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"abstract": "An <inline-formula><tex-math notation=\"LaTeX\">Z_$(n,m,t)$_Z</tex-math></inline-formula>-homomorphic secret sharing (HSS) scheme for a function family <inline-formula><tex-math notation=\"LaTeX\">Z_$\\cal F$_Z</tex-math></inline-formula> allows <inline-formula><tex-math notation=\"LaTeX\">Z_$n$_Z</tex-math></inline-formula> clients to share their data <inline-formula><tex-math notation=\"LaTeX\">Z_$x_{1}, \\ldots ,x_{n}$_Z</tex-math></inline-formula> among <inline-formula><tex-math notation=\"LaTeX\">Z_$m$_Z</tex-math></inline-formula> servers and then distribute the computation of any function <inline-formula><tex-math notation=\"LaTeX\">Z_$f\\in {\\cal F}$_Z</tex-math></inline-formula> to the servers such that: (i) any <inline-formula><tex-math notation=\"LaTeX\">Z_$t$_Z</tex-math></inline-formula> colluding servers learn no information about the data; (ii) each server is able to compute a partial result and <inline-formula><tex-math notation=\"LaTeX\">Z_$f(x_{1}, \\ldots ,x_{n})$_Z</tex-math></inline-formula> can be reconstructed from the servers' partial results. HSS schemes cannot guarantee correct reconstruction, if some servers are malicious and provide wrong partial results. Recently, verifiable HSS (VHSS) has been introduced to achieve an additional property: (iii) any <inline-formula><tex-math notation=\"LaTeX\">Z_$t$_Z</tex-math></inline-formula> colluding servers cannot persuade the client(s) to accept their partial results and reconstruct a wrong value. The property (iii) is usually achieved by the client verifying the servers' partial results. A VHSS scheme is compact if the verification is substantially faster than locally computing <inline-formula><tex-math notation=\"LaTeX\">Z_$f(x_{1},\\ldots ,x_{n})$_Z</tex-math></inline-formula>. Of the existing VHSS schemes for polynomials, some are not compact; the others are compact but impose very heavy workload on the servers, even for low degree polynomials (e.g., they are at least 4000× slower than the existing HSS schemes in order to evaluate polynomials of degree <inline-formula><tex-math notation=\"LaTeX\">Z_$\\leq 5$_Z</tex-math></inline-formula>, which have many applications such as privacy-preserving machine learning). In this paper, we propose both a single-client VHSS (SVHSS) model and a multi-client VHSS (MVHSS) model. Our SVHSS allows a client to use a secret key to share its data among servers; our MVHSS allows multiple clients to share their data with a public key. For any integers <inline-formula><tex-math notation=\"LaTeX\">Z_$m,t>0$_Z</tex-math></inline-formula>, we constructed both an <inline-formula><tex-math notation=\"LaTeX\">Z_$(m,t)$_Z</tex-math></inline-formula>-SVHSS scheme and an <inline-formula><tex-math notation=\"LaTeX\">Z_$(m,t)$_Z</tex-math></inline-formula>-MVHSS scheme that satisfy the properties of (i)-(iii). Our constructions are based on level-<inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula> homomorphic encryptions. The <inline-formula><tex-math notation=\"LaTeX\">Z_$(m,t)$_Z</tex-math></inline-formula>-SVHSS and <inline-formula><tex-math notation=\"LaTeX\">Z_$(m,t)$_Z</tex-math></inline-formula>-MVHSS are compact and allow the computations of degree-<inline-formula><tex-math notation=\"LaTeX\">Z_$d$_Z</tex-math></inline-formula> polynomials for <inline-formula><tex-math notation=\"LaTeX\">Z_$d\\leq ((k+1)m-1)/t$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$d\\leq ((k+1)(m-t)-1)/t$_Z</tex-math></inline-formula>, respectively. Experiments show that our schemes are much more efficient than the existing compact VHSS for low degree polynomials. For example, to compute polynomials of degree <inline-formula><tex-math notation=\"LaTeX\">Z_$\\leq 5$_Z</tex-math></inline-formula>, our MVHSS scheme is at least 420×faster. By applying SVHSS and MVHSS, we may add verifiability to privacy-preserving machine learning (PPML) algorithms. Experiments show that the resulting schemes are at least 52× and 20× faster than the existing verifiable PPML schemes.",
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"content": "An <inline-formula><tex-math notation=\"LaTeX\">$(n,m,t)$</tex-math></inline-formula>-homomorphic secret sharing (HSS) scheme for a function family <inline-formula><tex-math notation=\"LaTeX\">$\\cal F$</tex-math></inline-formula> allows <inline-formula><tex-math notation=\"LaTeX\">$n$</tex-math></inline-formula> clients to share their data <inline-formula><tex-math notation=\"LaTeX\">$x_{1}, \\ldots ,x_{n}$</tex-math></inline-formula> among <inline-formula><tex-math notation=\"LaTeX\">$m$</tex-math></inline-formula> servers and then distribute the computation of any function <inline-formula><tex-math notation=\"LaTeX\">$f\\in {\\cal F}$</tex-math></inline-formula> to the servers such that: (i) any <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math></inline-formula> colluding servers learn no information about the data; (ii) each server is able to compute a partial result and <inline-formula><tex-math notation=\"LaTeX\">$f(x_{1}, \\ldots ,x_{n})$</tex-math></inline-formula> can be reconstructed from the servers' partial results. HSS schemes cannot guarantee correct reconstruction, if some servers are malicious and provide wrong partial results. Recently, verifiable HSS (VHSS) has been introduced to achieve an additional property: (iii) any <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math></inline-formula> colluding servers cannot persuade the client(s) to accept their partial results and reconstruct a wrong value. The property (iii) is usually achieved by the client verifying the servers' partial results. A VHSS scheme is compact if the verification is substantially faster than locally computing <inline-formula><tex-math notation=\"LaTeX\">$f(x_{1},\\ldots ,x_{n})$</tex-math></inline-formula>. Of the existing VHSS schemes for polynomials, some are not compact; the others are compact but impose very heavy workload on the servers, even for low degree polynomials (e.g., they are at least 4000× slower than the existing HSS schemes in order to evaluate polynomials of degree <inline-formula><tex-math notation=\"LaTeX\">$\\leq 5$</tex-math></inline-formula>, which have many applications such as privacy-preserving machine learning). In this paper, we propose both a single-client VHSS (SVHSS) model and a multi-client VHSS (MVHSS) model. Our SVHSS allows a client to use a secret key to share its data among servers; our MVHSS allows multiple clients to share their data with a public key. For any integers <inline-formula><tex-math notation=\"LaTeX\">$m,t>0$</tex-math></inline-formula>, we constructed both an <inline-formula><tex-math notation=\"LaTeX\">$(m,t)$</tex-math></inline-formula>-SVHSS scheme and an <inline-formula><tex-math notation=\"LaTeX\">$(m,t)$</tex-math></inline-formula>-MVHSS scheme that satisfy the properties of (i)-(iii). Our constructions are based on level-<inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math></inline-formula> homomorphic encryptions. The <inline-formula><tex-math notation=\"LaTeX\">$(m,t)$</tex-math></inline-formula>-SVHSS and <inline-formula><tex-math notation=\"LaTeX\">$(m,t)$</tex-math></inline-formula>-MVHSS are compact and allow the computations of degree-<inline-formula><tex-math notation=\"LaTeX\">$d$</tex-math></inline-formula> polynomials for <inline-formula><tex-math notation=\"LaTeX\">$d\\leq ((k+1)m-1)/t$</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$d\\leq ((k+1)(m-t)-1)/t$</tex-math></inline-formula>, respectively. Experiments show that our schemes are much more efficient than the existing compact VHSS for low degree polynomials. For example, to compute polynomials of degree <inline-formula><tex-math notation=\"LaTeX\">$\\leq 5$</tex-math></inline-formula>, our MVHSS scheme is at least 420×faster. By applying SVHSS and MVHSS, we may add verifiability to privacy-preserving machine learning (PPML) algorithms. Experiments show that the resulting schemes are at least 52× and 20× faster than the existing verifiable PPML schemes.",
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"normalizedAbstract": "An --homomorphic secret sharing (HSS) scheme for a function family - allows - clients to share their data - among - servers and then distribute the computation of any function - to the servers such that: (i) any - colluding servers learn no information about the data; (ii) each server is able to compute a partial result and - can be reconstructed from the servers' partial results. HSS schemes cannot guarantee correct reconstruction, if some servers are malicious and provide wrong partial results. Recently, verifiable HSS (VHSS) has been introduced to achieve an additional property: (iii) any - colluding servers cannot persuade the client(s) to accept their partial results and reconstruct a wrong value. The property (iii) is usually achieved by the client verifying the servers' partial results. A VHSS scheme is compact if the verification is substantially faster than locally computing -. Of the existing VHSS schemes for polynomials, some are not compact; the others are compact but impose very heavy workload on the servers, even for low degree polynomials (e.g., they are at least 4000× slower than the existing HSS schemes in order to evaluate polynomials of degree -, which have many applications such as privacy-preserving machine learning). In this paper, we propose both a single-client VHSS (SVHSS) model and a multi-client VHSS (MVHSS) model. Our SVHSS allows a client to use a secret key to share its data among servers; our MVHSS allows multiple clients to share their data with a public key. For any integers -, we constructed both an --SVHSS scheme and an --MVHSS scheme that satisfy the properties of (i)-(iii). Our constructions are based on level-- homomorphic encryptions. The --SVHSS and --MVHSS are compact and allow the computations of degree-- polynomials for - and -, respectively. Experiments show that our schemes are much more efficient than the existing compact VHSS for low degree polynomials. For example, to compute polynomials of degree -, our MVHSS scheme is at least 420×faster. By applying SVHSS and MVHSS, we may add verifiability to privacy-preserving machine learning (PPML) algorithms. Experiments show that the resulting schemes are at least 52× and 20× faster than the existing verifiable PPML schemes.",
"title": "Verifiable Homomorphic Secret Sharing for Low Degree Polynomials",
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"abstract": "Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering. However, the interpretation of the clustering result from NMF is difficult, especially for the high-dimensional biological data without effective feature selection. To address this problem, we introduce a row-sparse NMF with <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _{2,0}$_Z</tex-math></inline-formula>-norm constraint (NMF<inline-formula><tex-math notation=\"LaTeX\">Z_$\\_\\ell _{20}$_Z</tex-math></inline-formula>), where the basis matrix <inline-formula><tex-math notation=\"LaTeX\">Z_$\\bm {W}$_Z</tex-math></inline-formula> is constrained by using the <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _{2,0}$_Z</tex-math></inline-formula>-norm constraint such that <inline-formula><tex-math notation=\"LaTeX\">Z_$\\bm {W}$_Z</tex-math></inline-formula> has a row-sparsity pattern with feature selection. However, it is a challenge to solve the model, because the <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _{2,0}$_Z</tex-math></inline-formula>-norm constraint is a non-convex and non-smooth function. Fortunately, we prove that the <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _{2,0}$_Z</tex-math></inline-formula>-norm constraint satisfies the Kurdyka-Łojasiewicz property. Based on this finding, we present a proximal alternating linearized minimization algorithm and its monotone accelerated version to solve the NMF<inline-formula><tex-math notation=\"LaTeX\">Z_$\\_\\ell _{20}$_Z</tex-math></inline-formula> model. In addition, we further present a orthogonal NMF with <inline-formula><tex-math notation=\"LaTeX\">Z_$\\ell _{2,0}$_Z</tex-math></inline-formula>-norm constraint (ONMF<inline-formula><tex-math notation=\"LaTeX\">Z_$\\_\\ell _{20}$_Z</tex-math></inline-formula>) to enhance the clustering performance by using a non-negative orthogonal constraint. The ONMF<inline-formula><tex-math notation=\"LaTeX\">Z_$\\_\\ell _{20}$_Z</tex-math></inline-formula> model is solved by transforming into a series of constrained and penalized matrix factorization problems. The convergence and guarantees for these proposed algorithms are proved and the computational complexity is well evaluated. The results on numerical and scRNA-seq datasets demonstrate the efficiency of our methods in comparison with existing methods.",
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"content": "Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering. However, the interpretation of the clustering result from NMF is difficult, especially for the high-dimensional biological data without effective feature selection. To address this problem, we introduce a row-sparse NMF with <inline-formula><tex-math notation=\"LaTeX\">$\\ell _{2,0}$</tex-math></inline-formula>-norm constraint (NMF<inline-formula><tex-math notation=\"LaTeX\">$\\_\\ell _{20}$</tex-math></inline-formula>), where the basis matrix <inline-formula><tex-math notation=\"LaTeX\">$\\bm {W}$</tex-math></inline-formula> is constrained by using the <inline-formula><tex-math notation=\"LaTeX\">$\\ell _{2,0}$</tex-math></inline-formula>-norm constraint such that <inline-formula><tex-math notation=\"LaTeX\">$\\bm {W}$</tex-math></inline-formula> has a row-sparsity pattern with feature selection. However, it is a challenge to solve the model, because the <inline-formula><tex-math notation=\"LaTeX\">$\\ell _{2,0}$</tex-math></inline-formula>-norm constraint is a non-convex and non-smooth function. Fortunately, we prove that the <inline-formula><tex-math notation=\"LaTeX\">$\\ell _{2,0}$</tex-math></inline-formula>-norm constraint satisfies the Kurdyka-Łojasiewicz property. Based on this finding, we present a proximal alternating linearized minimization algorithm and its monotone accelerated version to solve the NMF<inline-formula><tex-math notation=\"LaTeX\">$\\_\\ell _{20}$</tex-math></inline-formula> model. In addition, we further present a orthogonal NMF with <inline-formula><tex-math notation=\"LaTeX\">$\\ell _{2,0}$</tex-math></inline-formula>-norm constraint (ONMF<inline-formula><tex-math notation=\"LaTeX\">$\\_\\ell _{20}$</tex-math></inline-formula>) to enhance the clustering performance by using a non-negative orthogonal constraint. The ONMF<inline-formula><tex-math notation=\"LaTeX\">$\\_\\ell _{20}$</tex-math></inline-formula> model is solved by transforming into a series of constrained and penalized matrix factorization problems. The convergence and guarantees for these proposed algorithms are proved and the computational complexity is well evaluated. The results on numerical and scRNA-seq datasets demonstrate the efficiency of our methods in comparison with existing methods.",
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"abstract": "Test data compression allows compressed tests to be stored on a tester and decompressed on-chip. Test data compression has not been applied before to transparent-scan sequences in spite of their ability to provide test compaction beyond conventional scan-based tests. A transparent-scan sequence <inline-formula> <tex-math notation=\"LaTeX\">Z_$T$_Z</tex-math></inline-formula> can be obtained from a conventional compact scan-based test set <inline-formula> <tex-math notation=\"LaTeX\">Z_$C$_Z</tex-math></inline-formula>. In this case, the length of <inline-formula> <tex-math notation=\"LaTeX\">Z_$T$_Z</tex-math></inline-formula> is equal to the number of clock cycles required for applying <inline-formula> <tex-math notation=\"LaTeX\">Z_$C$_Z</tex-math></inline-formula>. Test compaction applied to <inline-formula> <tex-math notation=\"LaTeX\">Z_$T$_Z</tex-math></inline-formula> can reduce its length well below the number of clock cycles required for <inline-formula> <tex-math notation=\"LaTeX\">Z_$C$_Z</tex-math></inline-formula>. To allow test data compression and test compaction to be applied together to a transparent-scan sequence, this brief defines a format for a conventional compressed scan-based test set <inline-formula> <tex-math notation=\"LaTeX\">Z_$C$_Z</tex-math></inline-formula> that supports features of a compact transparent-scan sequence, and allows a compact transparent-scan sequence <inline-formula> <tex-math notation=\"LaTeX\">Z_$T$_Z</tex-math></inline-formula> to be obtained from <inline-formula> <tex-math notation=\"LaTeX\">Z_$C$_Z</tex-math></inline-formula>. Using this format, compaction of <inline-formula> <tex-math notation=\"LaTeX\">Z_$T$_Z</tex-math></inline-formula> is achieved by modifying certain parameters of <inline-formula> <tex-math notation=\"LaTeX\">Z_$C$_Z</tex-math></inline-formula>. The decompression logic remains similar to the conventional logic used for <inline-formula> <tex-math notation=\"LaTeX\">Z_$C$_Z</tex-math></inline-formula>. Experimental results for the set of single stuck-at faults in benchmark circuits demonstrate significant reductions in the length of <inline-formula> <tex-math notation=\"LaTeX\">Z_$T$_Z</tex-math></inline-formula> starting from a compressed and compacted scan-based test set <inline-formula> <tex-math notation=\"LaTeX\">Z_$C$_Z</tex-math></inline-formula>.",
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"content": "Test data compression allows compressed tests to be stored on a tester and decompressed on-chip. Test data compression has not been applied before to transparent-scan sequences in spite of their ability to provide test compaction beyond conventional scan-based tests. A transparent-scan sequence <inline-formula> <tex-math notation=\"LaTeX\">$T$ </tex-math></inline-formula> can be obtained from a conventional compact scan-based test set <inline-formula> <tex-math notation=\"LaTeX\">$C$ </tex-math></inline-formula>. In this case, the length of <inline-formula> <tex-math notation=\"LaTeX\">$T$ </tex-math></inline-formula> is equal to the number of clock cycles required for applying <inline-formula> <tex-math notation=\"LaTeX\">$C$ </tex-math></inline-formula>. Test compaction applied to <inline-formula> <tex-math notation=\"LaTeX\">$T$ </tex-math></inline-formula> can reduce its length well below the number of clock cycles required for <inline-formula> <tex-math notation=\"LaTeX\">$C$ </tex-math></inline-formula>. To allow test data compression and test compaction to be applied together to a transparent-scan sequence, this brief defines a format for a conventional compressed scan-based test set <inline-formula> <tex-math notation=\"LaTeX\">$C$ </tex-math></inline-formula> that supports features of a compact transparent-scan sequence, and allows a compact transparent-scan sequence <inline-formula> <tex-math notation=\"LaTeX\">$T$ </tex-math></inline-formula> to be obtained from <inline-formula> <tex-math notation=\"LaTeX\">$C$ </tex-math></inline-formula>. Using this format, compaction of <inline-formula> <tex-math notation=\"LaTeX\">$T$ </tex-math></inline-formula> is achieved by modifying certain parameters of <inline-formula> <tex-math notation=\"LaTeX\">$C$ </tex-math></inline-formula>. The decompression logic remains similar to the conventional logic used for <inline-formula> <tex-math notation=\"LaTeX\">$C$ </tex-math></inline-formula>. Experimental results for the set of single stuck-at faults in benchmark circuits demonstrate significant reductions in the length of <inline-formula> <tex-math notation=\"LaTeX\">$T$ </tex-math></inline-formula> starting from a compressed and compacted scan-based test set <inline-formula> <tex-math notation=\"LaTeX\">$C$ </tex-math></inline-formula>.",
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"abstract": "We investigate the potential role of haptics in climate visualization. In existing approaches to climate visualization, different dimensions of climate data such as temperature, humidity, wind, precipitation, and cloud water are typically represented using different visual markers and dimensions such as color, size, intensity, and orientation. Since the number of dimensions in climate data is large and climate data needs to be represented in connection with the topography, purely visual representations overwhelm users. Rather than overloading the visual channel, we investigate an alternative approach in which some of the climate information is displayed through the haptic channel in order to alleviate the perceptual and cognitive load of the user. In this approach, haptic feedback is further used to provide guidance while exploring climate data in order to enable natural and intuitive learning of cause and effect relationships between climate variables. Our experiments with 33 human subjects show that haptic feedback significantly improves the understanding of climate data and the cause and effect relations between climate variables as well as the interpretation of the variations in climate due to changes in terrain.",
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"content": "A comprehensive and comprehensible summary of existing deep neural networks (DNNs) helps practitioners understand the behaviour and evolution of DNNs, offers insights for architecture optimization, and sheds light on the working mechanisms of DNNs. However, this summary is hard to obtain because of the complexity and diversity of DNN architectures. To address this issue, we develop DNN Genealogy, an interactive visualization tool, to offer a visual summary of representative DNNs and their evolutionary relationships. DNN Genealogy enables users to learn DNNs from multiple aspects, including architecture, performance, and evolutionary relationships. Central to this tool is a systematic analysis and visualization of 66 representative DNNs based on our analysis of 140 papers. A directed acyclic graph is used to illustrate the evolutionary relationships among these DNNs and highlight the representative DNNs. A focus + context visualization is developed to orient users during their exploration. A set of network glyphs is used in the graph to facilitate the understanding and comparing of DNNs in the context of the evolution. Case studies demonstrate that DNN Genealogy provides helpful guidance in understanding, applying, and optimizing DNNs. DNN Genealogy is extensible and will continue to be updated to reflect future advances in DNNs.",
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"abstract": "3D point clouds acquired by scanning real-world objects or scenes have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc. They are often perturbed by noise or suffer from low density, which obstructs downstream tasks such as surface reconstruction and understanding. In this paper, we propose a novel paradigm of point set resampling for restoration, which learns continuous gradient fields of point clouds that converge points towards the underlying surface. In particular, we represent a point cloud via its gradient field—the gradient of the log-probability density function, and enforce the gradient field to be continuous, thus guaranteeing the continuity of the model for solvable optimization. Based on the continuous gradient fields estimated via a proposed neural network, resampling a point cloud amounts to performing gradient-based Markov Chain Monte Carlo (MCMC) on the input noisy or sparse point cloud. Further, we propose to introduce regularization into the gradient-based MCMC during point cloud restoration, which essentially refines the intermediate resampled point cloud iteratively and accommodates various priors in the resampling process. Extensive experimental results demonstrate that the proposed point set resampling achieves the state-of-the-art performance in representative restoration tasks including point cloud denoising and upsampling.",
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"content": "3D point clouds acquired by scanning real-world objects or scenes have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc. They are often perturbed by noise or suffer from low density, which obstructs downstream tasks such as surface reconstruction and understanding. In this paper, we propose a novel paradigm of point set resampling for restoration, which learns continuous gradient fields of point clouds that converge points towards the underlying surface. In particular, we represent a point cloud via its gradient field—the gradient of the log-probability density function, and enforce the gradient field to be continuous, thus guaranteeing the continuity of the model for solvable optimization. Based on the continuous gradient fields estimated via a proposed neural network, resampling a point cloud amounts to performing gradient-based Markov Chain Monte Carlo (MCMC) on the input noisy or sparse point cloud. Further, we propose to introduce regularization into the gradient-based MCMC during point cloud restoration, which essentially refines the intermediate resampled point cloud iteratively and accommodates various priors in the resampling process. Extensive experimental results demonstrate that the proposed point set resampling achieves the state-of-the-art performance in representative restoration tasks including point cloud denoising and upsampling.",
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"content": "There has been rapid progress recently on 3D human rendering, including novel view synthesis and pose animation, based on the advances of neural radiance fields (NeRF). However, most existing methods focus on person-specific training and their training typically requires multi-view videos. This paper deals with a new challenging task – rendering novel views and novel poses for a person <italic>unseen</italic> in training, using only multiview <italic>still images</italic> as input without videos. For this task, we propose a simple yet surprisingly effective method to train a generalizable NeRF with multiview images as conditional input. The key ingredient is a dedicated representation combining a canonical NeRF and a volume deformation scheme. Using a canonical space enables our method to learn shared properties of human and easily generalize to different people. Volume deformation is used to connect the canonical space with input and target images and query image features for radiance and density prediction. We leverage the parametric 3D human model fitted on the input images to derive the deformation, which works quite well in practice when combined with our canonical NeRF. The experiments on both real and synthetic data with the novel view synthesis and pose animation tasks collectively demonstrate the efficacy of our method.",
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"givenName": "Xiangjun",
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"fullName": "Jiaolong Yang",
"affiliation": "Microsoft Research Asia, Beijing, China",
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"givenName": "Jongyoo",
"surname": "Kim",
"fullName": "Jongyoo Kim",
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"abstract": "We introduce a novel posture guidance office chair and evaluate the effectiveness of vibrotactile and visual feedback methods for guiding seated postures. For visually dominant office work such as typing on the computer, it is possible that delivering posture feedback visually may overload the visual sense while haptic feedback may be a viable alternative. We performed two experiments to compare vibrotactile and visual feedback-posture compliance and dual-task cognitive workload assessment. In the first experiment, our results showed no statistically significant difference in effectiveness between using vibrotactile and visual feedback to obtain postural compliance to a reference posture. In the second experiment, participants experienced typing performance and response time degradations from both types of feedback. However the differences in performance degradation were not statistically significant between the two feedback methods. We conclude that vibrotactile and visual feedback are similarly effective for guiding quasistatic postures in routine tasks such as seated office work.",
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"content": "We introduce a novel posture guidance office chair and evaluate the effectiveness of vibrotactile and visual feedback methods for guiding seated postures. For visually dominant office work such as typing on the computer, it is possible that delivering posture feedback visually may overload the visual sense while haptic feedback may be a viable alternative. We performed two experiments to compare vibrotactile and visual feedback-posture compliance and dual-task cognitive workload assessment. In the first experiment, our results showed no statistically significant difference in effectiveness between using vibrotactile and visual feedback to obtain postural compliance to a reference posture. In the second experiment, participants experienced typing performance and response time degradations from both types of feedback. However the differences in performance degradation were not statistically significant between the two feedback methods. We conclude that vibrotactile and visual feedback are similarly effective for guiding quasistatic postures in routine tasks such as seated office work.",
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"title": "Comparison of Visual and Vibrotactile Feedback Methods for Seated Posture Guidance",
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"abstract": "Virtual reality (VR) is a valuable experimental tool for studying human movement, including the analysis of interactions during locomotion tasks for developing crowd simulation algorithms. However, these studies are generally limited to <italic>distant</italic> interactions in crowds, due to the difficulty of rendering realistic sensations of collisions in VR. In this article, we explore the use of wearable haptics to render contacts during virtual crowd navigation. We focus on the behavioral changes occurring with or without haptic rendering during a navigation task in a dense crowd, as well as on potential after-effects introduced by the use haptic rendering. Our objective is to provide recommendations for designing VR setup to study crowd navigation behavior. To the end, we designed an experiment (N=23) where participants navigated in a crowded virtual train station without, then with, and then again without haptic feedback of their collisions with virtual characters. Results show that providing haptic feedback improved the overall realism of the interaction, as participants more actively avoided collisions. We also noticed a significant after-effect in the users’ behavior when haptic rendering was once again disabled in the third part of the experiment. Nonetheless, haptic feedback did not have any significant impact on the users’ sense of presence and embodiment.",
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"content": "Virtual reality (VR) is a valuable experimental tool for studying human movement, including the analysis of interactions during locomotion tasks for developing crowd simulation algorithms. However, these studies are generally limited to <italic>distant</italic> interactions in crowds, due to the difficulty of rendering realistic sensations of collisions in VR. In this article, we explore the use of wearable haptics to render contacts during virtual crowd navigation. We focus on the behavioral changes occurring with or without haptic rendering during a navigation task in a dense crowd, as well as on potential after-effects introduced by the use haptic rendering. Our objective is to provide recommendations for designing VR setup to study crowd navigation behavior. To the end, we designed an experiment (N=23) where participants navigated in a crowded virtual train station without, then with, and then again without haptic feedback of their collisions with virtual characters. Results show that providing haptic feedback improved the overall realism of the interaction, as participants more actively avoided collisions. We also noticed a significant after-effect in the users’ behavior when haptic rendering was once again disabled in the third part of the experiment. Nonetheless, haptic feedback did not have any significant impact on the users’ sense of presence and embodiment.",
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"normalizedAbstract": "Virtual reality (VR) is a valuable experimental tool for studying human movement, including the analysis of interactions during locomotion tasks for developing crowd simulation algorithms. However, these studies are generally limited to distant interactions in crowds, due to the difficulty of rendering realistic sensations of collisions in VR. In this article, we explore the use of wearable haptics to render contacts during virtual crowd navigation. We focus on the behavioral changes occurring with or without haptic rendering during a navigation task in a dense crowd, as well as on potential after-effects introduced by the use haptic rendering. Our objective is to provide recommendations for designing VR setup to study crowd navigation behavior. To the end, we designed an experiment (N=23) where participants navigated in a crowded virtual train station without, then with, and then again without haptic feedback of their collisions with virtual characters. Results show that providing haptic feedback improved the overall realism of the interaction, as participants more actively avoided collisions. We also noticed a significant after-effect in the users’ behavior when haptic rendering was once again disabled in the third part of the experiment. Nonetheless, haptic feedback did not have any significant impact on the users’ sense of presence and embodiment.",
"title": "Crowd Navigation in VR: Exploring Haptic Rendering of Collisions",
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"Locomotion Tasks",
"Crowd Simulation Algorithms",
"Distant Interactions",
"Wearable Haptics",
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"Navigation Task",
"Dense Crowd",
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"Designing VR Setup",
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"Virtual Characters",
"Haptic Interfaces",
"Rendering Computer Graphics",
"Navigation",
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"givenName": "Florian",
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"affiliation": "Inria, Univ Rennes, M2S, CNRS, IRISA, Rennes, France",
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"affiliation": "Inria, Univ Rennes, M2S, CNRS, IRISA, Rennes, France",
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"givenName": "Alexandre",
"surname": "Bonneau",
"fullName": "Alexandre Bonneau",
"affiliation": "Inria, Univ Rennes, M2S, CNRS, IRISA, Rennes, France",
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{
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"fullName": "Alberto Jovane",
"affiliation": "Inria, Univ Rennes, M2S, CNRS, IRISA, Rennes, France",
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"affiliation": "Inria, Univ Rennes, M2S, CNRS, IRISA, Rennes, France",
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"affiliation": "Inria, Univ Rennes, M2S, CNRS, IRISA, Rennes, France",
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{
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"surname": "Pacchierotti",
"fullName": "Claudio Pacchierotti",
"affiliation": "Inria, Univ Rennes, M2S, CNRS, IRISA, Rennes, France",
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"fullName": "Julien Pettré",
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"abstract": "Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series analysis. In all kinds of attention networks, the attention maps are crucial as they encode semantic dependencies between input tokens. However, most existing attention networks perform modeling or reasoning based on <italic>representations</italic>, wherein the <italic>attention maps</italic> of different layers are learned separately <italic>without</italic> explicit interactions. In this paper, we propose a <italic>novel</italic> and <italic>generic</italic> evolving attention mechanism, which directly models the <italic>evolution</italic> of <italic>inter-token relationships</italic> through a chain of residual convolutional modules. The major motivations are twofold. On the one hand, the attention maps in different layers share transferable knowledge, thus adding a residual connection can facilitate the information flow of inter-token relationships across layers. On the other hand, there is naturally an evolutionary trend among attention maps at different abstraction levels, so it is beneficial to exploit a dedicated convolution-based module to capture this process. Equipped with the proposed mechanism, the convolution-enhanced evolving attention networks achieve superior performance in various applications, including time-series representation, natural language understanding, machine translation, and image classification. Especially on time-series representation tasks, Evolving Attention-enhanced Dilated Convolutional (EA-DC-) Transformer outperforms state-of-the-art models significantly, achieving an average of 17% improvement compared to the best SOTA. To the best of our knowledge, this is the first work that explicitly models the layer-wise evolution of attention maps. Our implementation is available at <italic><uri>https://github.com/pkuyym/EvolvingAttention</uri></italic>.",
"abstracts": [
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"content": "Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series analysis. In all kinds of attention networks, the attention maps are crucial as they encode semantic dependencies between input tokens. However, most existing attention networks perform modeling or reasoning based on <italic>representations</italic>, wherein the <italic>attention maps</italic> of different layers are learned separately <italic>without</italic> explicit interactions. In this paper, we propose a <italic>novel</italic> and <italic>generic</italic> evolving attention mechanism, which directly models the <italic>evolution</italic> of <italic>inter-token relationships</italic> through a chain of residual convolutional modules. The major motivations are twofold. On the one hand, the attention maps in different layers share transferable knowledge, thus adding a residual connection can facilitate the information flow of inter-token relationships across layers. On the other hand, there is naturally an evolutionary trend among attention maps at different abstraction levels, so it is beneficial to exploit a dedicated convolution-based module to capture this process. Equipped with the proposed mechanism, the convolution-enhanced evolving attention networks achieve superior performance in various applications, including time-series representation, natural language understanding, machine translation, and image classification. Especially on time-series representation tasks, Evolving Attention-enhanced Dilated Convolutional (EA-DC-) Transformer outperforms state-of-the-art models significantly, achieving an average of 17% improvement compared to the best SOTA. To the best of our knowledge, this is the first work that explicitly models the layer-wise evolution of attention maps. Our implementation is available at <italic><uri>https://github.com/pkuyym/EvolvingAttention</uri></italic>.",
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"abstract": "It has been well documented that laughter is an important communicative and expressive signal in face-to-face conversations. Our work aims at building a laughter behavior controller for a virtual character which is able to generate upper body animations from laughter audio given as input. This controller relies on the tight correlations between laughter audio and body behaviors. A unified continuous-state statistical framework, inspired by Kalman filter, is proposed to learn the correlations between laughter audio and head/torso behavior from a recorded laughter human dataset. Due to the lack of shoulder behavior data in the recorded human dataset, a rule-based method is defined to model the correlation between laughter audio and shoulder behavior. In the synthesis step, these characterized correlations are rendered in the animation of a virtual character. To validate our controller, a subjective evaluation is conducted where participants viewed the videos of a laughing virtual character. It compares the animations of a virtual character using our controller and a state of the art method. The evaluation results show that the laughter animations computed with our controller are perceived as more natural, expressing amusement more freely and appearing more authentic than with the state of the art method.",
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"content": "It has been well documented that laughter is an important communicative and expressive signal in face-to-face conversations. Our work aims at building a laughter behavior controller for a virtual character which is able to generate upper body animations from laughter audio given as input. This controller relies on the tight correlations between laughter audio and body behaviors. A unified continuous-state statistical framework, inspired by Kalman filter, is proposed to learn the correlations between laughter audio and head/torso behavior from a recorded laughter human dataset. Due to the lack of shoulder behavior data in the recorded human dataset, a rule-based method is defined to model the correlation between laughter audio and shoulder behavior. In the synthesis step, these characterized correlations are rendered in the animation of a virtual character. To validate our controller, a subjective evaluation is conducted where participants viewed the videos of a laughing virtual character. It compares the animations of a virtual character using our controller and a state of the art method. The evaluation results show that the laughter animations computed with our controller are perceived as more natural, expressing amusement more freely and appearing more authentic than with the state of the art method.",
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"abstract": "In this paper, we explore how virtual replicas can enhance Mixed Reality (MR) remote collaboration with a 3D reconstruction of the task space. People in different locations may need to work together remotely on complicated tasks. For example, a local user could follow a remote expert's instructions to complete a physical task. However, it could be challenging for the local user to fully understand the remote expert's intentions without effective spatial referencing and action demonstration. In this research, we investigate how virtual replicas can work as a spatial communication cue to improve MR remote collaboration. This approach segments the foreground manipulable objects in the local environment and creates corresponding virtual replicas of physical task objects. The remote user can then manipulate these virtual replicas to explain the task and guide their partner. This enables the local user to rapidly and accurately understand the remote expert's intentions and instructions. Our user study with an object assembly task found that using virtual replica manipulation was more efficient than using 3D annotation drawing in an MR remote collaboration scenario. We report and discuss the findings and limitations of our system and study, and present directions for future research.",
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"affiliation": "University of South Australia, Australia",
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"affiliation": "University of Auckland, New Zealand",
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"abstract": "Spin Torque Transfer Magnetic RAM (STT-MRAM) is a promising Non-Volatile Memory (NVM) technology achieving high density, low leakage power, and relatively small read/write delays. It provides a solution to improve the performance and to mitigate the leakage power consumption compared to SRAM-based processors. However, the process heterogeneity and the sophisticated back-end-of-line (BEOL) structure make it difficult to integrate the STT-MRAM in two-dimensional integrated circuits (2D ICs). In this article, we implement a RISC-V-based processor with STT-MRAM using a heterogeneous 3D integration methodology. Compared with the SRAM-based 2D counterpart, the MRAM-based 3D IC provides up to 17.55 percent silicon area saving, together with either 34.74 percent performance gain or 13.90 percent energy reduction.",
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"content": "Spin Torque Transfer Magnetic RAM (STT-MRAM) is a promising Non-Volatile Memory (NVM) technology achieving high density, low leakage power, and relatively small read/write delays. It provides a solution to improve the performance and to mitigate the leakage power consumption compared to SRAM-based processors. However, the process heterogeneity and the sophisticated back-end-of-line (BEOL) structure make it difficult to integrate the STT-MRAM in two-dimensional integrated circuits (2D ICs). In this article, we implement a RISC-V-based processor with STT-MRAM using a heterogeneous 3D integration methodology. Compared with the SRAM-based 2D counterpart, the MRAM-based 3D IC provides up to 17.55 percent silicon area saving, together with either 34.74 percent performance gain or 13.90 percent energy reduction.",
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"abstract": "Relations amongst entities play a central role in image understanding. Due to the complexity of modeling (<italic>subject</italic>, <italic>predicate</italic>, <italic>object</italic>) relation triplets, it is crucial to develop a method that can not only recognize seen relations, but also generalize to unseen cases. Inspired by a previously proposed visual translation embedding model, or VTransE <xref ref-type=\"bibr\" rid=\"ref1\">[1]</xref> , we propose a context-augmented translation embedding model that can capture both common and rare relations. The previous VTransE model maps entities and predicates into a low-dimensional embedding vector space where the predicate is interpreted as a translation vector between the embedded features of the bounding box regions of the <italic>subject</italic> and the <italic>object</italic>. Our model additionally incorporates the contextual information captured by the bounding box of the <italic>union</italic> of the subject and the object, and learns the embeddings guided by the constraint <italic>predicate</italic> <inline-formula><tex-math notation=\"LaTeX\">Z_$\\approx$_Z</tex-math></inline-formula> <italic>union</italic> (<italic>subject</italic>, <italic>object</italic>) <inline-formula><tex-math notation=\"LaTeX\">Z_$-$_Z</tex-math></inline-formula> <italic>subject</italic> <inline-formula><tex-math notation=\"LaTeX\">Z_$-$_Z</tex-math></inline-formula> <italic>object</italic>. In a comprehensive evaluation on multiple challenging benchmarks, our approach outperforms previous translation-based models and comes close to or exceeds the state of the art across a range of settings, from small-scale to large-scale datasets, from common to previously unseen relations. It also achieves promising results for the recently introduced task of scene graph generation.",
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"content": "Relations amongst entities play a central role in image understanding. Due to the complexity of modeling (<italic>subject</italic>, <italic>predicate</italic>, <italic>object</italic>) relation triplets, it is crucial to develop a method that can not only recognize seen relations, but also generalize to unseen cases. Inspired by a previously proposed visual translation embedding model, or VTransE <xref ref-type=\"bibr\" rid=\"ref1\">[1]</xref> , we propose a context-augmented translation embedding model that can capture both common and rare relations. The previous VTransE model maps entities and predicates into a low-dimensional embedding vector space where the predicate is interpreted as a translation vector between the embedded features of the bounding box regions of the <italic>subject</italic> and the <italic>object</italic>. Our model additionally incorporates the contextual information captured by the bounding box of the <italic>union</italic> of the subject and the object, and learns the embeddings guided by the constraint <italic>predicate</italic> <inline-formula><tex-math notation=\"LaTeX\">$\\approx$</tex-math><alternatives><mml:math><mml:mo>≈</mml:mo></mml:math><inline-graphic xlink:href=\"hung-ieq1-2992222.gif\"/></alternatives></inline-formula> <italic>union</italic> (<italic>subject</italic>, <italic>object</italic>) <inline-formula><tex-math notation=\"LaTeX\">$-$</tex-math><alternatives><mml:math><mml:mo>-</mml:mo></mml:math><inline-graphic xlink:href=\"hung-ieq2-2992222.gif\"/></alternatives></inline-formula> <italic>subject</italic> <inline-formula><tex-math notation=\"LaTeX\">$-$</tex-math><alternatives><mml:math><mml:mo>-</mml:mo></mml:math><inline-graphic xlink:href=\"hung-ieq3-2992222.gif\"/></alternatives></inline-formula> <italic>object</italic>. In a comprehensive evaluation on multiple challenging benchmarks, our approach outperforms previous translation-based models and comes close to or exceeds the state of the art across a range of settings, from small-scale to large-scale datasets, from common to previously unseen relations. It also achieves promising results for the recently introduced task of scene graph generation.",
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"abstract": "Malware variant attacks have been becoming serious threats in the Internet ecosystem. However, prior arts on malware variants detection over-rely on the supervised learning methods to identify the malware variants using a large number of labeled samples, resulting in their inability to detect the few-shot malware without sufficient samples and ground-truth labels. In this paper, we propose FewM-HGCL, a self-supervised <underline>Few</underline>-shot <underline>M</underline>alware variants detection framework based on <underline>H</underline>eterogeneous <underline>G</underline>raph <underline>C</underline>ontrastive <underline>L</underline>earning, which models the execution behavior of each malware variant as a heterogeneous graph and performs graph instance-based discrimination. Particularly, FewM-HGCL first models the execution behavior of each malware variant with a fine-grained attribute heterogeneous graph, which effectively depicts the interactive relationships between malware objects (<italic>e.g.</italic>, API, process, etc). Then three types of heterogeneous graph data augmentations are proposed, <italic>i.e.</italic>, API attribute masking, interaction enhancing, and meth-path sampling, to generate more robust positive and negative samples for each instance, incorporating semantic prior or structural prior, respectively. Afterward, FewM-HGCL utilizes heterogeneous graph contrastive learning to empower graph attention networks (GATs) to learn the graph-level representations for few-shot malware variants in a self-supervised manner. Experimental results show that the proposed FewM-HGCL on diverse datasets can achieve 70.47%<inline-formula><tex-math notation=\"LaTeX\">Z_$\\sim$_Z</tex-math></inline-formula>98.65% accuracy, which are 0.45%<inline-formula><tex-math notation=\"LaTeX\">Z_$\\sim$_Z</tex-math></inline-formula>8.46% improvements over previous state-of-the-art methods on the few-shot malware variants detection tasks.",
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"content": "Malware variant attacks have been becoming serious threats in the Internet ecosystem. However, prior arts on malware variants detection over-rely on the supervised learning methods to identify the malware variants using a large number of labeled samples, resulting in their inability to detect the few-shot malware without sufficient samples and ground-truth labels. In this paper, we propose FewM-HGCL, a self-supervised <underline>Few</underline>-shot <underline>M</underline>alware variants detection framework based on <underline>H</underline>eterogeneous <underline>G</underline>raph <underline>C</underline>ontrastive <underline>L</underline>earning, which models the execution behavior of each malware variant as a heterogeneous graph and performs graph instance-based discrimination. Particularly, FewM-HGCL first models the execution behavior of each malware variant with a fine-grained attribute heterogeneous graph, which effectively depicts the interactive relationships between malware objects (<italic>e.g.</italic>, API, process, etc). Then three types of heterogeneous graph data augmentations are proposed, <italic>i.e.</italic>, API attribute masking, interaction enhancing, and meth-path sampling, to generate more robust positive and negative samples for each instance, incorporating semantic prior or structural prior, respectively. Afterward, FewM-HGCL utilizes heterogeneous graph contrastive learning to empower graph attention networks (GATs) to learn the graph-level representations for few-shot malware variants in a self-supervised manner. Experimental results show that the proposed FewM-HGCL on diverse datasets can achieve 70.47%<inline-formula><tex-math notation=\"LaTeX\">$\\sim$</tex-math></inline-formula>98.65% accuracy, which are 0.45%<inline-formula><tex-math notation=\"LaTeX\">$\\sim$</tex-math></inline-formula>8.46% improvements over previous state-of-the-art methods on the few-shot malware variants detection tasks.",
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