OpenRaiser/Intern-Atlas huggingface dataset:
3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN
Venue: arXiv cs.CV 2022
Year: 2022

In this work we propose a novel solution for 3D skeleton-based human motion prediction. The objective of this task consists in forecasting future human poses based on a prior skeleton pose sequence. This involves solving two main challenges still present in recent literature; (1) discontinuity of the predicted motion which results in unrealistic motions and (2) performance deterioration in long-term horizons resulting from error accumulation across time. We tackle these issues by using a compact manifold-valued representation of 3D human skeleton motion. Specifically, we model the temporal evolution of the 3D poses as trajectory, what allows us to map human motions to single points on a sphere manifold. Using such a compact representation avoids error accumulation and provides robust representation for long-term prediction while ensuring the smoothness and the coherence of the whole motion. To learn these non-Euclidean representations, we build a manifold-aware Wasserstein generative adversarial model that captures the temporal and spatial dependencies of human motion through different losses. Experiments have been conducted on CMU MoCap and Human 3.6M datasets and demonstrate the superiority of our approach over the state-of-the-art both in short and long term horizons. The smoothness of the generated motion is highlighted in the qualitative results.
Topic Analysis for Text with Side Data
Venue: arXiv cs.LG 2022
Year: 2022

Although latent factor models (e.g., matrix factorization) obtain good performance in predictions, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendations. In this paper, we employ text with side data to tackle these limitations. We introduce a hybrid generative probabilistic model that combines a neural network with a latent topic model, which is a four-level hierarchical Bayesian model. In the model, each document is modeled as a finite mixture over an underlying set of topics and each topic is modeled as an infinite mixture over an underlying set of topic probabilities. Furthermore, each topic probability is modeled as a finite mixture over side data. In the context of text, the neural network provides an overview distribution about side data for the corresponding text, which is the prior distribution in LDA to help perform topic grouping. The approach is evaluated on several different datasets, where the model is shown to outperform standard LDA and Dirichlet-multinomial regression (DMR) in terms of topic grouping, model perplexity, classification and comment generation.
Multi-Layer Perceptron Neural Network for Improving Detection Performance of Malicious Phishing URLs Without Affecting Other Attack Types Classification
Venue: arXiv cs.CR 2022
Year: 2022

The hypothesis here states that neural network algorithms such as Multi-layer Perceptron (MLP) have higher accuracy in differentiating malicious and semi-structured phishing URLs. Compared to classical machine learning algorithms such as Logistic Regression and Multinomial Naive Bayes, the classical algorithms rely heavily on substantial corpus data training and machine learning experts' domain knowledge to perform complex feature engineering. MLP could perform non-linear separable multi-classes classification and focus less on corpus feature training. In addition, backpropagation weight adjustment could learn which features are more important in differentiating phishing from other attack types.
Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution
Venue: arXiv cs.DB 2022
Year: 2022

Clustering aims to group unlabeled objects based on similarity inherent among them into clusters. It is important for many tasks such as anomaly detection, database sharding, record linkage, and others. Some clustering methods are taken as batch algorithms that incur a high overhead as they cluster all the objects in the database from scratch or assume an incremental workload. In practice, database objects are updated, added, and removed from databases continuously which makes previous results stale. Running batch algorithms is infeasible in such scenarios as it would incur a significant overhead if performed continuously. This is particularly the case for high-velocity scenarios such as ones in Internet of Things applications. In this paper, we tackle the problem of clustering in high-velocity dynamic scenarios, where the objects are continuously updated, inserted, and deleted. Specifically, we propose a generally dynamic approach to clustering that utilizes previous clustering results. Our system, DynamicC, uses a machine learning model that is augmented with an existing batch algorithm. The DynamicC model trains by observing the clustering decisions made by the batch algorithm. After training, the DynamicC model is usedin cooperation with the batch algorithm to achieve both accurate and fast clustering decisions. The experimental results on four real-world and one synthetic datasets show that our approach has a better performance compared to the state-of-the-art method while achieving similarly accurate clustering results to the baseline batch algorithm.
Driver State Modeling through Latent Variable State Space Framework in the Wild
Venue: arXiv cs.HC 2022
Year: 2022

Analyzing the impact of the environment on drivers' stress level and workload is of high importance for designing human-centered driver-vehicle interaction systems and to ultimately help build a safer driving experience. However, driver's state, including stress level and workload, are psychological constructs that cannot be measured on their own and should be estimated through sensor measurements such as psychophysiological measures. We propose using a latent-variable state-space modeling framework for driver state analysis. By using latent-variable state-space models, we model drivers' workload and stress levels as latent variables estimated through multimodal human sensing data, under the perturbations of the environment in a state-space format and in a holistic manner. Through using a case study of multimodal driving data collected from 11 participants, we first estimate the latent stress level and workload of drivers from their heart rate, gaze measures, and intensity of facial action units. We then show that external contextual elements such as the number of vehicles as a proxy for traffic density and secondary task demands may be associated with changes in driver's stress levels and workload. We also show that different drivers may be impacted differently by the aforementioned perturbations. We found out that drivers' latent states at previous timesteps are highly associated with their current states. Additionally, we discuss the utility of state-space models in analyzing the possible lag between the two constructs of stress level and workload, which might be indicative of information transmission between the different parts of the driver's psychophysiology in the wild.
D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention
Venue: arXiv cs.CV 2022
Year: 2022

DETR is the first fully end-to-end detector that predicts a final set of predictions without post-processing. However, it suffers from problems such as low performance and slow convergence. A series of works aim to tackle these issues in different ways, but the computational cost is yet expensive due to the sophisticated encoder-decoder architecture. To alleviate this issue, we propose a decoder-only detector called D^2ETR. In the absence of encoder, the decoder directly attends to the fine-fused feature maps generated by the Transformer backbone with a novel computationally efficient cross-scale attention module. D^2ETR demonstrates low computational complexity and high detection accuracy in evaluations on the COCO benchmark, outperforming DETR and its variants.
Beyond Virtual Bazaar: How Social Commerce Promotes Inclusivity for the Traditionally Underserved Community in Chinese Developing Regions
Venue: arXiv cs.CY 2022
Year: 2022

The disadvantaged population is often underserved and marginalized in technology engagement: prior works show they are generally more reluctant and experience more barriers in adopting and engaging with mainstream technology. Here, we contribute to the HCI4D and ICTD literature through a novel "counter" case study on Chinese social commerce (e.g., Pinduoduo), which 1) first prospers among the traditionally underserved community from developing regions ahead of the more technologically advantaged communities, and 2) has been heavily engaged by this community. Through 12 in-depth interviews with social commerce users from the traditionally underserved community in Chinese developing regions, we demonstrate how social commerce, acting as a "counter", brings online the traditional offline socioeconomic lives the community has lived for ages, fits into the community's social, cultural, and economic context, and thus effectively promotes technology inclusivity. Our work provides novel insights and implications for building inclusive technology for the "next billion" population.
An Instance Space Analysis of Constrained Multi-Objective Optimization Problems
Venue: arXiv cs.NE 2022
Year: 2022

Multi-objective optimization problems with constraints (CMOPs) are generally considered more challenging than those without constraints. This in part can be attributed to the creation of infeasible regions generated by the constraint functions, and/or the interaction between constraints and objectives. In this paper, we explore the relationship between constrained multi-objective evolutionary algorithms (CMOEAs) performance and CMOP instances characteristics using Instance Space Analysis (ISA). To do this, we extend recent work focused on the use of Landscape Analysis features to characterise CMOP. Specifically, we scrutinise the multi-objective landscape and introduce new features to describe the multi-objective-violation landscape, formed by the interaction between constraint violation and multi-objective fitness. Detailed evaluation of problem-algorithm footprints spanning six CMOP benchmark suites and fifteen CMOEAs, illustrates that ISA can effectively capture the strength and weakness of the CMOEAs. We conclude that two key characteristics, the isolation of non-dominate set and the correlation between constraints and objectives evolvability, have the greatest impact on algorithm performance. However, the current benchmarks problems do not provide enough diversity to fully reveal the efficacy of CMOEAs evaluated.
Providing A Compiler Technology-Based Alternative For Big Data Application Infrastructures
Venue: arXiv cs.DC 2022
Year: 2022

The unprecedented growth of data volumes has caused traditional approaches to computing to be re-evaluated. This has started a transition towards the use of very large-scale clusters of commodity hardware and has given rise to the development of many new languages and paradigms for data processing and analysis. In this paper, we propose a compiler technology-based alternative to the development of many different Big Data application infrastructures. Key to this approach is the development of a single intermediate representation that enables the integration of compiler optimization and query optimization, and the re-use of many traditional compiler techniques for parallelization such as data distribution and loop scheduling. We show how the single intermediate can act as a generic intermediate for Big Data languages by mapping SQL and MapReduce onto this intermediate.
Cell-Free Massive MIMO-OFDM for High-Speed Train Communications
Venue: arXiv cs.IT 2022
Year: 2022

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems show great potentials in low-mobility scenarios, due to cell boundary disappearance and strong macro diversity. However, the great Doppler frequency offset (DFO) leads to serious inter-carrier interference in orthogonal frequency division multiplexing (OFDM) technology, which makes it difficult to provide high-quality transmissions for both high-speed train (HST) operation control systems and passengers. In this paper, we focus on the performance of CF massive MIMO-OFDM systems with both fully centralized and local minimum mean square error (MMSE) combining in HST communications. Considering the local maximum ratio (MR) combining, the large-scale fading decoding (LSFD) cooperation and the practical effect of DFO on system performance, exact closed-form expressions for uplink spectral efficiency (SE) expressions are derived. We observe that cooperative MMSE combining achieves better SE performance than uncooperative MR combining. In addition, HST communications with small cell and cellular massive MIMO-OFDM systems are compared in terms of SE. Numerical results reveal that the CF massive MIMO-OFDM system achieves a larger and more uniform SE than the other systems. Finally, the train antenna centric (TA-centric) CF massive MIMO-OFDM system is designed for practical implementation in HST communications, and three power control schemes are adopted to optimize the propagation of TAs for reducing the impact of the DFO.
Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems
Venue: arXiv cs.AI 2022
Year: 2022

Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued recently. However, these principles are high-level and difficult to put into practice. In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis. Responsible AI issues go beyond data and algorithms and are often at the system-level crosscutting many system components and the entire software engineering lifecycle. Based on the result of a systematic literature review, this paper identifies one missing element as the system-level guidance - how to design the architecture of responsible AI systems. We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design.
A Principled Design of Image Representation: Towards Forensic Tasks
Venue: arXiv cs.CV 2022
Year: 2022

Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on the proper image representation. Despite the importance, current theoretical understanding for such representation remains limited, with varying degrees of neglect for its key role. For this gap, we attempt to investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. Our work starts from the abstraction of basic principles that the representation for forensics should satisfy, especially revealing the criticality of robustness, interpretability, and coverage. At the theoretical level, we propose a new representation framework for forensics, called Dense Invariant Representation (DIR), which is characterized by stable description with mathematical guarantees. At the implementation level, the discrete calculation problems of DIR are discussed, and the corresponding accurate and fast solutions are designed with generic nature and constant complexity. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors. Also, at the application level, the proposed DIR is initially explored in passive and active forensics, namely copy-move forgery detection and perceptual hashing, exhibiting the benefits in fulfilling the requirements of such forensic tasks.
Asymptotic Properties of Quasi-Group Codes
Venue: arXiv cs.IT 2022
Year: 2022

This is a manuscript of a chapter prepared for a book. The good codes possess large information length and large minimum distance. A class of codes is said to be asymptotically good if there exists a positive real $δ$ such that, for any positive integer $N$ we can find a code in the class with code length greater than $N$, and with both the rate and the relative minimum distance greater than $δ$. The linear codes over any finite field are asymptotically good. More interestingly, the (asymptotic) GV-bound is a phase transition point for the linear codes; i.e., asymptotically speaking, the parameters of most linear codes attain the GV-bound. It is a long-standing open question: whether or not the cyclic codes over a finite field (which are an important class of codes) are asymptotically good? However, from a long time ago the quasi-cyclic codes of index $2$ were proved to be asymptotically good. This chapter consists of some of our studies on the asymptotic properties of several classes of quasi-group codes. We'll explain the studies in a consistent and self-contained style. We begin with the classical results on linear codes. In many cases we consider the quasi-group codes over finite abelian groups (including the cyclic case as a subcase of course), and study their asymptotic properties along two directions: (1) the order of the group (the coindex) is fixed while the index is going to infinity; (2) the index is small while the order of the group (the coindex) is going to infinity. Finally we describe the story on dihedral codes. The dihedral groups are non-abelian but near to cyclic groups (they have cyclic subgroups of index $2$). The asymptotic goodness of binary dihedral codes was obtained in the beginning of this century, and extended to the general dihedral codes recently.
Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training
Venue: arXiv cs.CV 2022
Year: 2022

The paper presents a simple and effective learning-based method for computing a discriminative 3D point cloud descriptor for place recognition purposes. Recent state-of-the-art methods have relatively complex architectures such as multi-scale oyramid of point Transformers combined with a pyramid of feature aggregation modules. Our method uses a simple and efficient 3D convolutional feature extraction, based on a sparse voxelized representation, enhanced with channel attention blocks. We employ recent advances in image retrieval and propose a modified version of a loss function based on a differentiable average precision approximation. Such loss function requires training with very large batches for the best results. This is enabled by using multistaged backpropagation. Experimental evaluation on the popular benchmarks proves the effectiveness of our approach, with a consistent improvement over the state of the art
Beyond GAP screening for Lasso by exploiting new dual cutting half-spaces with supplementary material
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we propose a novel safe screening test for Lasso. Our procedure is based on a safe region with a dome geometry and exploits a canonical representation of the set of half-spaces (referred to as "dual cutting half-spaces" in this paper) containing the dual feasible set. The proposed safe region is shown to be always included in the state-of-the-art "GAP Sphere" and "GAP Dome" proposed by Fercoq et al. (and strictly so under very mild conditions) while involving the same computational burden. Numerical experiments confirm that our new dome enables to devise more powerful screening tests than GAP regions and lead to significant acceleration to solve Lasso.
Continual Feature Selection: Spurious Features in Continual Learning
Venue: arXiv cs.LG 2022
Year: 2022

Continual Learning (CL) is the research field addressing learning without forgetting when the data distribution is not static. This paper studies spurious features' influence on continual learning algorithms. We show that continual learning algorithms solve tasks by selecting features that are not generalizable. Our experiments highlight that continual learning algorithms face two related problems: (1) spurious features and (2) local spurious features. The first one is due to a covariate shift between training and testing data, while the second is due to the limited access to data at each training step. We study (1) through a consistent set of continual learning experiments varying spurious correlation amount and data distribution support. We show that (2) is a major cause of performance decrease in continual learning along with catastrophic forgetting. This paper presents a different way of understanding performance decrease in continual learning by highlighting the influence of (local) spurious features in algorithms capabilities.
Sensitive Pictures: Emotional Interpretation in the Museum
Venue: arXiv cs.HC 2022
Year: 2022

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction.
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
Venue: arXiv cs.CL 2022
Year: 2022

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.