OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID
Venue: arXiv cs.CV 2021
Year: 2021

Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intradomain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks.
High-Accuracy RGB-D Face Recognition via Segmentation-Aware Face Depth Estimation and Mask-Guided Attention Network
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning approaches have achieved highly accurate face recognition by training the models with very large face image datasets. Unlike the availability of large 2D face image datasets, there is a lack of large 3D face datasets available to the public. Existing public 3D face datasets were usually collected with few subjects, leading to the over-fitting problem. This paper proposes two CNN models to improve the RGB-D face recognition task. The first is a segmentation-aware depth estimation network, called DepthNet, which estimates depth maps from RGB face images by including semantic segmentation information for more accurate face region localization. The other is a novel mask-guided RGB-D face recognition model that contains an RGB recognition branch, a depth map recognition branch, and an auxiliary segmentation mask branch with a spatial attention module. Our DepthNet is used to augment a large 2D face image dataset to a large RGB-D face dataset, which is used for training an accurate RGB-D face recognition model. Furthermore, the proposed mask-guided RGB-D face recognition model can fully exploit the depth map and segmentation mask information and is more robust against pose variation than previous methods. Our experimental results show that DepthNet can produce more reliable depth maps from face images with the segmentation mask. Our mask-guided face recognition model outperforms state-of-the-art methods on several public 3D face datasets.
The Time Perception Control and Regulation in VR Environment
Venue: arXiv cs.HC 2021
Year: 2021

To adapt to different environments, human circadian rhythms will be constantly adjusted as the environment changes, which follows the principle of survival of the fittest. According to this principle, objective factors (such as circadian rhythms, and light intensity) can be utilized to control time perception. The subjective judgment on the estimation of elapsed time is called time perception. In the physical world, factors that can affect time perception, represented by illumination, are called the Zeitgebers. In recent years, with the development of Virtual Reality (VR) technology, effective control of zeitgebers has become possible, which is difficult to achieve in the physical world. Based on previous studies, this paper deeply explores the actual performance in VR environment of four types of time zeitgebers (music, color, cognitive load, and concentration) that have been proven to have a certain impact on time perception in the physical world. It discusses the study of the measurement of the difference between human time perception and objective escaped time in the physical world.
Towards Malicious address identification in Bitcoin
Venue: arXiv cs.CR 2021
Year: 2021

The temporal aspect of blockchain transactions enables us to study the address's behavior and detect if it is involved in any illicit activity. However, due to the concept of change addresses (used to thwart replay attacks), temporal aspects are not directly applicable in the Bitcoin blockchain. Several pre-processing steps should be performed before such temporal aspects are utilized. We are motivated to study the Bitcoin transaction network and use the temporal features such as burst, attractiveness, and inter-event time along with several graph-based properties such as the degree of node and clustering coefficient to validate the applicability of already existing approaches known for other cryptocurrency blockchains on the Bitcoin blockchain. We generate the temporal and non-temporal feature set and train the Machine Learning (ML) algorithm over different temporal granularities to validate the state-of-the-art methods. We study the behavior of the addresses over different time granularities of the dataset. We identify that after applying change-address clustering, in Bitcoin, existing temporal features can be extracted and ML approaches can be applied. A comparative analysis of results show that the behavior of addresses in Ethereum and Bitcoin is similar with respect to in-degree, out-degree and inter-event time. Further, we identify 3 suspects that showed malicious behavior across different temporal granularities. These suspects are not marked as malicious in Bitcoin.
GUX-Analyzer: A Deep Multi-modal Analyzer Via Motivational Flow For Game User Experience
Venue: arXiv cs.HC 2021
Year: 2021

Quantitative analysis of Game User eXperience (GUX) is important to the game industry. Different from the typical questionnaire analysis, this paper focuses on the computational analysis of GUX. We aim to analyze the relationship between game and players using the multi-modal data including physiological data and game process data. We theoretically extend the Flow model from the classic skill-and-challenge plane by expanding new dimension on motivation, which is the result of the multi-modal data analysis on affect, and physiological data. We call this 3D Flow as Motivational Flow, MovFlow. Meanwhile, we implement a quantitative GUX Analysis System (GUXAS), which can predict the player's in-game experience state by only using game process data. It analyzes the correlation among not only in-game state, but the player's psychological-and-physiological reaction in the entire interactive game-play process. The experiments demonstrated our MovFlow model efficiently distinguished the users' in-game experience states from the perspective of GUX.
An Efficient Pruning Process with Locality Aware Exploration and Dynamic Graph Editing for Subgraph Matching
Venue: arXiv cs.IR 2021
Year: 2021

Subgraph matching is a NP-complete problem that extracts isomorphic embeddings of a query graph $q$ in a data graph $G$. In this paper, we present a framework with three components: Preprocessing, Reordering and Enumeration. While pruning is the core technique for almost all existing subgraph matching solvers, it mainly eliminates unnecessary enumeration over data graph without alternation of query graph. By formulating a problem: Assignment under Conditional Candidate Set(ACCS), which is proven to be equivalent to Subgraph matching problem, we propose Dynamic Graph Editing(DGE) that is for the first time designed to tailor the query graph to achieve pruning effect and performance acceleration. As a result, we proposed DGEE(Dynamic Graph Editing Enumeration), a novel enumeration algorithm combines Dynamic Graph Editing and Failing Set optimization. Our second contribution is proposing fGQL , an optimized version of GQL algorithm, that is utilized during the Preprocessing phase. Extensive experimental results show that the DGEE-based framework can outperform state-of-the-art subgraph matching algorithms.
Parametric Church's Thesis: Synthetic Computability without Choice
Venue: arXiv cs.LO 2021
Year: 2021

In synthetic computability, pioneered by Richman, Bridges, and Bauer, one develops computability theory without an explicit model of computation. This is enabled by assuming an axiom equivalent to postulating a function $ϕ$ to be universal for the space $\mathbb{N}\to\mathbb{N}$ ($\mathsf{CT}_ϕ$, a consequence of the constructivist axiom $\mathsf{CT}$), Markov's principle, and at least the axiom of countable choice. Assuming $\mathsf{CT}$ and countable choice invalidates the law of excluded middle, thereby also invalidating classical intuitions prevalent in textbooks on computability. On the other hand, results like Rice's theorem are not provable without a form of choice. In contrast to existing work, we base our investigations in constructive type theory with a separate, impredicative universe of propositions where countable choice does not hold and thus a priori $\mathsf{CT}_ϕ$ and the law of excluded middle seem to be consistent. We introduce various parametric strengthenings of $\mathsf{CT}_ϕ$, which are equivalent to assuming $\mathsf{CT}_ϕ$ and an $S^m_n$ operator for $ϕ$ like in the $S^m_n$ theorem. The strengthened axioms allow developing synthetic computability theory without choice, as demonstrated by elegant synthetic proofs of Rice's theorem. Moreover, they seem to be not in conflict with classical intuitions since they are consequences of the traditional analytic form of $\mathsf{CT}$. Besides explaining the novel axioms and proofs of Rice's theorem we contribute machine-checked proofs of all results in the Coq proof assistant.
Movie Recommender System using critic consensus
Venue: arXiv cs.IR 2021
Year: 2021

Recommendation systems are perhaps one of the most important agents for industry growth through the modern Internet world. Previous approaches on recommendation systems include collaborative filtering and content based filtering recommendation systems. These 2 methods are disjointed in nature and require the continuous storage of user preferences for a better recommendation. To provide better integration of the two processes, we propose a hybrid recommendation system based on the integration of collaborative and content-based content, taking into account the top critic consensus and movie rating score. We would like to present a novel model that recommends movies based on the combination of user preferences and critical consensus scores.
End to End Software Engineering Research
Venue: arXiv cs.SE 2021
Year: 2021

End to end learning is machine learning starting in raw data and predicting a desired concept, with all steps done automatically. In software engineering context, we see it as starting from the source code and predicting process metrics. This framework can be used for predicting defects, code quality, productivity and more. End-to-end improves over features based machine learning by not requiring domain experts and being able to extract new knowledge. We describe a dataset of 5M files from 15k projects constructed for this goal. The dataset is constructed in a way that enables not only predicting concepts but also investigating their causes.
A Min-plus Model of Age-of-Information with Worst-case and Statistical Bounds
Venue: arXiv cs.NI 2021
Year: 2021

We consider networked sources that generate update messages with a defined rate and we investigate the age of that information at the receiver. Typical applications are in cyber-physical systems that depend on timely sensor updates. We phrase the age of information in the min-plus algebra of the network calculus. This facilitates a variety of models including wireless channels and schedulers with random cross-traffic, as well as sources with periodic and random updates, respectively. We show how the age of information depends on the network service where, e.g., outages of a wireless channel cause delays. Further, our analytical expressions show two regimes depending on the update rate, where the age of information is either dominated by congestive delays or by idle waiting. We find that the optimal update rate strikes a balance between these two effects.
An Interface Preserving Moving Mesh in Multiple Space Dimensions
Venue: arXiv cs.CG 2021
Year: 2021

An interface preserving moving mesh algorithm in two or higher dimensions is presented. It resolves a moving $(d-1)$-dimensional manifold directly within the $d$-dimensional mesh, which means that the interface is represented by a subset of moving mesh cell-surfaces. The underlying mesh is a conforming simplicial partition that fulfills the Delaunay property. The local remeshing algorithms allow for strong interface deformations. We give a proof that the given algorithms preserve the interface after interface deformation and remeshing steps. Originating from various numerical methods, data is attached cell-wise to the mesh. After each remeshing operation the interface preserving moving mesh retains valid data by projecting the data to the new mesh cells.\newline An open source implementation of the moving mesh algorithm is available at [1].
Catch Me If You GAN: Using Artificial Intelligence for Fake Log Generation
Venue: arXiv cs.CR 2021
Year: 2021

With artificial intelligence (AI) becoming relevant in various parts of everyday life, other technologies are already widely influenced by the new way of handling large amounts of data. Although widespread already, AI has had only punctual influences on the cybersecurity field specifically. Many techniques and technologies used by cybersecurity experts function through manual labor and barely draw on automation, e.g., logs are often reviewed manually by system admins for potentially malicious keywords. This work evaluates the use of a special type of AI called generative adversarial networks (GANs) for log generation. More precisely, three different generative adversarial networks, SeqGAN, MaliGAN, and CoT, are reviewed in this research regarding their performance, focusing on generating new logs as a means of deceiving system admins for red teams. Although static generators for fake logs have been around for a while, their produces are usually easy to reveal as such. Using AI as an approach to this problem has not been widely researched. Identified challenges consist of formatting, dates and times, and overall consistency. Summing up the results, GANs seem not to be a good fit for generating fake logs. Their capability to detect fake logs, however, might be of use in practical scenarios.
Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents a solutions for the MediaEval 2021 task namely "Visual Sentiment Analysis: A Natural Disaster Use-case". The task aims to extract and classify sentiments perceived by viewers and the emotional message conveyed by natural disaster-related images shared on social media. The task is composed of three sub-tasks including, one single label multi-class image classification task, and, two multi-label multi-class image classification tasks, with different sets of labels. In our proposed solutions, we rely mainly on two different state-of-the-art models namely, Inception-v3 and VggNet-19, pre-trained on ImageNet, which are fine-tuned for each of the three task using different strategies. Overall encouraging results are obtained on all the three tasks. On the single-label classification task (i.e. Task 1), we obtained the weighted average F1-scores of 0.540 and 0.526 for the Inception-v3 and VggNet-19 based solutions, respectively. On the multi-label classification i.e., Task 2 and Task 3, the weighted F1-score of our Inception-v3 based solutions was 0.572 and 0.516, respectively. Similarly, the weighted F1-score of our VggNet-19 based solution on Task 2 and Task 3 was 0.584 and 0.495, respectively.
Deeper Learning with CoLU Activation
Venue: arXiv cs.LG 2021
Year: 2021

In neural networks, non-linearity is introduced by activation functions. One commonly used activation function is Rectified Linear Unit (ReLU). ReLU has been a popular choice as an activation but has flaws. State-of-the-art functions like Swish and Mish are now gaining attention as a better choice as they combat many flaws presented by other activation functions. CoLU is an activation function similar to Swish and Mish in properties. It is defined as f(x)=x/(1-xe^-(x+e^x)). It is smooth, continuously differentiable, unbounded above, bounded below, non-saturating, and non-monotonic. Based on experiments done with CoLU with different activation functions, it is observed that CoLU usually performs better than other functions on deeper neural networks. While training different neural networks on MNIST on an incrementally increasing number of convolutional layers, CoLU retained the highest accuracy for more layers. On a smaller network with 8 convolutional layers, CoLU had the highest mean accuracy, closely followed by ReLU. On VGG-13 trained on Fashion-MNIST, CoLU had a 4.20% higher accuracy than Mish and 3.31% higher accuracy than ReLU. On ResNet-9 trained on Cifar-10, CoLU had 0.05% higher accuracy than Swish, 0.09% higher accuracy than Mish, and 0.29% higher accuracy than ReLU. It is observed that activation functions may behave better than other activation functions based on different factors including the number of layers, types of layers, number of parameters, learning rate, optimizer, etc. Further research can be done on these factors and activation functions for more optimal activation functions and more knowledge on their behavior.
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems
Venue: arXiv cs.CR 2021
Year: 2021

In the last decade, the use of Machine Learning techniques in anomaly-based intrusion detection systems has seen much success. However, recent studies have shown that Machine learning in general and deep learning specifically are vulnerable to adversarial attacks where the attacker attempts to fool models by supplying deceptive input. Research in computer vision, where this vulnerability was first discovered, has shown that adversarial images designed to fool a specific model can deceive other machine learning models. In this paper, we investigate the transferability of adversarial network traffic against multiple machine learning-based intrusion detection systems. Furthermore, we analyze the robustness of the ensemble intrusion detection system, which is notorious for its better accuracy compared to a single model, against the transferability of adversarial attacks. Finally, we examine Detect & Reject as a defensive mechanism to limit the effect of the transferability property of adversarial network traffic against machine learning-based intrusion detection systems.
Safety assurance of an industrial robotic control system using hardware/software co-verification
Venue: arXiv cs.RO 2021
Year: 2021

As a general trend in industrial robotics, an increasing number of safety functions are being developed or re-engineered to be handled in software rather than by physical hardware such as safety relays or interlock circuits. This trend reinforces the importance of supplementing traditional, input-based testing and quality procedures which are widely used in industry today, with formal verification and model-checking methods. To this end, this paper focuses on a representative safety-critical system in an ABB industrial paint robot, namely the High-Voltage electrostatic Control system (HVC). The practical convergence of the high-voltage produced by the HVC, essential for safe operation, is formally verified using a novel and general co-verification framework where hardware and software models are related via platform mappings. This approach enables the pragmatic combination of highly diverse and specialised tools. The paper's main contribution includes details on how hardware abstraction and verification results can be transferred between tools in order to verify system-level safety properties. It is noteworthy that the HVC application considered in this paper has a rather generic form of a feedback controller. Hence, the co-verification framework and experiences reported here are also highly relevant for any cyber-physical system tracking a setpoint reference.
Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles
Venue: arXiv cs.CV 2021
Year: 2021

Acquiring data to train deep learning-based object detectors on Unmanned Aerial Vehicles (UAVs) is expensive, time-consuming and may even be prohibited by law in specific environments. On the other hand, synthetic data is fast and cheap to access. In this work, we explore the potential use of synthetic data in object detection from UAVs across various application environments. For that, we extend the open-source framework DeepGTAV to work for UAV scenarios. We capture various large-scale high-resolution synthetic data sets in several domains to demonstrate their use in real-world object detection from UAVs by analyzing multiple training strategies across several models. Furthermore, we analyze several different data generation and sampling parameters to provide actionable engineering advice for further scientific research. The DeepGTAV framework is available at https://git.io/Jyf5j.
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks
Venue: arXiv cs.LG 2021
Year: 2021

Measures of Activity of Daily Living (ADL) are an important indicator of overall health but difficult to measure in-clinic. Automated and accurate human activity recognition (HAR) using wrist-worn accelerometers enables practical and cost efficient remote monitoring of ADL. Key obstacles in developing high quality HAR is the lack of large labeled datasets and the performance loss when applying models trained on small curated datasets to the continuous stream of heterogeneous data in real-life. In this work we design a self-supervised learning paradigm to create a robust representation of accelerometer data that can generalize across devices and subjects. We demonstrate that this representation can separate activities of daily living and achieve strong HAR accuracy (on multiple benchmark datasets) using very few labels. We also propose a segmentation algorithm which can identify segments of salient activity and boost HAR accuracy on continuous real-life data.