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"abstract": "Current augmented reality displays still have a very limited field of view compared to the human vision. In order to localize out-of-view objects, researchers have predominantly explored visual guidance approaches to visualize information in the limited (in-view) screen space. Unfortunately, visual conflicts like cluttering or occlusion of information often arise, which can lead to search performance issues and a decreased awareness about the physical environment. In this paper, we compare an innovative non-visual guidance approach based on audio-tactile cues with the state-of-the-art visual guidance technique EyeSee360 for localizing out-of-view objects in augmented reality displays with limited field of view. In our user study, we evaluate both guidance methods in terms of search performance and situation awareness. We show that although audio-tactile guidance is generally slower than the well-performing EyeSee360 in terms of search times, it is on a par regarding the hit rate. Even more so, the audio-tactile method provides a significant improvement in situation awareness compared to the visual approach.",
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"content": "Current augmented reality displays still have a very limited field of view compared to the human vision. In order to localize out-of-view objects, researchers have predominantly explored visual guidance approaches to visualize information in the limited (in-view) screen space. Unfortunately, visual conflicts like cluttering or occlusion of information often arise, which can lead to search performance issues and a decreased awareness about the physical environment. In this paper, we compare an innovative non-visual guidance approach based on audio-tactile cues with the state-of-the-art visual guidance technique EyeSee360 for localizing out-of-view objects in augmented reality displays with limited field of view. In our user study, we evaluate both guidance methods in terms of search performance and situation awareness. We show that although audio-tactile guidance is generally slower than the well-performing EyeSee360 in terms of search times, it is on a par regarding the hit rate. Even more so, the audio-tactile method provides a significant improvement in situation awareness compared to the visual approach.",
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"title": "Comparing Non-Visual and Visual Guidance Methods for Narrow Field of View Augmented Reality Displays",
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"abstract": "With advances in Virtual Reality (VR) technology, user expectation for a near-perfect experience is also increasing. The push for a wider field-of-view can increase the challenges of correcting lens distortion. Past studies on imperfect VR experiences have focused on motion sickness provoked by vection-inducing VR stimuli and discomfort due to mismatches in accommodation and binocular convergence. Disorientation and discomfort due to unintended optical flow induced by lens distortion, referred to as dynamic distortion (DD), has, to date, received little attention. This study examines and models the effects of DD during head rotations with various fixed gazes stabilized by vestibulo-ocular reflex (VOR). Increases in DD levels comparable to lens parameters from poorly designed commercial VR lenses significantly increase discomfort scores of viewers in relation to disorientation, dizziness, and eye strain. Cross-validated results indicate that the model is able to predict significant differences in subjective scores resulting from different commercial VR lenses and these predictions correlated with empirical data. The present work provides new insights to understand symptoms of discomfort in VR during user interactions with static world-locked / space-stabilized scenes and contributes to the design of discomfort-free VR headset lenses.",
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"content": "With advances in Virtual Reality (VR) technology, user expectation for a near-perfect experience is also increasing. The push for a wider field-of-view can increase the challenges of correcting lens distortion. Past studies on imperfect VR experiences have focused on motion sickness provoked by vection-inducing VR stimuli and discomfort due to mismatches in accommodation and binocular convergence. Disorientation and discomfort due to unintended optical flow induced by lens distortion, referred to as dynamic distortion (DD), has, to date, received little attention. This study examines and models the effects of DD during head rotations with various fixed gazes stabilized by vestibulo-ocular reflex (VOR). Increases in DD levels comparable to lens parameters from poorly designed commercial VR lenses significantly increase discomfort scores of viewers in relation to disorientation, dizziness, and eye strain. Cross-validated results indicate that the model is able to predict significant differences in subjective scores resulting from different commercial VR lenses and these predictions correlated with empirical data. The present work provides new insights to understand symptoms of discomfort in VR during user interactions with static world-locked / space-stabilized scenes and contributes to the design of discomfort-free VR headset lenses.",
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"normalizedAbstract": "With advances in Virtual Reality (VR) technology, user expectation for a near-perfect experience is also increasing. The push for a wider field-of-view can increase the challenges of correcting lens distortion. Past studies on imperfect VR experiences have focused on motion sickness provoked by vection-inducing VR stimuli and discomfort due to mismatches in accommodation and binocular convergence. Disorientation and discomfort due to unintended optical flow induced by lens distortion, referred to as dynamic distortion (DD), has, to date, received little attention. This study examines and models the effects of DD during head rotations with various fixed gazes stabilized by vestibulo-ocular reflex (VOR). Increases in DD levels comparable to lens parameters from poorly designed commercial VR lenses significantly increase discomfort scores of viewers in relation to disorientation, dizziness, and eye strain. Cross-validated results indicate that the model is able to predict significant differences in subjective scores resulting from different commercial VR lenses and these predictions correlated with empirical data. The present work provides new insights to understand symptoms of discomfort in VR during user interactions with static world-locked / space-stabilized scenes and contributes to the design of discomfort-free VR headset lenses.",
"title": "Predicting Subjective Discomfort Associated with Lens Distortion in VR Headsets During Vestibulo-Ocular Response to VR Scenes",
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"givenName": "Tsz Tai",
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"abstract": "The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). Firstly, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations.",
"abstracts": [
{
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"content": "The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). Firstly, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations.",
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"title": "Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation",
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"abstract": "The quality of daily spontaneous conversations is of importance towards both our well-being as well as the development of interactive social agents. Prior research directly studying the quality of social conversations has operationalized it in narrow terms, associating greater quality to less small talk. Other works taking a broader perspective of interaction experience have indirectly studied quality through one of the several overlapping constructs such as rapport or engagement, in isolation. In this work we bridge this gap by proposing a holistic conceptualization of conversation quality, building upon the collaborative attributes of cooperative conversation floors. Taking a multilevel perspective of conversation, we develop and validate two instruments for perceived conversation quality (PCQ) at the individual and group levels. Specifically, we motivate capturing external raters' gestalt impressions of participant experiences from thin slices of behavior, and collect annotations of PCQ on the publicly available MatchNMingle dataset of in-the-wild mingling conversations. Finally, we present an analysis of behavioral features that are predictive of PCQ. We find that for the conversations in MatchNMingle, raters tend to associate smaller group sizes, equitable speaking turns with fewer interruptions, and time taken for synchronous bodily coordination with higher PCQ.",
"abstracts": [
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"content": "The quality of daily spontaneous conversations is of importance towards both our well-being as well as the development of interactive social agents. Prior research directly studying the quality of social conversations has operationalized it in narrow terms, associating greater quality to less small talk. Other works taking a broader perspective of interaction experience have indirectly studied quality through one of the several overlapping constructs such as rapport or engagement, in isolation. In this work we bridge this gap by proposing a holistic conceptualization of conversation quality, building upon the collaborative attributes of cooperative conversation floors. Taking a multilevel perspective of conversation, we develop and validate two instruments for perceived conversation quality (PCQ) at the individual and group levels. Specifically, we motivate capturing external raters' gestalt impressions of participant experiences from thin slices of behavior, and collect annotations of PCQ on the publicly available MatchNMingle dataset of in-the-wild mingling conversations. Finally, we present an analysis of behavioral features that are predictive of PCQ. We find that for the conversations in MatchNMingle, raters tend to associate smaller group sizes, equitable speaking turns with fewer interruptions, and time taken for synchronous bodily coordination with higher PCQ.",
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"abstract": "Alignment-free sequence comparison approaches have become increasingly popular in computational biology, because alignment-based approaches are inefficient to process large-scale datasets. Still, there is no way to determine the optimal value of the critical parameter k for alignment-free approaches in general. In this article, we tried to solve the problem by involving multiple k values simultaneously. The method counts the occurrence of each k-mer with different k values in a sequence. Two weighting schemes, based on maximizing deviation method and genetic algorithm, are then used on these counts. We applied the method to enhance the three common alignment-free approaches <inline-formula><tex-math notation=\"LaTeX\">Z_${D_2}$_Z</tex-math></inline-formula>, <inline-formula><tex-math notation=\"LaTeX\">Z_$D_2^S$_Z</tex-math></inline-formula>, and <inline-formula><tex-math notation=\"LaTeX\">Z_$D_2^*$_Z</tex-math></inline-formula>, and evaluated its performance on similarity search and functionally related regulatory sequences recognition. The enhanced approaches achieve better performance than the original approaches in all cases, and much better performance than some other common measures, such as <italic>Pcc</italic>, <italic>Eu</italic>, <italic>Ma, Ch, Kld</italic>, and <italic>Cos</italic>.",
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"abstract": "Cognitive radio networks (CRNs) have been proposed to solve the spectrum scarcity problem. One of their fundamental procedures is to construct a communication link on a common channel for the users, which is referred to as <italic>rendezvous</italic>. In reality, the capability to sense the spectrum may vary from user to user. We study distributed rendezvous for heterogeneous sensing capabilities in this paper. The licensed spectrum is divided into <inline-formula><tex-math notation=\"LaTeX\">Z_$n$_Z</tex-math></inline-formula> channels, <inline-formula><tex-math notation=\"LaTeX\">Z_$U = \\lbrace 1,2,\\ldots,n\\rbrace$_Z</tex-math></inline-formula>. We denote the sensing capability of user <inline-formula><tex-math notation=\"LaTeX\">Z_$i$_Z</tex-math></inline-formula> as <inline-formula><tex-math notation=\"LaTeX\">Z_$C_i \\subseteq U$_Z</tex-math></inline-formula> and the set of available channels (i.e., the channels not occupied by paying users) as <inline-formula><tex-math notation=\"LaTeX\">Z_$V_i \\subseteq C_i$_Z</tex-math></inline-formula>. Due to hardware differences, the users may have different sensing capabilities: <inline-formula><tex-math notation=\"LaTeX\">Z_$C_i \\ne C_j$_Z</tex-math></inline-formula>, and this is called <italic>heterogeneous sensing capability</italic>. In this paper, we propose efficient algorithms for two scenarios: the fully available scenario where <inline-formula><tex-math notation=\"LaTeX\">Z_$V_i = C_i$_Z</tex-math></inline-formula> and the partially available scenario where <inline-formula><tex-math notation=\"LaTeX\">Z_$V_i \\subseteq C_i$_Z</tex-math></inline-formula>. Our idea is to utilize two ‘pointers’ to traverse the sensing capability set, which sets our algorithms apart from the extant rendezvous algorithms. Considering any two neighboring users <inline-formula><tex-math notation=\"LaTeX\">Z_$a, b$_Z</tex-math></inline-formula>, we propose the Traversing Pointer (TP) algorithm that guarantees rendezvous in <inline-formula><tex-math notation=\"LaTeX\">Z_$O(\\max \\lbrace |C_a|,|C_b|\\rbrace \\log \\log n)$_Z</tex-math></inline-formula> time slots for the fully available scenario. This result is only <inline-formula><tex-math notation=\"LaTeX\">Z_$O(\\log \\log n)$_Z</tex-math></inline-formula> larger than the theoretical lower bound. Moreover, it removes an <inline-formula><tex-math notation=\"LaTeX\">Z_$O(\\min \\lbrace |C_a|,|C_b|\\rbrace)$_Z</tex-math></inline-formula> factor when compared to the state-of-the-art result (<inline-formula><tex-math notation=\"LaTeX\">Z_$O(|C_a||C_b|)$_Z</tex-math></inline-formula> in S.-H. Wu <italic>et al.</italic> For the partially available scenario, we propose the Moving Traversing Pointers (MTP) and Prime based Moving Traversing Pointers (P-MTP) algorithms that can guarantee rendezvous within <inline-formula><tex-math notation=\"LaTeX\">Z_$O((\\max \\lbrace |V_a|,|V_b|\\rbrace)^2\\log \\log n)$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$O(|V_a||V_b|\\log \\log n)$_Z</tex-math></inline-formula> time slots respectively, where the latter one combines the pointers and a common technique of plugging in a prime number. The proposed algorithms work more efficiently than the previous best result (<inline-formula><tex-math notation=\"LaTeX\">Z_$O(|C_a||C_b|)$_Z</tex-math></inline-formula> in C.-C. Wu <italic>et al.</italic> under various circumstances. We also conduct extensive simulations and the results corroborate our analyses.",
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"content": "Cognitive radio networks (CRNs) have been proposed to solve the spectrum scarcity problem. One of their fundamental procedures is to construct a communication link on a common channel for the users, which is referred to as <italic>rendezvous</italic>. In reality, the capability to sense the spectrum may vary from user to user. We study distributed rendezvous for heterogeneous sensing capabilities in this paper. The licensed spectrum is divided into <inline-formula><tex-math notation=\"LaTeX\">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href=\"gu-ieq1-2997077.gif\"/></alternatives></inline-formula> channels, <inline-formula><tex-math notation=\"LaTeX\">$U = \\lbrace 1,2,\\ldots,n\\rbrace$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>U</mml:mi><mml:mo>=</mml:mo><mml:mo>{</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mo>...</mml:mo><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo>}</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq2-2997077.gif\"/></alternatives></inline-formula>. We denote the sensing capability of user <inline-formula><tex-math notation=\"LaTeX\">$i$</tex-math><alternatives><mml:math><mml:mi>i</mml:mi></mml:math><inline-graphic xlink:href=\"gu-ieq3-2997077.gif\"/></alternatives></inline-formula> as <inline-formula><tex-math notation=\"LaTeX\">$C_i \\subseteq U$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>⊆</mml:mo><mml:mi>U</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq4-2997077.gif\"/></alternatives></inline-formula> and the set of available channels (i.e., the channels not occupied by paying users) as <inline-formula><tex-math notation=\"LaTeX\">$V_i \\subseteq C_i$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>⊆</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq5-2997077.gif\"/></alternatives></inline-formula>. Due to hardware differences, the users may have different sensing capabilities: <inline-formula><tex-math notation=\"LaTeX\">$C_i \\ne C_j$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>≠</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq6-2997077.gif\"/></alternatives></inline-formula>, and this is called <italic>heterogeneous sensing capability</italic>. In this paper, we propose efficient algorithms for two scenarios: the fully available scenario where <inline-formula><tex-math notation=\"LaTeX\">$V_i = C_i$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq7-2997077.gif\"/></alternatives></inline-formula> and the partially available scenario where <inline-formula><tex-math notation=\"LaTeX\">$V_i \\subseteq C_i$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>⊆</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq8-2997077.gif\"/></alternatives></inline-formula>. Our idea is to utilize two ‘pointers’ to traverse the sensing capability set, which sets our algorithms apart from the extant rendezvous algorithms. Considering any two neighboring users <inline-formula><tex-math notation=\"LaTeX\">$a, b$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>b</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq9-2997077.gif\"/></alternatives></inline-formula>, we propose the Traversing Pointer (TP) algorithm that guarantees rendezvous in <inline-formula><tex-math notation=\"LaTeX\">$O(\\max \\lbrace |C_a|,|C_b|\\rbrace \\log \\log n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo movablelimits=\"true\" form=\"prefix\">max</mml:mo><mml:mo>{</mml:mo><mml:mo>|</mml:mo></mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mrow><mml:mo>|</mml:mo><mml:mo>,</mml:mo><mml:mo>|</mml:mo></mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mrow><mml:mo>|</mml:mo><mml:mo>}</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq10-2997077.gif\"/></alternatives></inline-formula> time slots for the fully available scenario. This result is only <inline-formula><tex-math notation=\"LaTeX\">$O(\\log \\log n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq11-2997077.gif\"/></alternatives></inline-formula> larger than the theoretical lower bound. Moreover, it removes an <inline-formula><tex-math notation=\"LaTeX\">$O(\\min \\lbrace |C_a|,|C_b|\\rbrace)$</tex-math><alternatives><mml:math><mml:mrow><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo movablelimits=\"true\" form=\"prefix\">min</mml:mo><mml:mo>{</mml:mo><mml:mo>|</mml:mo></mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mrow><mml:mo>|</mml:mo><mml:mo>,</mml:mo><mml:mo>|</mml:mo></mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mrow><mml:mo>|</mml:mo><mml:mo>}</mml:mo><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq12-2997077.gif\"/></alternatives></inline-formula> factor when compared to the state-of-the-art result (<inline-formula><tex-math notation=\"LaTeX\">$O(|C_a||C_b|)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo>|</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:mo>|</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq13-2997077.gif\"/></alternatives></inline-formula> in S.-H. Wu <italic>et al.</italic> For the partially available scenario, we propose the Moving Traversing Pointers (MTP) and Prime based Moving Traversing Pointers (P-MTP) algorithms that can guarantee rendezvous within <inline-formula><tex-math notation=\"LaTeX\">$O((\\max \\lbrace |V_a|,|V_b|\\rbrace)^2\\log \\log n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo>(</mml:mo><mml:mo movablelimits=\"true\" form=\"prefix\">max</mml:mo><mml:mo>{</mml:mo><mml:mo>|</mml:mo></mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mrow><mml:mo>|</mml:mo><mml:mo>,</mml:mo><mml:mo>|</mml:mo></mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:msup><mml:mrow><mml:mo>|</mml:mo><mml:mo>}</mml:mo><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mrow><mml:mo form=\"prefix\">log</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq14-2997077.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$O(|V_a||V_b|\\log \\log n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo>|</mml:mo><mml:msub><mml:mi>V</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:mo>|</mml:mo><mml:msub><mml:mi>V</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq15-2997077.gif\"/></alternatives></inline-formula> time slots respectively, where the latter one combines the pointers and a common technique of plugging in a prime number. The proposed algorithms work more efficiently than the previous best result (<inline-formula><tex-math notation=\"LaTeX\">$O(|C_a||C_b|)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mo>|</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:mo>|</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>b</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"gu-ieq16-2997077.gif\"/></alternatives></inline-formula> in C.-C. Wu <italic>et al.</italic> under various circumstances. We also conduct extensive simulations and the results corroborate our analyses.",
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"normalizedAbstract": "Cognitive radio networks (CRNs) have been proposed to solve the spectrum scarcity problem. One of their fundamental procedures is to construct a communication link on a common channel for the users, which is referred to as rendezvous. In reality, the capability to sense the spectrum may vary from user to user. We study distributed rendezvous for heterogeneous sensing capabilities in this paper. The licensed spectrum is divided into - channels, -. We denote the sensing capability of user - as - and the set of available channels (i.e., the channels not occupied by paying users) as -. Due to hardware differences, the users may have different sensing capabilities: -, and this is called heterogeneous sensing capability. In this paper, we propose efficient algorithms for two scenarios: the fully available scenario where - and the partially available scenario where -. Our idea is to utilize two ‘pointers’ to traverse the sensing capability set, which sets our algorithms apart from the extant rendezvous algorithms. Considering any two neighboring users -, we propose the Traversing Pointer (TP) algorithm that guarantees rendezvous in - time slots for the fully available scenario. This result is only - larger than the theoretical lower bound. Moreover, it removes an - factor when compared to the state-of-the-art result (- in S.-H. Wu et al. For the partially available scenario, we propose the Moving Traversing Pointers (MTP) and Prime based Moving Traversing Pointers (P-MTP) algorithms that can guarantee rendezvous within - and - time slots respectively, where the latter one combines the pointers and a common technique of plugging in a prime number. The proposed algorithms work more efficiently than the previous best result (- in C.-C. Wu et al. under various circumstances. We also conduct extensive simulations and the results corroborate our analyses.",
"title": "On Heterogeneous Sensing Capability for Distributed Rendezvous in Cognitive Radio Networks",
"normalizedTitle": "On Heterogeneous Sensing Capability for Distributed Rendezvous in Cognitive Radio Networks",
"fno": "09099372",
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"keywords": [
"Cognitive Radio",
"Computational Complexity",
"Radio Spectrum Management",
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"Heterogeneous Sensing Capability",
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"CRN",
"Spectrum Scarcity Problem",
"Licensed Spectrum",
"Available Channels Set",
"Efficient Algorithms",
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"Extant Rendezvous Algorithms",
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{
"givenName": "Zhaoquan",
"surname": "Gu",
"fullName": "Zhaoquan Gu",
"affiliation": "Cyberspace Institute of Advanced Technology (CIAT), Guangzhou University, Guangzhou, China",
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{
"givenName": "Yuexuan",
"surname": "Wang",
"fullName": "Yuexuan Wang",
"affiliation": "College of Computer Science, Zhejiang University, Zhejiang, China",
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{
"givenName": "Tong",
"surname": "Shen",
"fullName": "Tong Shen",
"affiliation": "College of Computer Science, Zhejiang University, Zhejiang, China",
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{
"givenName": "Francis C.M.",
"surname": "Lau",
"fullName": "Francis C.M. Lau",
"affiliation": "Department of Computer Science, The University of Hong Kong, Hong Kong",
"__typename": "ArticleAuthorType"
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"abstract": "Transfer learning provides an effective solution for feasibly and fast customize accurate <italic>Student</italic> models, by transferring the learned knowledge of pre-trained <italic>Teacher</italic> models over large datasets via fine-tuning. Many pre-trained Teacher models used in transfer learning are publicly available and maintained by public platforms, increasing their vulnerability to backdoor attacks. In this article, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly adopted defenses: <italic>pruning-based</italic>, <italic>retraining-based</italic> and <italic>input pre-processing-based defenses</italic>. Specifically, (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {A}$_Z</tex-math></inline-formula>) ranking-based selection mechanism to speed up the backdoor trigger generation and perturbation process while defeating <italic>pruning-based</italic> and/or <italic>retraining-based defenses</italic>. (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {B}$_Z</tex-math></inline-formula>) autoencoder-powered trigger generation is proposed to produce a robust trigger that can defeat the <italic>input pre-processing-based defense</italic>, while guaranteeing that selected neuron(s) can be significantly activated. (<inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {C}$_Z</tex-math></inline-formula>) defense-aware retraining to generate the manipulated model using reverse-engineered model inputs. We launch effective misclassification attacks on Student models over real-world images, brain Magnetic Resonance Imaging (MRI) data and Electrocardiography (ECG) learning systems. The experiments reveal that our enhanced attack can maintain the 98.4 and 97.2 percent classification accuracy as the genuine model on clean image and time series inputs while improving <inline-formula><tex-math notation=\"LaTeX\">Z_$27.9\\%-100\\%$_Z</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">Z_$27.1\\%-56.1\\%$_Z</tex-math></inline-formula> attack success rate on trojaned image and time series inputs respectively in the presence of pruning-based and/or retraining-based defenses.",
"abstracts": [
{
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"content": "Transfer learning provides an effective solution for feasibly and fast customize accurate <italic>Student</italic> models, by transferring the learned knowledge of pre-trained <italic>Teacher</italic> models over large datasets via fine-tuning. Many pre-trained Teacher models used in transfer learning are publicly available and maintained by public platforms, increasing their vulnerability to backdoor attacks. In this article, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly adopted defenses: <italic>pruning-based</italic>, <italic>retraining-based</italic> and <italic>input pre-processing-based defenses</italic>. Specifically, (<inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {A}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">A</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq1-3000900.gif\"/></alternatives></inline-formula>) ranking-based selection mechanism to speed up the backdoor trigger generation and perturbation process while defeating <italic>pruning-based</italic> and/or <italic>retraining-based defenses</italic>. (<inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {B}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">B</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq2-3000900.gif\"/></alternatives></inline-formula>) autoencoder-powered trigger generation is proposed to produce a robust trigger that can defeat the <italic>input pre-processing-based defense</italic>, while guaranteeing that selected neuron(s) can be significantly activated. (<inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {C}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">C</mml:mi></mml:math><inline-graphic xlink:href=\"wang-ieq3-3000900.gif\"/></alternatives></inline-formula>) defense-aware retraining to generate the manipulated model using reverse-engineered model inputs. We launch effective misclassification attacks on Student models over real-world images, brain Magnetic Resonance Imaging (MRI) data and Electrocardiography (ECG) learning systems. The experiments reveal that our enhanced attack can maintain the 98.4 and 97.2 percent classification accuracy as the genuine model on clean image and time series inputs while improving <inline-formula><tex-math notation=\"LaTeX\">$27.9\\%-100\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>27</mml:mn><mml:mo>.</mml:mo><mml:mn>9</mml:mn><mml:mo>%</mml:mo><mml:mo>-</mml:mo><mml:mn>100</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wang-ieq4-3000900.gif\"/></alternatives></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$27.1\\%-56.1\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>27</mml:mn><mml:mo>.</mml:mo><mml:mn>1</mml:mn><mml:mo>%</mml:mo><mml:mo>-</mml:mo><mml:mn>56</mml:mn><mml:mo>.</mml:mo><mml:mn>1</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"wang-ieq5-3000900.gif\"/></alternatives></inline-formula> attack success rate on trojaned image and time series inputs respectively in the presence of pruning-based and/or retraining-based defenses.",
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"title": "Backdoor Attacks Against Transfer Learning With Pre-Trained Deep Learning Models",
"normalizedTitle": "Backdoor Attacks Against Transfer Learning With Pre-Trained Deep Learning Models",
"fno": "09112322",
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"Brain",
"Deep Learning Artificial Intelligence",
"Electrocardiography",
"Invasive Software",
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"content": "The behavior of ordered binary decision diagrams (OBDD) for general Boolean functions is studied. A tight upper bound of (2/sup n//n)(2+ epsilon ) for the worst case OBDD size is derived. Although the size of an OBDD is dependent on the ordering of decision variables, it is shown that almost all functions are not sensitive to variable ordering.",
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"content": "Abstract—Consider a hypercube of $2^n$ points described by $n$ Boolean variables and a subcube of $2^m$ points, $m \\leq n$. As is well-known, the Boolean function with value 1 in the points of the subcube can be expressed as the product (AND) of $n-m$ variables. The standard synthesis of arbitrary functions exploits this property. We extend the concept of subcube to the more powerful pseudocube. The basic set is still composed of $2^m$ points, but has a more general form. The function with value 1 in a pseudocube, called pseudoproduct, is expressed as the AND of $n-m$ EXOR-factors, each containing at most $m+1$ variables. Subcubes are special cases of pseudocubes and their corresponding pseudoproducts reduce to standard products. An arbitrary Boolean function can be expressed as a sum of pseudoproducts (SPP). This expression is in general much shorter than the standard sum of products, as demonstrated on some known benchmarks. The logical network of an $n$-bit adder is designed in SPP, as a relevant example of application of this new technique. A class of symmetric functions is also defined, particularly suitable for SPP representation.",
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"abstract": " Boolean functions are often represented by ordered binary-decision diagrams (OBDDs) introduced by Bryant (1986). Liaw and Lin (1992) have proved upper and lower bounds on the minimal OBDD size of almost all Boolean functions. Now tight bounds are proved for the minimal OBDD size for arbitrary or optimal variable orderings and for the minimal read-once branching program size of almost all functions. Almost all Boolean functions have a sensitivity of almost 1, i.e., the minimal OBDD size for an optimal variable ordering differs from the minimal OBDD size for a worst variable ordering by a factor of at most 1+/spl epsi/(n) where /spl epsi/(n) converges exponentially fast to 0.",
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"content": " Boolean functions are often represented by ordered binary-decision diagrams (OBDDs) introduced by Bryant (1986). Liaw and Lin (1992) have proved upper and lower bounds on the minimal OBDD size of almost all Boolean functions. Now tight bounds are proved for the minimal OBDD size for arbitrary or optimal variable orderings and for the minimal read-once branching program size of almost all functions. Almost all Boolean functions have a sensitivity of almost 1, i.e., the minimal OBDD size for an optimal variable ordering differs from the minimal OBDD size for a worst variable ordering by a factor of at most 1+/spl epsi/(n) where /spl epsi/(n) converges exponentially fast to 0.",
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"title": "The Size of Reduced OBDD's and Optimal Read-Once Branching Programs for Almost all Boolean Functions",
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"abstract": "Abstract—For 2D iteration space tiling, we address the problem of determining the tile parameters that minimize the total execution time on a parallel machine. We consider uniform dependency computations tiled so that (at least) one of the tile boundaries is parallel to the domain boundaries. We determine the optimal tile size as a closed form solution. In addition, we determine the optimal number of processors and also the optimal slope of the oblique tile boundary. Our results are based on the bsp model, which assures the portability of the results. Our predictions are justified on a sequence global alignment problem specialized to similar sequences using Fickett's k-band algorithm, for which our optimal semi-oblique tiling yields an improvement of a factor of 2.5 over orthogonal tiling. Our optimal solution requires a block-cyclic distribution of tiles to processors. The best one can obtain with only block distribution (as many authors require) is three times slower. Furthermore, our best running time is within 10 percent of the \"predicted theoretical peak\" performance of the machine!",
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"content": "Abstract—For 2D iteration space tiling, we address the problem of determining the tile parameters that minimize the total execution time on a parallel machine. We consider uniform dependency computations tiled so that (at least) one of the tile boundaries is parallel to the domain boundaries. We determine the optimal tile size as a closed form solution. In addition, we determine the optimal number of processors and also the optimal slope of the oblique tile boundary. Our results are based on the bsp model, which assures the portability of the results. Our predictions are justified on a sequence global alignment problem specialized to similar sequences using Fickett's k-band algorithm, for which our optimal semi-oblique tiling yields an improvement of a factor of 2.5 over orthogonal tiling. Our optimal solution requires a block-cyclic distribution of tiles to processors. The best one can obtain with only block distribution (as many authors require) is three times slower. Furthermore, our best running time is within 10 percent of the \"predicted theoretical peak\" performance of the machine!",
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"abstract": "The existence of various types of correlations among the expressions of a group of biologically significant genes poses challenges in developing effective methods of gene expression data analysis. The initial focus of computational biologists was to work with only absolute and shifting correlations. However, researchers have found that the ability to handle shifting-and-scaling correlation enables them to extract more biologically relevant and interesting patterns from gene microarray data. In this paper, we introduce an effective shifting-and-scaling correlation measure named Shifting and Scaling Similarity (SSSim), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named Intensive Correlation Search (ICS) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression data set. The technique performs satisfactorily with a number of benchmarked gene expression data sets when evaluated in terms of functional categories in Gene Ontology database.",
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"abstract": "Hutchinson Gilford progeria syndrome (HGPS) is a rare genetic disease with symptoms of aging at a very early age. Its molecular basis is not entirely clear, although profound gene expression changes have been reported, and there are some known and other presumed overlaps with normal aging process. Identification of genes with aging- or HGPS-associated expression changes is thus an important problem. However, standard regression approaches are currently unsuitable for this task due to limited sample sizes, thus motivating development of alternative approaches. Here, we report a novel iterative multiple regression approach that leverages co-expressed gene clusters to identify gene clusters whose expression co-varies with age and/or HGPS. We have applied our approach to novel RNA-seq profiles in fibroblast cell cultures at three different cellular ages, both from HGPS patients and normal samples. After establishing the robustness of our approach, we perform a comparative investigation of biological processes underlying normal aging and HGPS. Our results recapitulate previously known processes underlying aging as well as suggest numerous unique processes underlying aging and HGPS. The approach could also be useful in detecting phenotype-dependent co-expression gene clusters in other contexts with limited sample sizes.",
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"abstracts": [
{
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"content": "Microarray technology enables the collection of vast amounts of gene expression data from biological experiments. Clustering algorithms have been successfully applied to exploring the gene expression data. Since a set of genes may be only correlated to a subset of samples, it is useful to use co-clustering to recover co-clusters in the gene expression data. In this paper, we propose a novel algorithm, called Subspace Weighting Co-Clustering (SWCC), for high dimensional gene expression data. In SWCC, a gene subspace weight matrix is introduced to identify the contribution of gene objects in distinguishing different sample clusters. We design a new co-clustering objective function to recover the co-clusters in the gene expression data, in which the subspace weight matrix is introduced. An iterative algorithm is developed to solve the objective function, in which the subspace weight matrix is automatically computed during the iterative co-clustering process. Our empirical study shows encouraging results of the proposed algorithm in comparison with six state-of-the-art clustering algorithms on ten gene expression data sets. We also propose to use SWCC for gene clustering and selection. The experimental results show that the selected genes can improve the classification performance of Random Forests.",
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"normalizedAbstract": "Microarray technology enables the collection of vast amounts of gene expression data from biological experiments. Clustering algorithms have been successfully applied to exploring the gene expression data. Since a set of genes may be only correlated to a subset of samples, it is useful to use co-clustering to recover co-clusters in the gene expression data. In this paper, we propose a novel algorithm, called Subspace Weighting Co-Clustering (SWCC), for high dimensional gene expression data. In SWCC, a gene subspace weight matrix is introduced to identify the contribution of gene objects in distinguishing different sample clusters. We design a new co-clustering objective function to recover the co-clusters in the gene expression data, in which the subspace weight matrix is introduced. An iterative algorithm is developed to solve the objective function, in which the subspace weight matrix is automatically computed during the iterative co-clustering process. Our empirical study shows encouraging results of the proposed algorithm in comparison with six state-of-the-art clustering algorithms on ten gene expression data sets. We also propose to use SWCC for gene clustering and selection. The experimental results show that the selected genes can improve the classification performance of Random Forests.",
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