OpenRaiser/Intern-Atlas huggingface dataset:
Uncertainty Estimation in Cancer Survival Prediction
Venue: arXiv cs.LG 2020
Year: 2020

Survival models are used in various fields, such as the development of cancer treatment protocols. Although many statistical and machine learning models have been proposed to achieve accurate survival predictions, little attention has been paid to obtain well-calibrated uncertainty estimates associated with each prediction. The currently popular models are opaque and untrustworthy in that they often express high confidence even on those test cases that are not similar to the training samples, and even when their predictions are wrong. We propose a Bayesian framework for survival models that not only gives more accurate survival predictions but also quantifies the survival uncertainty better. Our approach is a novel combination of variational inference for uncertainty estimation, neural multi-task logistic regression for estimating nonlinear and time-varying risk models, and an additional sparsity-inducing prior to work with high dimensional data.
Hybrid Model For Intrusion Detection Systems
Venue: arXiv cs.CR 2020
Year: 2020

With the increasing number of new attacks on ever growing network traffic, it is becoming challenging to alert immediately any malicious activities to avoid loss of sensitive data and money. This is making intrusion detection as one of the major areas of concern in network security. Anomaly based network intrusion detection technique is one of the most commonly used technique. Depending upon the dataset used to test those techniques, the accuracy varies. Most of the times this dataset does not represent the real network traffic. Considering this, this project involves analysis of different machine learning algorithms used in intrusion detection systems, when tested upon two datasets which are similar to current real world network traffic(CICIDS2017) and an improvement of KDD 99 (NSL-KDD). After the analysis of different intrusion detection systems on both the datasets, this project aimed to develop a new hybrid model for intrusion detection systems. This new hybrid approach combines decision tree and random forest algorithms using stacking scheme to achieve an accuracy of 85.2% and precision of 86.2% for NSL-KDD dataset, and achieve an accuracy of 98% and precision of 98% for CICIDS2017 dataset.
Curriculum DeepSDF
Venue: arXiv cs.CV 2020
Year: 2020

When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions. In this paper, we design a "shape curriculum" for learning continuous Signed Distance Function (SDF) on shapes, namely Curriculum DeepSDF. Inspired by how humans learn, Curriculum DeepSDF organizes the learning task in ascending order of difficulty according to the following two criteria: surface accuracy and sample difficulty. The former considers stringency in supervising with ground truth, while the latter regards the weights of hard training samples near complex geometry and fine structure. More specifically, Curriculum DeepSDF learns to reconstruct coarse shapes at first, and then gradually increases the accuracy and focuses more on complex local details. Experimental results show that a carefully-designed curriculum leads to significantly better shape reconstructions with the same training data, training epochs and network architecture as DeepSDF. We believe that the application of shape curricula can benefit the training process of a wide variety of 3D shape representation learning methods.
Formation and Reconfiguration of Tight Multi-Lane Platoons
Venue: arXiv cs.RO 2020
Year: 2020

Advances in vehicular communication technologies are expected to facilitate cooperative driving. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons travelling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: 1) formation reconfiguration 2) obstacle avoidance, and 3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives. Videos and software can be found online https://github.com/RoyaFiroozi/Centralized-Planning.
Deep convolutional embedding for digitized painting clustering
Venue: arXiv cs.CV 2020
Year: 2020

Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns in accordance with domain knowledge and visual perception is extremely difficult. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To address these issues, we propose to use a deep convolutional embedding model for digitized painting clustering, in which the task of mapping the raw input data to an abstract, latent space is jointly optimized with the task of finding a set of cluster centroids in this latent feature space. Quantitative and qualitative experimental results show the effectiveness of the proposed method. The model is also capable of outperforming other state-of-the-art deep clustering approaches to the same problem. The proposed method can be useful for several art-related tasks, in particular visual link retrieval and historical knowledge discovery in painting datasets.
A Hybrid Model-based and Data-driven Approach to Spectrum Sharing in mmWave Cellular Networks
Venue: arXiv cs.IT 2020
Year: 2020

Inter-operator spectrum sharing in millimeter-wave bands has the potential of substantially increasing the spectrum utilization and providing a larger bandwidth to individual user equipment at the expense of increasing inter-operator interference. Unfortunately, traditional model-based spectrum sharing schemes make idealistic assumptions about inter-operator coordination mechanisms in terms of latency and protocol overhead, while being sensitive to missing channel state information. In this paper, we propose hybrid model-based and data-driven multi-operator spectrum sharing mechanisms, which incorporate model-based beamforming and user association complemented by data-driven model refinements. Our solution has the same computational complexity as a model-based approach but has the major advantage of having substantially less signaling overhead. We discuss how limited channel state information and quantized codebook-based beamforming affect the learning and the spectrum sharing performance. We show that the proposed hybrid sharing scheme significantly improves spectrum utilization under realistic assumptions on inter-operator coordination and channel state information acquisition.
Detecting Deepfakes with Metric Learning
Venue: arXiv cs.CV 2020
Year: 2020

With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenario and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.
Independent sets in ($P_4+P_4$,Triangle)-free graphs
Venue: arXiv cs.DM 2020
Year: 2020

The Maximum Weight Independent Set Problem (WIS) is a well-known NP-hard problem. A popular way to study WIS is to detect graph classes for which WIS can be solved in polynomial time, with particular reference to hereditary graph classes, i.e., defined by a hereditary graph property or equivalently by forbidding one or more induced subgraphs. Given two graphs $G$ and $H$, $G+H$ denotes the disjoint union of $G$ and $H$. This manuscript shows that (i) WIS can be solved for ($P_4+P_4$, Triangle)-free graphs in polynomial time, where a $P_4$ is an induced path of four vertices and a Triangle is a cycle of three vertices, and that in particular it turns out that (ii) for every ($P_4+P_4$, Triangle)-free graph $G$ there is a family ${\cal S}$ of subsets of $V(G)$ inducing (complete) bipartite subgraphs of $G$, which contains polynomially many members and can be computed in polynomial time, such that every maximal independent set of $G$ is contained in some member of ${\cal S}$. These results seem to be harmonic with respect to other polynomial results for WIS on certain [subclasses of] $S_{i,j,k}$-free graphs and to other structure results on [subclasses of] Triangle-free graphs.
Computational universality of fungal sandpile automata
Venue: arXiv cs.ET 2020
Year: 2020

Hyphae within the mycelia of the ascomycetous fungi are compartmentalised by septa. Each septum has a pore that allows for inter-compartmental and inter-hyphal streaming of cytosol and even organelles. The compartments, however, have special organelles, Woronin bodies, that can plug the pores. When the pores are blocked, no flow of cytoplasm takes place. Inspired by the controllable compartmentalisation within the mycelium of the ascomycetous fungi we designed two-dimensional fungal automata. A fungal automaton is a cellular automaton where communication between neighbouring cells can be blocked on demand. We demonstrate computational universality of the fungal automata by implementing sandpile cellular automata circuits there. We reduce the Monotone Circuit Value Problem to the Fungal Automaton Prediction Problem. We construct families of wires, cross-overs and gates to prove that the fungal automata are P-complete.
Toward Enabling a Reliable Quality Monitoring System for Additive Manufacturing Process using Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Additive Manufacturing (AM) is a crucial component of the smart industry. In this paper, we propose an automated quality grading system for the AM process using a deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures. The model demonstrates the accuracy of 94% and specificity of 96%, as well as above 75% in three classifier measures of the Fscore, the sensitivity, and precision for classifying the quality of the printing process in five grades in real-time. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built. The quality monitoring signal can also be used by the machine to suggest remedial actions by adjusting the parameters in real-time. The proposed quality predictive model serves as a proof-of-concept for any type of AM machines to produce reliable parts with fewer quality hiccups while limiting the waste of both time and materials.
Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection
Venue: arXiv cs.CV 2020
Year: 2020

Balancing methods for single-label data cannot be applied to multi-label problems as they would also resample the samples with high occurrences. We propose to reformulate this problem as an optimization problem in order to balance multi-label data. We apply this balancing algorithm to training datasets for detecting isolated facial movements, so-called Action Units. Several Action Units can describe combined emotions or physical states such as pain. As datasets in this area are limited and mostly imbalanced, we show how optimized balancing and then augmentation can improve Action Unit detection. At the IEEE Conference on Face and Gesture Recognition 2020, we ranked third in the Affective Behavior Analysis in-the-wild (ABAW) challenge for the Action Unit detection task.
Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images
Venue: arXiv cs.CV 2020
Year: 2020

Empirical evaluation of breast tissue biopsies for mitotic nuclei detection is considered an important prognostic biomarker in tumor grading and cancer progression. However, automated mitotic nuclei detection poses several challenges because of the unavailability of pixel-level annotations, different morphological configurations of mitotic nuclei, their sparse representation, and close resemblance with non-mitotic nuclei. These challenges undermine the precision of the automated detection model and thus make detection difficult in a single phase. This work proposes an end-to-end detection system for mitotic nuclei identification in breast cancer histopathological images. Deep object detection-based Mask R-CNN is adapted for mitotic nuclei detection that initially selects the candidate mitotic region with maximum recall. However, in the second phase, these candidate regions are refined by multi-object loss function to improve the precision. The performance of the proposed detection model shows improved discrimination ability (F-score of 0.86) for mitotic nuclei with significant precision (0.86) as compared to the two-stage detection models (F-score of 0.701) on TUPAC16 dataset. Promising results suggest that the deep object detection-based model has the potential to learn the characteristic features of mitotic nuclei from weakly annotated data and suggests that it can be adapted for the identification of other nuclear bodies in histopathological images.
Functional Data Analysis and Visualisation of Three-dimensional Surface Shape
Venue: arXiv cs.CV 2020
Year: 2020

The advent of high resolution imaging has made data on surface shape widespread. Methods for the analysis of shape based on landmarks are well established but high resolution data require a functional approach. The starting point is a systematic and consistent description of each surface shape. Three innovative forms of analysis are then introduced. The first uses surface integration to address issues of registration, principal component analysis and the measurement of asymmetry, all in functional form. Computational issues are handled through discrete approximations to integrals, based in this case on appropriate surface area weighted sums. The second innovation is to focus on sub-spaces where interesting behaviour such as group differences are exhibited, rather than on individual principal components. The third innovation concerns the comparison of individual shapes with a relevant control set, where the concept of a normal range is extended to the highly multivariate setting of surface shape. This has particularly strong applications to medical contexts where the assessment of individual patients is very important. All of these ideas are developed and illustrated in the important context of human facial shape, with a strong emphasis on the effective visual communication of effects of interest.
Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media
Venue: arXiv cs.SI 2020
Year: 2020

Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e. the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting, representing and understanding mindsets' structure, in Latin "forma mentis", from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts, without supervision, in benchmark texts. Once validated, TFMNs were applied to the case study of the gender gap in science, which was strongly linked to distorted mindsets by recent studies. Focusing over social media perception and online discourse, this work analysed 10,000 relevant tweets. "Gender" and "gap" elicited a mostly positive perception, with a trustful/joyous emotional profile and semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of "woman" highlighted discussion about sexual harassment and stereotype threat (a form of implicit cognitive bias) relative to women in science "sacrificing personal skills for success". The reconstructed perception of "man" highlighted social users' awareness of the myth of male superiority in science. No anger was detected around "person", suggesting that gap-focused discourse got less tense around genderless terms. No stereotypical perception of "scientist" was identified online, differently from real-world surveys. The overall analysis identified the online discourse as promoting a mostly stereotype-free, positive/trustful perception of gender disparity, aware of implicit/explicit biases and projected to closing the gap. TFMNs opened new ways for investigating perceptions in different groups, offering detailed data-informed grounding for policy making.
Vulnerabilities of Connectionist AI Applications: Evaluation and Defence
Venue: arXiv cs.CR 2020
Year: 2020

This article deals with the IT security of connectionist artificial intelligence (AI) applications, focusing on threats to integrity, one of the three IT security goals. Such threats are for instance most relevant in prominent AI computer vision applications. In order to present a holistic view on the IT security goal integrity, many additional aspects such as interpretability, robustness and documentation are taken into account. A comprehensive list of threats and possible mitigations is presented by reviewing the state-of-the-art literature. AI-specific vulnerabilities such as adversarial attacks and poisoning attacks as well as their AI-specific root causes are discussed in detail. Additionally and in contrast to former reviews, the whole AI supply chain is analysed with respect to vulnerabilities, including the planning, data acquisition, training, evaluation and operation phases. The discussion of mitigations is likewise not restricted to the level of the AI system itself but rather advocates viewing AI systems in the context of their supply chains and their embeddings in larger IT infrastructures and hardware devices. Based on this and the observation that adaptive attackers may circumvent any single published AI-specific defence to date, the article concludes that single protective measures are not sufficient but rather multiple measures on different levels have to be combined to achieve a minimum level of IT security for AI applications.
Face-Off: Adversarial Face Obfuscation
Venue: arXiv cs.CR 2020
Year: 2020

Advances in deep learning have made face recognition technologies pervasive. While useful to social media platforms and users, this technology carries significant privacy threats. Coupled with the abundant information they have about users, service providers can associate users with social interactions, visited places, activities, and preferences--some of which the user may not want to share. Additionally, facial recognition models used by various agencies are trained by data scraped from social media platforms. Existing approaches to mitigate these privacy risks from unwanted face recognition result in an imbalanced privacy-utility trade-off to users. In this paper, we address this trade-off by proposing Face-Off, a privacy-preserving framework that introduces strategic perturbations to the user's face to prevent it from being correctly recognized. To realize Face-Off, we overcome a set of challenges related to the black-box nature of commercial face recognition services, and the scarcity of literature for adversarial attacks on metric networks. We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++. Our user study with 423 participants further shows that the perturbations come at an acceptable cost for the users.
A Real Polynomial for Bipartite Graph Minimum Weight Perfect Matchings
Venue: arXiv cs.DM 2020
Year: 2020

In a recent paper, Beniamini and Nisan gave a closed-form formula for the unique multilinear polynomial for the Boolean function determining whether a given bipartite graph $G \subseteq K_{n,n}$ has a perfect matching, together with an efficient algorithm for computing the coefficients of the monomials of this polynomial. We give the following generalization: Given an arbitrary non-negative weight function $w$ on the edges of $K_{n,n}$, consider its set of minimum weight perfect matchings. We give the real multilinear polynomial for the Boolean function which determines if a graph $G \subseteq K_{n,n}$ contains one of these minimum weight perfect matchings.
Utilizing Language Relatedness to improve Machine Translation: A Case Study on Languages of the Indian Subcontinent
Venue: arXiv cs.CL 2020
Year: 2020

In this work, we present an extensive study of statistical machine translation involving languages of the Indian subcontinent. These languages are related by genetic and contact relationships. We describe the similarities between Indic languages arising from these relationships. We explore how lexical and orthographic similarity among these languages can be utilized to improve translation quality between Indic languages when limited parallel corpora is available. We also explore how the structural correspondence between Indic languages can be utilized to re-use linguistic resources for English to Indic language translation. Our observations span 90 language pairs from 9 Indic languages and English. To the best of our knowledge, this is the first large-scale study specifically devoted to utilizing language relatedness to improve translation between related languages.