OpenRaiser/Intern-Atlas huggingface dataset:
Explainable AI Enabled Inspection of Business Process Prediction Models
Venue: arXiv cs.AI 2021
Year: 2021

Modern data analytics underpinned by machine learning techniques has become a key enabler to the automation of data-led decision making. As an important branch of state-of-the-art data analytics, business process predictions are also faced with a challenge in regard to the lack of explanation to the reasoning and decision by the underlying `black-box' prediction models. With the development of interpretable machine learning techniques, explanations can be generated for a black-box model, making it possible for (human) users to access the reasoning behind machine learned predictions. In this paper, we aim to present an approach that allows us to use model explanations to investigate certain reasoning applied by machine learned predictions and detect potential issues with the underlying methods thus enhancing trust in business process prediction models. A novel contribution of our approach is the proposal of model inspection that leverages both the explanations generated by interpretable machine learning mechanisms and the contextual or domain knowledge extracted from event logs that record historical process execution. Findings drawn from this work are expected to serve as a key input to developing model reliability metrics and evaluation in the context of business process predictions.
Communication and Computation Reduction for Split Learning using Asynchronous Training
Venue: arXiv cs.LG 2021
Year: 2021

Split learning is a promising privacy-preserving distributed learning scheme that has low computation requirement at the edge device but has the disadvantage of high communication overhead between edge device and server. To reduce the communication overhead, this paper proposes a loss-based asynchronous training scheme that updates the client-side model less frequently and only sends/receives activations/gradients in selected epochs. To further reduce the communication overhead, the activations/gradients are quantized using 8-bit floating point prior to transmission. An added benefit of the proposed communication reduction method is that the computations at the client side are reduced due to reduction in the number of client model updates. Furthermore, the privacy of the proposed communication reduction based split learning method is almost the same as traditional split learning. Simulation results on VGG11, VGG13 and ResNet18 models on CIFAR-10 show that the communication cost is reduced by 1.64x-106.7x and the computations in the client are reduced by 2.86x-32.1x when the accuracy degradation is less than 0.5% for the single-client case. For 5 and 10-client cases, the communication cost reduction is 11.9x and 11.3x on VGG11 for 0.5% loss in accuracy.
A Factor Graph-based approach to vehicle sideslip angle estimation
Venue: arXiv cs.RO 2021
Year: 2021

Sideslip angle is an important variable for understanding and monitoring vehicle dynamics but it lacks an inexpensive method for direct measurement. Therefore, it is typically estimated from inertial and other proprioceptive sensors onboard using filtering methods from the family of the Kalman Filter. As a novel alternative, this work proposes modelling the problem directly as a graphical model (factor graph), which can then be optimized using a variety of methods, such as whole dataset batch optimization for offline processing or fixed-lag smoother for on-line operation. Experimental results on real vehicle datasets validate the proposal with a good agreement between estimated and actual sideslip angle, showing similar performance than the state-of-the-art with a great potential for future extensions due to the flexible mathematical framework.
On the Modulus in Matching Vector Codes
Venue: arXiv cs.IT 2021
Year: 2021

A $k$-query locally decodable code (LDC) $C$ allows one to encode any $n$-symbol message $x$ as a codeword $C(x)$ of $N$ symbols such that each symbol of $x$ can be recovered by looking at $k$ symbols of $C(x)$, even if a constant fraction of $C(x)$ have been corrupted. Currently, the best known LDCs are matching vector codes (MVCs). A modulus $m=p_1^{α_1}p_2^{α_2}\cdots p_r^{α_r}$ may result in an MVC with $k\leq 2^r$ and $N=\exp(\exp(O((\log n)^{1-1/r} (\log\log n)^{1/r})))$. The $m$ is {\em good} if it is possible to have $k<2^r$. The good numbers yield more efficient MVCs. Prior to this work, there are only {\em finitely many} good numbers. All of them were obtained via computer search and have the form $m=p_1p_2$. In this paper, we study good numbers of the form $m=p_1^{α_1}p_2^{α_2}$. We show that if $m=p_1^{α_1}p_2^{α_2}$ is good, then any multiple of $m$ of the form $p_1^{β_1}p_2^{β_2}$ must be good as well. Given a good number $m=p_1^{α_1}p_2^{α_2}$, we show an explicit method of obtaining smaller good numbers that have the same prime divisors. Our approach yields {\em infinitely many} new good numbers.
Bidirectional Approximate Message Passing for RIS-Assisted Multi-User MISO Communications
Venue: arXiv cs.IT 2021
Year: 2021

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and lowpower configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted multi-user wireless communication system and present a joint channel estimation and signal recovery algorithm in this paper. Specifically, we propose a bidirectional approximate message passing algorithm that applies the Taylor series expansion and Gaussian approximation to simplify the sum-product algorithm in the formulated problem. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed algorithms.
MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis
Venue: arXiv cs.LG 2021
Year: 2021

The emergence of novel pathogens and zoonotic diseases like the SARS-CoV-2 have underlined the need for developing novel diagnosis and intervention pipelines that can learn rapidly from small amounts of labeled data. Combined with technological advances in next-generation sequencing, metagenome-based diagnostic tools hold much promise to revolutionize rapid point-of-care diagnosis. However, there are significant challenges in developing such an approach, the chief among which is to learn self-supervised representations that can help detect novel pathogen signatures with very low amounts of labeled data. This is particularly a difficult task given that closely related pathogens can share more than 90% of their genome structure. In this work, we address these challenges by proposing MG-Net, a self-supervised representation learning framework that leverages multi-modal context using pseudo-imaging data derived from clinical metagenome sequences. We show that the proposed framework can learn robust representations from unlabeled data that can be used for downstream tasks such as metagenome sequence classification with limited access to labeled data. Extensive experiments show that the learned features outperform current baseline metagenome representations, given only 1000 samples per class.
Extension of Additive Valuations to General Valuations on the Existence of EFX
Venue: arXiv cs.GT 2021
Year: 2021

Envy-freeness is one of the most widely studied notions in fair division. Since envy-free allocations do not always exist when items are indivisible, several relaxations have been considered. Among them, possibly the most compelling concept is envy-freeness up to any item (EFX). We study the existence of EFX allocations for general valuations. The existence of EFX allocations is a major open problem. For general valuations, it is known that an EFX allocation always exists (i) when $n=2$ or (ii) when all agents have identical valuations, where $n$ is the number of agents. it is also known that an EFX allocation always exists when one can leave at most $n-1$ items unallocated. We develop new techniques and extend some results of additive valuations to general valuations on the existence of EFX allocations. We show that an EFX allocation always exists (i) when all agents have one of two general valuations or (ii) when the number of items is at most $n+3$. We also show that an EFX allocation always exists when one can leave at most $n-2$ items unallocated. In addition to the positive results, we construct an instance with $n=3$ in which an existing approach does not work as it is.
Predicting Issue Types on GitHub
Venue: arXiv cs.SE 2021
Year: 2021

Software maintenance and evolution involves critical activities for the success of software projects. To support such activities and keep code up-to-date and error-free, software communities make use of issue trackers, i.e., tools for signaling, handling, and addressing the issues occurring in software systems. However, in popular projects, tens or hundreds of issue reports are daily submitted. In this context, identifying the type of each submitted report (e.g., bug report, feature request, etc.) would facilitate the management and the prioritization of the issues to address. To support issue handling activities, in this paper, we propose Ticket Tagger, a GitHub app analyzing the issue title and description through machine learning techniques to automatically recognize the types of reports submitted on GitHub and assign labels to each issue accordingly. We empirically evaluated the tool's prediction performance on about 30,000 GitHub issues. Our results show that the Ticket Tagger can identify the correct labels to assign to GitHub issues with reasonably high effectiveness. Considering these results and the fact that the tool is designed to be easily integrated in the GitHub issue management process, Ticket Tagger consists in a useful solution for developers.
Towards Plug-and-Play Visual Graph Query Interfaces: Data-driven Canned Pattern Selection for Large Networks
Venue: arXiv cs.DB 2021
Year: 2021

Canned patterns (i.e. small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIs for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel generic and extensible framework called TATTOO that takes a data-driven approach to automatically selecting canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity, and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.
Provenance, Anonymisation and Data Environments: a Unifying Construction
Venue: arXiv cs.DB 2021
Year: 2021

The Anonymisation Decision-making Framework (ADF) operationalizes the risk management of data exchange between organizations, referred to as "data environments". The second edition of ADF has increased its emphasis on modeling data flows, highlighting a potential new use of provenance information to support anonymisation decision-making. In this paper, we provide a use case that showcases this functionality more. Based on this use case, we identify how provenance information could be utilized within the ADF framework, and identify a currently un-met requirement which is the modeling of \textit{data environments}. We show how data environments can be implemented within the W3C PROV in four different ways. We analyze the costs and benefits of each approach, and consider another use case as a partial check for completeness. We then summarize our findings and suggest ways forward.
SkyCell: A Space-Pruning Based Parallel Skyline Algorithm
Venue: arXiv cs.DB 2021
Year: 2021

Skyline computation is an essential database operation that has many applications in multi-criteria decision making scenarios such as recommender systems. Existing algorithms have focused on checking point domination, which lack efficiency over large datasets. We propose a grid-based structure that enables grid cell domination checks. We show that only a small constant number of cells need to be checked which is independent from the number of data points. Our structure also enables parallel processing. We thus obtain a highly efficient parallel skyline algorithm named SkyCell, taking advantage of the parallelization power of graphics processing units. Experimental results confirm the effectiveness and efficiency of SkyCell -- it outperforms state-of-the-art algorithms consistently and by up to over two orders of magnitude in the computation time.
Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN
Venue: arXiv cs.CV 2021
Year: 2021

The parsing of windows in building facades is a long-desired but challenging task in computer vision. It is crucial to urban analysis, semantic reconstruction, lifecycle analysis, digital twins, and scene parsing amongst other building-related tasks that require high-quality semantic data. This article investigates the usage of the mask R-CNN framework to be used for window detection of facade imagery input. We utilize transfer learning to train our proposed method on COCO weights with our own collected dataset of street view images of facades to produce instance segmentations of our new window class. Experimental results show that our suggested approach with a relatively small dataset trains the network only with transfer learning and augmentation achieves results on par with prior state-of-the-art window detection approaches, even without post-optimization techniques.
Relational Graph Convolutional Networks: A Closer Look
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model. Our reproduction results empirically validate the correctness of our implementations using benchmark Knowledge Graph datasets on node classification and link prediction tasks. Our explanation provides a friendly understanding of the different components of the RGCN for both users and researchers extending the RGCN approach. Furthermore, we introduce two new configurations of the RGCN that are more parameter efficient. The code and datasets are available at https://github.com/thiviyanT/torch-rgcn.
Differentiable Feature Selection, a Reparameterization Approach
Venue: arXiv cs.LG 2021
Year: 2021

We consider the task of feature selection for reconstruction which consists in choosing a small subset of features from which whole data instances can be reconstructed. This is of particular importance in several contexts involving for example costly physical measurements, sensor placement or information compression. To break the intrinsic combinatorial nature of this problem, we formulate the task as optimizing a binary mask distribution enabling an accurate reconstruction. We then face two main challenges. One concerns differentiability issues due to the binary distribution. The second one corresponds to the elimination of redundant information by selecting variables in a correlated fashion which requires modeling the covariance of the binary distribution. We address both issues by introducing a relaxation of the problem via a novel reparameterization of the logitNormal distribution. We demonstrate that the proposed method provides an effective exploration scheme and leads to efficient feature selection for reconstruction through evaluation on several high dimensional image benchmarks. We show that the method leverages the intrinsic geometry of the data, facilitating reconstruction.
Learning Theorem Proving Components
Venue: arXiv cs.LO 2021
Year: 2021

Saturation-style automated theorem provers (ATPs) based on the given clause procedure are today the strongest general reasoners for classical first-order logic. The clause selection heuristics in such systems are, however, often evaluating clauses in isolation, ignoring other clauses. This has changed recently by equipping the E/ENIGMA system with a graph neural network (GNN) that chooses the next given clause based on its evaluation in the context of previously selected clauses. In this work, we describe several algorithms and experiments with ENIGMA, advancing the idea of contextual evaluation based on learning important components of the graph of clauses.
Pushing the Limits: Resilience Testing for Mission-Critical Machine-Type Communication
Venue: arXiv cs.NI 2021
Year: 2021

Interdisciplinary application fields, such as automotive, industrial applications or field robotics show an increasing need for reliable and resilient wireless communication even under high load conditions. These mission-critical applications require dependable service quality characteristics in terms of latency and especially stability. Current deployments often use either wired links that lack the flexibility to accommodate them, or wireless technologies that are susceptible to interference. Depending on the application and the surrounding environments, different technologies can meet the associated requirements and have to be tested deliberately to prevent unexpected system failure. To stress test these infrastructures in a reproducible and application-aware manner, we propose STING, a spatially distributed traffic and interference generation framework. STING is evaluated in a remote control test case of an Unmanned Ground Vehicle that serves as a scout in Search and Rescue missions. A significant impact of interference on the remote control quality of experience is shown in tests with different operators, which result in an 80% increase in completion time in our test scenario with high interference on the radio channel. With this case study, we have proven STING to be a reliable and reproducible way to asses resilience against interference of wireless machine-type communication use cases. Our concept can find use for any type of wireless technology, in unlicensed (e.g. Wi-Fi) as well as licensed bands (e.g. 5G).
How Lightning's Routing Diminishes its Anonymity
Venue: arXiv cs.CR 2021
Year: 2021

The system shows the error of "Bad character(s) in field Abstract" for no reason. Please refer to manuscript for the full abstract
Training Electric Vehicle Charging Controllers with Imitation Learning
Venue: arXiv cs.LG 2021
Year: 2021

The problem of coordinating the charging of electric vehicles gains more importance as the number of such vehicles grows. In this paper, we develop a method for the training of controllers for the coordination of EV charging. In contrast to most existing works on this topic, we require the controllers to preserve the privacy of the users, therefore we do not allow any communication from the controller to any third party. In order to train the controllers, we use the idea of imitation learning -- we first find an optimum solution for a relaxed version of the problem using quadratic optimization and then train the controllers to imitate this solution. We also investigate the effects of regularization of the optimum solution on the performance of the controllers. The method is evaluated on realistic data and shows improved performance and training speed compared to similar controllers trained using evolutionary algorithms.
Global Outliers Detection in Wireless Sensor Networks: A Novel Approach Integrating Time-Series Analysis, Entropy, and Random Forest-based Classification
Venue: arXiv cs.NI 2021
Year: 2021

Wireless Sensor Networks (WSNs) have recently attracted greater attention worldwide due to their practicality in monitoring, communicating, and reporting specific physical phenomena. The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signal weakness, or intrusion attacks depending on the specific situation. Sending high-noise data has negative effects not just on data accuracy and network reliability, but also regarding the decision-making processes in the base station. Anomaly detection, or outlier detection, is the process of detecting noisy data amidst the contexts thus described. The literature contains relatively few noise detection techniques in the context of WSNs, particularly for outlier-detection algorithms applying time series analysis, which considers the effective neighbors to ensure a global-collaborative detection. Hence, the research presented in this paper is intended to design and implement a global outlier-detection approach, which allows us to find and select appropriate neighbors to ensure an adaptive collaborative detection based on time-series analysis and entropy techniques. The proposed approach applies a random forest algorithm for identifying the best results. To measure the effectiveness and efficiency of the proposed approach, a comprehensive and real scenario provided by the Intel Berkeley Research lab has been simulated. Noisy data have been injected into the collected data randomly. The results obtained from the experiment then conducted experimentation demonstrate that our approach can detect anomalies with up to 99% accuracy.
On the Memorization Properties of Contrastive Learning
Venue: arXiv cs.LG 2021
Year: 2021

Memorization studies of deep neural networks (DNNs) help to understand what patterns and how do DNNs learn, and motivate improvements to DNN training approaches. In this work, we investigate the memorization properties of SimCLR, a widely used contrastive self-supervised learning approach, and compare them to the memorization of supervised learning and random labels training. We find that both training objects and augmentations may have different complexity in the sense of how SimCLR learns them. Moreover, we show that SimCLR is similar to random labels training in terms of the distribution of training objects complexity.