OpenRaiser/Intern-Atlas huggingface dataset:
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning
Venue: arXiv cs.CV 2022
Year: 2022

Inspired by strategies like Active Learning, it is intuitive that intelligently selecting the training classes from a dataset for Zero-Shot Learning (ZSL) can improve the performance of existing ZSL methods. In this work, we propose a framework called Diverse and Rare Class Identifier (DiRaC-I) which, given an attribute-based dataset, can intelligently yield the most suitable "seen classes" for training ZSL models. DiRaC-I has two main goals - constructing a diversified set of seed classes, followed by a visual-semantic mining algorithm initialized by these seed classes that acquires the classes capturing both diversity and rarity in the object domain adequately. These classes can then be used as "seen classes" to train ZSL models for image classification. We adopt a real-world scenario where novel object classes are available to neither DiRaC-I nor the ZSL models during training and conducted extensive experiments on two benchmark data sets for zero-shot image classification - CUB and SUN. Our results demonstrate DiRaC-I helps ZSL models to achieve significant classification accuracy improvements.
Depression Diagnosis and Analysis via Multimodal Multi-order Factor Fusion
Venue: arXiv cs.MM 2022
Year: 2022

Depression is a leading cause of death worldwide, and the diagnosis of depression is nontrivial. Multimodal learning is a popular solution for automatic diagnosis of depression, and the existing works suffer two main drawbacks: 1) the high-order interactions between different modalities can not be well exploited; and 2) interpretability of the models are weak. To remedy these drawbacks, we propose a multimodal multi-order factor fusion (MMFF) method. Our method can well exploit the high-order interactions between different modalities by extracting and assembling modality factors under the guide of a shared latent proxy. We conduct extensive experiments on two recent and popular datasets, E-DAIC-WOZ and CMDC, and the results show that our method achieve significantly better performance compared with other existing approaches. Besides, by analyzing the process of factor assembly, our model can intuitively show the contribution of each factor. This helps us understand the fusion mechanism.
Comparative Analysis of Terahertz Propagation Under Dust Storm Conditions on Mars and Earth
Venue: arXiv cs.IT 2022
Year: 2022

Reliable Terahertz (THz) links are necessary for outdoor point-to-point communication with the exponential growth of wireless data traffic. This study presents a modified Monte Carlo simulation procedure for estimating THz link attenuation due to multiple scattering by dust particles on the THz beam propagation path. Scattering models are developed for beams through dust, based on Mie and Rayleigh approximations for corresponding frequencies for Earth (0.24 THz) and Mars (1.64 THz). The simulation results are compared, considering parameters such as the number of Monte-Carlo photon (MCP) packets, visibility, dust particle placement density along the beam, frequency, and distance between the transmitter and the receiver. Moreover, a channel capacity model was proposed, considering THz link attenuation due to dust storms, spreading loss and molecular absorption loss for Earth and Mars outdoor environments. Simulation results for Earth show that link attenuation increases with dust particle placement density, distance and frequency, and attenuation decreases with visibility. On Mars, similar results are obtained, except that the attenuation is variate around a constant value with the frequency increase. Channel capacity is estimated for Earth and Mars environments considering time and distance-dependent scenarios. Time windows that show a sudden drop of dust particles along the beam provide opportunities to communicate with high reliability. Moreover, increasing the distance between the transmitter and receiver severely reduces the channel capacity measurement in strong dust storm conditions in both environments. Our study has found that weak dust storms have relatively little effect on Mars, but much larger effects on Earth.
Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following
Venue: arXiv cs.LG 2022
Year: 2022

Agents that can follow language instructions are expected to be useful in a variety of situations such as navigation. However, training neural network-based agents requires numerous paired trajectories and languages. This paper proposes using multimodal generative models for semi-supervised learning in the instruction following tasks. The models learn a shared representation of the paired data, and enable semi-supervised learning by reconstructing unpaired data through the representation. Key challenges in applying the models to sequence-to-sequence tasks including instruction following are learning a shared representation of variable-length mulitimodal data and incorporating attention mechanisms. To address the problems, this paper proposes a novel network architecture to absorb the difference in the sequence lengths of the multimodal data. In addition, to further improve the performance, this paper shows how to incorporate the generative model-based approach with an existing semi-supervised method called a speaker-follower model, and proposes a regularization term that improves inference using unpaired trajectories. Experiments on BabyAI and Room-to-Room (R2R) environments show that the proposed method improves the performance of instruction following by leveraging unpaired data, and improves the performance of the speaker-follower model by 2\% to 4\% in R2R.
Robust Consensus Clustering and its Applications for Advertising Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions. In practice, there exist noise and outliers in clustering task, which, however, may significantly degrade the performance. To address this issue, we propose a novel algorithm -- robust consensus clustering that can find common ground truth among experts' opinions, which tends to be minimally affected by the bias caused by the outliers. In particular, we formalize the robust consensus clustering problem as a constraint optimization problem, and then derive an effective algorithm upon alternating direction method of multipliers (ADMM) with rigorous convergence guarantee. Our method outperforms the baselines on benchmarks. We apply the proposed method to the real-world advertising campaign segmentation and forecasting tasks using the proposed consensus clustering results based on the similarity computed via Kolmogorov-Smirnov Statistics. The accurate clustering result is helpful for building the advertiser profiles so as to perform the forecasting.
Quantum Circuit Resizing
Venue: arXiv cs.ET 2022
Year: 2022

Existing quantum systems provide very limited physical qubit counts, trying to execute a quantum algorithm/circuit on them that have a higher number of logical qubits than physically available lead to a compile-time error. Given that it is unrealistic to expect existing quantum systems to provide, in near future, sufficient number of qubits that can accommodate large circuit, there is a pressing need to explore strategies that can somehow execute large circuits on small systems. In this paper, first, we perform an analysis to identify the qubits that are most suitable for circuit resizing. Our results reveal that, in most quantum programs, there exist qubits that can be reused mid-program to serially/sequentially execute the circuit employing fewer qubits. Motivated by this observation, we design, implement and evaluate a compiler-based approach that i) identifies the qubits that can be most beneficial for serial circuit execution; ii) selects those qubits to reuse at each step of execution for size minimization of the circuit; and iii) minimizes Middle Measurement (MM) delays due to impractical implementation of shots to improve the circuit reliability. Furthermore, since our approach intends to execute the circuits sequentially, the crosstalk errors can also be optimized as a result of the reduced number of concurrent gates. The experimental results indicate that our proposed approach can (i) execute large circuits that initially cannot fit into small circuits, on small quantum hardware, and (ii) can significantly improve the PST of the results by 2.1X when both original and our serialized programs can fit into the target quantum hardware.
3-D Markerless Tracking of Human Gait by Geometric Trilateration of Multiple Kinects
Venue: arXiv cs.MM 2022
Year: 2022

In this paper, we develop an integrated markerless gait tracking system with three Kinect v2 sensors. A geometric principle-based trilateration method is proposed for optimizing the accuracy of the measured gait data. To tackle the data synchronization problem among the Kinect clients and the server, a synchronization mechanism based on NTP (Network Time Protocol) is designed for synchronizing the server and Kinect clients' clocks. Furthermore, a time schedule is designed for timing each Kinect client's data transmission. In the experiment, participants are asked to perform a 60 s walk while the proposed tracking system obtains the participant's gait data. Six joints (including left hip, right hip, left knee, right knee, left ankle and right ankle) of the participants are tracked where the obtained gait data are described as 6000 {movements} of joint positions (1000 {movements} for each joint). The results show that the trilateration tracking result by the three Kinect sensors has a much higher accuracy compared with the accuracy measured by a single Kinect sensor. Within a randomly sampled time period (67.726 s in the experiment), 98.37% of the frames generated by the gait tracking system have timing errors less than 1 ms, which is much better than the default NTP service embedded in the Windows 8.1 operating system. The accuracy of the proposed system is quantitatively evaluated and verified by a comparison with a commercial medical system (Delsys Trigno Smart Sensor System).
DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images, and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.
Modeling Effective Lifespan of Payment Channels
Venue: arXiv cs.DC 2022
Year: 2022

While being decentralized, secure, and reliable, Bitcoin and many other blockchain-based cryptocurrencies suffer from scalability issues. One of the promising proposals to address this problem is off-chain payment channels. Since, not all nodes are connected directly to each other, they can use a payment network to route their payments. Each node allocates a balance that is frozen during the channel's lifespan. Spending and receiving transactions will shift the balance to one side of the channel. A channel becomes unbalanced when there is not sufficient balance in one direction. In this case, we say the effective lifespan of the channel has ended. In this paper, we develop a mathematical model to predict the expected effective lifespan of a channel based on the network's topology. We investigate the impact of channel unbalancing on the payment network and individual channels. We also discuss the effect of certain characteristics of payment channels on their lifespan. Our case study on a snapshot of the Lightning Network shows how the effective lifespan is distributed, and how it is correlated with other network characteristics. Our results show that central unbalanced channels have a drastic effect on the network performance.
RegTraffic: A Regression Based Traffic Simulator for Spatiotemporal Traffic Modeling, Simulation and Visualization
Venue: arXiv cs.NI 2022
Year: 2022

Traffic simulation is a great tool to demonstrate complex traffic structures which can be extremely useful for the planning, development, and management of road traffic networks. Current traffic simulators offer limited features when it comes to interactive and adaptive traffic modeling. This paper presents RegTraffic, a novel interactive traffic simulator that integrates dynamic regression-based spatiotemporal traffic analysis to predict congestion of intercorrelated road segments. The simulator models traffic congestion of road segments depending on neighboring road links and temporal features of the dynamic traffic flow. The simulator provides a user-friendly web interface to select road segments of interest, receive user-defined traffic parameters, and visualize the traffic for the flow of correlated road links based on the user inputs and the underlying correlation of these road links. Performance evaluation shows that RegTraffic can effectively predict traffic congestion with a Mean Squared Error of 1.3 Km/h and a Root Mean Squared Error of 1.71 Km/h. RegTraffic can effectively simulate the results and provide visualization on interactive geographical maps.
Review of Cookie Synchronization Detection Methods
Venue: arXiv cs.CR 2022
Year: 2022

The research community has deemed cookie synchronization detection an inherently challenging task. Studies aiming to identify cookie synchronizations often share high-level design choices, but deviate amongst low-level implementations. For example, the majority of studies label a cookie synchronization iff a user identifier is shared with a third party; however, there is a lack of consistency among implementations, such as party relations or identifier value definitions, or whether such definitions are even included. This review intends to provide a record of established methods and promote standardization of methods choice in future work.
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks
Venue: arXiv cs.NI 2022
Year: 2022

In this paper, we study a network slicing problem for edge-cloud orchestrated vehicular networks, in which the edge and cloud servers are orchestrated to process computation tasks for reducing network slicing cost while satisfying the quality of service requirements. We propose a two-stage network slicing framework, which consists of 1) network planning stage in a large timescale to perform slice deployment, edge resource provisioning, and cloud resource provisioning, and 2) network operation stage in a small timescale to perform resource allocation and task dispatching. Particularly, we formulate the network slicing problem as a two-timescale stochastic optimization problem to minimize the network slicing cost. Since the problem is NP-hard due to coupled network planning and network operation stages, we develop a Two timescAle netWork Slicing (TAWS) algorithm by collaboratively integrating reinforcement learning (RL) and optimization methods, which can jointly make network planning and operation decisions. Specifically, by leveraging the timescale separation property of decisions, we decouple the problem into a large-timescale network planning subproblem and a small-timescale network operation subproblem. The former is solved by an RL method, and the latter is solved by an optimization method. Simulation results based on real-world vehicle traffic traces show that the TAWS can effectively reduce the network slicing cost as compared to the benchmark scheme.
A Critique of Sopin's "${\rm PH} = {\rm PSPACE}$"
Venue: arXiv cs.CC 2022
Year: 2022

We critique Valerii Sopin's paper "${\rm PH} = {\rm PSPACE}$" [Sop14]. The paper claims to resolve one of the major open problems of theoretical computer science by leveraging the Skolemization of existential quantifiers of quantified boolean formulas to show that ${\rm QBF}$ (a well-known ${\rm PSPACE}$-complete problem) is in $Π_4^p$, and thus ${\rm PH} = {\rm PSPACE}$. In this critique, we highlight problems in that paper and conclude that it fails to establish that ${\rm PH} = {\rm PSPACE}$.
Balanced Datasets for IoT IDS
Venue: arXiv cs.CR 2022
Year: 2022

As the Internet of Things (IoT) continues to grow, cyberattacks are becoming increasingly common. The security of IoT networks relies heavily on intrusion detection systems (IDSs). The development of an IDS that is accurate and efficient is a challenging task. As a result, this challenge is made more challenging by the absence of balanced datasets for training and testing the proposed IDS. In this study, four commonly used datasets are visualized and analyzed visually. Moreover, it proposes a sampling algorithm that generates a sample that represents the original dataset. In addition, it proposes an algorithm to generate a balanced dataset. Researchers can use this paper as a starting point when investigating cybersecurity and machine learning. The proposed sampling algorithms showed reliability in generating well-representing and balanced samples from NSL-KDD, UNSW-NB15, BotNetIoT-01, and BoTIoT datasets.
A Functorial Perspective on (Multi)computational Irreducibility
Venue: arXiv cs.CC 2022
Year: 2022

This article aims to provide a novel formalization of the concept of computational irreducibility in terms of the exactness of functorial correspondence between a category of data structures and elementary computations and a corresponding category of (1-dimensional) cobordisms. We proceed to demonstrate that, by equipping both categories with a symmetric monoidal structure and considering the case of higher-dimensional cobordism categories, we obtain a natural extension of this formalism that serves also to encompass non-deterministic or ``multiway'' computations, in which one quantifies not only the irreducibility in the behavior of a single (deterministic) computation path, but in the branching and merging behavior of an entire ``multiway system'' of such paths too. We finally outline how, in the most general case, the resulting symmetric monoidal functor may be considered to be adjoint to the functor characterizing the Atiyah-Segal axiomatization of a functorial quantum field theory. Thus, we conclude by arguing that the irreducibility of (multi)computations may be thought of as being dual to the locality of time evolution in functorial approaches to quantum mechanics and quantum field theory. In the process, we propose an extension of the methods of standard (monoidal) category theory, in which morphisms are effectively equipped with intrinsic computational complexity data, together with an algebra for how those complexities compose (both in sequence and in parallel, subject to the monoidal structure).
The use of Octree in point cloud analysis with application to cultural heritage
Venue: arXiv cs.CV 2022
Year: 2022

In this article we present the effects of our work on the subject of the technical approach to the 3D point cloud data analysis through the use of the Octree method to compress, analyse and compute the initial data.
Combating harmful Internet use with peer assessment and differential evolution
Venue: arXiv cs.CY 2022
Year: 2022

Harmful Internet use (HIU) is a term coined for the unintended use of the Internet. In this study, we propose a more accurate HIU measuring method based on the peer assessment and differential evolution approach. The sample data comprises a juvenile population in Poland; 267 subjects assessed 1,513 peers. In addition to classic statistical analysis, differential evolution has been employed. Results indicate that there may be a substantially higher rate of HIU than other studies have indicated. More accurate measurement of the adolescent population influx affected by HIU is needed for healthcare and welfare system planning. Presented in Prague, Czech Republic, 20-22 July 2022.
RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory
Venue: arXiv cs.CV 2022
Year: 2022

This paper introduces a novel framework, i.e., RFPose-OT, to enable the 3D human pose estimation from Radio Frequency (RF) signals. Different from existing methods that predict human poses from RF signals on the signal level directly, we consider the structure difference between the RF signals and the human poses, propose to transform the RF signals to the pose domain on the feature level based on Optimal Transport (OT) theory, and generate human poses from the transformed features. To evaluate RFPose-OT, we build a radio system and a multi-view camera system to acquire the RF signal data and the ground-truth human poses. The experimental results in basic indoor environment, occlusion indoor environment, and outdoor environment, all demonstrate that RFPose-OT can predict 3D human poses with higher precision than the state-of-the-art methods.
Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs
Venue: arXiv cs.CV 2022
Year: 2022

Existing deep neural networks (DNNs) that achieve state-of-the-art (SOTA) performance on both clean and adversarially-perturbed images rely on either activation or weight conditioned convolution operations. However, such conditional learning costs additional multiply-accumulate (MAC) or addition operations, increasing inference memory and compute costs. To that end, we present a sparse mixture once for all adversarial training (SMART), that allows a model to train once and then in-situ trade-off between accuracy and robustness, that too at a reduced compute and parameter overhead. In particular, SMART develops two expert paths, for clean and adversarial images, respectively, that are then conditionally trained via respective dedicated sets of binary sparsity masks. Extensive evaluations on multiple image classification datasets across different models show SMART to have up to 2.72x fewer non-zero parameters costing proportional reduction in compute overhead, while yielding SOTA accuracy-robustness trade-off. Additionally, we present insightful observations in designing sparse masks to successfully condition on both clean and perturbed images.
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications
Venue: arXiv cs.MA 2022
Year: 2022

This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications.