OpenRaiser/Intern-Atlas huggingface dataset:
Distortion based Light-weight Security for Cyber-Physical Systems
Venue: arXiv cs.IT 2020
Year: 2020

In Cyber-Physical Systems (CPS), inference based on communicated data is of critical significance as it can be used to manipulate or damage the control operations by adversaries. This calls for efficient mechanisms for secure transmission of data since control systems are becoming increasingly distributed over larger geographical areas. Distortion based security, recently proposed as one candidate for secure transmissions in CPS, is not only more appropriate for these applications but also quite frugal in terms of prior requirements on shared keys. In this paper, we propose distortion-based metrics to protect CPS communication and show that it is possible to confuse adversaries with just a few bits of pre-shared keys. In particular, we will show that a linear dynamical system can communicate its state in a manner that prevents an eavesdropper from accurately learning the state.
Local Neighbor Propagation Embedding
Venue: arXiv cs.LG 2020
Year: 2020

Manifold Learning occupies a vital role in the field of nonlinear dimensionality reduction and its ideas also serve for other relevant methods. Graph-based methods such as Graph Convolutional Networks (GCN) show ideas in common with manifold learning, although they belong to different fields. Inspired by GCN, we introduce neighbor propagation into LLE and propose Local Neighbor Propagation Embedding (LNPE). With linear computational complexity increase compared with LLE, LNPE enhances the local connections and interactions between neighborhoods by extending $1$-hop neighbors into $n$-hop neighbors. The experimental results show that LNPE could obtain more faithful and robust embeddings with better topological and geometrical properties.
Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison
Venue: arXiv cs.IT 2020
Year: 2020

Deep learning based physical layer design, i.e., using dense neural networks as encoders and decoders, has received considerable interest recently. However, while such an approach is naturally training data-driven, actions of the wireless channel are mimicked using standard channel models, which only partially reflect the physical ground truth. Very recently, neural network based mutual information (MI) estimators have been proposed that directly extract channel actions from the input-output measurements and feed these outputs into the channel encoder. This is a promising direction as such a new design paradigm is fully adaptive and training data-based. This paper implements further recent improvements of such MI estimators, analyzes theoretically their suitability for the channel coding problem, and compares their performance. To this end, a new MI estimator using a \emph{``reverse Jensen''} approach is proposed.
Creating Artificial Modalities to Solve RGB Liveness
Venue: arXiv cs.CV 2020
Year: 2020

Special cameras that provide useful features for face anti-spoofing are desirable, but not always an option. In this work we propose a method to utilize the difference in dynamic appearance between bona fide and spoof samples by creating artificial modalities from RGB videos. We introduce two types of artificial transforms: rank pooling and optical flow, combined in end-to-end pipeline for spoof detection. We demonstrate that using intermediate representations that contain less identity and fine-grained features increase model robustness to unseen attacks as well as to unseen ethnicities. The proposed method achieves state-of-the-art on the largest cross-ethnicity face anti-spoofing dataset CASIA-SURF CeFA (RGB).
Gamification and engagement of tourists and residents in public transportation exploiting location-based technologies
Venue: arXiv cs.HC 2020
Year: 2020

Cities are becoming very congested. There is a need to reduce the number of private cars on the roads, by maximising the potential for local public transport. With the increasing awareness of transport that is sustainable in the sense of environmental impact, but also climate and social, there is the need to create engagement into public transportation. Gamification, which is the use of game elements in non-game contexts, has proven to deliver very positive results, by turning regular activities into engaging ones, which are fun to perform. We have designed a mobile application, that interacts with short-range wireless communication technologies, inviting people to use public transport. To evaluate the solution, we have created a questionnaire based on the System Usability Scale, but also using usability testing with specific tasks.
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery
Venue: arXiv cs.CV 2020
Year: 2020

Automatic recognition of surgical activities in the operating room (OR) is a key technology for creating next generation intelligent surgical devices and workflow monitoring/support systems. Such systems can potentially enhance efficiency in the OR, resulting in lower costs and improved care delivery to the patients. In this paper, we investigate automatic surgical activity recognition in robot-assisted operations. We collect the first large-scale dataset including 400 full-length multi-perspective videos from a variety of robotic surgery cases captured using Time-of-Flight cameras. We densely annotate the videos with 10 most recognized and clinically relevant classes of activities. Furthermore, we investigate state-of-the-art computer vision action recognition techniques and adapt them for the OR environment and the dataset. First, we fine-tune the Inflated 3D ConvNet (I3D) for clip-level activity recognition on our dataset and use it to extract features from the videos. These features are then fed to a stack of 3 Temporal Gaussian Mixture layers which extracts context from neighboring clips, and eventually go through a Long Short Term Memory network to learn the order of activities in full-length videos. We extensively assess the model and reach a peak performance of 88% mean Average Precision.
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Venue: arXiv cs.AI 2020
Year: 2020

Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both the interpretable inference of new facts, and the explanation of known facts. Among these systems, the walk-based methods that generate the instantiated rules containing constants by abstracting sampled paths in KGs demonstrate strong predictive performance and expressivity. However, due to the large volume of possible rules, these systems do not scale well where computational resources are often wasted on generating and evaluating unpromising rules. In this work, we address such scalability issues by proposing new methods for pruning unpromising rules using rule hierarchies. The approach consists of two phases. Firstly, since rule hierarchies are not readily available in walk-based methods, we have built a Rule Hierarchy Framework (RHF), which leverages a collection of subsumption frameworks to build a proper rule hierarchy from a set of learned rules. And secondly, we adapt RHF to an existing rule learner where we design and implement two methods for Hierarchical Pruning (HPMs), which utilize the generated hierarchies to remove irrelevant and redundant rules. Through experiments over four public benchmark datasets, we show that the application of HPMs is effective in removing unpromising rules, which leads to significant reductions in the runtime as well as in the number of learned rules, without compromising the predictive performance.
Link Prediction Using Supervised Machine Learning based on Aggregated and Topological Features
Venue: arXiv cs.SI 2020
Year: 2020

Link prediction is an important task in social network analysis. There are different characteristics (features) in a social network that can be used for link prediction. In this paper, we evaluate the effectiveness of aggregated features and topological features in link prediction using supervised learning. The aggregated features, in a social network, are some aggregation functions of the attributes of the nodes. Topological features describe the topology or structure of a social network, and its underlying graph. We evaluated the effectiveness of these features by measuring the performance of different supervised machine learning methods. Specifically, we selected five well-known supervised methods including J48 decision tree, multi-layer perceptron (MLP), support vector machine (SVM), logistic regression and Naive Bayes (NB). We measured the performance of these five methods with different sets of features of the DBLP Dataset. Our results indicate that the combination of aggregated and topological features generates the best performance. For evaluation purposes, we used accuracy, area under the ROC curve (AUC) and F-Measure. Our selected features can be used for the analysis of almost any social network. This is because these features provide the important characteristics of the underlying graph of the social networks. The significance of our work is that the selected features can be very effective in the analysis of big social networks. In such networks we usually deal with big data sets, with millions or billions of instances. Using fewer, but more effective, features can help us for the analysis of big social networks.
New developer metrics: Are comments as crucial as code contributions?
Venue: arXiv cs.SE 2020
Year: 2020

Open-source code development has become widespread in recent years. As a result, open-source software platforms have also become popular, and millions of developers from diverse locations are able to contribute to the same projects. On these platforms, various knowledge about them is obtained from user activity. This information is used in the form of developer metrics to solve a variety of challenges. In this study, we proposed new developer metrics, including commenting and issue-related activity, that require less information. We concluded that commenting on any feature of a project can be equally as valuable as code contribution. In addition, besides the quantitative ones, metrics based on only the existence of the activity have been shown to offer also considerable results. We saw that issues were crucial in identifying user contributions. Even if a developer makes a contribution to only one issue on a project, the relation between the developer and the project is tight. The hit scores are relatively lower because of the sparsity problem of our dataset; even so, we believe that we have presented improvable and remarkable new developer metrics.
Unsupervised Calibration under Covariate Shift
Venue: arXiv cs.LG 2020
Year: 2020

A probabilistic model is said to be calibrated if its predicted probabilities match the corresponding empirical frequencies. Calibration is important for uncertainty quantification and decision making in safety-critical applications. While calibration of classifiers has been widely studied, we find that calibration is brittle and can be easily lost under minimal covariate shifts. Existing techniques, including domain adaptation ones, primarily focus on prediction accuracy and do not guarantee calibration neither in theory nor in practice. In this work, we formally introduce the problem of calibration under domain shift, and propose an importance sampling based approach to address it. We evaluate and discuss the efficacy of our method on both real-world datasets and synthetic datasets.
Hands-off Model Integration in Spatial Index Structures
Venue: arXiv cs.DB 2020
Year: 2020

Spatial indexes are crucial for the analysis of the increasing amounts of spatial data, for example generated through IoT applications. The plethora of indexes that has been developed in recent decades has primarily been optimised for disk. With increasing amounts of memory even on commodity machines, however, moving them to main memory is an option. Doing so opens up the opportunity to use additional optimizations that are only amenable to main memory. In this paper we thus explore the opportunity to use light-weight machine learning models to accelerate queries on spatial indexes. We do so by exploring the potential of using interpolation and similar techniques on the R-tree, arguably the most broadly used spatial index. As we show in our experimental analysis, the query execution time can be reduced by up to 60% while simultaneously shrinking the index's memory footprint by over 90%
Hierarchical Jacobi Iteration for Structured Matrices on GPUs using Shared Memory
Venue: arXiv cs.MS 2020
Year: 2020

High fidelity scientific simulations modeling physical phenomena typically require solving large linear systems of equations which result from discretization of a partial differential equation (PDE) by some numerical method. This step often takes a vast amount of computational time to complete, and therefore presents a bottleneck in simulation work. Solving these linear systems efficiently requires the use of massively parallel hardware with high computational throughput, as well as the development of algorithms which respect the memory hierarchy of these hardware architectures to achieve high memory bandwidth. In this paper, we present an algorithm to accelerate Jacobi iteration for solving structured problems on graphics processing units (GPUs) using a hierarchical approach in which multiple iterations are performed within on-chip shared memory every cycle. A domain decomposition style procedure is adopted in which the problem domain is partitioned into subdomains whose data is copied to the shared memory of each GPU block. Jacobi iterations are performed internally within each block's shared memory, avoiding the need to perform expensive global memory accesses every step. We test our algorithm on the linear systems arising from discretization of Poisson's equation in 1D and 2D, and observe speedup in convergence using our shared memory approach compared to a traditional Jacobi implementation which only uses global memory on the GPU. We observe a x8 speedup in convergence in the 1D problem and a nearly x6 speedup in the 2D case from the use of shared memory compared to a conventional GPU approach.
Method for the generation of depth images for view-based shape retrieval of 3D CAD model from partial point cloud
Venue: arXiv cs.CV 2020
Year: 2020

A laser scanner can easily acquire the geometric data of physical environments in the form of a point cloud. Recognizing objects from a point cloud is often required for industrial 3D reconstruction, which should include not only geometry information but also semantic information. However, recognition process is often a bottleneck in 3D reconstruction because it requires expertise on domain knowledge and intensive labor. To address this problem, various methods have been developed to recognize objects by retrieving the corresponding model in the database from an input geometry query. In recent years, the technique of converting geometric data into an image and applying view-based 3D shape retrieval has demonstrated high accuracy. Depth image which encodes depth value as intensity of pixel is frequently used for view-based 3D shape retrieval. However, geometric data collected from objects is often incomplete due to the occlusions and the limit of line of sight. Image generated by occluded point clouds lowers the performance of view-based 3D object retrieval due to loss of information. In this paper, we propose a method of viewpoint and image resolution estimation method for view-based 3D shape retrieval from point cloud query. Automatic selection of viewpoint and image resolution by calculating the data acquisition rate and density from the sampled viewpoints and image resolutions are proposed. The retrieval performance from the images generated by the proposed method is experimented and compared for various dataset. Additionally, view-based 3D shape retrieval performance with deep convolutional neural network has been experimented with the proposed method.
AdaSGD: Bridging the gap between SGD and Adam
Venue: arXiv cs.LG 2020
Year: 2020

In the context of stochastic gradient descent(SGD) and adaptive moment estimation (Adam),researchers have recently proposed optimization techniques that transition from Adam to SGD with the goal of improving both convergence and generalization performance. However, precisely how each approach trades off early progress and generalization is not well understood; thus, it is unclear when or even if, one should transition from one approach to the other. In this work, by first studying the convex setting, we identify potential contributors to observed differences in performance between SGD and Adam. In particular,we provide theoretical insights for when and why Adam outperforms SGD and vice versa. We ad-dress the performance gap by adapting a single global learning rate for SGD, which we refer to as AdaSGD. We justify this proposed approach with empirical analyses in non-convex settings. On several datasets that span three different domains,we demonstrate how AdaSGD combines the benefits of both SGD and Adam, eliminating the need for approaches that transition from Adam to SGD.
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
Venue: arXiv cs.CR 2020
Year: 2020

Malware remains a big threat to cyber security, calling for machine learning based malware detection. While promising, such detectors are known to be vulnerable to evasion attacks. Ensemble learning typically facilitates countermeasures, while attackers can leverage this technique to improve attack effectiveness as well. This motivates us to investigate which kind of robustness the ensemble defense or effectiveness the ensemble attack can achieve, particularly when they combat with each other. We thus propose a new attack approach, named mixture of attacks, by rendering attackers capable of multiple generative methods and multiple manipulation sets, to perturb a malware example without ruining its malicious functionality. This naturally leads to a new instantiation of adversarial training, which is further geared to enhancing the ensemble of deep neural networks. We evaluate defenses using Android malware detectors against 26 different attacks upon two practical datasets. Experimental results show that the new adversarial training significantly enhances the robustness of deep neural networks against a wide range of attacks, ensemble methods promote the robustness when base classifiers are robust enough, and yet ensemble attacks can evade the enhanced malware detectors effectively, even notably downgrading the VirusTotal service.
Hierarchical Graph Matching Network for Graph Similarity Computation
Venue: arXiv cs.DB 2020
Year: 2020

Graph edit distance / similarity is widely used in many tasks, such as graph similarity search, binary function analysis, and graph clustering. However, computing the exact graph edit distance (GED) or maximum common subgraph (MCS) between two graphs is known to be NP-hard. In this paper, we propose the hierarchical graph matching network (HGMN), which learns to compute graph similarity from data. HGMN is motivated by the observation that two similar graphs should also be similar when they are compressed into more compact graphs. HGMN utilizes multiple stages of hierarchical clustering to organize a graph into successively more compact graphs. At each stage, the earth mover distance (EMD) is adopted to obtain a one-to-one mapping between the nodes in two graphs (on which graph similarity is to be computed), and a correlation matrix is also derived from the embeddings of the nodes in the two graphs. The correlation matrices from all stages are used as input for a convolutional neural network (CNN), which is trained to predict graph similarity by minimizing the mean squared error (MSE). Experimental evaluation on 4 datasets in different domains and 4 performance metrics shows that HGMN consistently outperforms existing baselines in the accuracy of graph similarity approximation.
Mitigating undesirable emergent behavior arising between driver and semi-automated vehicle
Venue: arXiv cs.HC 2020
Year: 2020

Emergent behavior arising in a joint human-robot system cannot be fully predicted based on an understanding of the individual agents. Typically, robot behavior is governed by algorithms that optimize a reward function that should quantitatively capture the joint system's goal. Although reward functions can be updated to better match human needs, this is no guarantee that no misalignment with the complex and variable human needs will occur. Algorithms may learn undesirable behavior when interacting with the human and the intrinsically unpredictable human-inhabited world, thereby producing further misalignment with human users or bystanders. As a result, humans might behave differently than anticipated, causing robots to learn differently and undesirable behavior to emerge. With this short paper, we state that to design for Human-Robot Interaction that mitigates such undesirable emergent behavior, we need to complement advancements in human-robot interaction algorithms with human factors knowledge and expertise. More specifically, we advocate a three-pronged approach that we illustrate using a particularly challenging example of safety-critical human-robot interaction: a driver interacting with a semi-automated vehicle. Undesirable emergent behavior should be mitigated by a combination of 1) including driver behavioral mechanisms in the vehicle's algorithms and reward functions, 2) model-based approaches that account for interaction-induced driver behavioral adaptations and 3) driver-centered interaction design that promotes driver engagement with the semi-automated vehicle, and the transparent communication of each agent's actions that allows mutual support and adaptation. We provide examples from recent empirical work in our group, in the hope this proves to be fruitful for discussing emergent human-robot interaction.
Haptic human-human interaction does not improve individual visuomotor adaptation
Venue: arXiv cs.HC 2020
Year: 2020

Haptic interaction between two humans, for example, a physiotherapist assisting a patient regaining the ability to grasp a cup, likely facilitates motor skill acquisition. Haptic human-human interaction has been shown to enhance individual performance improvement in a tracking task with a visuomotor rotation perturbation. These results are remarkable given that haptically assisting or guiding an individual rarely benefits their individual improvement when the assistance is removed. We, therefore, replicated a study that reported that haptic interaction between humans was beneficial for individual improvement for tracking a target in a visuomotor rotation perturbation. In addition, we tested the effect of more interaction time and a stronger haptic coupling between the partners on individual improvement in the same task. We found no benefits of haptic interaction on individual improvement compared to individuals who practised the task alone, independent of interaction time or interaction strength.