Reviewerly/exHarmony huggingface dataset:
Moving Virtual Reality out of its Comfort Zone and Into the African Kalahari Desert Field: Experiences From Technological Co-Exploration With an Indigenous San Community in Namibia

Indigenous people (IP) living in remote areas, at the margins of mainstream society, are often the last ones to experience emerging technologies and even less to shape those experiences. It could be argued technology exposure and experience is necessary for IP to gain agency in making informed decisions on the rejection or appropriation of novel technologies. In this paper, VR is introduced to a remote San community within a broader community-based research collaboration considering political and ethical perspectives of technology inclusion. The intent was to familiarise the community with the technology through the development and playthrough of a game, to explore future opportunities for joint co-designs of VR applications, meanwhile gauging the barriers for how VR operates outside of its intended setting. The community members expressed their excitement about the experience and the desire to re-create traditional San games in VR. The paper reflects on the community experiences, the setup and use of VR in remote settings, and the choices made to facilitate the familiarization of emerging technology.
Multivariate Visualization of Game Metrics

Visualizations have become a relevant instrument in games user research and analytics as well as for players to facilitate the analysis of behavioral data, including the evaluation of spatial game metrics. A fundamental task in this respect is to understand how different metrics vary across the game environment and how they relate to each other. In this paper, we evaluate a visualization based on hexagonal binning paired with visual glyphs to represent multivariate information. To study how much information can be encoded in a reasonable manner we created variants with varying complexity and measured correctness and efficiency for different types of tasks through an online study (N = 197). Our results indicate that players appreciated the clear display of multivariate data but that correctness and efficiency were also largely influenced by the visual coding and the task at hand. Based on gained insights, we discuss implications for the design of such and similar gameplay visualizations.
User Identity Linkage Across Social Media via Attentive Time-Aware User Modeling

In this paper, we work towards linking users’ identities on different social media platforms by exploring the user-generated contents (UGCs). This task is non-trivial due to the following challenges. 1) As UGCs involve multiple modalities (e.g., text and image), how to accurately characterize the user account based on their heterogeneous multi-modal UGCs poses the main challenge. 2) As people tend to post similar UGCs on different social media platforms during the same period, how to effectively model the temporal post correlation is a crucial challenge. And 3) no public benchmark dataset is available to support our user identity linkage based on heterogeneous UGCs with timestamps. Towards this end, we present an attentive time-aware user identity linkage scheme, which seamlessly integrates the temporal post correlation modeling and attentive user similarity modeling. To facilitate the evaluation, we create a comprehensive large-scale user identity linkage dataset from two popular social media platforms: Instagram and Twitter. Extensive experiments have been conducted on our dataset and the results verify the effectiveness of the proposed scheme. As a residual product, we have released the dataset, codes, and parameters to facilitate other researchers.
Kernel‐based low‐rank tensorized multiview spectral clustering

Multiview spectral clustering aims to separate data into different clusters efficiently by the use of multiview information. Many studies learn the affinity matrix from the original high-dimensional data, whose noise goes against the clustering results. Besides, some methods based on self-representation subspace clustering have a high time complexity. In this paper, we propose a simple, yet effective, and efficient method named Kernel-based Low-rank Tensorized Multiview Spectral Clustering (KLTMSC) to address these issues. Instead of using the original data to get the affinity matrix, KLTMSC learns the affinity matrix from kernel representation of the high-dimensional data to reduce the noisy information. Furthermore, to be robust to noise, the low-rank tensor is learned in the process of exploring the high-order correlations between data. Experiments on real-world data sets show that our method not only yields better results but also is quite time-saving compared with other state-of-the-art models.
The Causal Foundations of Applied Probability and Statistics

chapter Share on The Causal Foundations of Applied Probability and Statistics Author: Sander Greenland University of California, Los Angeles University of California, Los AngelesSearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 605–624https://doi.org/10.1145/3501714.3501747Online:04 March 2022Publication History 0citation18DownloadsMetricsTotal Citations0Total Downloads18Last 12 Months18Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Deep Dynamic Interest Learning With Session Local and Global Consistency for Click-Through Rate Predictions

Click-through rate (CTR) prediction is the core task in an online advertising system. How to capture the users’ dynamic interests through the behavior sequences to predict CTR is a challenging problem in the real-world applications. To address this challenge, we propose a deep dynamic interest learning by learning the local sessions and global sessions within sequences for CTR prediction. The local sessions and global sessions are used to capture the short-term dynamic interests and the long-term interests of users, respectively. To explore the heterogeneous behaviors within the global session efficiently, the interests in global sessions are forced to be consistent with that in local sessions. Then, the bi-LSTM network is adopted to capture the dynamic interests with the behaviors across global sessions effectively. Experimental results on various datasets show that the proposed method outperforms several state-of-the-art CTR prediction methods.
Kinematic joint descriptor and depth motion descriptor with convolutional neural networks for human action recognition

Human Action Recognition has gained a huge research interest due to its widespread applications in various fields. However, due to several challenges like noisy and occluded data, view-point variations, body sizes etc., still the action recognition remains a challenging task. Most of the existing action recognition methods focused on the single data type thereby the recognition system has limited performance. To improve the recognition performance, we have modeled a new approach for human action recognition from two different data types; they are depth images and skeleton joints. Two different descriptors are developed for action representation; they are Differential Depth Motion History Image for depth maps and Motion Kinematic Joint Descriptor for skeleton joints. To attain a discriminative feature set, we have trained three different Convolutional Neural Network Models and the results are fused for final action classification. Simulation is carried out over two public datasets and the obtained results indicate that the proposed approach outperforms state-of-art methods.
Influence of COVID-19 confinement on physics learning in engineering and science students

Abstract Due to the global alert about the risk of contagion and propagation of Covid-19, a situation that has changed in the educational processes, this has forced the system to migrate towards a more dynamic and connected model of teaching and learning, which has allowed not to interrupt and to continue with the training processes, in a remote and assisted way. In this research, the profile of the engineering and science student is analyzed, and its relationship with the learning of physics in this crisis. For this purpose, the technique of simple random sampling of a certain population was applied, a research instrument was designed and validated by pairs of experts, its reliability was established based on Cronbach’s Alpha test, and the instrument was applied to 354 students of the faculty of engineering and basic sciences, of the Universidad Francisco de Paula Santander, located in the city of Cúcuta, Colombia, during a 15-day time window, the analysis of the data and variables is carried out from the design of a mathematical model of logistic regression, where the dependent variable represents the feeling of emotional affect by the obligatory social isolation. The results indicated that the students feel emotionally affected in 88.56% generating implications in the learning of physics and in their academic performance, especially those who comply the profile: being female, single, not having received training in the use of digital platforms, without academic difficulties but who feel that their academic performance has been affected.
Attack-Aware Detection and Defense to Resist Adversarial Examples

This article approaches to design an attack-aware detection and defense framework to resist adversarial attacks on the security-critical artificial intelligent systems. We first make efforts to test the performances of adversarial attacks and present classifying and grading rule (CGR) for the fine-grained grouping of adversarial example attacks. According to CGR, adversarial attacks can be divided into six groups. Then, we propose a feature squeezing and CGR-based detector to detect adversarial attacks, which can be aware of the detailed attack group and is evaluated to be effective by extensive experiments. We also test the defense performances of typical defense methods against these six groups of adversarial attacks, and finally give the defense recommendations for each type of adversarial attack.
Inequality-Constrained and Robust 3D Face Model Fitting

Fitting 3D morphable models (3DMMs) on faces is a well-studied problem, motivated by various industrial and research applications. 3DMMs express a 3D facial shape as a linear sum of basis functions. The resulting shape, however, is a plausible face only when the basis coefficients take values within limited intervals. Methods based on unconstrained optimization address this issue with a weighted ℓ2 penalty on coefficients; however, determining the weight of this penalty is difficult, and the existence of a single weight that works universally is questionable. We propose a new formulation that does not require the tuning of any weight parameter. Specifically, we formulate 3DMM fitting as an inequality-constrained optimization problem, where the primary constraint is that basis coefficients should not exceed the interval that is learned when the 3DMM is constructed. We employ additional constraints to exploit sparse landmark detectors, by forcing the facial shape to be within the error bounds of a reliable detector. To enable operation "in-the-wild", we use a robust objective function, namely Gradient Correlation. Our approach performs comparably with deep learning (DL) methods on "in-the-wild" data that have inexact ground truth, and better than DL methods on more controlled data with exact ground truth. Since our formulation does not require any learning, it enjoys a versatility that allows it to operate with multiple frames of arbitrary sizes. This study's results encourage further research on 3DMM fitting with inequality-constrained optimization methods, which have been unexplored compared to unconstrained methods.
CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images

Limited by the GPU memory, the current mainstream detectors fail to directly apply to large-scale remote sensing images for object detection. Moreover, the scale range of objects in remote sensing images is much wider than that of general images, which also greatly hinders the existing methods to effectively detect geospatial objects of various scales. For achieving high-performance object detection on large-scale remote sensing images, this article proposes a much faster and more accurate detecting framework, called cropping region proposal network-based scale folding network (CRPN-SFNet). In our framework, the CRPN includes a weak semantic RPN for quickly locating interesting regions and a strategy of generating cropping regions to effectively filter out meaningless regions, which can greatly reduce the computation and storage burden. Meanwhile, the proposed SFNet leverages the scale folding-based training and testing methods to extend the valid detection range of existing detectors, which is beneficial for detecting remote sensing objects of various scales, including very small and very large geospatial objects. Extensive experiments on the public Dataset for Object deTection in Aerial images data set indicate that our CRPN can help our detector deal the larger image faster with the limited GPU memory; meanwhile, the SFNet is beneficial to achieve more accurate detection of geospatial objects with wide-scale range. For large-scale remote sensing images, the proposed detection framework outperforms the state-of-the-art object detection methods in terms of accuracy and speed.
From Image to Stability: Learning Dynamics from Human Pose

We propose and validate two end-to-end deep learning architectures to learn foot pressure distribution maps (dynamics) from 2D or 3D human pose (kinematics). The networks are trained using 1.36 million synchronized pose+pressure data pairs from 10 subjects performing multiple takes of a 5-min long choreographed Taiji sequence. Using leave-one-subject-out cross validation, we demonstrate reliable and repeatable foot pressure prediction, setting the first baseline for solving a non-obvious pose to pressure cross-modality mapping problem in computer vision. Furthermore, we compute and quantitatively validate Center of Pressure (CoP) and Base of Support (BoS), two key components for stability analysis, from the predicted foot pressure distributions.
Desirable Features of a Chatbot-building Platform

There is a visible eagerness in the business community to integrate chatbots with their websites and mobile apps. They provide a humanised interface to information and can serve as digital assistants that can perform tasks on behalf of an individual. There are many commercial platforms which provide interfaces to build these chatbots. They are used by both professional software developers as well as people from non-IT backgrounds. Based on our experiences with three popular chatbot-building platforms - Google Dialogflow, IBM Watson Assistant and Amazon Lex, we present a list of desirable features that these platforms should exhibit in order to cater to their mixed user base. We also rate the availability and ease of use of these features on the current versions of these platforms.
Using chatbots and life events to provide public service information

A main objective of eGovernment is the provision of user-centric public services (PS) in a personalized and user-friendly way. Life events (LE) have been proposed as an intuitive method for personalised and integrated PS provision. A technology that could be employed for providing personalised PS and LE information is chatbots, which however is currently unexploited. The aim of this paper is twofold: (a) to present an architecture for the integration of chatbot technology and LE for better PS information provision and (b) to implement a proof-of-concept chatbot prototype that exploits LE information available at the Greek eGovernment portal ERMIS. As a result, we propose a layered architecture consisting of four layers, namely graphical user interface, chatbot engine, application programming interface and linked data repositories. A chatbot pilot is implemented based on the proposed architecture. Two usage scenarios of this pilot are demonstrated and evaluated using a TAM based questionnaire. Benefits and challenges are also discussed.
Defense against neural trojan attacks: A survey

Deep learning techniques have become significantly prevalent in many real-world problems including a variety of detection, recognition, and classification tasks. To obtain high-performance neural networks, an enormous amount of training datasets, memory, and time-consuming computations are required which has increased the demands for outsource training among users. As a result, the machine-learning-as-a-service(MLaaS) providers or a third party can gain an opportunity to put the model's security at risk by training the model with malicious inputs. The malicious functionality inserted into the neural network by the adversary will be activated in the presence of specific inputs. These kinds of attacks to neural networks, called trojan or backdoor attacks, are very stealthy and hard to detect because they do not affect the network performance on clean datasets. In this paper, we refer to two important threat models and we focus on the detection and mitigation techniques against these types of attacks on neural networks which has been proposed recently. We summarize, discuss, and compare the defense methods and their corresponding results.
Predicting Visual Overlap of Images Through Interpretable Non-metric Box Embeddings

To what extent are two images picturing the same 3D surfaces? Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features. This expense is further multiplied when a query image is evaluated against a gallery, e.g. in visual relocalization. While we don’t obviate the need for geometric verification, we propose an interpretable image-embedding that cuts the search in scale space to essentially a lookup. Our approach measures the asymmetric relation between two images. The model then learns a scene-specific measure of similarity, from training examples with known 3D visible-surface overlaps. The result is that we can quickly identify, for example, which test image is a close-up version of another, and by what scale factor. Subsequently, local features need only be detected at that scale. We validate our scene-specific model by showing how this embedding yields competitive image-matching results, while being simpler, faster, and also interpretable by humans.
An Adaptive Batch-Image Based Driver Status Monitoring System on a Lightweight GPU-Equipped SBC

Most Driver Status Monitoring (DSM) systems consist of spatial features extractions and temporal status recognition in sequence. Extracting the spatial features, which include driver facial behavior such as eye closing and mouth opening, generally requires considerable computation. It causes DSM systems to lose the valuable instantly-occurring driver facial information during real-time processing. Loss of facial information affects the accuracy of the system, and its impact is more severe on restricted computing resources. To solve this problem, this paper proposes an Adaptive Batch-Image (ABI) based DSM (ABI-DSM) system. The ABI enables the DSM system to use images captured in real-time while the DSM process previous input images. For real-time operation on a lightweight GPU-equipped Single-Board Computer (SBC), the ABI-DSM system is designed as follows. First, the system uses the driver's facial behavior to reduce the dimension of the time-series data for recognizing the status of the driver. The second, detection and tracking of driver's faces are not used for facial behavior recognition. Also, the system works with PydMobileNet, which has lower parameters and FLOPs than MobileNetV2, for facial behavior recognition. Experiments show that the ABI-DSM systems based on MobileNetV2 and PydMobileNet perform better than others in terms of both FPS and Precision. In particular, the PydMobileNet-based ABI-DSM system outperforms competitors when the size of Batch-Image is over six images.
Accurate Smart Contract Verification Through Direct Modelling

Smart contracts challenge the existing, highly efficient techniques applied in symbolic model checking of software by their unique traits not present in standard programming models. Still, the majority of reported smart contract verification projects either reuse off-the-shelf model checking tools resulting in inefficient and even unsound models, or apply generic solutions that typically require highly-trained human intervention. In this paper, we present the solution adopted in the formal analysis engine of the official Solidity compiler. We focus on the accurate modeling of the central aspects of smart contracts. For that, we specify purpose-built rules defined in the expressive and highly automatable logic of constrained Horn clauses, which are readily supported by an effective solving infrastructure for establishing sound safety proofs or finite-length counterexamples. We evaluated our approach on an extensive set of smart contracts recently deployed in the Ethereum platform. The reported results show that the approach is able to prove correctness and discover bugs in significantly more contracts than comparable publicly available systems.
VarSR: Variational Super-Resolution Network for Very Low Resolution Images

As is well known, single image super-resolution (SR) is an ill-posed problem where multiple high resolution (HR) images can be matched to one low resolution (LR) image due to the difference in their representation capabilities. Such many-to-one nature is particularly magnified when super-resolving with large upscaling factors from very low dimensional domains such as 8 $$\times $$ 8 resolution where detailed information of HR is hardly discovered. Most existing methods are optimized for deterministic generation of SR images under pre-defined objectives such as pixel-level reconstruction and thus limited to the one-to-one correspondence between LR and SR images against the nature. In this paper, we propose VarSR, Variational Super Resolution Network, that matches latent distributions of LR and HR images to recover the missing details. Specifically, we draw samples from the learned common latent distribution of LR and HR to generate diverse SR images as the many-to-one relationship. Experimental results validate that our method can produce more accurate and perceptually plausible SR images from very low resolutions compared to the deterministic techniques.