OpenRaiser/Intern-Atlas huggingface dataset:
Machine Learning-based Selection of Graph Partitioning Strategy Using the Characteristics of Graph Data and Algorithm
Venue: arXiv cs.DC 2022
Year: 2022

Analyzing large graph data is an essential part of many modern applications, such as social networks. Due to its large computational complexity, distributed processing is frequently employed. This requires graph data to be divided across nodes, and the choice of partitioning strategy has a great impact on the execution time of the task. Yet, there is no one-size-fits-all partitioning strategy that performs well on arbitrary graph data and algorithms. The performance of a strategy depends on the characteristics of the graph data and algorithms. Moreover, due to the complexity of graph data and algorithms, manually identifying the best partitioning strategy is also infeasible. In this work, we propose a machine learning-based approach to select the most appropriate partitioning strategy for a given graph and processing algorithm. Our approach enumerates viable partitioning strategies, predicts the execution time of the target algorithm for each, and selects the partitioning strategy with the fastest estimated execution time. Our machine learning model is trained on features extracted from graph data and algorithm pseudo-code. We also propose a method that augments real execution logs of graph tasks to create a large synthetic dataset. Evaluation results show that the strategies selected by our approach lead to 1.46X faster execution time on average compared with the mean execution time of the partitioning strategies and about 0.95X the performance compared to the best partitioning strategy.
Constellations on the Sphere with Efficient Encoding-Decoding for Noncoherent Communications
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we propose a new structured Grassmannian constellation for noncoherent communications over single-input multiple-output (SIMO) Rayleigh block-fading channels. The constellation, which we call Grass-Lattice, is based on a measure preserving mapping from the unit hypercube to the Grassmannian of lines. The constellation structure allows for on-the-fly symbol generation, low-complexity decoding, and simple bit-to-symbol Gray coding. Simulation results show that Grass-Lattice has symbol and bit error rate performance close to that of a numerically optimized unstructured constellation, and is more power efficient than other structured constellations proposed in the literature and a coherent pilot-based scheme.
An Indian Roads Dataset for Supported and Suspended Traffic Lights Detection
Venue: arXiv cs.CV 2022
Year: 2022

Autonomous vehicles are growing rapidly, in well-developed nations like America, Europe, and China. Tech giants like Google, Tesla, Audi, BMW, and Mercedes are building highly efficient self-driving vehicles. However, the technology is still not mainstream for developing nations like India, Thailand, Africa, etc., In this paper, we present a thorough comparison of the existing datasets based on well-developed nations as well as Indian roads. We then developed a new dataset "Indian Roads Dataset" (IRD) having more than 8000 annotations extracted from 3000+ images shot using a 64 (megapixel) camera. All the annotations are manually labelled adhering to the strict rules of annotations. Real-time video sequences have been captured from two different cities in India namely New Delhi and Chandigarh during the day and night-light conditions. Our dataset exceeds previous Indian traffic light datasets in size, annotations, and variance. We prove the amelioration of our dataset by providing an extensive comparison with existing Indian datasets. Various dataset criteria like size, capturing device, a number of cities, and variations of traffic light orientations are considered. The dataset can be downloaded from here https://sites.google.com/view/ird-dataset/home
Managing a blockchain-based platform ecosystem for industry-wide adoption: The case of TradeLens
Venue: arXiv cs.CY 2022
Year: 2022

The proliferation of blockchain-based platform ecosystems in recent years has prompted scholars across various disciplines to explore the conditions leading to their successful deployment. However, developing a blockchain-based platform ecosystem creates various challenges for the platform sponsor that may influence industry-wide adoption and, ultimately, the platform's success. This study follows the development of TradeLens, a leading global shipping platform ecosystem underpinned by blockchain technology. We examine the factors affecting industry-wide adoption among global supply chain actors by unpacking platform value drivers and platform governance mechanisms identified at TradeLens. While the platform value hinges on the digitalization of workflows and the ecosystem leverage, the platform governance includes strategic (off-chain), technology (on-chain), and interoperability (on- and off-chain) governance - as mechanisms for effectively managing a blockchain-based platform ecosystem. This paper contributes to the literature on blockchain-based platform ecosystems and the platform literature.
Selecting Related Knowledge via Efficient Channel Attention for Online Continual Learning
Venue: arXiv cs.CV 2022
Year: 2022

Continual learning aims to learn a sequence of tasks by leveraging the knowledge acquired in the past in an online-learning manner while being able to perform well on all previous tasks, this ability is crucial to the artificial intelligence (AI) system, hence continual learning is more suitable for most real-word and complex applicative scenarios compared to the traditional learning pattern. However, the current models usually learn a generic representation base on the class label on each task and an effective strategy is selected to avoid catastrophic forgetting. We postulate that selecting the related and useful parts only from the knowledge obtained to perform each task is more effective than utilizing the whole knowledge. Based on this fact, in this paper we propose a new framework, named Selecting Related Knowledge for Online Continual Learning (SRKOCL), which incorporates an additional efficient channel attention mechanism to pick the particular related knowledge for every task. Our model also combines experience replay and knowledge distillation to circumvent the catastrophic forgetting. Finally, extensive experiments are conducted on different benchmarks and the competitive experimental results demonstrate that our proposed SRKOCL is a promised approach against the state-of-the-art.
Survey on Deep Fuzzy Systems in regression applications: a view on interpretability
Venue: arXiv cs.LG 2022
Year: 2022

Regression problems have been more and more embraced by deep learning (DL) techniques. The increasing number of papers recently published in this domain, including surveys and reviews, shows that deep regression has captured the attention of the community due to efficiency and good accuracy in systems with high-dimensional data. However, many DL methodologies have complex structures that are not readily transparent to human users. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas such as cyber-security systems, medical, financial surveillance, and industrial processes. Fuzzy logic systems (FLS) are inherently interpretable models, well known in the literature, capable of using nonlinear representations for complex systems through linguistic terms with membership degrees mimicking human thought. Within an atmosphere of explainable artificial intelligence, it is necessary to consider a trade-off between accuracy and interpretability for developing intelligent models. This paper aims to investigate the state-of-the-art on existing methodologies that combine DL and FLS, namely deep fuzzy systems, to address regression problems, configuring a topic that is currently not sufficiently explored in the literature and thus deserves a comprehensive survey.
Conformance Checking for Trace Fragments Using Infix and Postfix Alignments
Venue: arXiv cs.SE 2022
Year: 2022

Conformance checking deals with collating modeled process behavior with observed process behavior recorded in event data. Alignments are a state-of-the-art technique to detect, localize, and quantify deviations in process executions, i.e., traces, compared to reference process models. Alignments, however, assume complete process executions covering the entire process from start to finish or prefixes of process executions. This paper defines infix/postfix alignments, proposes approaches to their computation, and evaluates them using real-life event data.
Minimizing Information Leakage under Padding Constraints
Venue: arXiv cs.CR 2022
Year: 2022

An attacker can gain information of a user by analyzing its network traffic. The size of transferred data leaks information about the file being transferred or the service being used, and this is particularly revealing when the attacker has background knowledge about the files or services available for transfer. To prevent this, servers may pad their files using a padding scheme, changing the file sizes and preventing anyone from guessing their identity uniquely. This work focuses on finding optimal padding schemes that keep a balance between privacy and the costs of bandwidth increase. We consider Rényi-min leakage as our main measure for privacy, since it is directly related with the success of a simple attacker, and compare our algorithms with an existing solution that minimizes Shannon leakage. We provide improvements to our algorithms in order to optimize average total padding and Shannon leakage while minimizing Rényi-min leakage. Moreover, our algorithms are designed to handle a more general and important scenario in which multiple servers wish to compute padding schemes in a way that protects the servers' identity in addition to the identity of the files.
Programming abstractions for preemptive scheduling in FPGAs using partial reconfiguration
Venue: arXiv cs.AR 2022
Year: 2022

FPGAs are an attractive type of accelerator for all-purpose HPC computing systems due to the possibility of deploying tailored hardware on demand. However, the common tools for programming and operating FPGAs are still complex to use, specially in scenarios where diverse types of tasks should be dynamically executed. In this work we present a programming abstraction with a simple interface that internally leverages High-Level Synthesis, Dynamic Partial Reconfiguration and synchronisation mechanisms to use an FPGA as a multi-tasking server with preemptive scheduling and priority queues. This leads to a better use of the FPGA resources, allowing the execution of several kernels at the same time and deploying the most urgent ones as fast as possible. The results of our experimental study show that our approach incurs only a 1.66% overhead when using only one Reconfigurable Region (RR), and 4.04% when using two RRs, whilst presenting a significant performance improvement over the traditional non-preemptive full reconfiguration approach.
Trustworthy Federated Learning via Blockchain
Venue: arXiv cs.LG 2022
Year: 2022

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm compared with baseline algorithms.
PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
Venue: arXiv cs.CV 2022
Year: 2022

In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition. To tackle the domain-shift which exists under the UDA setting, we first exploited a recent Domain Generalization (DG) technique, called Relative Norm Alignment (RNA). Secondly, we extended this approach to work on unlabelled target data, enabling a simpler adaptation of the model to the target distribution in an unsupervised fashion. To this purpose, we included in our framework UDA algorithms, such as multi-level adversarial alignment and attentive entropy. By analyzing the challenge setting, we notice the presence of a secondary concurrence shift in the data, which is usually called environmental bias. It is caused by the existence of different environments, i.e., kitchens. To deal with these two shifts (environmental and temporal), we extended our system to perform Multi-Source Multi-Target Domain Adaptation. Finally, we employed distinct models in our final proposal to leverage the potential of popular video architectures, and we introduced two more losses for the ensemble adaptation. Our submission (entry 'plnet') is visible on the leaderboard and ranked in 2nd position for 'verb', and in 3rd position for both 'noun' and 'action'.
Evaluating the (in)accessibility of data behind papers in astronomy
Venue: arXiv cs.DL 2022
Year: 2022

This paper presents results of a survey of authors of journal articles published over several decades in astronomy. The study focuses on determining the characteristics and accessibility of data behind papers, referring to the spectrum of raw and derived data that would be needed to validate the results of a particular published article as a capsule of scientific knowledge. Curating the data behind papers can arguably lead to new discoveries through reuse. However, as shown through related research and confirmed by the results of the present study, a fully accessible portrait of the data behind papers is often unavailable. These findings have implications for reusability efforts and are presented alongside a discussion of open science.
Deep Baseline Network for Time Series Modeling and Anomaly Detection
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning has seen increasing applications in time series in recent years. For time series anomaly detection scenarios, such as in finance, Internet of Things, data center operations, etc., time series usually show very flexible baselines depending on various external factors. Anomalies unveil themselves by lying far away from the baseline. However, the detection is not always easy due to some challenges including baseline shifting, lacking of labels, noise interference, real time detection in streaming data, result interpretability, etc. In this paper, we develop a novel deep architecture to properly extract the baseline from time series, namely Deep Baseline Network (DBLN). By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result. Empirical evaluation on both synthetic and public real-world datasets shows that our purely unsupervised algorithm achieves superior performance compared with state-of-art methods and has good practical applications.
Towards Diversity-tolerant RDF-stores
Venue: arXiv cs.DB 2022
Year: 2022

We propose a novel approach to designing RDF-stores with the goal of improving the consistency and predictability of query performance. When designing these systems, three properties are commonly desired: support for the full range of SPARQL query features (Q), support for widely varying RDF datasets in terms of structuredness and size (S), and high performance (P). We develop the empirical SPQ conjecture which states that it may be impossible to achieve all the three desiderata simultaneously. We present a strong case for its plausibility based on our experimental results. The tradeoffs among the three and design guidelines based on the SPQ conjecture are also discussed.
Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional neural networks trained using manually generated labels are commonly used for semantic or instance segmentation. In precision agriculture, automated flower detection methods use supervised models and post-processing techniques that may not perform consistently as the appearance of the flowers and the data acquisition conditions vary. We propose a self-supervised learning strategy to enhance the sensitivity of segmentation models to different flower species using automatically generated pseudo-labels. We employ a data augmentation and refinement approach to improve the accuracy of the model predictions. The augmented semantic predictions are then converted to panoptic pseudo-labels to iteratively train a multi-task model. The self-supervised model predictions can be refined with existing post-processing approaches to further improve their accuracy. An evaluation on a multi-species fruit tree flower dataset demonstrates that our method outperforms state-of-the-art models without computationally expensive post-processing steps, providing a new baseline for flower detection applications.
Gradient Descent Temporal Difference-difference Learning
Venue: arXiv cs.LG 2022
Year: 2022

Off-policy algorithms, in which a behavior policy differs from the target policy and is used to gain experience for learning, have proven to be of great practical value in reinforcement learning. However, even for simple convex problems such as linear value function approximation, these algorithms are not guaranteed to be stable. To address this, alternative algorithms that are provably convergent in such cases have been introduced, the most well known being gradient descent temporal difference (GTD) learning. This algorithm and others like it, however, tend to converge much more slowly than conventional temporal difference learning. In this paper we propose gradient descent temporal difference-difference (Gradient-DD) learning in order to improve GTD2, a GTD algorithm, by introducing second-order differences in successive parameter updates. We investigate this algorithm in the framework of linear value function approximation, theoretically proving its convergence by applying the theory of stochastic approximation. %analytically showing its improvement over GTD2. Studying the model empirically on the random walk task, the Boyan-chain task, and the Baird's off-policy counterexample, we find substantial improvement over GTD2 and, in several cases, better performance even than conventional TD learning.
Air Computing: A Survey on a New Generation Computation Paradigm in 6G Wireless Networks
Venue: arXiv cs.NI 2022
Year: 2022

There is an ever-growing race between what novel applications demand from the infrastructure and what the continuous technological breakthroughs bring in. Especially after the proliferation of smart devices and diverse IoT requirements, we observe the dominance of cutting-edge applications with ever-increased user expectations in terms of mobility, pervasiveness, and real-time response. Over the years, to meet the requirements of those applications, cloud computing provides the necessary capacity for computation, while edge computing ensures low latency. However, these two essential solutions would be insufficient for the next-generation applications since computational and communicational bottlenecks are inevitable due to the highly dynamic load. Therefore, a 3D networking structure using different air layers including Low Altitude Platforms, High Altitude Platforms, and Low Earth Orbits in a harmonized manner for both urban and rural areas should be applied to satisfy the requirements of the dynamic environment. In this perspective, we put forward a novel, next-generation paradigm called Air Computing that presents a dynamic, responsive, and high-resolution computation and communication environment for all spectrum of applications using the 6G Wireless Networks as the fundamental communication system. In this survey, we define the components of air computing, investigate its architecture in detail, and discuss its essential use cases and the advantages it brings for next-generation application scenarios. We provide a detailed and technical overview of the benefits and challenges of air computing as a novel paradigm and spot the important future research directions.
A generic framework for coded caching and distributed computation schemes
Venue: arXiv cs.IT 2022
Year: 2022

Several network communication problems are highly related such as coded caching and distributed computation. The centralized coded caching focuses on reducing the network burden in peak times in a wireless network system and the coded distributed computation studies the tradeoff between computation and communication in distributed system. In this paper, motivated by the study of the only rainbow $3$-term arithmetic progressions set, we propose a unified framework for constructing coded caching schemes. This framework builds bridges between coded caching schemes and lots of combinatorial objects due to the freedom of the choices of families and operations. We prove that any scheme based on a placement delivery array (PDA) can be represented by a rainbow scheme under this framework and lots of other known schemes can also be included in this framework. Moreover, we also present a new coded caching scheme with linear subpacketization and near constant rate using the only rainbow $3$-term arithmetic progressions set. Next, we modify the framework to be applicable to the distributed computing problem. We present a new transmission scheme in the shuffle phase and show that in certain cases it could have a lower communication load than the schemes based on PDAs or resolvable designs with the same number of files.
Mixed Criticality Communication within an Unmanned Delivery Rotorcraft
Venue: arXiv cs.NI 2022
Year: 2022

Stand-alone functions additional to a UAV flight-controller, such as safety-relevant flight-path monitoring or payload-monitoring and control, may be SORA-required or advised for specific flight paths of delivery-drones. These functions, articulated as discrete electronic components either internal or external to the main fuselage, can be networked with other on-board electronics systems. Such an integration requires respecting the integrity levels of each component on the network both in terms of function and in terms of power-supply. In this body of work we detail an intra-component communication system for small autonomous and semi-autonomous unmanned aerial vehicles (UAVs.) We discuss the context and the (conservative) design decisions before detailing the hardware and software interfaces and reporting on a first implementation. We finish by drawing conclusions and proposing future work.