric Analysis of the Surveys in Blockchain

—Blockchain has become an emerging technology in the ﬁeld of computer science and is used for asset security, anonymity, and veriﬁability, etc. Enormous use cases of blockchain, e.g., for making online payments, storing or sharing of private data in a tamper-proof way has attracted the research community in the past decade. In this regard, a lot of survey papers have been published focusing on the domain-speciﬁc exploratory study of applying blockchain in that particular domain. In this paper, we collected those survey papers published and collected 542 unique papers covering the span of 2017 to 2020 published in different venues. We then perform a bibliometric study on those papers and provide interesting insights on different features extracted from that papers. We hope this study will serve as a valuable resource for the blockchain community.
What do fact checkers fact-check when?

Recent research suggests that not all fact checking efforts are equal: when and what is fact checked plays a pivotal role in effectively correcting misconceptions. In this paper, we propose a framework to study fact checking efforts using Google Trends, a signal that captures search interest over topics on the world's largest search engine. Our framework consists of extracting claims from fact checking efforts, linking such claims with knowledge graph entities, and estimating the online attention they receive. We use this framework to study a dataset of 879 COVID-19-related fact checks done in 2020 by 81 international organizations. Our findings suggest that there is often a disconnect between online attention and fact checking efforts. For example, in around 40% of countries where 10 or more claims were fact checked, half or more than half of the top 10 most popular claims were not fact checked. Our analysis also shows that claims are first fact checked after receiving, on average, 35% of the total online attention they would eventually receive in 2020. Yet, there is a big variation among claims: some were fact checked before receiving a surge of misinformation-induced online attention, others are fact checked much later. Overall, our work suggests that the incorporation of online attention signals may help organizations better assess and prioritize their fact checking efforts. Also, in the context of international collaboration, where claims are fact checked multiple times across different countries, online attention could help organizations keep track of which claims are "migrating" between different countries.
HYouTube: Video Harmonization Dataset

Video composition aims to generate a composite video by combining the foreground of one video with the background of another video, but the inserted foreground may be incompatible with the background in terms of color and illumination. Video harmonization aims to adjust the foreground of a composite video to make it compatible with the background. So far, video harmonization has only received limited attention and there is no public dataset for video harmonization. In this work, we construct a new video harmonization dataset HYouTube by adjusting the foreground of real videos to create synthetic composite videos. Considering the domain gap between real composite videos and synthetic composite videos, we additionally create 100 real composite videos via copy-and-paste. Datasets are available at https://github.com/bcmi/Video-Harmonization-Dataset-HYouTube.
Automatic 3D Ultrasound Segmentation of Uterus Using Deep Learning

On-line segmentation of the uterus can aid effective image-based guidance for precise delivery of dose to the target tissue (the uterocervix) during cervix cancer radiotherapy. 3D ultrasound (US) can be used to image the uterus, however, finding the position of uterine boundary in US images is a challenging task due to large daily positional and shape changes in the uterus, large variation in bladder filling, and the limitations of 3D US images such as low resolution in the elevational direction and imaging aberrations. Previous studies on uterus segmentation mainly focused on developing semi-automatic algorithms where require manual initialization to be done by an expert clinician. Due to limited studies on the automatic 3D uterus segmentation, the aim of the current study was to overcome the need for manual initialization in the semi-automatic algorithms using the recent deep learning-based algorithms. Therefore, we developed 2D UNet-based networks that are trained based on two scenarios. In the first scenario, we trained 3 different networks on each plane (i.e., sagittal, coronal, axial) individually. In the second scenario, our proposed network was trained using all the planes of each 3D volume. Our proposed schematic can overcome the initial manual selection of previous semi-automatic algorithm.
Exemplar-Controllable Paraphrasing and Translation using Bitext

Most prior work on exemplar-based syntactically controlled paraphrase generation relies on automatically-constructed large-scale paraphrase datasets, which are costly to create. We sidestep this prerequisite by adapting models from prior work to be able to learn solely from bilingual text (bitext). Despite only using bitext for training, and in near zero-shot conditions, our single proposed model can perform four tasks: controlled paraphrase generation in both languages and controlled machine translation in both language directions. To evaluate these tasks quantitatively, we create three novel evaluation datasets. Our experimental results show that our models achieve competitive results on controlled paraphrase generation and strong performance on controlled machine translation. Analysis shows that our models learn to disentangle semantics and syntax in their latent representations, but still suffer from semantic drift.
Learning and Adaptation for Millimeter-Wave Beam Tracking and Training: A Dual Timescale Variational Framework

Millimeter-wave vehicular networks incur enormous beam-training overhead to enable narrow-beam communications. This paper proposes a learning and adaptation framework in which the dynamics of the communication beams are learned and then exploited to design adaptive beam-tracking and training with low overhead: on a long-timescale, a deep recurrent variational autoencoder (DR-VAE) uses noisy beam-training feedback to learn a probabilistic model of beam dynamics and enable predictive beam-tracking; on a short-timescale, an adaptive beam-training procedure is formulated as a partially observable (PO-) Markov decision process (MDP) and optimized via point-based value iteration (PBVI) by leveraging beam-training feedback and a probabilistic prediction of the strongest beam pair provided by the DR-VAE. In turn, beam-training feedback is used to refine the DR-VAE via stochastic gradient ascent in a continuous process of learning and adaptation. The proposed DR-VAE learning framework learns accurate beam dynamics: it reduces the Kullback-Leibler divergence between the ground truth and the learned model of beam dynamics by ~95% over the Baum-Welch algorithm and a naive learning approach that neglects feedback errors. Numerical results on a line-of-sight scenario with multipath and 3D beamforming reveal that the proposed dual timescale approach yields near-optimal spectral efficiency, and improves it by 130% over a policy that scans exhaustively over the dominant beam pairs, and by 20% over a state-of-the-art POMDP policy. Finally, a low-complexity policy is proposed by reducing the POMDP to an error-robust MDP, and is shown to perform well in regimes with infrequent feedback errors.