Reviewerly/exHarmony huggingface dataset:
Long cycle of random permutations with polynomially growing cycle weights

We study the asymptotic behavior of the long cycles of a random permutation of $n$ objects with respect to multiplicative measures with polynomial growing cycle weights. We show that the longest cycle and the length differences between the longest cycles converge, after suitable normalisation, in distribution to iid random variables $(Z_j)_{j\in\mathbb{N}}$ such that $\exp(-Z_j)$ is exponentially distributed. Our method is based on generating functions and the saddle point method.
History of Neutrino Magnetic Moment

In this historic Lomonosov conference on the occasion of 150 year anniversary of the Mendeleev's periodic table, I present the history of neutrino magnetic moment. It was first thought by Wolfgang Pauli and its magnitude was calculated during the gauge theory era.
Forgetting to learn logic programs

Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hypothesis space and the sample complexity of an ILP learner. We introduce Forgetgol, a multi-task ILP learner which supports forgetting. We experimentally compare Forgetgol against approaches that either remember or forget everything. Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks.
Selection-based Question Answering of an MOOC

e-Yantra Robotics Competition (eYRC) is a unique Robotics Competition hosted by IIT Bombay that is actually an Embedded Systems and Robotics MOOC. Registrations have been growing exponentially in each year from 4500 in 2012 to over 34000 in 2019. In this 5-month long competition students learn complex skills under severe time pressure and have access to a discussion forum to post doubts about the learning material. Responding to questions in real-time is a challenge for project staff. Here, we illustrate the advantage of Deep Learning for real-time question answering in the eYRC discussion forum. We illustrate the advantage of Transformer based contextual embedding mechanisms such as Bidirectional Encoder Representation From Transformer (BERT) over word embedding mechanisms such as Word2Vec. We propose a weighted similarity metric as a measure of matching and find it more reliable than Content-Content or Title-Title similarities alone. The automation of replying to questions has brought the turn around response time(TART) down from a minimum of 21 mins to a minimum of 0.3 secs.
Weighted Parsing for Grammar-Based Language Models over Multioperator Monoids

We develop a general framework for weighted parsing which is built on top of grammar-based language models and employs multioperator monoids as weight algebras. It generalizes previous work in that area (semiring parsing, weighted deductive parsing) and also covers applications outside the classical scope of parsing, e.g., algebraic dynamic programming. We show an algorithm for weighted parsing and, for a large class of weighted grammar-based language models, we prove formally that it terminates and is correct.
Curriculum Self-Paced Learning for Cross-Domain Object Detection

Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e.g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN. On top of combining Cycle-GAN transformations and self-paced learning in a smart and efficient way, in this paper, we propose a novel self-paced algorithm that learns from easy to hard. Our method is simple and effective, without any overhead during inference. It uses only pseudo-labels for samples taken from the target domain, i.e. the domain adaptation is unsupervised. We conduct experiments on four cross-domain benchmarks, showing better results than the state of the art. We also perform an ablation study demonstrating the utility of each component in our framework. Additionally, we study the applicability of our framework to other object detectors. Furthermore, we compare our difficulty measure with other measures from the related literature, proving that it yields superior results and that it correlates well with the performance metric.
Multiple Style-Transfer in Real-Time

Style transfer aims to combine the content of one image with the artistic style of another. It was discovered that lower levels of convolutional networks captured style information, while higher levels captures content information. The original style transfer formulation used a weighted combination of VGG-16 layer activations to achieve this goal. Later, this was accomplished in real-time using a feed-forward network to learn the optimal combination of style and content features from the respective images. The first aim of our project was to introduce a framework for capturing the style from several images at once. We propose a method that extends the original real-time style transfer formulation by combining the features of several style images. This method successfully captures color information from the separate style images. The other aim of our project was to improve the temporal style continuity from frame to frame. Accordingly, we have experimented with the temporal stability of the output images and discussed the various available techniques that could be employed as alternatives.
Privacy and Utility Preserving Sensor-Data Transformations

Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users' devices. These transformations aim at eliminating patterns that can be used for user re-identification or for inferring potentially sensitive activities, while introducing a minor utility loss for the target application (or task). We show that, on gesture and activity recognition tasks, we can prevent inference of potentially sensitive activities while keeping the reduction in recognition accuracy of non-sensitive activities to less than 5 percentage points. We also show that we can reduce the accuracy of user re-identification and of the potential inference of gender to the level of a random guess, while keeping the accuracy of activity recognition comparable to that obtained on the original data.
Hardness of Learning DNFs using Halfspaces

The problem of learning $t$-term DNF formulas (for $t = O(1)$) has been studied extensively in the PAC model since its introduction by Valiant (STOC 1984). A $t$-term DNF can be efficiently learnt using a $t$-term DNF only if $t = 1$ i.e., when it is an AND, while even weakly learning a $2$-term DNF using a constant term DNF was shown to be NP-hard by Khot and Saket (FOCS 2008). On the other hand, Feldman et al. (FOCS 2009) showed the hardness of weakly learning a noisy AND using a halfspace -- the latter being a generalization of an AND, while Khot and Saket (STOC 2008) showed that an intersection of two halfspaces is hard to weakly learn using any function of constantly many halfspaces. The question of whether a $2$-term DNF is efficiently learnable using $2$ or constantly many halfspaces remained open. In this work we answer this question in the negative by showing the hardness of weakly learning a $2$-term DNF as well as a noisy AND using any function of a constant number of halfspaces. In particular we prove the following. For any constants $\nu, \zeta > 0$ and $\ell \in \mathbb{N}$, given a distribution over point-value pairs $\{0,1\}^n \times \{0,1\}$, it is NP-hard to decide whether, YES Case: There is a $2$-term DNF that classifies all the points of the distribution, and an AND that classifies at least $1-\zeta$ fraction of the points correctly. NO Case: Any boolean function depending on at most $\ell$ halfspaces classifies at most $1/2 + \nu$ fraction of the points of the distribution correctly. Our result generalizes and strengthens the previous best results mentioned above on the hardness of learning a $2$-term DNF, learning an intersection of two halfspaces, and learning a noisy AND.
NISQ computing for decision making under uncertainty

Noisy Intermediate-Scale Quantum computers are expected to be available this year. It is proposed to exploit such a device for decision making under uncertainty. The probabilistic character of quantum mechanics reflects this uncertainty. Concomitantly, the noise may add to it. The approach is standard in the sense that Bayes decision rule is used to decide on the basis of maximum expected reward. The novelty is to model the various action profiles and the development of `nature' as unitary transformations on a set of qubits. Measurement eventually yields samples of classical binary random variables in which the reward function has to be expressed. In order to achieve sufficiently low variances for reliable decision making more runs of such a quantum algorithm are necessary. Some simple examples have been worked out to elucidate the idea. Here the calculations are still analytically feasible. Presently lacking an operating quantum device, the QX simulator of Quantum Inspire has been used to generate the necessary samples for comparison and demonstration. First obtained results are promising and point at a possible useful application for noisy intermediate-scale quantum computers.
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique

We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural networks for image classification and adversarial attacks to embed secret information within images. Thus, we use the attacks to embed an encoding of the message within images and the related deep neural network outputs to extract it. The key properties of adversarial attacks (invisible perturbations, nontransferability, resilience to tampering) offer guarantees regarding the confidentiality and the integrity of the hidden messages. We empirically evaluate adversarial embedding using more than 100 models and 1,000 messages. Our results confirm that our embedding passes unnoticed by both humans and steganalysis methods, while at the same time impedes illicit retrieval of the message (less than 13% recovery rate when the interceptor has some knowledge about our model), and is resilient to soft and (to some extent) aggressive image tampering (up to 100% recovery rate under jpeg compression). We further develop our method by proposing a new type of adversarial attack which improves the embedding density (amount of hidden information) of our method to up to 10 bits per pixel.
A classification of flag-transitive block designs

In this article, we investigate $2$-$(v,k,\lambda)$ designs with $\gcd(r,\lambda)=1$ admitting flag-transitive automorphism groups $G$. We prove that if $G$ is an almost simple group, then such a design belongs to one of the seven infinite families of $2$-designs or it is one of the eleven well-known examples. We describe all these examples of designs. We, in particular, prove that if $\mathcal{D}$ is a symmetric $(v,k,\lambda)$ design with $\gcd(k,\lambda)=1$ admitting a flag-transitive automorphism group $G$, then either $G\leq A\Gamma L_{1}(q)$ for some odd prime power $q$, or $\mathcal{D}$ is a projective space or the unique Hadamard design with parameters $(11,5,2)$.
Naive cubical type theory

This paper proposes a way of doing type theory informally, assuming a cubical style of reasoning. It can thus be viewed as a first step toward a cubical alternative to the program of informalization of type theory carried out in the homotopy type theory book for dependent type theory augmented with axioms for univalence and higher inductive types. We adopt a cartesian cubical type theory proposed by Angiuli, Brunerie, Coquand, Favonia, Harper, and Licata as the implicit foundation, confining our presentation to elementary results such as function extensionality, the derivation of weak connections and path induction, the groupoid structure of types, and the Eckmman-Hilton duality.
A Generic Metaheuristic Approach to Sequential Security Games

The paper introduces a generic approach to solving Sequential Security Games (SGs) which utilizes Evolutionary Algorithms. Formulation of the method (named EASG) is general and largely game-independent, which allows for its application to a wide range of SGs with just little adjustments addressing game specificity. Comprehensive experiments performed on 3 different types of games (with 300 instances in total) demonstrate robustness and stability of EASG, manifested by repeatable achieving optimal or near-optimal solutions in the vast majority of the cases. The main advantage of EASG is time efficiency. The method scales visibly better than state-of-the-art approaches and consequently can be applied to SG instances which are beyond capabilities of the existing methods. Furthermore, due to anytime characteristics, EASG is very well suited for time-critical applications, as the method can be terminated at any moment and still provide a reasonably good solution - the best one found so far.
Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection

Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances. MIL is gaining traction for learning from weak labels but has not been widely applied to 3D medical imaging. MIL is well-suited to clinical CT acquisitions since (1) the highly anisotropic voxels hinder application of traditional 3D networks and (2) patch-based networks have limited ability to learn whole volume labels. In this work, we apply MIL with a deep convolutional neural network to identify whether clinical CT head image volumes possess one or more large hemorrhages (> 20cm$^3$), resulting in a learned 2D model without the need for 2D slice annotations. Individual image volumes are considered separate bags, and the slices in each volume are instances. Such a framework sets the stage for incorporating information obtained in clinical reports to help train a 2D segmentation approach. Within this context, we evaluate the data requirements to enable generalization of MIL by varying the amount of training data. Our results show that a training size of at least 400 patient image volumes was needed to achieve accurate per-slice hemorrhage detection. Over a five-fold cross-validation, the leading model, which made use of the maximum number of training volumes, had an average true positive rate of 98.10%, an average true negative rate of 99.36%, and an average precision of 0.9698. The models have been made available along with source code to enabled continued exploration and adaption of MIL in CT neuroimaging.
Recognizing Facial Expressions of Occluded Faces using Convolutional Neural Networks

In this paper, we present an approach based on convolutional neural networks (CNNs) for facial expression recognition in a difficult setting with severe occlusions. More specifically, our task is to recognize the facial expression of a person wearing a virtual reality (VR) headset which essentially occludes the upper part of the face. In order to accurately train neural networks for this setting, in which faces are severely occluded, we modify the training examples by intentionally occluding the upper half of the face. This forces the neural networks to focus on the lower part of the face and to obtain better accuracy rates than models trained on the entire faces. Our empirical results on two benchmark data sets, FER+ and AffectNet, show that our CNN models' predictions on lower-half faces are up to 13% higher than the baseline CNN models trained on entire faces, proving their suitability for the VR setting. Furthermore, our models' predictions on lower-half faces are no more than 10% under the baseline models' predictions on full faces, proving that there are enough clues in the lower part of the face to accurately predict facial expressions.
A Capsule Network-based Model for Learning Node Embeddings

In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leveraging a network of two capsule layers. Caps2NE induces a routing process to aggregate feature vectors of context neighbors of a given target node at the first capsule layer, then feed these features into the second capsule layer to infer a plausible embedding for the target node. Experimental results show that our proposed Caps2NE obtains state-of-the-art performances on benchmark datasets for the node classification task. Our code is available at: \url{https://github.com/daiquocnguyen/Caps2NE}.
Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

In this work, we investigate the application of trainable and spectrally initializable matrix transformations on the feature maps produced by convolution operations. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers, ImageNet) images to historical documents (CB55) and handwriting recognition (GPDS). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases by an average of 2.2 across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.
Robustness to Capitalization Errors in Named Entity Recognition

Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such noise. Existing methods to improve robustness to the noise completely discard given orthographic information, mwhich significantly degrades their performance on well-formed text. We propose a simple alternative approach based on data augmentation, which allows the model to \emph{learn} to utilize or ignore orthographic information depending on its usefulness in the context. It achieves competitive robustness to capitalization errors while making negligible compromise to its performance on well-formed text and significantly improving generalization power on noisy user-generated text. Our experiments clearly and consistently validate our claim across different types of machine learning models, languages, and dataset sizes.
The phonetic bases of vocal expressed emotion: natural versus acted

Can vocal emotions be emulated? This question has been a recurrent concern of the speech community, and has also been vigorously investigated. It has been fueled further by its link to the issue of validity of acted emotion databases. Much of the speech and vocal emotion research has relied on acted emotion databases as valid proxies for studying natural emotions. To create models that generalize to natural settings, it is crucial to work with valid prototypes -- ones that can be assumed to reliably represent natural emotions. More concretely, it is important to study emulated emotions against natural emotions in terms of their physiological, and psychological concomitants. In this paper, we present an on-scale systematic study of the differences between natural and acted vocal emotions. We use a self-attention based emotion classification model to understand the phonetic bases of emotions by discovering the most 'attended' phonemes for each class of emotions. We then compare these attended-phonemes in their importance and distribution across acted and natural classes. Our tests show significant differences in the manner and choice of phonemes in acted and natural speech, concluding moderate to low validity and value in using acted speech databases for emotion classification tasks.
Authentication of Smartphone Users Using Behavioral Biometrics

Smartphones and tablets have become ubiquitous in our daily lives. Smartphones, in particular, have become more than personal assistants. These devices have provided new avenues for consumers to play, work, and socialize whenever and wherever they want. Smartphones are small in size, so they are easy to handle and to stow and carry in users' pockets or purses. However, mobile devices are also susceptible to various problems. One of the greatest concerns is the possibility of breach in security and privacy if the device is seized by an outside party. It is possible that threats can come from friends as well as strangers. Due to the size of smart devices, they can be easily lost and may expose details of users' private lives. In addition, this might enable pervasive observation or imitation of one's movements and activities, such as sending messages to contacts, accessing private communication, shopping with a credit card, and relaying information about where one has been. This paper highlights the potential risks that occur when smartphones are stolen or seized, discusses the concept of continuous authentication, and analyzes current approaches and mechanisms of behavioral biometrics with respect to methodology, associated datasets and evaluation approaches.
Classification on the Computational Complexity of Spin Models

In this note, we provide a unifying framework to investigate the computational complexity of classical spin models and give the full classification on spin models in terms of system dimensions, randomness, external magnetic fields and types of spin coupling. We further discuss about the implications of NP-complete Hamiltonian models in physics and the fundamental limitations of all numerical methods imposed by such models. We conclude by a brief discussion on the picture when quantum computation and quantum complexity theory are included.
How to Evaluate Word Representations of Informal Domain?

Diverse word representations have surged in most state-of-the-art natural language processing (NLP) applications. Nevertheless, how to efficiently evaluate such word embeddings in the informal domain such as Twitter or forums, remains an ongoing challenge due to the lack of sufficient evaluation dataset. We derived a large list of variant spelling pairs from UrbanDictionary with the automatic approaches of weakly-supervised pattern-based bootstrapping and self-training linear-chain conditional random field (CRF). With these extracted relation pairs we promote the odds of eliding the text normalization procedure of traditional NLP pipelines and directly adopting representations of non-standard words in the informal domain. Our code is available.