OpenRaiser/Intern-Atlas huggingface dataset:
Transductive image segmentation: Self-training and effect of uncertainty estimation
Venue: arXiv cs.CV 2021
Year: 2021

Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracting imaging phenotypes. This work investigates an often overlooked aspect of SSL, transduction. It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization. We focus on the self-training framework and explore its potential for transduction. We analyze it through the lens of Information Gain and reveal that learning benefits from the use of calibrated or under-confident models. Our extensive experiments on a large MRI database for multi-class segmentation of traumatic brain lesions shows promising results when comparing transductive with inductive predictions. We believe this study will inspire further research on transductive learning, a well-suited paradigm for medical image analysis.
Maximizing Nash Social Welfare in 2-Value Instances
Venue: arXiv cs.GT 2021
Year: 2021

We consider the problem of maximizing the Nash social welfare when allocating a set $\mathcal{G}$ of indivisible goods to a set $\mathcal{N}$ of agents. We study instances, in which all agents have 2-value additive valuations: The value of every agent $i \in \mathcal{N}$ for every good $j \in \mathcal{G}$ is $v_{ij} \in \{p,q\}$, for $p,q \in \mathbb{N}$, $p \le q$. Maybe surprisingly, we design an algorithm to compute an optimal allocation in polynomial time if $p$ divides $q$, i.e., when $p=1$ and $q \in \mathbb{N}$ after appropriate scaling. The problem is \classNP-hard whenever $p$ and $q$ are coprime and $p \ge 3$. In terms of approximation, we present positive and negative results for general $p$ and $q$. We show that our algorithm obtains an approximation ratio of at most 1.0345. Moreover, we prove that the problem is \classAPX-hard, with a lower bound of $1.000015$ achieved at $p/q = 4/5$.
Wave-Informed Matrix Factorization with Global Optimality Guarantees
Venue: arXiv cs.LG 2021
Year: 2021

With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing representation learning techniques that can incorporate known physical constraints into the learned representation. As one example, in many applications that involve a signal propagating through physical media (e.g., optics, acoustics, fluid dynamics, etc), it is known that the dynamics of the signal must satisfy constraints imposed by the wave equation. Here we propose a matrix factorization technique that decomposes such signals into a sum of components, where each component is regularized to ensure that it satisfies wave equation constraints. Although our proposed formulation is non-convex, we prove that our model can be efficiently solved to global optimality in polynomial time. We demonstrate the benefits of our work by applications in structural health monitoring, where prior work has attempted to solve this problem using sparse dictionary learning approaches that do not come with any theoretical guarantees regarding convergence to global optimality and employ heuristics to capture desired physical constraints.
Heterogeneous network-based drug repurposing for COVID-19
Venue: arXiv cs.LG 2021
Year: 2021

The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses (HCoVs), which spreads rapidly around the world. Compared with new drug development, drug repurposing may be the best shortcut for treating COVID-19. Therefore, we constructed a comprehensive heterogeneous network based on the HCoVs-related target proteins and use the previously proposed deepDTnet, to discover potential drug candidates for COVID-19. We obtain high performance in predicting the possible drugs effective for COVID-19 related proteins. In summary, this work utilizes a powerful heterogeneous network-based deep learning method, which may be beneficial to quickly identify candidate repurposable drugs toward future clinical trials for COVID-19. The code and data are available at https://github.com/stjin-XMU/HnDR-COVID.
Discriminator-Free Generative Adversarial Attack
Venue: arXiv cs.CV 2021
Year: 2021

The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images. Most of the existing works for adversarial attack are gradient-based and suf-fer from the latency efficiencies and the load on GPU memory. Thegenerative-based adversarial attacks can get rid of this limitation,and some relative works propose the approaches based on GAN.However, suffering from the difficulty of the convergence of train-ing a GAN, the adversarial examples have either bad attack abilityor bad visual quality. In this work, we find that the discriminatorcould be not necessary for generative-based adversarial attack, andpropose theSymmetric Saliency-based Auto-Encoder (SSAE)to generate the perturbations, which is composed of the saliencymap module and the angle-norm disentanglement of the featuresmodule. The advantage of our proposed method lies in that it is notdepending on discriminator, and uses the generative saliency map to pay more attention to label-relevant regions. The extensive exper-iments among the various tasks, datasets, and models demonstratethat the adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.The code is available at https://github.com/BravoLu/SSAE.
Constellation Design of Remote Sensing Small Satellite for Infrastructure Monitoring in India
Venue: arXiv cs.RO 2021
Year: 2021

A constellation of remote sensing small satellite system has been developed for infrastructure monitoring in India by using SAR Payload. The LEO constellation of the small satellites is designed in a way, which can cover the entire footprint of India. Since India lies a little above the equatorial region, the orbital parameters are adjusted in a way that inclination of 36 degrees and RAAN varies from 70-130 degrees at a height of 600 km has been considered. A total number of 4 orbital planes are designed in which each orbital plane consisting 3 small satellites with 120-degrees true anomaly separation. Each satellite is capable of taking multiple look images with the minimum resolution of 1 meter per pixel and swath width of 10 km approx. The multiple look images captured by the SAR payload help in continuous infrastructure monitoring of our interested footprint area in India. Each small satellite is equipped with a communication payload that uses X-band and VHF antenna, whereas the TT&C will use a high data-rate S-band transmitter. The paper presents only a coverage metrics analysis method of our designed constellation for our India footprint by considering the important metrics like revisit time, response time, and coverage efficiency. The result shows that the average revisits time for our constellation ranges from about 15- 35 min which is less than an hour and the average response time for this iteratively designed constellation ranges from about 25-120 min along with hundred percent coverage efficiency most of the time. Finally, it was concluded that each satellite has 70kg of total mass and costs around $ 0.75M to develop.
A Markov Game Model for AI-based Cyber Security Attack Mitigation
Venue: arXiv cs.GT 2021
Year: 2021

The new generation of cyber threats leverages advanced AI-aided methods, which make them capable to launch multi-stage, dynamic, and effective attacks. Current cyber-defense systems encounter various challenges to defend against such new and emerging threats. Modeling AI-aided threats through game theory models can help the defender to select optimal strategies against the attacks and make wise decisions to mitigate the attack's impact. This paper first explores the current state-of-the-art in the new generation of threats in which AI techniques such as deep neural network is used for the attacker and discusses further challenges. We propose a Markovian dynamic game that can evaluate the efficiency of defensive methods against the AI-aided attacker under a cloud-based system in which the attacker utilizes an AI technique to launch an advanced attack by finding the shortest attack path. We use the CVSS metrics to quantify the values of this zero-sum game model for decision-making.
Robust Deep Learning Frameworks for Acoustic Scene and Respiratory Sound Classification
Venue: arXiv cs.SD 2021
Year: 2021

This thesis focuses on dealing with the task of acoustic scene classification (ASC), and then applied the techniques developed for ASC to a real-life application of detecting respiratory disease. To deal with ASC challenges, this thesis addresses three main factors that directly affect the performance of an ASC system. Firstly, this thesis explores input features by making use of multiple spectrograms (log-mel, Gamma, and CQT) for low-level feature extraction to tackle the issue of insufficiently discriminative or descriptive input features. Next, a novel Encoder network architecture is introduced. The Encoder firstly transforms each low-level spectrogram into high-level intermediate features, or embeddings, and thus combines these high-level features to form a very distinct composite feature. The composite or combined feature is then explored in terms of classification performance, with different Decoders such as Random Forest (RF), Multilayer Perception (MLP), and Mixture of Experts (MoE). By using this Encoder-Decoder framework, it helps to reduce the computation cost of the reference process in ASC systems which make use of multiple spectrogram inputs. Since the proposed techniques applied for general ASC tasks were shown to be highly effective, this inspired an application to a specific real-life application. This was namely the 2017 Internal Conference on Biomedical Health Informatics (ICBHI) respiratory sound dataset. Building upon the proposed ASC framework, the ICBHI tasks were tackled with a deep learning framework, and the resulting system shown to be capable at detecting respiratory anomaly cycles and diseases.
Multi-Layered Diagnostics for Smart Cities
Venue: arXiv cs.CY 2021
Year: 2021

Smart cities use technology to improve traffic patterns, energy distribution, air quality and more. The elements of a smart city can also increase the convenience for its citizens, by integrating IT technology into many aspects of citizen interaction such as simplifying access to many of the city services. The fields of healthcare, education, culture, and shopping can all be integrated into the core of a smart city to create an infrastructure that allows citizens to live more conveniently. Actual deployment cases exist in U.S., Europe, Singapore, and South Korea. With this environment, we need to think ahead about cybersecurity and prepare countermeasures as the cyberattacks in a smart city can threaten the lives of its citizens. In this paper, we examine smart city security threats from a multilayered perspective, targeting representative elements that make up a smart city. A summary of attack scenarios and threat countermeasures are also described.
PERSA+: A Deep Learning Front-End for Context-Agnostic Audio Classification
Venue: arXiv cs.SD 2021
Year: 2021

Deep learning has been applied to diverse audio semantics tasks, enabling the construction of models that learn hierarchical levels of features from high-dimensional raw data, delivering state-of-the-art performance. But do these algorithms perform similarly in real-world conditions, or just at the benchmark, where their high learning capability assures the complete memorization of the employed datasets? This work presents a deep learning front-end, aiming at discarding detrimental information before entering the modeling stage, bringing the learning process closer to the point, anticipating the development of robust and context-agnostic classification algorithms.
Similarity metrics for Different Market Scenarios in Abides
Venue: arXiv cs.CE 2021
Year: 2021

Markov Decision Processes (MDPs) are an effective way to formally describe many Machine Learning problems. In fact, recently MDPs have also emerged as a powerful framework to model financial trading tasks. For example, financial MDPs can model different market scenarios. However, the learning of a (near-)optimal policy for each of these financial MDPs can be a very time-consuming process, especially when nothing is known about the policy to begin with. An alternative approach is to find a similar financial MDP for which we have already learned its policy, and then reuse such policy in the learning of a new policy for a new financial MDP. Such a knowledge transfer between market scenarios raises several issues. On the one hand, how to measure the similarity between financial MDPs. On the other hand, how to use this similarity measurement to effectively transfer the knowledge between financial MDPs. This paper addresses both of these issues. Regarding the first one, this paper analyzes the use of three similarity metrics based on conceptual, structural and performance aspects of the financial MDPs. Regarding the second one, this paper uses Probabilistic Policy Reuse to balance the exploitation/exploration in the learning of a new financial MDP according to the similarity of the previous financial MDPs whose knowledge is reused.
Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language
Venue: arXiv cs.LG 2021
Year: 2021

Communication between agents in collaborative multi-agent settings is in general implicit or a direct data stream. This paper considers text-based natural language as a novel form of communication between multiple agents trained with reinforcement learning. This could be considered first steps toward a truly autonomous communication without the need to define a limited set of instructions, and natural collaboration between humans and robots. Inspired by the game of Blind Leads, we propose an environment where one agent uses natural language instructions to guide another through a maze. We test the ability of reinforcement learning agents to effectively communicate through discrete word-level symbols and show that the agents are able to sufficiently communicate through natural language with a limited vocabulary. Although the communication is not always perfect English, the agents are still able to navigate the maze. We achieve a BLEU score of 0.85, which is an improvement of 0.61 over randomly generated sequences while maintaining a 100% maze completion rate. This is a 3.5 times the performance of the random baseline using our reference set.
A Hybrid Approach to Fine-grained Automated Fault Localization
Venue: arXiv cs.SE 2021
Year: 2021

Fault localization is to identify faulty source code. It could be done on various granularities, e.g., classes, methods, and statements. Most of the automated fault localization (AFL) approaches are coarse-grained because it is challenging to accurately locate fine-grained faulty software elements, e.g., statements. SBFL, based on dynamic execution of test cases only, is simple, intuitive, and generic (working on various granularities). However, its accuracy deserves significant improvement. To this end, in this paper, we propose a hybrid fine-grained AFL approach based on both dynamic spectrums and static statement types. The rationale of the approach is that some types of statements are significantly more/less error-prone than others, and thus statement types could be exploited for fault localization. On a crop of faulty programs, we compute the error-proneness for each type of statements, and assign priorities to special statement types that are steadily more/less error-prone than others. For a given faulty program under test, we first leverage traditional spectrum-based fault localization algorithm to identify all suspicious statements and to compute their suspicious scores. For each of the resulting suspicious statements, we retrieve its statement type as well as the special priority associated with the type. The final suspicious score is the product of the SBFL suspicious score and the priority assigned to the statement type. A significant advantage of the approach is that it is simple and intuitive, making it efficient and easy to interpret/implement. We evaluate the proposed approach on widely used benchmark Defects4J. The evaluation results suggest that the proposed approach outperforms widely used SBFL, reducing the absolute waste effort (AWE) by 9.3% on average.
Predicting Driver Takeover Time in Conditionally Automated Driving
Venue: arXiv cs.LG 2021
Year: 2021

It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests (TORs), and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset from a meta-analysis study [1]. In addition, we used SHAP (SHapley Additive exPlanation) to analyze and explain the effects of the predictors on takeover time. We identified seven most critical predictors that resulted in the best prediction performance. Their main effects and interaction effects on takeover time were examined. The results showed that the proposed approach provided both good performance and explainability. Our findings have implications on the design of in-vehicle monitoring and alert systems to facilitate the interaction between the drivers and the automated vehicle.
Saliency for free: Saliency prediction as a side-effect of object recognition
Venue: arXiv cs.CV 2021
Year: 2021

Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects. Neural networks for saliency estimation require ground truth saliency maps for training which are usually achieved via eyetracking experiments. In the current paper, we demonstrate that saliency maps can be generated as a side-effect of training an object recognition deep neural network that is endowed with a saliency branch. Such a network does not require any ground-truth saliency maps for training.Extensive experiments carried out on both real and synthetic saliency datasets demonstrate that our approach is able to generate accurate saliency maps, achieving competitive results on both synthetic and real datasets when compared to methods that do require ground truth data.
Proximal Policy Optimization for Tracking Control Exploiting Future Reference Information
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, reinforcement learning (RL) has gained increasing attention in control engineering. Especially, policy gradient methods are widely used. In this work, we improve the tracking performance of proximal policy optimization (PPO) for arbitrary reference signals by incorporating information about future reference values. Two variants of extending the argument of the actor and the critic taking future reference values into account are presented. In the first variant, global future reference values are added to the argument. For the second variant, a novel kind of residual space with future reference values applicable to model-free reinforcement learning is introduced. Our approach is evaluated against a PI controller on a simple drive train model. We expect our method to generalize to arbitrary references better than previous approaches, pointing towards the applicability of RL to control real systems.
Mono2Micro: A Practical and Effective Tool for Decomposing Monolithic Java Applications to Microservices
Venue: arXiv cs.SE 2021
Year: 2021

In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results. How well does Mono2Micro perform against other decomposition techniques, and how do practitioners perceive the tool? This paper describes the technical foundations of Mono2Micro and presents results to answer these two questions. To answer the first question, we evaluated Mono2Micro against four existing techniques on a set of open-source and proprietary Java applications and using different metrics to assess the quality of decomposition and tool's efficiency. Our results show that Mono2Micro significantly outperforms state-of-the-art baselines in specific metrics well-defined for the problem domain. To answer the second question, we conducted a survey of twenty-one practitioners in various industry roles who have used Mono2Micro. This study highlights several benefits of the tool, interesting practitioner perceptions, and scope for further improvements. Overall, these results show that Mono2Micro can provide a valuable aid to practitioners in creating functionally cohesive and explainable microservice decompositions.
A Cooperative Optimal Mining Model for Bitcoin
Venue: arXiv cs.GT 2021
Year: 2021

We analyze Bitcoin mining from the perspective of a game and propose an optimal mining model that maximizes profits of pools and miners. The model is a two-stage Stackelberg game in which each stage forms a sub-game. In stage I, pools are the leaders who assign a computing power to be consumed by miners. In stage II, miners decide of their power consumption and distribution. They find themselves in a social dilemma in which they must choose between mining in solo, therefore prioritizing their individual preferences, and participating in a pool for the collective interest. The model relies on a pool protocol based on a simulated game in which the miners compete for the reward won by the pool. The solutions for the stage I sub-game and the simulated protocol game are unique and stable Nash equilibriums while the stage II sub-game leads to a stable cooperative equilibrium only when miners choose their strategies according to certain criteria. We conclude that the cooperative optimal mining model has the potential to favor Bitcoin decentralization and stability. Mainly, the social dilemma faced by miners together with the balance of incentives ensure a certain distribution of the network computing power between pools and solo miners, while equilibriums in the game solutions provide stability to the system.