OpenRaiser/Intern-Atlas huggingface dataset:
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning
Venue: arXiv cs.CV 2022
Year: 2022

We present a simple approach which can turn a ViT encoder into an efficient video model, which can seamlessly work with both image and video inputs. By sparsely sampling the inputs, the model is able to do training and inference from both inputs. The model is easily scalable and can be adapted to large-scale pre-trained ViTs without requiring full finetuning. The model achieves SOTA results and the code will be open-sourced.
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
Venue: arXiv cs.CV 2022
Year: 2022

Deformable image registration, i.e., the task of aligning multiple images into one coordinate system by non-linear transformation, serves as an essential preprocessing step for neuroimaging data. Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the source image is repeatedly deformed in stages by a same neural network until it is well-aligned with the target image. Conventional methods for multi-stage registration can often blur the source image as the pixel/voxel values are repeatedly interpolated from the image generated by the previous stage. However, maintaining image quality such as sharpness during image registration is crucial to medical data analysis. In this paper, we study the problem of anti-blur deformable image registration and propose a novel solution, called Anti-Blur Network (ABN), for multi-stage image registration. Specifically, we use a pair of short-term registration and long-term memory networks to learn the nonlinear deformations at each stage, where the short-term registration network learns how to improve the registration accuracy incrementally and the long-term memory network combines all the previous deformations to allow an interpolation to perform on the raw image directly and preserve image sharpness. Extensive experiments on both natural and medical image datasets demonstrated that ABN can accurately register images while preserving their sharpness. Our code and data can be found at https://github.com/anonymous3214/ABN
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System
Venue: arXiv cs.LO 2022
Year: 2022

In this short paper, we present our ongoing work on the veriFIRE project -- a collaboration between industry and academia, aimed at using verification for increasing the reliability of a real-world, safety-critical system. The system we target is an airborne platform for wildfire detection, which incorporates two deep neural networks. We describe the system and its properties of interest, and discuss our attempts to verify the system's consistency, i.e., its ability to continue and correctly classify a given input, even if the wildfire it describes increases in intensity. We regard this work as a step towards the incorporation of academic-oriented verification tools into real-world systems of interest.
Estimation Large- Scale Fading Channels for Transmit Orthogonal Pilot Reuse Sequences in Massive MIMO System
Venue: arXiv cs.NI 2022
Year: 2022

Massive multiple-input multiple-output (MIMO) is a critical technology for future fifth-generation (5G) systems. Reduced pilot contamination (PC) enhanced system performance, and reduced inter-cell interference and improved channel estimation. However, because the pilot sequence transmitted by users in a single cell to neighboring cells is not orthogonal, massive MIMO systems are still constrained. We propose channel evaluation using orthogonal pilot reuse sequences (PRS) and zero forced (ZF) pre-coding techniques to eliminate channel quality in end users with poor channel quality based on channel evaluation, large-scale shutdown evaluation, and analysis of maximum transmission efficiency. We derived the lower bounds on the downlink data rate (DR) and signal-to-interference noise ratio (SINR) that can be achieved based on PRS assignment to a group of users where the number of antenna elements mitigated the interference when the number of antennas reaches infinity. The channel coherence interval limitation, the orthogonal PRS cannot be allocated to all UEs in each cell. The short coherence intervals able to reduce the PC and improve the quality of channel. The results of the modelling showed that higher DR can be achieved due to better channel evaluation and lower loss.
WiSwarm: Age-of-Information-based Wireless Networking for Collaborative Teams of UAVs
Venue: arXiv cs.NI 2022
Year: 2022

The Age-of-Information (AoI) metric has been widely studied in the theoretical communication networks and queuing systems literature. However, experimental evaluation of its applicability to complex real-world time-sensitive systems is largely lacking. In this work, we develop, implement, and evaluate an AoI-based application layer middleware that enables the customization of WiFi networks to the needs of time-sensitive applications. By controlling the storage and flow of information in the underlying WiFi network, our middleware can: (i) prevent packet collisions; (ii) discard stale packets that are no longer useful; and (iii) dynamically prioritize the transmission of the most relevant information. To demonstrate the benefits of our middleware, we implement a mobility tracking application using a swarm of UAVs communicating with a central controller via WiFi. Our experimental results show that, when compared to WiFi-UDP/WiFi-TCP, the middleware can improve information freshness by a factor of 109x/48x and tracking accuracy by a factor of 4x/6x, respectively. Most importantly, our results also show that the performance gains of our approach increase as the system scales and/or the traffic load increases.
Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation
Venue: arXiv cs.LG 2022
Year: 2022

Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. Recently, light-weight and portable EEG wearable devices based on low-density montages have increased the convenience and usability of BCI applications. However, loss of EEG decoding performance is often inevitable due to reduced number of electrodes and coverage of scalp regions of a low-density EEG montage. To address this issue, we introduce knowledge distillation (KD), a learning mechanism developed for transferring knowledge/information between neural network models, to enhance the performance of low-density EEG decoding. Our framework includes a newly proposed similarity-keeping (SK) teacher-student KD scheme that encourages a low-density EEG student model to acquire the inter-sample similarity as in a pre-trained teacher model trained on high-density EEG data. The experimental results validate that our SK-KD framework consistently improves motor-imagery EEG decoding accuracy when number of electrodes deceases for the input EEG data. For both common low-density headphone-like and headband-like montages, our method outperforms state-of-the-art KD methods across various EEG decoding model architectures. As the first KD scheme developed for enhancing EEG decoding, we foresee the proposed SK-KD framework to facilitate the practicality of low-density EEG-based BCI in real-world applications.
DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Human activity recognition (HAR) using drone-mounted cameras has attracted considerable interest from the computer vision research community in recent years. A robust and efficient HAR system has a pivotal role in fields like video surveillance, crowd behavior analysis, sports analysis, and human-computer interaction. What makes it challenging are the complex poses, understanding different viewpoints, and the environmental scenarios where the action is taking place. To address such complexities, in this paper, we propose a novel Sparse Weighted Temporal Attention (SWTA) module to utilize sparsely sampled video frames for obtaining global weighted temporal attention. The proposed SWTA is comprised of two parts. First, temporal segment network that sparsely samples a given set of frames. Second, weighted temporal attention, which incorporates a fusion of attention maps derived from optical flow, with raw RGB images. This is followed by a basenet network, which comprises a convolutional neural network (CNN) module along with fully connected layers that provide us with activity recognition. The SWTA network can be used as a plug-in module to the existing deep CNN architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. It has been evaluated on three publicly available benchmark datasets, namely Okutama, MOD20, and Drone-Action. The proposed model has received an accuracy of 72.76%, 92.56%, and 78.86% on the respective datasets thereby surpassing the previous state-of-the-art performances by a margin of 25.26%, 18.56%, and 2.94%, respectively.
A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids
Venue: arXiv cs.LG 2022
Year: 2022

This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of the system through the GNN framework along with the state measurements can improve the performance of the detection mechanism. The problem is formulated as a classification problem through a GNN with message passing mechanism to identify abnormal measurements. The residual block used in the aggregation process of message passing and the gated recurrent unit can lead to improved computational time and performance. The performance of the proposed model has been evaluated through extensive simulations of power system states and attack scenarios showing promising performance. The sensitivity of the model to intensity and location of the attacks and model's detection delay versus detection accuracy have also been evaluated.
Privacy protection and service evaluation methods for location-based services in edge computing environments
Venue: arXiv cs.CR 2022
Year: 2022

This paper proposes a privacy protection and evaluation method for location services based on edge computing environment. By constructing the site service data protection and system evaluation system in the edge computing environment, based on the existing user privacy protection work, the data processing module and service evaluation module are constructed, and the evaluation algorithm is designed. NPE evaluation model and POE evaluation model are designed according to relevant research recommended by IPE. Specifically, in the NPE evaluation model, we regard each user's decision as a group of factors, and propose a method to integrate learning factors. In the poe evaluation model, users' hidden intentions for the next action are understood by unifying metadata information, two time contexts and other different factors. The experiment verifies the effectiveness and feasibility of this method.
Learning Action-Effect Dynamics from Pairs of Scene-graphs
Venue: arXiv cs.CV 2022
Year: 2022

'Actions' play a vital role in how humans interact with the world. Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform 'Reasoning about Actions & Change' (RAC). Recently, there has been growing interest in the study of RAC with visual and linguistic inputs. Graphs are often used to represent semantic structure of the visual content (i.e. objects, their attributes and relationships among objects), commonly referred to as scene-graphs. In this work, we propose a novel method that leverages scene-graph representation of images to reason about the effects of actions described in natural language. We experiment with existing CLEVR_HYP (Sampat et. al, 2021) dataset and show that our proposed approach is effective in terms of performance, data efficiency, and generalization capability compared to existing models.
COVID-bit: Keep a Distance of (at least) 2m From My Air-Gap Computer!
Venue: arXiv cs.CR 2022
Year: 2022

Air-gapped systems are isolated from the Internet due to the sensitive information they handle. This paper presents COVID-bit, a new COVert channel attack that leaks sensitive information over the air from highly isolated systems. The information emanates from the air-gapped computer over the air to a distance of 2m and more and can be picked up by a nearby insider or spy with a mobile phone or laptop. Malware on an air-gapped computer can generate radio waves by executing crafted code on the target system. The malicious code exploits the dynamic power consumption of modern computers and manipulates the momentary loads on CPU cores. This technique allows the malware to control the computer's internal utilization and generate low-frequency electromagnetic radiation in the 0 - 60 kHz band. Sensitive information (e.g., files, encryption keys, biometric data, and keylogging) can be modulated over the emanated signals and received by a nearby mobile phone at a max speed of 1000 bits/sec. We show that a smartphone or laptop with a small \$1 antenna carried by a malicious insider or visitor can be used as a covert receiver. Notably, the attack is highly evasive since it executes from an ordinary user-level process, does not require root privileges, and is effective even within a Virtual Machine (VM). We discuss the attack model and provide technical details. We implement air-gap transmission of texts and files, and present signal generation and data modulation. We test the covert channel and show evaluation results. Finally, we present a set of countermeasures to this air-gap attack.
Von Data Warehouse bis Data Mesh: Ein Wegweiser durch den Dschungel analytischer Datenarchitekturen
Venue: arXiv cs.DB 2022
Year: 2022

Data warehouse, data lake, data lakehouse, data mesh ... many new names for analytical data architectures are currently circulating in the scene. But are the various approaches really so different? This article attempts a structured comparison of the different architecture paradigms, methodically based on DAMA-DMBOK and ArchiMate. Differences, similarities and dependencies as well as overlapping architectural building blocks are worked out and illustrated. This results in a first orientation guide for the choice of the right analytical data architecture for the respective use case. -- Data Warehouse, Data Lake, Date Lakehouse, Data Mesh ... in der Szene kursieren derzeit viele neue Namen für analytische Datenarchitekturen. Doch sind die diversen Ansätze wirklich so unterschiedlich? Dieser Beitrag versucht einen strukturierten Vergleich der verschiedenen Architekturparadigmen, methodisch basierend auf DAMA-DMBOK und ArchiMate. Es werden Unterschiede, Gemeinsamkeiten und Abhängigkeiten sowie überlappende Architekturbausteine herausgearbeitet und illustriert. Daraus entsteht eine erste Orientierungshilfe für die Wahl der richtigen analytischen Datenarchitektur für den jeweiligen Anwendungsfall.
Learning Double-Compression Video Fingerprints Left from Social-Media Platforms
Venue: arXiv cs.CV 2022
Year: 2022

Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication tool. However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential. Most of the work performed so far on social media provenance has concentrated on images; in this paper, we propose a CNN architecture that analyzes video content to trace videos back to their social network of origin. The experiments demonstrate that stating platform provenance is possible for videos as well as images with very good accuracy.
Face Presentation Attack Detection
Venue: arXiv cs.CV 2022
Year: 2022

Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment due to its convenience and high accuracy. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios. A presentation attack is first defined in ISO standard as: a presentation to the biometric data capture subsystem with the goal of interfering with the operation of the biometric system. Specifically, PAs range from simple 2D print, replay and more sophisticated 3D masks and partial masks. To defend the face recognition systems against PAs, both academia and industry have paid extensive attention to developing face presentation attack detection (PAD) technology (or namely `face anti-spoofing (FAS)').
Overview Of Satellite Image Recognition Models
Venue: arXiv cs.CV 2022
Year: 2022

In this article, the analysis of existing models of satellite image recognition was carried out, the problems in the field of satellite image recognition as a source of information were considered and analyzed, deep learning methods were compared, and existing image recognition methods were analyzed. The results obtained will be used as a basis for the prospective development of a fire recognition model based on satellite images and the use of recognition results as input data for a cognitive model of forecasting the macro-economic situation based on fuzzy cognitive maps.
Blockchain-based Payment Systems: A Bibliometric & Network Analysis
Venue: arXiv cs.DL 2022
Year: 2022

Blockchain is a shared, immutable ledger that has attracted the attention of researchers and practitioners across innumerable sectors, with its implications for modernizing payment systems having the possibility of inciting a digital revolution. In the scope of this study, 1,511 publications were obtained from Scopus to conduct a systematic review of the research space through bibliometric and network analyses. The main aim of this study was to determine key authors, significant studies, and collaboration patterns, to reveal the distributions and impacts of publications in the blockchain-based payments area between 2019 and 2022. The results indicate that the Khalifa University of Science and Technology is the most influential journal, while the most cited author is Salah, K. Additionally, the National Natural Science Foundation of China has sponsored most academic documents, with the US emerging as the most impactful country. This study has also found that blockchain-based payments literature congregated in 5 disciplines. These areas are computer science, engineering, decision science, mathematics, and business. A co-authorship analysis also networks relations between nations, authors, and organizations globally, creating unique clusters that maximize research productivity. In summary, this study designs an analytical map of the research landscape, which can guide future research.
Unsupervised spectral-band feature identification for optimal process discrimination
Venue: arXiv cs.LG 2022
Year: 2022

Changes in real-world dynamic processes are often described in terms of differences in energies $\textbf{E}(\underlineα)$ of a set of spectral-bands $\underlineα$. Given continuous spectra of two classes $A$ and $B$, or in general, two stochastic processes $S^{(A)}(f)$ and $S^{(B)}(f)$, $f \in \mathbb{R}^+$, we address the ubiquitous problem of identifying a subset of intervals of $f$ called spectral-bands $\underlineα \subset \mathbb{R}^+$ such that the energies $\textbf{E}(\underlineα)$ of these bands can optimally discriminate between the two classes. We introduce EGO-MDA, an unsupervised method to identify optimal spectral-bands $\underlineα^*$ for given samples of spectra from two classes. EGO-MDA employs a statistical approach that iteratively minimizes an adjusted multinomial log-likelihood (deviance) criterion $\mathcal{D}(\underlineα,\mathcal{M})$. Here, Mixture Discriminant Analysis (MDA) aims to derive MLE of two GMM distribution parameters, i.e., $\mathcal{M}^* = \underset{\mathcal{M}}{\rm argmin}~\mathcal{D}(\underlineα, \mathcal{M})$ and identify a classifier that optimally discriminates between two classes for a given spectral representation. The Efficient Global Optimization (EGO) finds the spectral-bands $\underlineα^* = \underset{\underlineα}{\rm argmin}~\mathcal{D}(\underlineα,\mathcal{M})$ for given GMM parameters $\mathcal{M}$. For pathological cases of low separation between mixtures and model misspecification, we discuss the effect of the sample size and the number of iterations on the estimates of parameters $\mathcal{M}$ and therefore the classifier performance. A case study on a synthetic data set is provided. In an engineering application of optimal spectral-banding for anomaly tracking, EGO-MDA achieved at least 70% improvement in the median deviance relative to other methods tested.
Generalized Lorenz dominance orders
Venue: arXiv cs.DM 2022
Year: 2022

We extend the discrete majorization theory by working with non-normalized Lorenz curves. Then we prove two generalizations of the Muirhead theorem. These not only use elementary transfers but also local increases. Together these operations are described as elementary impact increases. The first generalization shows that if an array X is dominated, in the generalized sense, by an array Y then Y can be derived from X by a finite number of elementary impact increases and this in such a way that each step transforms an array into a new one which is strictly larger in the generalized majorization sense. The other one shows that if the dominating array, Y, is ordered decreasingly then elementary impact increases starting from the dominated array, X, lead to the dominating one. Here each step transforms an array to a new one for which the decreasingly ordered version dominates the previous one and is dominated by Y.
Learning on Graphs for Mineral Asset Valuation Under Supply and Demand Uncertainty
Venue: arXiv cs.AI 2022
Year: 2022

Valuing mineral assets is a challenging task that is highly dependent on the supply (geological) uncertainty surrounding resources and reserves, and the uncertainty of demand (commodity prices). In this work, a graph-based reasoning, modeling and solution approach is proposed to jointly address mineral asset valuation and mine plan scheduling and optimization under supply and demand uncertainty in the "mining complex" framework. Three graph-based solutions are proposed: (i) a neural branching policy that learns a block-sampling ore body representation, (ii) a guiding policy that learns to explore a heuristic selection tree, (iii) a hyper-heuristic that manages the value/supply chain optimization and dynamics modeled as a graph structure. Results on two large-scale industrial mining complexes show a reduction of up to three orders of magnitude in primal suboptimality, execution time, and number of iterations, and an increase of up to 40% in the mineral asset value.