OpenRaiser/Intern-Atlas huggingface dataset:
Masking Neural Networks Using Reachability Graphs to Predict Process Events
Venue: arXiv cs.LG 2021
Year: 2021

Decay Replay Mining is a deep learning method that utilizes process model notations to predict the next event. However, this method does not intertwine the neural network with the structure of the process model to its full extent. This paper proposes an approach to further interlock the process model of Decay Replay Mining with its neural network for next event prediction. The approach uses a masking layer which is initialized based on the reachability graph of the process model. Additionally, modifications to the neural network architecture are proposed to increase the predictive performance. Experimental results demonstrate the value of the approach and underscore the importance of discovering precise and generalized process models.
Computation of the Activity-on-Node Binary-State Reliability with Uncertainty Components
Venue: arXiv cs.DM 2021
Year: 2021

Various networks such as cloud computing, water/gas/electricity networks, wireless sensor networks, transportation networks, and 4G/5G networks, have become an integral part of our daily lives. A binary-state network (BN) is often used to model network structures and applications. The BN reliability is the probability that a BN functions continuously; that is, that there is always a simple path connected between a specific pair of nodes. This metric is a popular index for designing, managing, controlling, and evaluating networks. The traditional BN reliability assumes that the reliability of each arc is known in advance. However, this is not always the case. Functioning components operate under different environments; moreover, a network might have newly installed components. Hence, the reliability of these components is not always known. To resolve the aforementioned problems, in which the reliability of some components of a network are uncertain, we introduce the fuzzy concept for the analysis of these components, and propose a new algorithm to solve this uncertainty-component BN reliability problem. The time complexity of the proposed algorithm is analyzed, and the superior performance of the algorithm is demonstrated through examples.
Automated Pest Detection with DNN on the Edge for Precision Agriculture
Venue: arXiv cs.LG 2021
Year: 2021

Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.
Charting closed-loop collective cultural decisions: From book best sellers and music downloads to Twitter hashtags and Reddit comments
Venue: arXiv cs.SI 2021
Year: 2021

Charts are used to measure relative success for a large variety of cultural items. Traditional music charts have been shown to follow self-organizing principles with regard to the distribution of item lifetimes, the on-chart residence times. Here we examine if this observation holds also for (a) music streaming charts (b) book best-seller lists and (c) for social network activity charts, such as Twitter hashtags and the number of comments Reddit postings receive. We find that charts based on the active production of items, like commenting, are more likely to be influenced by external factors, in particular by the 24 hour day-night cycle. External factors are less important for consumption-based charts (sales, downloads), which can be explained by a generic theory of decision-making. In this view, humans aim to optimize the information content of the internal representation of the outside world, which is logarithmically compressed. Further support for information maximization is argued to arise from the comparison of hourly, daily and weekly charts, which allow to gauge the importance of decision times with respect to the chart compilation period.
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Venue: arXiv cs.LG 2021
Year: 2021

Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against $\ell_2$-norm adversarial perturbations. However, the cost of MC sampling needed in RS for evaluation is high and computationally expensive. To address this issue, we investigate the possibility of performing randomized smoothing and establishing the robust certification in the latent space of a network, so that the overall dimensionality of tensors involved in computation could be drastically reduced. To this end, we propose Latent Space Randomized Smoothing. Another important aspect is that we use orthogonal modules, whose Lipschitz property is known for free by design, to propagate the certified radius estimated in the latent space back to the input space, providing valid certifiable regions for the test samples in the input space. Experiments on CIFAR10 and ImageNet show that our method achieves competitive certified robustness but with a significant improvement of efficiency during the test phase.
End to End Bangla Speech Synthesis
Venue: arXiv cs.SD 2021
Year: 2021

Text-to-Speech (TTS) system is a system where speech is synthesized from a given text following any particular approach. Concatenative synthesis, Hidden Markov Model (HMM) based synthesis, Deep Learning (DL) based synthesis with multiple building blocks, etc. are the main approaches for implementing a TTS system. Here, we are presenting our deep learning-based end-to-end Bangla speech synthesis system. It has been implemented with minimal human annotation using only 3 major components (Encoder, Decoder, Post-processing net including waveform synthesis). It does not require any frontend preprocessor and Grapheme-to-Phoneme (G2P) converter. Our model has been trained with phonetically balanced 20 hours of single speaker speech data. It has obtained a 3.79 Mean Opinion Score (MOS) on a scale of 5.0 as subjective evaluation and a 0.77 Perceptual Evaluation of Speech Quality(PESQ) score on a scale of [-0.5, 4.5] as objective evaluation. It is outperforming all existing non-commercial state-of-the-art Bangla TTS systems based on naturalness.
Geolocation differences of language use in urban areas
Venue: arXiv cs.CL 2021
Year: 2021

The explosion in the availability of natural language data in the era of social media has given rise to a host of applications such as sentiment analysis and opinion mining. Simultaneously, the growing availability of precise geolocation information is enabling visualization of global phenomena such as environmental changes and disease propagation. Opportunities for tracking spatial variations in language use, however, have largely been overlooked, especially on small spatial scales. Here we explore the use of Twitter data with precise geolocation information to resolve spatial variations in language use on an urban scale down to single city blocks. We identify several categories of language tokens likely to show distinctive patterns of use and develop quantitative methods to visualize the spatial distributions associated with these patterns. Our analysis concentrates on comparison of contrasting pairs of Tweet distributions from the same category, each defined by a set of tokens. Our work shows that analysis of small-scale variations can provide unique information on correlations between language use and social context which are highly valuable to a wide range of fields from linguistic science and commercial advertising to social services.
Cybonto: Towards Human Cognitive Digital Twins for Cybersecurity
Venue: arXiv cs.AI 2021
Year: 2021

Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response to the expanding ever-more-complex threat landscape, Digital Twins (DTs) and particularly Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple knowledge domains. Simulated results may offer insights into adversaries' behaviors and tactics, resulting in better proactive cyber-defense strategies. For the first time, this paper solidifies the vision of DTs and HDTs for cybersecurity via the Cybonto conceptual framework proposal. The paper also contributes the Cybonto ontology, formally documenting 108 constructs and thousands of cognitive-related paths based on 20 time-tested psychology theories. Finally, the paper applied 20 network centrality algorithms in analyzing the 108 constructs. The identified top 10 constructs call for extensions of current digital cognitive architectures in preparation for the DT future.
Experimental Findings on the Sources of Detected Unrecoverable Errors in GPUs
Venue: arXiv cs.DC 2021
Year: 2021

We investigate the sources of Detected Unrecoverable Errors (DUEs) in GPUs exposed to neutron beams. Illegal memory accesses and interface errors are among the more likely sources of DUEs. ECC increases the launch failure events. Our test procedure has shown that ECC can reduce the DUEs caused by Illegal Address access up to 92% for Kepler and 98% for Volta.
Resource Management in Edge and Fog Computing using FogBus2 Framework
Venue: arXiv cs.DC 2021
Year: 2021

Edge/Fog computing is a novel computing paradigm that provides resource-limited Internet of Things (IoT) devices with scalable computing and storage resources. Compared to cloud computing, edge/fog servers have fewer resources, but they can be accessed with higher bandwidth and less communication latency. Thus, integrating edge/fog and cloud infrastructures can support the execution of diverse latency-sensitive and computation-intensive IoT applications. Although some frameworks attempt to provide such integration, there are still several challenges to be addressed, such as dynamic scheduling of different IoT applications, scalability mechanisms, multi-platform support, and supporting different interaction models. FogBus2, as a new python-based framework, offers a lightweight and distributed container-based framework to overcome these challenges. In this chapter, we highlight key features of the FogBus2 framework alongside describing its main components. Besides, we provide a step-by-step guideline to set up an integrated computing environment, containing multiple cloud service providers (Hybrid-cloud) and edge devices, which is a prerequisite for any IoT application scenario. To obtain this, a low-overhead communication network among all computing resources is initiated by the provided scripts and configuration files. Next, we provide instructions and corresponding code snippets to install and run the main framework and its integrated applications. Finally, we demonstrate how to implement and integrate several new IoT applications and custom scheduling and scalability policies with the FogBus2 framework.
TabPert: An Effective Platform for Tabular Perturbation
Venue: arXiv cs.CL 2021
Year: 2021

To truly grasp reasoning ability, a Natural Language Inference model should be evaluated on counterfactual data. TabPert facilitates this by assisting in the generation of such counterfactual data for assessing model tabular reasoning issues. TabPert allows a user to update a table, change its associated hypotheses, change their labels, and highlight rows that are important for hypothesis classification. TabPert also captures information about the techniques used to automatically produce the table, as well as the strategies employed to generate the challenging hypotheses. These counterfactual tables and hypotheses, as well as the metadata, can then be used to explore an existing model's shortcomings methodically and quantitatively.
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning
Venue: arXiv cs.LG 2021
Year: 2021

Low-cost particulate matter sensors are transforming air quality monitoring because they have lower costs and greater mobility as compared to reference monitors. Calibration of these low-cost sensors requires training data from co-deployed reference monitors. Machine Learning based calibration gives better performance than conventional techniques, but requires a large amount of training data from the sensor, to be calibrated, co-deployed with a reference monitor. In this work, we propose novel transfer learning methods for quick calibration of sensors with minimal co-deployment with reference monitors. Transfer learning utilizes a large amount of data from other sensors along with a limited amount of data from the target sensor. Our extensive experimentation finds the proposed Model-Agnostic- Meta-Learning (MAML) based transfer learning method to be the most effective over other competitive baselines.
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators
Venue: arXiv cs.LG 2021
Year: 2021

In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable. In order to test trading strategies before deploying them to real-time trading, multi-agent market environments calibrated so that the time series that result from interaction of simulated agents resemble historical are often used. To ensure adequate testing, one must test trading strategies in a variety of market scenarios -- which includes both scenarios that represent ordinary market days as well as stressed markets (most recently observed due to the beginning of COVID pandemic). In this paper, we address the problem of multi-agent simulator parameter calibration to allow simulator capture characteristics of different market regimes. We propose a novel two-step method to train a discriminator that is able to distinguish between "real" and "fake" price and volume time series as a part of GAN with self-attention, and then utilize it within an optimization framework to tune parameters of a simulator model with known agent archetypes to represent a market scenario. We conclude with experimental results that demonstrate effectiveness of our method.
Recognizing and Embedding Simple Optimal 2-Planar Graphs
Venue: arXiv cs.DS 2021
Year: 2021

In the area of beyond-planar graphs, i.e. graphs that can be drawn with some local restrictions on the edge crossings, the recognition problem is prominent next to the density question for the different graph classes. For 1-planar graphs, the recognition problem has been settled, namely it is NP-complete for the general case, while optimal 1-planar graphs, i.e. those with maximum density, can be recognized in linear time. For 2-planar graphs, the picture is less complete. As expected, the recognition problem has been found to be NP-complete in general. In this paper, we consider the recognition of simple optimal 2-planar graphs. We exploit a combinatorial characterization of such graphs and present a linear time algorithm for recognition and embedding.
Three-Dimensional Trajectory Design for Multi-User MISO UAV Communications: A Deep Reinforcement Learning Approach
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we investigate a multi-user downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication system, where a multi-antenna UAV is employed to serve multiple ground terminals. Unlike existing approaches focus only on a simplified two-dimensional scenario, this paper considers a three-dimensional (3D) urban environment, where the UAV's 3D trajectory is designed to minimize data transmission completion time subject to practical throughput and flight movement constraints. Specifically, we propose a deep reinforcement learning (DRL)-based trajectory design for completion time minimization (DRL-TDCTM), which is developed from a deep deterministic policy gradient algorithm. In particular, to represent the state information of UAV and environment, we set an additional information, i.e., the merged pheromone, as a reference of reward which facilitates the algorithm design. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can continuously and adaptively learn how to adjust the UAV's movement strategy. Finally, simulation results show the superiority of the proposed DRL-TDCTM algorithm over the conventional baseline methods.
A Novel Internet-of-Drones and Blockchain-based System Architecture for Search and Rescue
Venue: arXiv cs.NI 2021
Year: 2021

With the development in information and communications technology (ICT) and drones such as Internet-of-Things (IoT), edge computing, image processing, and autonomous drones, solutions supporting search and rescue (SAR) missions can be developed with more intelligent capabilities. In most of the drone and unmanned aerial vehicle (UAV) based systems supporting SAR missions, several drones deployed in different areas acquire images and videos that are sent to a ground control station (GCS) for processing and detecting a missing person. Although this offers many advantages, such as easy management and deployment, the approach still has many limitations. For example, when a connection between a drone and a GCS has some problems, the quality of service cannot be maintained. Many drone and UAV-based systems do not support flexibility, transparency, security, and traceability. In this paper, we propose a novel Internet-of-Drones (IoD) architecture using blockchain technology. We implement the proposed architecture with different drones, edge servers, and a Hyperledger blockchain network. The proof-of-concept design demonstrates that the proposed architecture can offer high-level services such as prolonging the operating time of a drone, improving the capability of detecting humans accurately, and a high level of transparency, traceability, and security.
Flip Learning: Erase to Segment
Venue: arXiv cs.CV 2021
Year: 2021

Nodule segmentation from breast ultrasound images is challenging yet essential for the diagnosis. Weakly-supervised segmentation (WSS) can help reduce time-consuming and cumbersome manual annotation. Unlike existing weakly-supervised approaches, in this study, we propose a novel and general WSS framework called Flip Learning, which only needs the box annotation. Specifically, the target in the label box will be erased gradually to flip the classification tag, and the erased region will be considered as the segmentation result finally. Our contribution is three-fold. First, our proposed approach erases on superpixel level using a Multi-agent Reinforcement Learning framework to exploit the prior boundary knowledge and accelerate the learning process. Second, we design two rewards: classification score and intensity distribution reward, to avoid under- and over-segmentation, respectively. Third, we adopt a coarse-to-fine learning strategy to reduce the residual errors and improve the segmentation performance. Extensively validated on a large dataset, our proposed approach achieves competitive performance and shows great potential to narrow the gap between fully-supervised and weakly-supervised learning.
Angle Based Feature Learning in GNN for 3D Object Detection using Point Cloud
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we present new feature encoding methods for Detection of 3D objects in point clouds. We used a graph neural network (GNN) for Detection of 3D objects namely cars, pedestrians, and cyclists. Feature encoding is one of the important steps in Detection of 3D objects. The dataset used is point cloud data which is irregular and unstructured and it needs to be encoded in such a way that ensures better feature encapsulation. Earlier works have used relative distance as one of the methods to encode the features. These methods are not resistant to rotation variance problems in Graph Neural Networks. We have included angular-based measures while performing feature encoding in graph neural networks. Along with that, we have performed a comparison between other methods like Absolute, Relative, Euclidean distances, and a combination of the Angle and Relative methods. The model is trained and evaluated on the subset of the KITTI object detection benchmark dataset under resource constraints. Our results demonstrate that a combination of angle measures and relative distance has performed better than other methods. In comparison to the baseline method(relative), it achieved better performance. We also performed time analysis of various feature encoding methods.
Training face verification models from generated face identity data
Venue: arXiv cs.CV 2021
Year: 2021

Machine learning tools are becoming increasingly powerful and widely used. Unfortunately membership attacks, which seek to uncover information from data sets used in machine learning, have the potential to limit data sharing. In this paper we consider an approach to increase the privacy protection of data sets, as applied to face recognition. Using an auxiliary face recognition model, we build on the StyleGAN generative adversarial network and feed it with latent codes combining two distinct sub-codes, one encoding visual identity factors, and, the other, non-identity factors. By independently varying these vectors during image generation, we create a synthetic data set of fictitious face identities. We use this data set to train a face recognition model. The model performance degrades in comparison to the state-of-the-art of face verification. When tested with a simple membership attack our model provides good privacy protection, however the model performance degrades in comparison to the state-of-the-art of face verification. We find that the addition of a small amount of private data greatly improves the performance of our model, which highlights the limitations of using synthetic data to train machine learning models.
Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning (ML) has made incredible impacts and transformations in a wide range of vehicular applications. As the use of ML in Internet of Vehicles (IoV) continues to advance, adversarial threats and their impact have become an important subject of research worth exploring. In this paper, we focus on Sybil-based adversarial threats against a deep reinforcement learning (DRL)-assisted IoV framework and more specifically, DRL-based dynamic service placement in IoV. We carry out an experimental study with real vehicle trajectories to analyze the impact on service delay and resource congestion under different attack scenarios for the DRL-based dynamic service placement application. We further investigate the impact of the proportion of Sybil-attacked vehicles in the network. The results demonstrate that the performance is significantly affected by Sybil-based data poisoning attacks when compared to adversary-free healthy network scenario.