OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Explanations for Video Action Recognition
Venue: arXiv cs.CV 2023
Year: 2023

To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for the classification. However, existing methods often ignore the hierarchical relationship between these prototypes, and thus can not explain semantic concepts at both higher level (e.g., water sports) and lower level (e.g., swimming). In this paper inspired by human cognition system, we leverage hierarchal information to deal with uncertainty: When we observe water and human activity, but no definitive action it can be recognized as the water sports parent class. Only after observing a person swimming can we definitively refine it to the swimming action. To this end, we propose HIerarchical Prototype Explainer (HIPE) to build hierarchical relations between prototypes and classes. HIPE enables a reasoning process for video action classification by dissecting the input video frames on multiple levels of the class hierarchy, our method is also applicable to other video tasks. The faithfulness of our method is verified by reducing accuracy-explainability trade off on ActivityNet and UCF-101 while providing multi-level explanations.
IMC: A Classification of Identity Management Approaches
Venue: arXiv cs.CR 2023
Year: 2023

This paper presents a comprehensive classification of identity management approaches. The classification makes use of three axes: topology, type of user, and type of environment. The analysis of existing approaches using the resulting identity management cube (IMC) highlights the trade-off between user control and trust in attributes. A comparative analysis of IMC and established models identifies missing links between the approaches. The IMC is extended by a morphology of identity management, describing characteristics of cooperation. The morphology is then mapped to the life cycle of users and identity management in a further step. These classifications are practically underlined with current approaches. Both methods combined provide a comprehensive characterization of identity management approaches. The methods help to choose suited approaches and implement needed tools.
Time-Entanglement QKD: Secret Key Rates and Information Reconciliation Coding
Venue: arXiv cs.IT 2023
Year: 2023

In time entanglement-based quantum key distribution (QKD), Alice and Bob extract the raw key bits from the (identical) arrival times of entangled photon pairs by time-binning. Each of them individually discretizes time into bins and groups them into frames. They retain only the frames with a single occupied bin. Thus, Alice and Bob can use the position of the occupied bin within a frame to generate random key bits, as in PPM modulation. Because of entanglement, their occupied bins and their keys should be identical. However, practical photon detectors suffer from time jitter errors. These errors cause discrepancies between Alice's and Bob's keys. Alice sends information to Bob through the public channel to reconcile the keys. The amount of information determines the secret key rate. This paper computes the secret key rates possible with detector jitter errors and constructs codes for information reconciliation to approach these rates.
EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies
Venue: arXiv cs.SD 2023
Year: 2023

Vocal Bursts -- short, non-speech vocalizations that convey emotions, such as laughter, cries, sighs, moans, and groans -- are an often-overlooked aspect of speech emotion recognition, but an important aspect of human vocal communication. One barrier to study of these interesting vocalizations is a lack of large datasets. I am pleased to introduce the EmoGator dataset, which consists of 32,130 samples from 357 speakers, 16.9654 hours of audio; each sample classified into one of 30 distinct emotion categories by the speaker. Several different approaches to construct classifiers to identify emotion categories will be discussed, and directions for future research will be suggested. Data set is available for download from https://github.com/fredbuhl/EmoGator.
A Policy Optimization Method Towards Optimal-time Stability
Venue: arXiv cs.RO 2023
Year: 2023

In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization. However, these criteria only guarantee the infinite-time convergence of the system's state to an equilibrium point, which leads to sub-optimality of the policy. In this paper, we propose a policy optimization technique incorporating sampling-based Lyapunov stability. Our approach enables the system's state to reach an equilibrium point within an optimal time and maintain stability thereafter, referred to as "optimal-time stability". To achieve this, we integrate the optimization method into the Actor-Critic framework, resulting in the development of the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm. Through evaluations conducted on ten robotic tasks, our approach outperforms previous studies significantly, effectively guiding the system to generate stable patterns.
Bayesian Generalized Kernel Inference for Exploration of Autonomous Robots
Venue: arXiv cs.RO 2023
Year: 2023

This paper concerns realizing highly efficient information-theoretic robot exploration with desired performance in complex scenes. We build a continuous lightweight inference model to predict the mutual information (MI) and the associated prediction confidence of the robot's candidate actions which have not been evaluated explicitly. This allows the decision-making stage in robot exploration to run with a logarithmic complexity approximately, this will also benefit online exploration in large unstructured, and cluttered places that need more spatial samples to assess and decide. We also develop an objective function to balance the local optimal action with the highest MI value and the global choice with high prediction variance. Extensive numerical and dataset simulations show the desired efficiency of our proposed method without losing exploration performance in different environments. We also provide our open-source implementation codes released on GitHub for the robot community.
Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification
Venue: arXiv cs.CV 2023
Year: 2023

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.
Age-Optimal Multi-Channel-Scheduling under Energy and Tolerance Constraints
Venue: arXiv cs.IT 2023
Year: 2023

We study the optimal scheduling problem where n source nodes attempt to transmit updates over L shared wireless on/off fading channels to optimize their age performance under energy and age-violation tolerance constraints. Specifically, we provide a generic formulation of age-optimization in the form of a constrained Markov Decision Processes (CMDP), and obtain the optimal scheduler as the solution of an associated Linear Programming problem. We investigate the characteristics of the optimal single-user multi-channel scheduler for the important special cases of average-age and violation-rate minimization. This leads to several key insights on the nature of the optimal allocation of the limited energy, where a usual threshold-based policy does not apply and will be useful in guiding scheduler designers. We then investigate the stability region of the optimal scheduler for the multi-user case. We also develop an online scheduler using Lyapunov-drift-minimization methods that do not require the knowledge of channel statistics. Our numerical studies compare the stability region of our online scheduler to the optimal scheduler to reveal that it performs closely with unknown channel statistics.
Bent Partitions, Vectorial Dual-Bent Functions and Partial Difference Sets
Venue: arXiv cs.IT 2023
Year: 2023

It is known that partial spreads is a class of bent partitions. In \cite{AM2022Be,MP2021Be}, two classes of bent partitions whose forms are similar to partial spreads were presented. In \cite{AKM2022Ge}, more bent partitions $Γ_{1}, Γ_{2}, Γ_{1}^{\bullet}, Γ_{2}^{\bullet}, Θ_{1}, Θ_{2}$ were presented from (pre)semifields, including the bent partitions given in \cite{AM2022Be,MP2021Be}. In this paper, we investigate the relations between bent partitions and vectorial dual-bent functions. For any prime $p$, we show that one can generate certain bent partitions (called bent partitions satisfying Condition $\mathcal{C}$) from certain vectorial dual-bent functions (called vectorial dual-bent functions satisfying Condition A). In particular, when $p$ is an odd prime, we show that bent partitions satisfying Condition $\mathcal{C}$ one-to-one correspond to vectorial dual-bent functions satisfying Condition A. We give an alternative proof that $Γ_{1}, Γ_{2}, Γ_{1}^{\bullet}, Γ_{2}^{\bullet}, Θ_{1}, Θ_{2}$ are bent partitions. We present a secondary construction of vectorial dual-bent functions, which can be used to generate more bent partitions. We show that any ternary weakly regular bent function $f: V_{n}^{(3)}\rightarrow \mathbb{F}_{3}$ ($n$ even) of $2$-form can generate a bent partition. When such $f$ is weakly regular but not regular, the generated bent partition by $f$ is not coming from a normal bent partition, which answers an open problem proposed in \cite{AM2022Be}. We give a sufficient condition on constructing partial difference sets from bent partitions, and when $p$ is an odd prime, we provide a characterization of bent partitions satisfying Condition $\mathcal{C}$ in terms of partial difference sets.
Rate Region of MIMO RIS-assisted Broadcast Channels with Rate Splitting and Improper Signaling
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we study the achievable rate region of 1-layer rate splitting (RS) in the presence of hardware impairment (HWI) and improper Gaussian signaling (IGS) for a single-cell reconfigurable intelligent surface (RIS) assisted broadcast channel (BC). We assume that the transceivers may suffer from an imbalance in in-band and quadrature signals, which is known as I/Q imbalance (IQI). The received signal and noise can be improper when there exists IQI. Therefore, we employ IGS to compensate for IQI as well as to manage interference. Our results show that RS and RIS can significantly enlarge the rate region, where the role of RS is to manage interference while RIS mainly improves the coverage.
Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images
Venue: arXiv cs.CV 2023
Year: 2023

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.
Learning Road Scene-level Representations via Semantic Region Prediction
Venue: arXiv cs.CV 2023
Year: 2023

In this work, we tackle two vital tasks in automated driving systems, i.e., driver intent prediction and risk object identification from egocentric images. Mainly, we investigate the question: what would be good road scene-level representations for these two tasks? We contend that a scene-level representation must capture higher-level semantic and geometric representations of traffic scenes around ego-vehicle while performing actions to their destinations. To this end, we introduce the representation of semantic regions, which are areas where ego-vehicles visit while taking an afforded action (e.g., left-turn at 4-way intersections). We propose to learn scene-level representations via a novel semantic region prediction task and an automatic semantic region labeling algorithm. Extensive evaluations are conducted on the HDD and nuScenes datasets, and the learned representations lead to state-of-the-art performance for driver intention prediction and risk object identification.
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control
Venue: arXiv cs.NE 2023
Year: 2023

We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands. Such distributed control design is widespread across biological systems because it increases survivability and accuracy in certain and uncertain environments. We demonstrate that TLA can provide many advantages over existing approaches, including persistent exploration, adaptive control, explainable temporal behavior, compute efficiency and distributed control. We present two different algorithms for training TLA: (a) Closed-loop control, where the fast controller is trained over a pre-trained slow controller, allowing better exploration for the fast controller and closed-loop control where the fast controller decides whether to "act-or-not" at each timestep; and (b) Partially open loop control, where the slow controller is trained over a pre-trained fast controller, allowing for open loop-control where the slow controller picks a temporally extended action or defers the next n-actions to the fast controller. We evaluated our method on a suite of continuous control tasks and demonstrate the advantages of TLA over several strong baselines.
Interactive Control over Temporal Consistency while Stylizing Video Streams
Venue: arXiv cs.GR 2023
Year: 2023

Image stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per-frame basis. However, per-frame stylization usually lacks temporal consistency, expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal consistency suffer from one or more of the following drawbacks: They (1) are only suitable for a limited range of techniques, (2) do not support online processing as they require the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency control. Domain-agnostic techniques for temporal consistency aim to eradicate flickering completely but typically disregard aesthetic aspects. For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that stylizes video streams in real-time at full HD resolutions while providing interactive consistency control. We develop a lite optical-flow network that operates at 80 FPS on desktop systems with sufficient accuracy. Further, we employ an adaptive combination of local and global consistency features and enable interactive selection between them. Objective and subjective evaluations demonstrate that our method is superior to state-of-the-art video consistency approaches.
Parameterized Lower Bounds for Problems in P via Fine-Grained Cross-Compositions
Venue: arXiv cs.DS 2023
Year: 2023

We provide a general framework to exclude parameterized running times of the form $O(\ell^β+ n^γ)$ for problems that have polynomial running time lower bounds under hypotheses from fine-grained complexity. Our framework is based on cross-compositions from parameterized complexity. We (conditionally) exclude running times of the form $O(\ell^{γ/{(γ-1)} - ε} + n^γ)$ for any $1<γ<2$ and $ε>0$ for the following problems: - Longest Common Subsequence: Given two length-$n$ strings and $\ell\in\mathbb{N}$, is there a common subsequence of length $\ell$? - Discrete Fréchet Distance: Given two lists of $n$ points each and $k\in \mathbb{N}$, is the Fréchet distance of the lists at most $k$? Here $\ell$ is the maximum number of points which one list is ahead of the other list in an optimum traversal. Moreover, we exclude running times $O(\ell^{{2γ}/{(γ-1)}-ε} + n^γ)$ for any $1<γ<3$ and $ε>0$ for: - Negative Triangle: Given an edge-weighted graph with $n$ vertices, is there a triangle whose sum of edge-weights is negative? Here $\ell$ is the order of a maximum connected component. - Triangle Collection: Given a vertex-colored graph with $n$ vertices, is there for each triple of colors a triangle whose vertices have these three colors? Here $\ell$ is the order of a maximum connected component. - 2nd Shortest Path: Given an $n$-vertex edge-weighted directed graph, two vertices $s$ and $t$, and $k \in \mathbb{N}$, has the second longest $s$-$t$-path length at most $k$? Here $\ell$ is the directed feedback vertex set. Except for 2nd Shortest Path all these running time bounds are tight, that is, algorithms with running time $O(\ell^{γ/{(γ-1)}} + n^γ)$ for any $1 < γ< 2$ and $O(\ell^{{2γ}/{(γ-1)}} + n^γ)$ for any $1 < γ< 3$, respectively, are known.
Understanding the main failure scenarios of subsea blowout preventers systems: An approach through Latent Semantic Analysis
Venue: arXiv cs.IR 2023
Year: 2023

The blowout preventer (BOP) system is one of the most important well safety barriers during the drilling phase because it can prevent the development of blowout events. This paper investigates BOP system's main failures using an LSA-based methodology. A total of 1312 failure records from companies worldwide were collected from the International Association of Drilling Contractors' RAPID-S53 database. The database contains recordings of halted drilling operations due to BOP system's failures and component's function deviations. The main failure scenarios of the components annular preventer, shear rams preventer, compensated chamber solenoid valve, and hydraulic regulators were identified using the proposed methodology. The scenarios contained valuable information about corrective maintenance procedures, such as frequently observed failure modes, detection methods used, suspected causes, and corrective actions. The findings highlighted that the major failures of the components under consideration were leakages caused by damaged elastomeric seals. The majority of the failures were detected during function and pressure tests with the BOP system in the rig. This study provides an alternative safety analysis that contributes to understanding blowout preventer system's critical component failures by applying a methodology based on a well-established text mining technique and analyzing failure records from an international database.
Resident Population Density-Inspired Deployment of K-tier Aerial Cellular Network
Venue: arXiv cs.NI 2023
Year: 2023

Using unmanned aerial vehicles (UAVs) to enhance network coverage has proven a variety of benefits compared to terrestrial counterparts. One of the commonly used mathematical tools to model the locations of the UAVs is stochastic geometry (SG). However, in the existing studies, both users and UAVs are often modeled as homogeneous point processes. In this paper, we consider an inhomogeneous Poisson point process (PPP)-based model for the locations of the users that captures the degradation in the density of active users as we move away from the town center. In addition, we propose the deployment of aerial vehicles following the same inhomogeneity of the users to maximize the performance. In addition, a multi-tier network model is also considered to make better use of the rich space resources. Then, the analytical expressions of the coverage probability for a typical user and the total coverage probability are derived. Finally, we optimize the coverage probability with limitations of the total number of UAVs and the minimum local coverage probability. Finally we give the optimal UAV distribution parameters when the maximum overall coverage probability is reached.
Effect of emotions and personalisation on cancer website reuse intentions
Venue: arXiv cs.HC 2023
Year: 2023

The effect of emotions and personalisation on continuance use intentions in online health services is underexplored. Accordingly, we propose a research model for examining the impact of emotion- and personalisation-based factors on cancer website reuse intentions. We conducted a study using a real-world NGO cancer-support website, which was evaluated by 98 participants via an online questionnaire. Model relations were estimated using the PLS-SEM method. Our findings indicated that pre-use emotions did not significantly influence perceived personalisation. However, satisfaction with personalisation, and perceived usefulness mediated by satisfaction, increased reuse intentions. In addition, post-use positive emotions potentially influenced reuse intentions. Our paper, therefore, illustrates the applicability of theory regarding continuance use intentions to cancer-support websites and highlights the importance of personalisation for these purposes.