Miracle-dz/UniFAR-FTUs huggingface dataset:
Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study

We prove that idealised discriminative Bayesian neural networks, capturing perfect epistemic uncertainty, cannot have adversarial examples: Techniques for crafting adversarial examples will necessarily fail to generate perturbed images which fool the classifier. This suggests why MC dropout-based techniques have been observed to be fairly robust to adversarial examples. We support our claims mathematically and empirically. We experiment with HMC on synthetic data derived from MNIST for which we know the ground truth image density, showing that near-perfect epistemic uncertainty correlates to density under image manifold, and that adversarial images lie off the manifold. Using our new-found insights we suggest a new attack for MC dropout-based models by looking for imperfections in uncertainty estimation, and also suggest a mitigation. Lastly, we demonstrate our mitigation on a cats-vs-dogs image classification task with a VGG13 variant.
PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection

Context plays an important role in many computer vision tasks. Previous models usually construct contextual information from the whole context region. However, not all context locations are helpful and some of them may be detrimental to the final task. To solve this problem, we propose a novel pixel-wise contextual attention network, i.e., the PiCANet, to learn to selectively attend to informative context locations for each pixel. Specifically, it can generate an attention map over the context region for each pixel, where each attention weight corresponds to the contextual relevance of each context location w.r. t. the specified pixel location. Thus, an attended contextual feature can be constructed by using the attention map to aggregate the contextual features. We formulate PiCANet in a global form and a local form to attend to global contexts and local contexts, respectively. Our designs for the two forms are both fully differentiable. Thus they can be embedded into any CNN architectures for various computer vision tasks in an end-to-end manner. We take saliency detection as an example application to demonstrate the effectiveness of the proposed PiCANets. Specifically, we embed global and local PiCANets into an encoder-decoder Convnet hierarchically. Thorough analyses indicate that the global PiCANet helps to construct global contrast while the local PiCANets help to enhance the feature maps to be more homogenous, thus making saliency detection results more accurate and uniform. As a result, our proposed saliency model achieves state-of-the-art results on 4 benchmark datasets.
Knowledge Aggregation via Epsilon Model Spaces

In many practical applications, machine learning is divided over multiple agents, where each agent learns a different task and/or learns from a different dataset. We present Epsilon Model Spaces (EMS), a framework for learning a global model by aggregating local learnings performed by each agent. Our approach forgoes sharing of data between agents, makes no assumptions on the distribution of data across agents, and requires minimal communication between agents. We empirically validate our techniques on MNIST experiments and discuss how EMS can generalize to a wide range of problem settings, including federated averaging and catastrophic forgetting. We believe our framework to be among the first to lay out a general methodology for "combining" distinct models.
Number Sequence Prediction Problems and Computational Powers of Neural Network Models

Inspired by number series tests to measure human intelligence, we suggest number sequence prediction tasks to assess neural network models' computational powers for solving algorithmic problems. We define complexity and difficulty of a number sequence prediction task with the structure of the smallest automation that can generate the sequence. We suggest two types of number sequence prediction problems: the number-level and the digit-level problems. The number-level problems format sequences as 2-dimensional grids of digits, and the digit-level problem provides a single digit input per a time step, hence solving this problem is equivalent to modeling a sequential state automation. The complexity of a number-level sequence problem can be defined with the depth of an equivalent combinatorial logic. Experimental results with CNN models suggest that they are capable of learning the compound operations of the number-level sequence generation rules but the depths of the compound operations are limited. For the digit-level problems, GRU and LSTM models can solve the problems with complexity of finite state automations, but they cannot solve the problems with complexity of pushdown automations or Turing machines. The results show that our number sequence prediction problems effectively evaluate machine learning models' computational capabilities.
Automaton-Based Criteria for Membership in CTL

Computation Tree Logic (CTL) is widely used in formal verification, however, unlike linear temporal logic (LTL), its connection to automata over words and trees is not yet fully understood. Moreover, the long sought connection between LTL and CTL is still missing; It is not known whether their common fragment is decidable, and there are very limited necessary conditions and sufficient conditions for checking whether an LTL formula is definable in CTL. We provide sufficient conditions and necessary conditions for LTL formulas and ω-regular languages to be expressible in CTL. The conditions are automaton-based; We first tighten the automaton characterization of CTL to the class of Hesitant Alternating Linear Tree Automata (HLT), and then conduct the conditions by relating between the cycles of a word automaton for a given ω-regular language and the cycles of a potentially equivalent HLT. The new conditions allow to simplify proofs of known results on languages that are definable, or not, in CTL, as well as to prove new results. Among which, they allow us to refute a conjecture by Clarke and Draghicescu from 1988, regarding a condition for a CTL formula to be expressible in CTL.
Group Affect Prediction Using Emotion Heatmaps and Scene Information

In this paper, we describe our work on emotion detection for a group of people in an image. We use an ensemble of a Convolutional Neutral Network (CNN) trained on the emotion heatmaps and a fine-tuned CNN which had been trained on ImageNet data. This work was done for Emotion Recognition in in the Wild (EmotiW) challenge, 2017. Our best submission achieved a test accuracy of 70.64% which was 17.02% above the baseline accuracy provided by the organizers.
Publishing a Quality Context-aware Annotated Corpus and Lexicon for Harassment Research

Having a quality annotated corpus is essential especially for applied research. Despite the recent focus of Web science community on researching about cyberbullying, the community dose not still have standard benchmarks. In this paper, we publish first, a quality annotated corpus and second, an offensive words lexicon capturing different types type of harassment as (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment.We crawled data from Twitter using our offensive lexicon. Then relied on the human judge to annotate the collected tweets w.r. t. the contextual types because using offensive words is not sufficient to reliably detect harassment. Our corpus consists of 25,000 annotated tweets in five contextual types. We are pleased to share this novel annotated corpus and the lexicon with the research community. The instruction to acquire the corpus has been published on the Git repository.
Exploring Neural Architectures for Multilingual Customer Feedback Analysis: IJCNLP 2017 Shared Task

This paper describes our systems for IJCNLP 2017 Shared Task on Customer Feedback Analysis. We experimented with simple neural architectures that gave competitive performance on certain tasks. This includes shallow CNN and Bi-Directional LSTM architectures with Facebook's Fasttext as a baseline model. Our best performing model was in the Top 5 systems using the Exact-Accuracy and Micro-Average-F1 metrics for the Spanish (85.28% for both) and French (70% and 73.17% respectively) task, and outperformed all the other models on comment (87.28%) and meaningless (51.85%) tags using Micro Average F1 by Tags metric for the French task.
EmptyHeaded: Boolean Algebra Based Graph Processing

We present a graph pattern engine, EmptyHeaded, that uses recent algorithmic advances in join processing to compile patterns into Boolean algebra operations that exploit SIMD parallelism. The EmptyHeaded engine demonstrates that treating graph patterns as a general join processing problem can compete with and often outperform both specialized approaches and existing OLAP systems on graph queries. The core Boolean algebra operation performed in EmptyHeaded is set intersection. Extracting SIMD parallelism during set intersections on graph data is challenging because graph data can be skewed in several dierent ways. Our contributions are a demonstration of this new type of engine with Boolean algebra at its core, an exploration of set intersection representations and algorithms for set intersections that are optimized for skew. We demonstrate that EmptyHeaded outperforms specialized graph engines by over an order of magnitude and relational systems by over two orders of magnitude. Our results suggest that this new style of engine is a promising new direction for future graph engines and accelerators.
A Model for Performance Management and Estimation

Traditional cost estimation models in software engineering are based on the concept of productivity defined as the ratio of output to input; for instance, detailed software estimation models, such as COCOMO, can take multiple factors into account, but their multipliers lead to a single perspective based on the productivity concept. A less explored relationship in software engineering is the one between productivity and performance. This paper presents some classic concepts on the multidimensionality of performance, and proposes some suggestions to implement multidimensional performance models in software engineering based on certain fundamental concepts from geometry, that is, the QEST/LIME family of models.
Clustering of Naturalistic Driving Encounters Using Unsupervised Learning

Deep understanding of driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with surrounding vehicles engaged. This paper develops an unsupervised classifier to group naturalistic driving encounters into several distinguishable clusters by combining an auto-encoder with a k-means clustering (AE-kMC). In order to show the effectiveness of our developed classifier, the data of 10000 naturalistic driving encounters collected by the University of Michigan, Ann Arbor in the past five years were tested using the two proposed methods above. We compare our developed method with the k-means clustering methods. The comparison experiment results demonstrate that our developed AE-kMC outperforms the k-means clustering method.
Generative Adversarial Autoencoder Networks

We introduce an effective model to overcome the problem of mode collapse when training Generative Adversarial Networks (GAN). Firstly, we propose a new generator objective that finds it better to tackle mode collapse. And, we apply an independent Autoencoders (AE) to constrain the generator and consider its reconstructed samples as "real" samples to slow down the convergence of discriminator that enables to reduce the gradient vanishing problem and stabilize the model. Secondly, from mappings between latent and data spaces provided by AE, we further regularize AE by the relative distance between the latent and data samples to explicitly prevent the generator falling into mode collapse setting. This idea comes when we find a new way to visualize the mode collapse on MNIST dataset. To the best of our knowledge, our method is the first to propose and apply successfully the relative distance of latent and data samples for stabilizing GAN. Thirdly, our proposed model, namely Generative Adversarial Autoencoder Networks (GAAN), is stable and has suffered from neither gradient vanishing nor mode collapse issues, as empirically demonstrated on synthetic, MNIST, MNIST-1K, CelebA and CIFAR-10 datasets. Experimental results show that our method can approximate well multi-modal distribution and achieve better results than state-of-the-art methods on these benchmark datasets. Our model implementation is published here: this https URL
The FactChecker: Verifying Text Summaries of Relational Data Sets

We present a novel natural language query interface, the FactChecker, aimed at text summaries of relational data sets. The tool focuses on natural language claims that translate into an SQL query and a claimed query result. Similar in spirit to a spell checker, the FactChecker marks up text passages that seem to be inconsistent with the actual data. At the heart of the system is a probabilistic model that reasons about the input document in a holistic fashion. Based on claim keywords and the document structure, it maps each text claim to a probability distribution over associated query translations. By efficiently executing tens to hundreds of thousands of candidate translations for a typical input document, the system maps text claims to correctness probabilities. This process becomes practical via a specialized processing backend, avoiding redundant work via query merging and result caching. Verification is an interactive process in which users are shown tentative results, enabling them to take corrective actions if necessary. Our system was tested on a set of 53 public articles containing 392 claims. Our test cases include articles from major newspapers, summaries of survey results, and Wikipedia articles. Our tool revealed erroneous claims in roughly a third of test cases. A detailed user study shows that users using our tool are in average six times faster at checking text summaries, compared to generic SQL interfaces. In fully automated verification, our tool achieves significantly higher recall and precision than baselines from the areas of natural language query interfaces and fact checking.
A Simple Discrete-Time Survival Model for Neural Networks

There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation of predicted survival curves. In this paper, we describe a discrete-time survival model that is designed to be used with neural networks. The model is trained with the maximum likelihood method using minibatch stochastic gradient descent (SGD). The use of SGD enables rapid training speed. The model is flexible, so that the baseline hazard rate and the effect of the input data can vary with follow-up time. It has been implemented in the Keras deep learning framework, and source code for the model and several examples is available online. We demonstrated the high performance of the model by using it as part of a convolutional neural network to predict survival for over 10,000 patients with metastatic cancer, using the full text of 1,137,317 provider notes. The model's C-index on the validation set was 0.71, which was superior to a linear baseline model (C-index 0.69).
Data and Task Based Effectiveness of Basic Visualizations

Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks and data attribute types. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five visualization types --- Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart --- across ten common data analysis tasks and three data attribute types using two real world datasets. We found the effectiveness of these visualization types significantly varies across task and data attribute types, suggesting that visualization design would benefit from considering context dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different task and data contexts.
Distributed Optimal Power Flow using ALADIN

The present paper applies the recently proposed Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method to solve non-convex AC Optimal Power Flow Problems (OPF) in a distributed fashion. In contrast to the often used Alternaring Direction of Multipliers Method (ADMM), ALADIN guarantees locally quadratic convergence for AC-OPF. Numerical results for IEEE 5-300 bus test cases indicate that ALADIN is able to outperform ADMM and to reduce the number of iterations by at least one order of magnitude. We compare ALADIN to numerical results for ADMM documented in the literature. The improved convergence speed comes at cost of increasing the communication effort per iteration. Therefore, we also propose a variant of ALADIN that uses inexact Hessians to reduce communication. Finally, we provide a detailed comparison of both variants of ALADIN to ADMM from an algorithmic and communication perspective.
A Single-shot Camera-Projector Calibration System For Imperfect Planar Targets

Existing camera-projector calibration methods typically warp feature points from a camera image to a projector image using estimated homographies, and often suffer from errors in camera parameters and noise due to imperfect planarity of the calibration target. In this paper we propose a simple yet robust solution that explicitly deals with these challenges. Following the structured light (SL) camera-project calibration framework, a carefully designed correspondence algorithm is built on top of the De Bruijn patterns. Such correspondence is then used for initial camera-projector calibration. Then, to gain more robustness against noises, especially those from an imperfect planar calibration board, a novel bundle adjustment algorithm is developed to jointly optimize the estimated camera and projector models. Aside from the robustness, our solution requires only one shot of SL pattern for each calibration board pose, which is much more convenient than multi-shot solutions in practice. Data validations are conducted on both synthetic and real datasets, and our method shows clear advantages over existing methods in all experiments.
Implementing a fog/cloud architecture for supporting stream data management

The Internet of Mobile Things (IoMT) requires support for a data lifecycle process ranging from sorting, cleaning and monitoring data streams to more complex tasks such as querying, aggregation, and analytics. Current solutions for stream data management in IoMT have been focused on partial aspects of a data lifecycle process, with special emphasis on sensor networks. This paper aims to address this problem by developing an offline and real-time data lifecycle process that incorporates a layered, data-flow centric, and an edge/cloud computing approach that is needed for handling heterogeneous, streaming and geographicallydispersed IoMT data streams. We propose an end to end architecture to support an instant intra-layer communication that establishes a stream data flow in real-time to respond to immediate data lifecycle tasks at the edge layer of the system. Our architecture also provides offline functionalities for later analytics and visualization of IoMT data streams at the core layer of the system. Communication and process are thus the defining factors in the design of our stream data management solution for IoMT. We describe and evaluate our prototype implementation using real-time transit data feeds and a commercial edge-based platform. Preliminary results are showing the advantages of running data lifecycle tasks at the edge of the network for reducing the volume of data streams that are redundant and should not be transported to the cloud.
Tree-depth, quantifier elimination, and quantifier rank

For a class K of finite graphs we consider the following three statements. (i) K has bounded tree-depth. (ii) First-order logic FO has an effective generalized quantifier elimination on K. (iii) The parameterized model checking for FO on K is in para-AC0. We prove that (i) ⟹ (ii) and (ii) ⟺ (iii). All three statements are equivalent if K is closed under taking subgraphs, but not in general. By a result due to Elberfeld et al. [12] monadic second-order logic MSO and FO have the same expressive power on every class of graphs of bounded tree-depth. Hence the implication (i) ⟹ (iii) holds for MSO, too; it is the analogue of Courcelle's Theorem for tree-depth (instead of tree-width) and para-AC0 (instead of FPT). In [13] it was already shown that the model-checking for a fixed MSO-property on a class of graphs of bounded tree-depth is in AC0.
Query Focused Variable Centroid Vectors for Passage Re-ranking in Semantic Search

In this paper, we propose a new approach for passage re-ranking. We show that variable (i.e. non-static) centroid vectors for passages, created based on the given query, significantly improves passage re-ranking results compared to that obtained using static centroid vectors. We also show that the results are comparable to RWMD-Q, an existing (non-centroid based unsupervised) state of the art. The experiments reported are conducted on two different datasets in both neural and co-occurrence based distributional semantics settings.
Commonsense LocatedNear Relation Extraction

LocatedNear relation describes two typically co-located objects, which is a type of useful commonsense knowledge for computer vision, natural language understanding, machine comprehension, etc. We propose to automatically extract such relationship through a sentence-level classifier and aggregating the scores of entity pairs detected from a large number of sentences. To enable the research of these tasks, we release two benchmark datasets, one containing 5,000 sentences annotated with whether a mentioned entity pair has LocatedNear relation in the given sentence or not; the other containing 500 pairs of physical objects and whether they are commonly located nearby. We also propose some baseline methods for the tasks and compare the results with a state-of-the-art general-purpose relation classifier.
Arbitrarily-Oriented Text Recognition

Recognizing text from natural images is still a hot research topic in computer vision due to its various applications. Despite the enduring research of several decades on optical character recognition (OCR), recognizing texts from natural images is still a challenging task. This is because scene texts are often in irregular arrangements (curved, arbitrarily-oriented or seriously distorted), which have not yet been well addressed in the literature. Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts. In this paper, we develop the arbitrary orientation network (AON) to capture the deep features of irregular texts (e.g. arbitrarily-oriented, perspective or curved), which are combined into an attention-based decoder to generate character sequence. The whole network can be trained end-to-end by using only images and word-level labels. Extensive experiments on various benchmarks, including the CUTE80, SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed AON-based method substantially outperforms the existing methods.