OpenRaiser/Intern-Atlas huggingface dataset:
Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies
Venue: arXiv cs.LG 2021
Year: 2021

Traffic management in a city has become a major problem due to the increasing number of vehicles on roads. Intelligent Transportation System (ITS) can help the city traffic managers to tackle the problem by providing accurate traffic forecasts. For this, ITS requires a reliable traffic prediction algorithm that can provide accurate traffic prediction at multiple time steps based on past and current traffic data. In recent years, a number of different methods for traffic prediction have been proposed which have proved their effectiveness in terms of accuracy. However, most of these methods have either considered spatial information or temporal information only and overlooked the effect of other. In this paper, to address the above problem a deep learning based approach has been developed using both the spatial and temporal dependencies. To consider spatio-temporal dependencies, nearby road sensors at a particular instant are selected based on the attributes like traffic similarity and distance. Two pre-trained deep auto-encoders were cross-connected using the concept of latent space mapping and the resultant model was trained using the traffic data from the selected nearby sensors as input. The proposed deep learning based approach was trained using the real-world traffic data collected from loop detector sensors installed on different highways of Los Angeles and Bay Area. The traffic data is freely available from the web portal of the California Department of Transportation Performance Measurement System (PeMS). The effectiveness of the proposed approach was verified by comparing it with a number of machine/deep learning approaches. It has been found that the proposed approach provides accurate traffic prediction results even for 60-min ahead prediction with least error than other techniques.
Approximation Algorithms for Vertex-Connectivity Augmentation on the Cycle
Venue: arXiv cs.DS 2021
Year: 2021

Given a $k$-vertex-connected graph $G$ and a set $S$ of extra edges (links), the goal of the $k$-vertex-connectivity augmentation problem is to find a set $S' \subseteq S$ of minimum size such that adding $S'$ to $G$ makes it $(k+1)$-vertex-connected. Unlike the edge-connectivity augmentation problem, research for the vertex-connectivity version has been sparse. In this work we present the first polynomial time approximation algorithm that improves the known ratio of 2 for $2$-vertex-connectivity augmentation, for the case in which $G$ is a cycle. This is the first step for attacking the more general problem of augmenting a $2$-connected graph. Our algorithm is based on local search and attains an approximation ratio of $1.8704$. To derive it, we prove novel results on the structure of minimal solutions.
SERC: Syntactic and Semantic Sequence based Event Relation Classification
Venue: arXiv cs.CL 2021
Year: 2021

Temporal and causal relations play an important role in determining the dependencies between events. Classifying the temporal and causal relations between events has many applications, such as generating event timelines, event summarization, textual entailment and question answering. Temporal and causal relations are closely related and influence each other. So we propose a joint model that incorporates both temporal and causal features to perform causal relation classification. We use the syntactic structure of the text for identifying temporal and causal relations between two events from the text. We extract parts-of-speech tag sequence, dependency tag sequence and word sequence from the text. We propose an LSTM based model for temporal and causal relation classification that captures the interrelations between the three encoded features. Evaluation of our model on four popular datasets yields promising results for temporal and causal relation classification.
Maneuvering Digital Watermarking In Face Recognition
Venue: arXiv cs.CR 2021
Year: 2021

The challenges faced in the digital world are many, which could be resolved with some biometric recognition methods. These biometric recognition methods are encompassed within watermarking technology, steganography, cryptography, and many other schemes of security. These methods assist in securing digital images with the authentication of their owner. This paper briefly contextualizes the digital watermarking technique, which is referred to as Natural Preserving Transform (NPT) and Hartley Transform, which is endeavored in the face recognition process. The non-blind extraction and quasi-blind extraction techniques are used for extracting the watermark from the image in the proposed system. This paper articulates the application of this watermarking technique employed in face recognition through watermarking of various face images to multiple backgrounds encompassing gray-scale images. Natural Preserve Transform is employed as a part of the fuzzy logic watermarking. In the proposed system, NPT is employed for encoding a logo of grayscale watermarking text or logo image to a host image located anywhere. The robustness and performance of the proposed system are experimentally tested with the help of image processing operations like image compression, noise degradation, cropping. Due to its unique feature of uniform distribution of face images, this technique is selected among other methods in digital watermarking. The system is tested for its efficiency with experimental analyses, which could be confirmed with the results of the simulation. The above system is proposed for copyright protection, authentication, and security requirements.
Pull or Wait: How to Optimize Query Age of Information
Venue: arXiv cs.IT 2021
Year: 2021

We study a pull-based status update communication model where a source node submits update packets to a channel with random transmission delay, at times requested by a remote destination node. The objective is to minimize the average query-age-of-information (QAoI), defined as the average age-of-information (AoI) measured at query instants that occur at the destination side according to a stochastic arrival process. In reference to a push-based problem formulation defined in the literature where the source decides to \textit{update or wait} at will, with the objective of minimizing the time average AoI at the destination, we name this problem the \textit{Pull-or-Wait} (PoW) problem. We provide a comparison of the two formulations: (i) Under Poisson query arrivals, an optimal policy that minimizes the time average AoI also minimizes the average QAoI, and these minimum values are equal; and (ii) the optimal average QAoI under periodic query arrivals is always less than or equal to the optimal time average AoI. We identify the PoW problem in the case of a single query as a stochastic shortest path (SSP) problem with uncountable state and action spaces, which has been not solved in previous literature. We derive an optimal solution for this SSP problem and use it as a building block for the solution of the PoW problem under periodic query arrivals.
ML-PersRef: A Machine Learning-based Personalized Multimodal Fusion Approach for Referencing Outside Objects From a Moving Vehicle
Venue: arXiv cs.HC 2021
Year: 2021

Over the past decades, the addition of hundreds of sensors to modern vehicles has led to an exponential increase in their capabilities. This allows for novel approaches to interaction with the vehicle that go beyond traditional touch-based and voice command approaches, such as emotion recognition, head rotation, eye gaze, and pointing gestures. Although gaze and pointing gestures have been used before for referencing objects inside and outside vehicles, the multimodal interaction and fusion of these gestures have so far not been extensively studied. We propose a novel learning-based multimodal fusion approach for referencing outside-the-vehicle objects while maintaining a long driving route in a simulated environment. The proposed multimodal approaches outperform single-modality approaches in multiple aspects and conditions. Moreover, we also demonstrate possible ways to exploit behavioral differences between users when completing the referencing task to realize an adaptable personalized system for each driver. We propose a personalization technique based on the transfer-of-learning concept for exceedingly small data sizes to enhance prediction and adapt to individualistic referencing behavior. Our code is publicly available at https://github.com/amr-gomaa/ML-PersRef.
Augmenting Edge Connectivity via Isolating Cuts
Venue: arXiv cs.DS 2021
Year: 2021

We give an algorithm for augmenting the edge connectivity of an undirected graph by using the isolating cuts framework (Li and Panigrahi, FOCS '20). Our algorithm uses poly-logarithmic calls to any max-flow algorithm, which yields a running time of $\tilde O(m + n^{3/2})$ and improves on the previous best time of $\tilde O(n^2)$ (Benczúr and Karger, SODA '98) for this problem. We also obtain an identical improvement in the running time of the closely related edge splitting off problem in undirected graphs.
Video Salient Object Detection via Contrastive Features and Attention Modules
Venue: arXiv cs.CV 2021
Year: 2021

Video salient object detection aims to find the most visually distinctive objects in a video. To explore the temporal dependencies, existing methods usually resort to recurrent neural networks or optical flow. However, these approaches require high computational cost, and tend to accumulate inaccuracies over time. In this paper, we propose a network with attention modules to learn contrastive features for video salient object detection without the high computational temporal modeling techniques. We develop a non-local self-attention scheme to capture the global information in the video frame. A co-attention formulation is utilized to combine the low-level and high-level features. We further apply the contrastive learning to improve the feature representations, where foreground region pairs from the same video are pulled together, and foreground-background region pairs are pushed away in the latent space. The intra-frame contrastive loss helps separate the foreground and background features, and the inter-frame contrastive loss improves the temporal consistency. We conduct extensive experiments on several benchmark datasets for video salient object detection and unsupervised video object segmentation, and show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks
Venue: arXiv cs.RO 2021
Year: 2021

The co-adaptation of robot morphology and behaviour becomes increasingly important with the advent of fast 3D-manufacturing methods and efficient deep reinforcement learning algorithms. A major challenge for the application of co-adaptation methods to the real world is the simulation-to-reality-gap due to model and simulation inaccuracies. However, prior work focuses primarily on the study of evolutionary adaptation of morphologies exploiting analytical models and (differentiable) simulators with large population sizes, neglecting the existence of the simulation-to-reality-gap and the cost of manufacturing cycles in the real world. This paper presents a new approach combining classic high-frequency deep neural networks with computational expensive Graph Neural Networks for the data-efficient co-adaptation of agents with varying numbers of degrees-of-freedom. Evaluations in simulation show that the new method can co-adapt agents within such a limited number of production cycles by efficiently combining design optimization with offline reinforcement learning, that it allows for the direct application to real-world co-adaptation tasks in future work
Autonomous Attack Mitigation for Industrial Control Systems
Venue: arXiv cs.CR 2021
Year: 2021

Defending computer networks from cyber attack requires timely responses to alerts and threat intelligence. Decisions about how to respond involve coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations. Currently, playbooks are used to automate portions of a response process, but often leave complex decision-making to a human analyst. In this work, we present a deep reinforcement learning approach to autonomous response and recovery in large industrial control networks. We propose an attention-based neural architecture that is flexible to the size of the network under protection. To train and evaluate the autonomous defender agent, we present an industrial control network simulation environment suitable for reinforcement learning. Experiments show that the learned agent can effectively mitigate advanced attacks that progress with few observable signals over several months before execution. The proposed deep reinforcement learning approach outperforms a fully automated playbook method in simulation, taking less disruptive actions while also defending more nodes on the network. The learned policy is also more robust to changes in attacker behavior than playbook approaches.
Linear-time Minimization of Wheeler DFAs
Venue: arXiv cs.DS 2021
Year: 2021

Wheeler DFAs (WDFAs) are a sub-class of finite-state automata which is playing an important role in the emerging field of compressed data structures: as opposed to general automata, WDFAs can be stored in just $\logσ+ O(1)$ bits per edge, $σ$ being the alphabet's size, and support optimal-time pattern matching queries on the substring closure of the language they recognize. An important step to achieve further compression is minimization. When the input $\mathcal A$ is a general deterministic finite-state automaton (DFA), the state-of-the-art is represented by the classic Hopcroft's algorithm, which runs in $O(|\mathcal A|\log |\mathcal A|)$ time. This algorithm stands at the core of the only existing minimization algorithm for Wheeler DFAs, which inherits its complexity. In this work, we show that the minimum WDFA equivalent to a given input WDFA can be computed in linear $O(|\mathcal A|)$ time. When run on de Bruijn WDFAs built from real DNA datasets, an implementation of our algorithm reduces the number of nodes from 14% to 51% at a speed of more than 1 million nodes per second.
Secure Namespaced Kernel Audit for Containers
Venue: arXiv cs.CR 2021
Year: 2021

Despite the wide usage of container-based cloud computing, container auditing for security analysis relies mostly on built-in host audit systems, which often lack the ability to capture high-fidelity container logs. State-of-the-art reference-monitor-based audit techniques greatly improve the quality of audit logs, but their system-wide architecture is too costly to be adapted for individual containers. Moreover, these techniques typically require extensive kernel modifications, making them difficult to deploy in practical settings. In this paper, we present saBPF (secure audit BPF), an extension of the eBPF framework capable of deploying secure system-level audit mechanisms at the container granularity. We demonstrate the practicality of saBPF in Kubernetes by designing an audit framework, an intrusion detection system, and a lightweight access control mechanism. We evaluate saBPF and show that it is comparable in performance and security guarantees to audit systems from the literature that are implemented directly in the kernel.
Energy Efficiency of Uplink Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channel
Venue: arXiv cs.IT 2021
Year: 2021

Cell-free massive MIMO (CF-mMIMO) provides wireless connectivity for a large number of user equipments (UEs) using access points (APs) distributed across a wide area with high spectral efficiency (SE). The energy efficiency (EE) of the uplink is determined by (i) the transmit power control (TPC) algorithms, (ii) the numbers, configurations, and locations of the APs and the UEs, and (iii) the propagation channels between the APs and the UEs. This paper investigates all three aspects, based on extensive (~30,000 possible AP locations and 128 possible UE locations) channel measurement data at 3.5 GHz. We compare three different TPC algorithms, namely maximization of transmit power (max-power), maximization of minimum SE (max-min SE), and maximization of minimum EE (max-min EE) while guaranteeing a target SE. We also compare various antenna arrangements including fully-distributed and semi-distributed systems, where APs can be located on a regular grid or randomly, and the UEs can be placed in clusters or far apart. Overall, we show that the max-min EE TPC is highly effective in improving the uplink EE, especially when no UE within a set of served UEs is in a bad channel condition and when the BS antennas are fully-distributed.
A Meta-Learned Neuron model for Continual Learning
Venue: arXiv cs.LG 2021
Year: 2021

Continual learning is the ability to acquire new knowledge without forgetting the previously learned one, assuming no further access to past training data. Neural network approximators trained with gradient descent are known to fail in this setting as they must learn from a stream of data-points sampled from a stationary distribution to converge. In this work, we replace the standard neuron by a meta-learned neuron model whom inference and update rules are optimized to minimize catastrophic interference. Our approach can memorize dataset-length sequences of training samples, and its learning capabilities generalize to any domain. Unlike previous continual learning methods, our method does not make any assumption about how tasks are constructed, delivered and how they relate to each other: it simply absorbs and retains training samples one by one, whether the stream of input data is time-correlated or not.
Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval
Venue: arXiv cs.LG 2021
Year: 2021

As the Internet of Things (IoT) continues to grow, ensuring the security of systems that rely on wireless IoT devices has become critically important. Deep learning-based passive physical layer transmitter authorization systems have been introduced recently for this purpose, as they accommodate the limited computational and power budget of such devices. These systems have been shown to offer excellent outlier detection accuracies when trained and tested on a fixed authorized transmitter set. However in a real-life deployment, a need may arise for transmitters to be added and removed as the authorized set of transmitters changes. In such cases, the system could experience long down-times, as retraining the underlying deep learning model is often a time-consuming process. In this paper, we draw inspiration from information retrieval to address this problem: by utilizing feature vectors as RF fingerprints, we first demonstrate that training could be simplified to indexing those feature vectors into a database using locality sensitive hashing (LSH). Then we show that approximate nearest neighbor search could be performed on the database to perform transmitter authorization that matches the accuracy of deep learning models, while allowing for more than 100x faster retraining. Furthermore, dimensionality reduction techniques are used on the feature vectors to show that the authorization latency of our technique could be reduced to approach that of traditional deep learning-based systems.
Facial Emotion Recognition using Deep Residual Networks in Real-World Environments
Venue: arXiv cs.CV 2021
Year: 2021

Automatic affect recognition using visual cues is an important task towards a complete interaction between humans and machines. Applications can be found in tutoring systems and human computer interaction. A critical step towards that direction is facial feature extraction. In this paper, we propose a facial feature extractor model trained on an in-the-wild and massively collected video dataset provided by the RealEyes company. The dataset consists of a million labelled frames and 2,616 thousand subjects. As temporal information is important to the emotion recognition domain, we utilise LSTM cells to capture the temporal dynamics in the data. To show the favourable properties of our pre-trained model on modelling facial affect, we use the RECOLA database, and compare with the current state-of-the-art approach. Our model provides the best results in terms of concordance correlation coefficient.
Representation Edit Distance as a Measure of Novelty
Venue: arXiv cs.LG 2021
Year: 2021

Adaptation to novelty is viewed as learning to change and augment existing skills to confront unfamiliar situations. In this paper, we propose that the amount of editing of an effective representation (the Representation Edit Distance or RED) used in a set of skill programs in an agent's mental model is a measure of difficulty for adaptation to novelty. The RED is an intuitive approximation to the change in information content in bit strings measured by comparing pre-novelty and post-novelty skill programs. We also present some notional examples of how to use RED for predicting difficulty.
A Tutorial of Cyber-Syndrome viewed from Cyber-Physical-Social-Thinking Space and Maslow's Hierarchy of Needs
Venue: arXiv cs.HC 2021
Year: 2021

With the increase of active Internet users, various physical, social, and mental disorders have recently emerged because of the excessive use of technology. Cyber-Syndrome is known as the condition that appears due to the excessive interaction with the cyberspace, and it affects the users' physical, social, and mental states. In this paper, we discuss the etiology and symptoms of Cyber-Syndrome according to theories of Cyber-Physical-Social-Thinking (CPST) space and Maslow's Hierarchy of Needs. In addition, we also propose an entropy-based mechanism for recovery of Cyber-Syndrome, to provide potential guidance for clinical detection and diagnosis. Cyber-Syndrome has attracted much attention these days, and more in-depth exploration is needed in the future.