OpenRaiser/Intern-Atlas huggingface dataset:
DandelionTouch: High Fidelity Haptic Rendering of Soft Objects in VR by a Swarm of Drones
Venue: arXiv cs.RO 2022
Year: 2022

To achieve high fidelity haptic rendering of soft objects in a high mobility virtual environment, we propose a novel haptic display DandelionTouch. The tactile actuators are delivered to the fingertips of the user by a swarm of drones. Users of DandelionTouch are capable of experiencing tactile feedback in a large space that is not limited by the device's working area. Importantly, they will not experience muscle fatigue during long interactions with virtual objects. Hand tracking and swarm control algorithm allow guiding the swarm with hand motions and avoid collisions inside the formation. Several topologies of the impedance connection between swarm units were investigated in this research. The experiment, in which drones performed a point following task on a square trajectory in real-time, revealed that drones connected in a Star topology performed the trajectory with low mean positional error (RMSE decreased by 20.6% in comparison with other impedance topologies and by 40.9% in comparison with potential field-based swarm control). The achieved velocities of the drones in all formations with impedance behavior were 28% higher than for the swarm controlled with the potential field algorithm. Additionally, the perception of several vibrotactile patterns was evaluated in a user study with 7 participants. The study has shown that the proposed combination of temporal delay and frequency modulation allows users to successfully recognize the surface property and motion direction in VR simultaneously (mean recognition rate of 70%, maximum of 93%). DandelionTouch suggests a new type of haptic feedback in VR systems where no hand-held or wearable interface is required.
Downward Self-Reducibility in TFNP
Venue: arXiv cs.CC 2022
Year: 2022

A problem is \emph{downward self-reducible} if it can be solved efficiently given an oracle that returns solutions for strictly smaller instances. In the decisional landscape, downward self-reducibility is well studied and it is known that all downward self-reducible problems are in \textsc{PSPACE}. In this paper, we initiate the study of downward self-reducible search problems which are guaranteed to have a solution -- that is, the downward self-reducible problems in \textsc{TFNP}. We show that most natural $\PLS$-complete problems are downward self-reducible and any downward self-reducible problem in \textsc{TFNP} is contained in \textsc{PLS}. Furthermore, if the downward self-reducible problem is in \textsc{TFUP} (i.e. it has a unique solution), then it is actually contained in \textsc{UEOPL}, a subclass of \textsc{CLS}. This implies that if integer factoring is \emph{downward self-reducible} then it is in fact in \textsc{UEOPL}, suggesting that no efficient factoring algorithm exists using the factorization of smaller numbers.
Sustainable Venture Capital
Venue: arXiv cs.CY 2022
Year: 2022

Sustainability initiatives are set to benefit greatly from the growing involvement of venture capital, in the same way that other technological endeavours have been enabled and accelerated in the post-war period. With the spoils increasingly being shared between shareholders and other stakeholders, this requires a more nuanced view than the finance-first methodologies deployed to date. Indeed, it is possible for a venture-backed sustainability startup to deliver outstanding results to society in general without returning a cent to investors, though the most promising outcomes deliver profit with purpose, satisfying all stakeholders in ways that make existing 'extractive' venture capital seem hollow. To explore this nascent area, a review of related research was conducted and social entrepreneurs & investors interviewed to construct a questionnaire assessing the interests and intentions of current & future ecosystem participants. Analysis of 114 responses received via several sampling methods revealed statistically significant relationships between investing preferences and genders, generations, sophistication, and other variables, all the way down to the level of individual UN Sustainable Development Goals (SDGs).
Reconfigurable Intelligent Surface for Vehicular Communications: Exact Performance Analysis with Phase Noise and Mobility
Venue: arXiv cs.IT 2022
Year: 2022

Recent research provides an approximation on the performance for reconfigurable intelligent surface (RIS) assisted systems over generalized fading channels with phase noise resulting from imperfect phase compensation at the RIS. In this paper, we developed exact analysis and upper bounds on the performance of RIS-assisted vehicular communication system considering phase noise with mobility over asymmetric fading channels by coherently combining received signals reflected by RIS elements and direct transmissions from the source terminal. We employ a novel approach to represent the PDF and CDF of the product of four channel coefficients in terms of a single univariate Fox-H function. We use the derived PDF to develop an exact statistical analysis of the end-to-end SNR for the RIS-assisted system using multi-variate Fox-H function.
Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification
Venue: arXiv cs.DS 2022
Year: 2022

We present an algorithm that given any $n$-vertex, $m$-edge, rank $r$ hypergraph constructs a spectral sparsifier with $O(n \varepsilon^{-2} \log n \log r)$ hyperedges in nearly-linear $\widetilde{O}(mr)$ time. This improves in both size and efficiency over a line of work (Bansal-Svensson-Trevisan 2019, Kapralov-Krauthgamer-Tardos-Yoshida 2021) for which the previous best size was $O(\min\{n \varepsilon^{-4} \log^3 n,nr^3 \varepsilon^{-2} \log n\})$ and runtime was $\widetilde{O}(mr + n^{O(1)})$. Independent Result: In an independent work, Lee (Lee 2022) also shows how to compute a spectral hypergraph sparsifier with $O(n \varepsilon^{-2} \log n \log r)$ hyperedges.
A Tent Lévy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study
Venue: arXiv cs.LG 2022
Year: 2022

The "Curse of Dimensionality" induced by the rapid development of information science, might have a negative impact when dealing with big datasets. In this paper, we propose a variant of the sparrow search algorithm (SSA), called Tent Lévy flying sparrow search algorithm (TFSSA), and use it to select the best subset of features in the packing pattern for classification purposes. SSA is a recently proposed algorithm that has not been systematically applied to feature selection problems. After verification by the CEC2020 benchmark function, TFSSA is used to select the best feature combination to maximize classification accuracy and minimize the number of selected features. The proposed TFSSA is compared with nine algorithms in the literature. Nine evaluation metrics are used to properly evaluate and compare the performance of these algorithms on twenty-one datasets from the UCI repository. Furthermore, the approach is applied to the coronavirus disease (COVID-19) dataset, yielding the best average classification accuracy and the average number of feature selections, respectively, of 93.47% and 2.1. Experimental results confirm the advantages of the proposed algorithm in improving classification accuracy and reducing the number of selected features compared to other wrapper-based algorithms.
Improved Perception of AEC Construction Details via Immersive Teaching in Virtual Reality
Venue: arXiv cs.HC 2022
Year: 2022

This work proposes, implements and tests an immersive framework upon Virtual Reality (VR) for comprehension, knowledge development and learning process assisting an improved perception of complex spatial arrangements in AEC in comparison to the traditional 2D projection drawing-based method. The research focuses on the prototypical example of construction details as a traditionally difficult teaching task for conveying geometric and semantic information to students. Our mixed-methods study analyses test results of two test panel groups upon different questions about geometric and functional aspects of the construction detail as well as surveys and interviews of participating lecturers, students and laypersons towards their experience using the VR tool. The quantitative analysis of the test results prove that for participants with little pre-existing knowledge (such as novice students), a significantly better learning score for the test group is detected. Moreover, both groups rated the VR experience as an enjoyable and engaging way of learning. Analysis of survey results towards the VR experience reveals, that students, lecturers and professionals alike enjoyed the VR experience more than traditional learning of the construction detail. During the post-experiment qualitative evaluation in the form of interviews, the panel expressed an improved understanding, increased enthusiasm for the topic, and greater desire for other topics to be presented using VR tools. The expressed better understanding of design concepts after the VR experience by the students is statistically significant on average in the exam results. The results support our core assumption, that the presentation of contextual 3D models is a promising teaching approach to illustrate content.
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images
Venue: arXiv cs.CV 2022
Year: 2022

Segmentation of thermal facial images is a challenging task. This is because facial features often lack salience due to high-dynamic thermal range scenes and occlusion issues. Limited availability of datasets from unconstrained settings further limits the use of the state-of-the-art segmentation networks, loss functions and learning strategies which have been built and validated for RGB images. To address the challenge, we propose Self-Adversarial Multi-scale Contrastive Learning (SAM-CL) framework as a new training strategy for thermal image segmentation. SAM-CL framework consists of a SAM-CL loss function and a thermal image augmentation (TiAug) module as a domain-specific augmentation technique. We use the Thermal-Face-Database to demonstrate effectiveness of our approach. Experiments conducted on the existing segmentation networks (UNET, Attention-UNET, DeepLabV3 and HRNetv2) evidence the consistent performance gains from the SAM-CL framework. Furthermore, we present a qualitative analysis with UBComfort and DeepBreath datasets to discuss how our proposed methods perform in handling unconstrained situations.
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles
Venue: arXiv cs.LG 2022
Year: 2022

Advanced researches on connected vehicles have recently targeted to the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks. Rather than sharing and uploading the training data to the server, the updating of model parameters (e.g., neural networks' weights and biases) is applied by large populations of interconnected vehicles, acting as local learners. Despite these benefits, the limitation of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters, leading to the drawback of a single point of failure and scaling issues for increasing V2X network size. Meanwhile, in intelligent transport scenarios, data collected from onboard sensors are redundant, which degrades the performance of aggregation. To tackle these problems, we explore a novel idea of decentralized data processing and introduce a federated learning framework for in-network vehicles, C-DFL(Consensus based Decentralized Federated Learning), to tackle federated learning on connected vehicles and improve learning quality. Extensive simulations have been implemented to evaluate the performance of C-DFL, that demonstrates C-DFL outperforms the performance of conventional methods in all cases.
Acoustic SLAM based on the Direction-of-Arrival and the Direct-to-Reverberant Energy Ratio
Venue: arXiv cs.RO 2022
Year: 2022

This paper proposes a new method that fuses acoustic measurements in the reverberation field and low-accuracy inertial measurement unit (IMU) motion reports for simultaneous localization and mapping (SLAM). Different from existing studies that only use acoustic data for direction-of-arrival (DoA) estimates, the source's distance from sensors is calculated with the direct-to-reverberant energy ratio (DRR) and applied as a new constraint to eliminate the nonlinear noise from motion reports. A particle filter is applied to estimate the critical distance, which is key for associating the source's distance with the DRR. A keyframe method is used to eliminate the deviation of the source position estimation toward the robot. The proposed DoA-DRR acoustic SLAM (D-D SLAM) is designed for three-dimensional motion and is suitable for most robots. The method is the first acoustic SLAM algorithm that has been validated on a real-world indoor scene dataset that contains only acoustic data and IMU measurements. Compared with previous methods, D-D SLAM has acceptable performance in locating the robot and building a source map from a real-world indoor dataset. The average location accuracy is 0.48 m, while the source position error converges to less than 0.25 m within 2.8 s. These results prove the effectiveness of D-D SLAM in real-world indoor scenes, which may be especially useful in search and rescue missions after disasters where the environment is foggy, i.e., unsuitable for light or laser irradiation.
In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning
Venue: arXiv cs.LG 2022
Year: 2022

When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or PATE, computes output labels by aggregating the predictions of a (possibly distributed) collection of teacher models via a voting mechanism. The mechanism adds noise to attain a differential privacy guarantee with respect to the teachers' training data. In this work, we observe that this use of noise, which makes PATE predictions stochastic, enables new forms of leakage of sensitive information. For a given input, our adversary exploits this stochasticity to extract high-fidelity histograms of the votes submitted by the underlying teachers. From these histograms, the adversary can learn sensitive attributes of the input such as race, gender, or age. Although this attack does not directly violate the differential privacy guarantee, it clearly violates privacy norms and expectations, and would not be possible at all without the noise inserted to obtain differential privacy. In fact, counter-intuitively, the attack becomes easier as we add more noise to provide stronger differential privacy. We hope this encourages future work to consider privacy holistically rather than treat differential privacy as a panacea.
Reinforcement Learning in Computing and Network Convergence Orchestration
Venue: arXiv cs.NI 2022
Year: 2022

As computing power is becoming the core productivity of the digital economy era, the concept of Computing and Network Convergence (CNC), under which network and computing resources can be dynamically scheduled and allocated according to users' needs, has been proposed and attracted wide attention. Based on the tasks' properties, the network orchestration plane needs to flexibly deploy tasks to appropriate computing nodes and arrange paths to the computing nodes. This is a orchestration problem that involves resource scheduling and path arrangement. Since CNC is relatively new, in this paper, we review some researches and applications on CNC. Then, we design a CNC orchestration method using reinforcement learning (RL), which is the first attempt, that can flexibly allocate and schedule computing resources and network resources. Which aims at high profit and low latency. Meanwhile, we use multi-factors to determine the optimization objective so that the orchestration strategy is optimized in terms of total performance from different aspects, such as cost, profit, latency and system overload in our experiment. The experiments shows that the proposed RL-based method can achieve higher profit and lower latency than the greedy method, random selection and balanced-resource method. We demonstrate RL is suitable for CNC orchestration. This paper enlightens the RL application on CNC orchestration.
AIR-JPMC@SMM4H'22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models
Venue: arXiv cs.CL 2022
Year: 2022

This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English). The goal of this task was to accurately determine if the contents of a given tweet demonstrated someone reporting their own experience with intimate partner violence. The submitted system is an ensemble of five RoBERTa models each weighted by their respective F1-scores on the validation data-set. This system performed 13% better than the baseline and was the best performing system overall for this shared task.
Why More Text is (Often) Better: Themes from Reader Preferences for Integration of Charts and Text
Venue: arXiv cs.HC 2022
Year: 2022

Given a choice between charts with minimal text and those with copious textual annotations, participants in a study (Stokes et al.) tended to prefer the charts with more text. This paper examines the qualitative responses of the participants' preferences for various stimuli integrating charts and text, including a text-only variant. A thematic analysis of these responses resulted in three main findings. First, readers commented most frequently on the presence or lack of context; they preferred to be informed, even when it sacrificed simplicity. Second, readers discussed the story-like component of the text-only variant and made little mention of narrative in relation to the chart variants. Finally, readers showed suspicion around possible misleading elements of the chart or text. These themes support findings from previous work on annotations, captions, and alternative text. We raise further questions regarding the combination of text and visual communication.
STING: Self-attention based Time-series Imputation Networks using GAN
Venue: arXiv cs.LG 2022
Year: 2022

Time series data are ubiquitous in real-world applications. However, one of the most common problems is that the time series data could have missing values by the inherent nature of the data collection process. So imputing missing values from multivariate (correlated) time series data is imperative to improve a prediction performance while making an accurate data-driven decision. Conventional works for imputation simply delete missing values or fill them based on mean/zero. Although recent works based on deep neural networks have shown remarkable results, they still have a limitation to capture the complex generation process of the multivariate time series. In this paper, we propose a novel imputation method for multivariate time series data, called STING (Self-attention based Time-series Imputation Networks using GAN). We take advantage of generative adversarial networks and bidirectional recurrent neural networks to learn latent representations of the time series. In addition, we introduce a novel attention mechanism to capture the weighted correlations of the whole sequence and avoid potential bias brought by unrelated ones. Experimental results on three real-world datasets demonstrate that STING outperforms the existing state-of-the-art methods in terms of imputation accuracy as well as downstream tasks with the imputed values therein.
Popular Edges with Critical Nodes
Venue: arXiv cs.DS 2022
Year: 2022

In the popular edge problem, the input is a bipartite graph $G = (A \cup B,E)$ where $A$ and $B$ denote a set of men and a set of women respectively, and each vertex in $A\cup B$ has a strict preference ordering over its neighbours. A matching $M$ in $G$ is said to be {\em popular} if there is no other matching $M'$ such that the number of vertices that prefer $M'$ to $M$ is more than the number of vertices that prefer $M$ to $M'$. The goal is to determine, whether a given edge $e$ belongs to some popular matching in $G$. A polynomial-time algorithm for this problem appears in \cite{CK18}. We consider the popular edge problem when some men or women are prioritized or critical. A matching that matches all the critical nodes is termed as a feasible matching. It follows from \cite{Kavitha14,Kavitha21,NNRS21,NN17} that, when $G$ admits a feasible matching, there always exists a matching that is popular among all feasible matchings. We give a polynomial-time algorithm for the popular edge problem in the presence of critical men or women. We also show that an analogous result does not hold in the many-to-one setting, which is known as the Hospital-Residents Problem in literature, even when there are no critical nodes.
Boosting as Frank-Wolfe
Venue: arXiv cs.LG 2022
Year: 2022

Some boosting algorithms, such as LPBoost, ERLPBoost, and C-ERLPBoost, aim to solve the soft margin optimization problem with the $\ell_1$-norm regularization. LPBoost rapidly converges to an $ε$-approximate solution in practice, but it is known to take $Ω(m)$ iterations in the worst case, where $m$ is the sample size. On the other hand, ERLPBoost and C-ERLPBoost are guaranteed to converge to an $ε$-approximate solution in $O(\frac{1}{ε^2} \ln \frac{m}ν)$ iterations. However, the computation per iteration is very high compared to LPBoost. To address this issue, we propose a generic boosting scheme that combines the Frank-Wolfe algorithm and any secondary algorithm and switches one to the other iteratively. We show that the scheme retains the same convergence guarantee as ERLPBoost and C-ERLPBoost. One can incorporate any secondary algorithm to improve in practice. This scheme comes from a unified view of boosting algorithms for soft margin optimization. More specifically, we show that LPBoost, ERLPBoost, and C-ERLPBoost are instances of the Frank-Wolfe algorithm. In experiments on real datasets, one of the instances of our scheme exploits the better updates of the secondary algorithm and performs comparably with LPBoost.
Physical Interaction: Reconstructing Hand-object Interactions with Physics
Venue: arXiv cs.CV 2022
Year: 2022

Single view-based reconstruction of hand-object interaction is challenging due to the severe observation missing caused by occlusions. This paper proposes a physics-based method to better solve the ambiguities in the reconstruction. It first proposes a force-based dynamic model of the in-hand object, which not only recovers the unobserved contacts but also solves for plausible contact forces. Next, a confidence-based slide prevention scheme is proposed, which combines both the kinematic confidences and the contact forces to jointly model static and sliding contact motion. Qualitative and quantitative experiments show that the proposed technique reconstructs both physically plausible and more accurate hand-object interaction and estimates plausible contact forces in real-time with a single RGBD sensor.