OpenRaiser/Intern-Atlas huggingface dataset:
Time-Incremental Learning from Data Using Temporal Logics
Venue: arXiv cs.LG 2021
Year: 2021

Real-time and human-interpretable decision-making in cyber-physical systems is a significant but challenging task, which usually requires predictions of possible future events from limited data. In this paper, we introduce a time-incremental learning framework: given a dataset of labeled signal traces with a common time horizon, we propose a method to predict the label of a signal that is received incrementally over time, referred to as prefix signal. Prefix signals are the signals that are being observed as they are generated, and their time length is shorter than the common horizon of signals. We present a novel decision-tree based approach to generate a finite number of Signal Temporal Logic (STL) specifications from the given dataset, and construct a predictor based on them. Each STL specification, as a binary classifier of time-series data, captures the temporal properties of the dataset over time. The predictor is constructed by assigning time-variant weights to the STL formulas. The weights are learned by using neural networks, with the goal of minimizing the misclassification rate for the prefix signals defined over the given dataset. The learned predictor is used to predict the label of a prefix signal, by computing the weighted sum of the robustness of the prefix signal with respect to each STL formula. The effectiveness and classification performance of our algorithm are evaluated on an urban-driving and a naval-surveillance case studies.
Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data
Venue: arXiv cs.LG 2021
Year: 2021

Cognition in midlife is an important predictor of age-related mental decline and statistical models that predict cognitive performance can be useful for predicting decline. However, existing models struggle to capture complex relationships between physical, sociodemographic, psychological and mental health factors that effect cognition. Using data from an observational, cohort study, Midlife in the United States (MIDUS), we modeled a large number of variables to predict executive function and episodic memory measures. We used cross-sectional and longitudinal outcomes with varying sparsity, or amount of missing data. Deep neural network (DNN) models consistently ranked highest in all of the cognitive performance prediction tasks, as assessed with root mean squared error (RMSE) on out-of-sample data. RMSE differences between DNN and other model types were statistically significant (T(8) = -3.70; p < 0.05). The interaction effect between model type and sparsity was significant (F(9)=59.20; p < 0.01), indicating the success of DNNs can partly be attributed to their robustness and ability to model hierarchical relationships between health-related factors. Our findings underscore the potential of neural networks to model clinical datasets and allow better understanding of factors that lead to cognitive decline.
Invertible Image Dataset Protection
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning has achieved enormous success in various industrial applications. Companies do not want their valuable data to be stolen by malicious employees to train pirated models. Nor do they wish the data analyzed by the competitors after using them online. We propose a novel solution for dataset protection in this scenario by robustly and reversibly transform the images into adversarial images. We develop a reversible adversarial example generator (RAEG) that introduces slight changes to the images to fool traditional classification models. Even though malicious attacks train pirated models based on the defensed versions of the protected images, RAEG can significantly weaken the functionality of these models. Meanwhile, the reversibility of RAEG ensures the performance of authorized models. Extensive experiments demonstrate that RAEG can better protect the data with slight distortion against adversarial defense than previous methods.
Forking Around: Correlation of forking practices with the success of a project
Venue: arXiv cs.SE 2021
Year: 2021

Forking-based development has made it easier and straightforward for developers to contribute to open-source software (OSS). Developers can fork an existing project and add changes in their local version without interrupting the development process in the main project. Despite the efficiency of OSS, more than 80% of the projects are not sustainable. Identifying the elements related to OSS success can enlighten developers regarding the sustainability of a project. In our study, we explore whether or not the inefficiencies which arise due to forking-based development like redundant development, fragmented communities, lack of modularity, etc. have any relation to the outcome of a project in terms of sustainability. We formulate eight metrics to quantify attributes for projects in the ASFI dataset. To find the correlation between the metrics and the success of a project, we built a logistic regression model to metrics with significant p-values and performed backward stepwise regression analysis, using the stepAIC function in R to cross-check our findings. The findings show that modularity, centralized management index, and hard forks are consequential for the success of a project. Developers can use the outcomes of our research to plan and structure their projects to increase the probability of their success.
Physical Layer Security Techniques for Future Wireless Networks
Venue: arXiv cs.CR 2021
Year: 2021

The broadcast nature of wireless communication systems makes wireless transmission extremely susceptible to eavesdropping and even malicious interference. Physical layer security technology can effectively protect the private information sent by the transmitter from being listened to by illegal eavesdroppers, thus ensuring the privacy and security of communication between the transmitter and legitimate users. The development of mobile communication presents new challenges to physical layer security research. This paper provides a comprehensive survey of the physical layer security research on various promising mobile technologies, including directional modulation (DM), spatial modulation (SM), covert communication, intelligent reflecting surface (IRS)-aided communication, and so on. Finally, future trends and the unresolved technical challenges are summarized in physical layer security for mobile communications.
On some Foundational Aspects of Human-Centered Artificial Intelligence
Venue: arXiv cs.AI 2021
Year: 2021

The burgeoning of AI has prompted recommendations that AI techniques should be "human-centered". However, there is no clear definition of what is meant by Human Centered Artificial Intelligence, or for short, HCAI. This paper aims to improve this situation by addressing some foundational aspects of HCAI. To do so, we introduce the term HCAI agent to refer to any physical or software computational agent equipped with AI components and that interacts and/or collaborates with humans. This article identifies five main conceptual components that participate in an HCAI agent: Observations, Requirements, Actions, Explanations and Models. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI. In this paper, we focus our analysis on scenarios consisting of a single agent operating in dynamic environments in presence of humans.
Signal and Image Reconstruction with Tight Frames via Unconstrained $\ell_1-α\ell_2$-Analysis Minimizations
Venue: arXiv cs.IT 2021
Year: 2021

In the paper, we introduce an unconstrained analysis model based on the $\ell_{1}-α\ell_{2}$ $(0< α\leq1)$ minimization for the signal and image reconstruction. We develop some new technology lemmas for tight frame, and the recovery guarantees based on the restricted isometry property adapted to frames. The effective algorithm is established for the proposed nonconvex analysis model. We illustrate the performance of the proposed model and algorithm for the signal and compressed sensing MRI reconstruction via extensive numerical experiments. And their performance is better than that of the existing methods.
Source Detection via Contact Tracing in the Presence of Asymptomatic Patients
Venue: arXiv cs.SI 2021
Year: 2021

Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. A main limitation of current source detection algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent contact tracing algorithms, we propose a new framework, which we call Source Detection via Contact Tracing Framework (SDCTF). In the SDCTF, the source detection task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We propose two local search algorithms for the SDCTF: the LS algorithm is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform state of the art adaptive and non-adaptive source detection algorithms adapted to the SDCTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the probability of success of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator developed by Lorch et al., which is the first time source detection algorithms are tested on such a complex dataset.
Mutation Testing in Evolving Systems: Studying the relevance of mutants to code evolution
Venue: arXiv cs.SE 2021
Year: 2021

When software evolves, opportunities for introducing faults appear. Therefore, it is important to test the evolved program behaviors during each evolution cycle. We conduct an exploratory study to investigate the properties of commit-relevant mutants, i.e., the test elements of commit-aware mutation testing, by offering a general definition and an experimental approach to identify them. We thus, aim at investigating the prevalence, location, comparative advantages of commit-aware mutation testing over time (i.e., the program evolution) and the predictive power of several commit-related features to understand the essential properties for its best-effort application case. Our approach utilizes the impact of mutants and the effects of one mutant on another in capturing and analyzing the implicit interactions between the changed and unchanged code parts. The study analyses millions of mutants (over 10 million), 288 commits, five (5) different open-source software projects involving over 68,213 CPU days of computation and sets a ground truth where we perform our analysis. Our analysis shows that commit-relevant mutants are located mainly outside of program commit change (81%), while an effective selection of commit-relevant mutants can reduce the number of mutants by up to 93%. In addition, we demonstrate that commit relevant mutation testing is significantly more effective and efficient than state-of-the-art baselines. Our analysis of the predictive power of mutants and commit-related features in predicting commit-relevant mutants found that most proxy features do not reliably predict commit-relevant mutants. This empirical study highlights the properties of commit-relevant mutants and demonstrates the importance of identifying and selecting commit-relevant mutants when testing evolving software systems.
On the Relational Translation Method for Propositional Modal Logics
Venue: arXiv cs.LO 2021
Year: 2021

One way of proving theorems in modal logics is translating them into the predicate calculus and then using conventional resolution-style theorem provers. This approach has been regarded as inappropriate in practice, because the resulting formulas are too lengthy and it is impossible to show the non-theoremhood of modal formulas. In this paper, we demonstrate the practical feasibility of the (relational) translation method. Using a state-of-the-art theorem prover for first-order predicate logic, we proved many benchmark theorems available from the modal logic literature. We show the invalidity of propositional modal and temporal logic formulas, using model generators or satisfiability testers for the classical logic. Many satisfiable formulas are found to have very small models. Finally, several different approaches are compared.
Managing Home Routers with NETCONF over TLS and NETCONF Call Home
Venue: arXiv cs.NI 2021
Year: 2021

The Network Configuration (NETCONF) protocol and the associated YANG data modeling language are the foundations of contemporary network management frameworks evolving within the Internet Engineering Task Force (IETF). netopeer (a NETCONF server) and ncclient (a NETCONF client) are popular open-source projects that support the latest NETCONF v1.1 protocol using the mandatory Secure Shell (SSH) transport. We recently implemented and integrated NETCONF over Transport Layer Security (TLS) transport and NETCONF Call Home (CH) mechanisms using reverse TLS and SSH in both projects. The CH mechanism allows a managed device behind a Network Address Translation (NAT) running a NETCONF server (netopeer) to successfully establish a NETCONF session with a Network Management System (NMS) running a NETCONF client (ncclient). In this article, we describe how these standards allow home routers and NAT boxes (in particular) to be managed using these latest additions to the NETCONF protocol.
IoT Security Challenges and Mitigations: An Introduction
Venue: arXiv cs.CR 2021
Year: 2021

The use of IoT in society is perhaps already ubiquitous, with a vast attack surface offering multiple opportunities for malicious actors. This short paper first presents an introduction to IoT and its security issues, including an overview of IoT layer models and topologies, IoT standardisation efforts and protocols. The focus then moves to IoT vulnerabilities and specific suggestions for mitigations. This work's intended audience are those relatively new to IoT though with existing network-related knowledge. It is concluded that device resource constraints and a lack of IoT standards are significant issues. Research opportunities exist to develop efficient IoT IDS and energy-saving cryptography techniques lightweight enough to reasonably deploy. The need for standardised protocols and channel-based security solutions is clear, underpinned by legislative directives to ensure high standards that prevent cost-cutting on the device manufacturing side.
Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning
Venue: arXiv cs.IT 2021
Year: 2021

Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make autonomous decisions on their transmission strategies with only local information. We demonstrate that with limited information exchange and faster convergence, DRL-CT algorithm can achieve 90% of the performance achieved by the combination of weighted minimum mean square error (WMMSE) algorithm for power control and exhaustive search for dynamic channel access. In the second part of this paper, we consider distributed multi-agent DRL scenario in which each user conducts its own training and makes its decisions individually, acting as a DRL agent. Finally, as a compromise between centralized and fully distributed scenarios, we consider federated DRL (FDRL) to approach the performance of DRL-CT with the use of a central unit in training while limiting the information exchange and preserving privacy of the users in the wireless system. Via simulation results, we show that proposed learning frameworks lead to efficient adaptive channel access and power control policies in dynamic environments.
Automated Code Optimization with E-Graphs
Venue: arXiv cs.PL 2021
Year: 2021

This thesis proposes an advanced, generic and high-level code rewriting and analysis system in the Julia programming language, providing applied equality saturation in the presence of multiple dispatch and metaprogramming. We show how our system can practically solve some challenging problems: Can programmers implement their own high-level compiler optimizations for their domain-specific scientific programs, without the requirement of them being compiler experts at all? Can these optimizers be implemented by users in the same language and inside the same programs they want to optimize, solving the two-language problem? Can these compiler optimizers be written in a high-level fashion, as equations, without the need to worry about the rewriting ordering? Thus, can symbolic mathematics do high-level compiler optimizations or vice-versa?
Exploring Aspects of Polyglot High-Performance Virtual Machine GraalVM
Venue: arXiv cs.PL 2021
Year: 2021

Contemporary software often becomes vastly complex, and we are required to use a variety of technologies and different programming languages for its development. As interoperability between programming languages could cause high overhead resulting in a performance loss, it is important to examine how a current polyglot virtual machine with a compiler written in a high-level object-oriented language deals with it. OpenJDK's Project Metropolis presented the GraalVM, an open-source, high-performance polyglot virtual machine, mostly written in Java. This paper presents GraalVM's architecture and its features; furthermore, examining how it resolves common interoperability and performance problems. GraalVM makes software ecosystem productive when combining various programming languages, for example, Java, JavaScript, C/C++, Python, Ruby, R, and others. The vital part of GraalVM is the Graal compiler written in Java, which allows developers to maintain and optimize code faster, simpler, and more efficient, in comparison to traditional compilers in C/C++ languages. Graal can be used as a just-in-time (JIT) or as static, ahead-of-time (AOT) compiler. Graal is an aggressively optimizing compiler implementing common compiler optimizations, with emphasis on outstanding inlining and escape analysis algorithms. This paper compares Graal with some of the best-specialized competitors, and presents our results tested within an academic environment.
Attention-based Bidirectional LSTM for Deceptive Opinion Spam Classification
Venue: arXiv cs.CL 2021
Year: 2021

Online Reviews play a vital role in e commerce for decision making. Much of the population makes the decision of which places, restaurant to visit, what to buy and from where to buy based on the reviews posted on the respective platforms. A fraudulent review or opinion spam is categorized as an untruthful or deceptive review. Positive reviews of a product or a restaurant helps attract customers and thereby lead to an increase in sales whereas negative reviews may hamper the progress of a restaurant or sales of a product and thereby lead to defamed reputation and loss. Fraudulent reviews are deliberately posted on various online review platforms to trick customers to buy, visit or distract against a product or a restaurant. They are also written to commend or discredit the product's repute. The work aims at detecting and classifying the reviews as deceptive or truthful. It involves use of various deep learning techniques for classifying the reviews and an overview of proposed approach involving Attention based Bidirectional LSTM to tackle issues related to semantic information in reviews and a comparative study over baseline machine learning techniques for review classification.
K-Core Decomposition on Super Large Graphs with Limited Resources
Venue: arXiv cs.DC 2021
Year: 2021

K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-and-conquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.
Retrieving Black-box Optimal Images from External Databases
Venue: arXiv cs.IR 2021
Year: 2021

Suppose we have a black-box function (e.g., deep neural network) that takes an image as input and outputs a value that indicates preference. How can we retrieve optimal images with respect to this function from an external database on the Internet? Standard retrieval problems in the literature (e.g., item recommendations) assume that an algorithm has full access to the set of items. In other words, such algorithms are designed for service providers. In this paper, we consider the retrieval problem under different assumptions. Specifically, we consider how users with limited access to an image database can retrieve images using their own black-box functions. This formulation enables a flexible and finer-grained image search defined by each user. We assume the user can access the database through a search query with tight API limits. Therefore, a user needs to efficiently retrieve optimal images in terms of the number of queries. We propose an efficient retrieval algorithm Tiara for this problem. In the experiments, we confirm that our proposed method performs better than several baselines under various settings.
SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences
Venue: arXiv cs.CV 2021
Year: 2021

We present a dataset that contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. Ground-truth annotations for 13 sequences were prepared and released as the dataset called SFU-HW-Tracks-v1. For each video frame, ground truth annotations include object class ID, object ID, and bounding box location and its dimensions. The dataset can be used to evaluate object tracking performance on uncompressed video sequences and study the relationship between video compression and object tracking.