OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging Rationales to Improve Human Task Performance
Venue: arXiv cs.HC 2020
Year: 2020

Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans. In response to the proliferation of such models, the field of Explainable AI (XAI) has sought to develop techniques that enhance the transparency and interpretability of machine learning methods. In this work, we consider a question not previously explored within the XAI and ML communities: Given a computational system whose performance exceeds that of its human user, can explainable AI capabilities be leveraged to improve the performance of the human? We study this question in the context of the game of Chess, for which computational game engines that surpass the performance of the average player are widely available. We introduce the Rationale-Generating Algorithm, an automated technique for generating rationales for utility-based computational methods, which we evaluate with a multi-day user study against two baselines. The results show that our approach produces rationales that lead to statistically significant improvement in human task performance, demonstrating that rationales automatically generated from an AI's internal task model can be used not only to explain what the system is doing, but also to instruct the user and ultimately improve their task performance.
Trust dynamics and user attitudes on recommendation errors: preliminary results
Venue: arXiv cs.SI 2020
Year: 2020

Artificial Intelligence based systems may be used as digital nudging techniques that can steer or coerce users to make decisions not always aligned with their true interests. When such systems properly address the issues of Fairness, Accountability, Transparency, and Ethics, then the trust of the user in the system would just depend on the system's output. The aim of this paper is to propose a model for exploring how good and bad recommendations affect the overall trust in an idealized recommender system that issues recommendations over a resource with limited capacity. The impact of different users attitudes on trust dynamics is also considered. Using simulations, we ran a large set of experiments that allowed to observe that: 1) under certain circumstances, all the users ended accepting the recommendations; and 2) the user attitude (controlled by a single parameter balancing the gain/loss of trust after a good/bad recommendation) has a great impact in the trust dynamics.
Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural networks to data with many irrelevant variables often leads to overfitting. In an attempt to circumvent this issue, a prior knowledge pertaining to what features are relevant and their possible feature interactions can be encoded into these networks. In this work, we use decision trees to capture such relevant features and their interactions and define a mapping to encode extracted relationships into a neural network. This addresses the initialization related concern of fully connected neural networks. At the same time through feature selection it enables learning of compact representations compared to state of the art tree-based approaches. Empirical evaluations and simulation studies show the superiority of such an approach over fully connected neural networks and tree-based approaches
Incremental Fast Subclass Discriminant Analysis
Venue: arXiv cs.LG 2020
Year: 2020

This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kernelized variant. Extensive experiments on eight image datasets with different incremental batch sizes show the superiority of the proposed approach in terms of training time and accuracy being equal or close to fastSDA solution and outperforming other methods.
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification
Venue: arXiv cs.CV 2020
Year: 2020

A big challenge of person re-identification (Re-ID) using a multi-branch network architecture is to learn diverse features from the ID-labeled dataset. The 2-branch Batch DropBlock (BDB) network was recently proposed for achieving diversity between the global branch and the feature-dropping branch. In this paper, we propose to move the dropping operation from the intermediate feature layer towards the input (image dropping). Since it may drop a large portion of input images, this makes the training hard to converge. Hence, we propose a novel double-batch-split co-training approach for remedying this problem. In particular, we show that the feature diversity can be well achieved with the use of multiple dropping branches by setting individual dropping ratio for each branch. Empirical evidence demonstrates that the proposed method performs superior to BDB on popular person Re-ID datasets, including Market-1501, DukeMTMC-reID and CUHK03 and the use of more dropping branches can further boost the performance.
A 3D Beamforming Scheme Based on The Spatial Distribution of User Locations
Venue: arXiv cs.NI 2020
Year: 2020

Multi-antenna technologies such as massive Multiple-Input Multiple-Output (massive MIMO) and beamforming are key features to enhance performance, in terms of capacity and coverage, by using a large number of antennas intelligently. With the upcoming 5G New Radio (NR), FD-MIMO (Full Dimension MIMO) will play a major key role. FD-MIMO consists in arranging a large number of antennas in a 2D array, which enables to use 3D beamforming i.e., beamforming in both horizontal and vertical dimensions. The present paper provides a 3D beamforming model where beam steering depends on the random spatial distribution of users. We attempt to derive some analytical results regarding the probability distribution of antenna beamforming radiation pattern. Also, through system level simulations, we show how 3D beamforming can reduce interference impact, compared to the traditional 2D beamforming, and enhances system performance in terms of the coverage probability and users throughput.
Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition
Venue: arXiv cs.SI 2020
Year: 2020

We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.
Reaching, Grasping and Re-grasping: Learning Multimode Grasping Skills
Venue: arXiv cs.RO 2020
Year: 2020

The ability to adapt to uncertainties, recover from failures, and coordinate between hand and fingers are essential sensorimotor skills for fully autonomous robotic grasping. In this paper, we aim to study a unified feedback control policy for generating the finger actions and the motion of hand to accomplish seamlessly coordinated tasks of reaching, grasping and re-grasping. We proposed a set of quantified metrics for task-orientated rewards to guide the policy exploration, and we analyzed and demonstrated the effectiveness of each reward term. To acquire a robust re-grasping motion, we deployed different initial states in training to experience failures that the robot would encounter during grasping due to inaccurate perception or disturbances. The performance of learned policy is evaluated on three different tasks: grasping a static target, grasping a dynamic target, and re-grasping. The quality of learned grasping policy was evaluated based on success rates in different scenarios and the recovery time from failures. The results indicate that the learned policy is able to achieve stable grasps of a static or moving object. Moreover, the policy can adapt to new environmental changes on the fly and execute collision-free re-grasp after a failed attempt within a short recovery time even in difficult configurations.
Modeling Programs Hierarchically with Stack-Augmented LSTM
Venue: arXiv cs.SE 2020
Year: 2020

Programming language modeling has attracted extensive attention in recent years, and it plays an essential role in program processing fields. Statistical language models, which are initially designed for natural languages, have been generally used for modeling programming languages. However, different from natural languages, programming languages contain explicit and hierarchical structure that is hard to learn by traditional statistical language models. To address this challenge, we propose a novel Stack-Augmented LSTM neural network for programming language modeling. Adding a stack memory component into the LSTM network enables our model to capture the hierarchical information of programs through the PUSH and POP operations, which further allows our model capturing the long-term dependency in the programs. We evaluate the proposed model on three program analysis tasks, i.e., code completion, program classification, and code summarization. Evaluation results show that our proposed model outperforms baseline models in all the three tasks, indicating that by capturing the structural information of programs with a stack, our proposed model can represent programs more precisely.
Progress Report on Numerical Modeling of a Prototype Fuel Cell
Venue: arXiv cs.CE 2020
Year: 2020

Progress on the numerical modeling of a prototype fuel cell is reported. Some known limitations of the previously published Alpha model are addressed, and the numerical uncertainty due to discretization of the improved model, Beta, was estimated. In Part 1, the Beta model is compared to Alpha, where significant albeit small differences are seen. Shortcomings of the improved model are discussed, paving the way forward, while a discrepancy with previous results is addressed, further suggesting the use of the Darcy-Brinkman over Stokes-Darcy formulation for free and porous media flow. Furthermore, a parametric study is carried out, constraining plausible values of the reaction rate constants identifying additional opportunities for validation. In Part 2, a mesh convergence study is carried out to estimate the discretization error of Beta model. A reduced, proxy geometry and two extrapolation schemes are used to estimate the exact solution, which is then used to estimate the model's uncertainty through the Grid Convergence Index framework. Error estimates are on average $\sim 10\%$ for the flow rate range simulated, larger than experimental ones available. Results suggest a difficulty in achieving mesh convergence in fuel cell-like models, even in simpler cases. Caution is thus suggested during validation or when devising predictions from numerical models. Finally, given the uncertainties in the numerical data and the available experimental data, the results lack validation power, highlighting the need for additional experimental data and improved precision for the numerical data.
Diffusion in arrays of obstacles: beyond homogenisation
Venue: arXiv cs.CE 2020
Year: 2020

We revisit the classical problem of diffusion of a scalar (or heat) released in a two-dimensional medium with an embedded periodic array of impermeable obstacles such as perforations. Homogenisation theory provides a coarse-grained description of the scalar at large times and predicts that it diffuses with a certain effective diffusivity, so the concentration is approximately Gaussian. We improve on this by developing a large-deviation approximation which also captures the non-Gaussian tails of the concentration through a rate function obtained by solving a family of eigenvalue problems. We focus on cylindrical obstacles and on the dense limit, when the obstacles occupy a large area fraction and non-Gaussianity is most marked. We derive an asymptotic approximation for the rate function in this limit, valid uniformly over a wide range of distances. We use finite-element implementations to solve the eigenvalue problems yielding the rate function for arbitrary obstacle area fractions and an elliptic boundary-value problem arising in the asymptotics calculation. Comparison between numerical results and asymptotic predictions confirm the validity of the latter.
AnySeq: A High Performance Sequence Alignment Library based on Partial Evaluation
Venue: arXiv cs.DC 2020
Year: 2020

Sequence alignments are fundamental to bioinformatics which has resulted in a variety of optimized implementations. Unfortunately, the vast majority of them are hand-tuned and specific to certain architectures and execution models. This not only makes them challenging to understand and extend, but also difficult to port to other platforms. We present AnySeq - a novel library for computing different types of pairwise alignments of DNA sequences. Our approach combines high performance with an intuitively understandable implementation, which is achieved through the concept of partial evaluation. Using the AnyDSL compiler framework, AnySeq enables the compilation of algorithmic variants that are highly optimized for specific usage scenarios and hardware targets with a single, uniform codebase. The resulting domain-specific library thus allows the variation of alignment parameters (such as alignment type, scoring scheme, and traceback vs.~plain score) by simple function composition rather than metaprogramming techniques which are often hard to understand. Our implementation supports multithreading and SIMD vectorization on CPUs, CUDA-enabled GPUs, and FPGAs. AnySeq is at most 7% slower and in many cases faster (up to 12%) than state-of-the art manually optimized alignment libraries on CPUs (SeqAn) and on GPUs (NVBio).
Social Internet of Things: Architectural Approaches and Challenges
Venue: arXiv cs.SE 2020
Year: 2020

Social Internet of Things (SIoT) takes a step forward over the traditional Internet of Things (IoT), introducing a new paradigm that combines the concepts of social networks with the IoT, to obtain the benefits of both worlds, as in the case of the Social Internet of Vehicles. With the emergence of the Social Internet of Things, new challenges also arise that need to be analyzed in depth. In this article, the key challenges around the software architecture of the various SIoT system described in the literature are analyzed. One of the conclusions is that SIoT is still at an early stage of development, and therefore, SIoT systems architecture will be concerned by this fact. Challenging quality attributes specific for SIoT include scalability, navigability and trust
Local WL Invariance and Hidden Shades of Regularity
Venue: arXiv cs.CC 2020
Year: 2020

The $k$-dimensional Weisfeiler-Leman algorithm is a powerful tool in graph isomorphism testing. For an input graph $G$, the algorithm determines a canonical coloring of $s$-tuples of vertices of $G$ for each $s$ between 1 and $k$. We say that a numerical parameter of $s$-tuples is $k$-WL-invariant if it is determined by the tuple color. As an application of Dvořák's result on $k$-WL-invariance of homomorphism counts, we spot some non-obvious regularity properties of strongly regular graphs and related graph families. For example, if $G$ is a strongly regular graph, then the number of paths of length 6 between vertices $x$ and $y$ in $G$ depends only on whether or not $x$ and $y$ are adjacent (and the length 6 is here optimal). Or, the number of cycles of length 7 passing through a vertex $x$ in $G$ is the same for every $x$ (where the length 7 is also optimal).
Constructing a Highlight Classifier with an Attention Based LSTM Neural Network
Venue: arXiv cs.CL 2020
Year: 2020

Data is being produced in larger quantities than ever before in human history. It's only natural to expect a rise in demand for technology that aids humans in sifting through and analyzing this inexhaustible supply of information. This need exists in the market research industry, where large amounts of consumer research data is collected through video recordings. At present, the standard method for analyzing video data is human labor. Market researchers manually review the vast majority of consumer research video in order to identify relevant portions - highlights. The industry state of the art turnaround ratio is 2.2 - for every hour of video content 2.2 hours of manpower are required. In this study we present a novel approach for NLP-based highlight identification and extraction based on a supervised learning model that aides market researchers in sifting through their data. Our approach hinges on a manually curated user-generated highlight clips constructed from long and short-form video data. The problem is best suited for an NLP approach due to the availability of video transcription. We evaluate multiple classes of models, from gradient boosting to recurrent neural networks, comparing their performance in extraction and identification of highlights. The best performing models are then evaluated using four sampling methods designed to analyze documents much larger than the maximum input length of the classifiers. We report very high performances for the standalone classifiers, ROC AUC scores in the range 0.93-0.94, but observe a significant drop in effectiveness when evaluated on large documents. Based on our results we suggest combinations of models/sampling algorithms for various use cases.
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data
Venue: arXiv cs.LG 2020
Year: 2020

A novel correction algorithm is proposed for multi-class classification problems with corrupted training data. The algorithm is non-intrusive, in the sense that it post-processes a trained classification model by adding a correction procedure to the model prediction. The correction procedure can be coupled with any approximators, such as logistic regression, neural networks of various architectures, etc. When training dataset is sufficiently large, we prove that the corrected models deliver correct classification results as if there is no corruption in the training data. For datasets of finite size, the corrected models produce significantly better recovery results, compared to the models without the correction algorithm. All of the theoretical findings in the paper are verified by our numerical examples.
Adjusting Image Attributes of Localized Regions with Low-level Dialogue
Venue: arXiv cs.CL 2020
Year: 2020

Natural Language Image Editing (NLIE) aims to use natural language instructions to edit images. Since novices are inexperienced with image editing techniques, their instructions are often ambiguous and contain high-level abstractions that tend to correspond to complex editing steps to accomplish. Motivated by this inexperience aspect, we aim to smooth the learning curve by teaching the novices to edit images using low-level commanding terminologies. Towards this end, we develop a task-oriented dialogue system to investigate low-level instructions for NLIE. Our system grounds language on the level of edit operations, and suggests options for a user to choose from. Though compelled to express in low-level terms, a user evaluation shows that 25% of users found our system easy-to-use, resonating with our motivation. An analysis shows that users generally adapt to utilizing the proposed low-level language interface. In this study, we identify that object segmentation as the key factor to the user satisfaction. Our work demonstrates the advantages of the low-level, direct language-action mapping approach that can be applied to other problem domains beyond image editing such as audio editing or industrial design.
Think Global, Act Local: Relating DNN generalisation and node-level SNR
Venue: arXiv cs.LG 2020
Year: 2020

The reasons behind good DNN generalisation remain an open question. In this paper we explore the problem by looking at the Signal-to-Noise Ratio of nodes in the network. Starting from information theory principles, it is possible to derive an expression for the SNR of a DNN node output. Using this expression we construct figures-of-merit that quantify how well the weights of a node optimise SNR (or, equivalently, information rate). Applying these figures-of-merit, we give examples indicating that weight sets that promote good SNR performance also exhibit good generalisation. In addition, we are able to identify the qualities of weight sets that exhibit good SNR behaviour and hence promote good generalisation. This leads to a discussion of how these results relate to network training and regularisation. Finally, we identify some ways that these observations can be used in training design.
Task-Aware Variational Adversarial Active Learning
Venue: arXiv cs.LG 2020
Year: 2020

Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques. Active learning (AL) tackles this by querying the most informative samples to be annotated among unlabeled pool. Two promising directions for AL that have been recently explored are task-agnostic approach to select data points that are far from the current labeled pool and task-aware approach that relies on the perspective of task model. Unfortunately, the former does not exploit structures from tasks and the latter does not seem to well-utilize overall data distribution. Here, we propose task-aware variational adversarial AL (TA-VAAL) that modifies task-agnostic VAAL, that considered data distribution of both label and unlabeled pools, by relaxing task learning loss prediction to ranking loss prediction and by using ranking conditional generative adversarial network to embed normalized ranking loss information on VAAL. Our proposed TA-VAAL outperforms state-of-the-arts on various benchmark datasets for classifications with balanced / imbalanced labels as well as semantic segmentation and its task-aware and task-agnostic AL properties were confirmed with our in-depth analyses.
Generalized Bayesian Cramér-Rao Inequality via Information Geometry of Relative $α$-Entropy
Venue: arXiv cs.IT 2020
Year: 2020

The relative $α$-entropy is the Rényi analog of relative entropy and arises prominently in information-theoretic problems. Recent information geometric investigations on this quantity have enabled the generalization of the Cramér-Rao inequality, which provides a lower bound for the variance of an estimator of an escort of the underlying parametric probability distribution. However, this framework remains unexamined in the Bayesian framework. In this paper, we propose a general Riemannian metric based on relative $α$-entropy to obtain a generalized Bayesian Cramér-Rao inequality. This establishes a lower bound for the variance of an unbiased estimator for the $α$-escort distribution starting from an unbiased estimator for the underlying distribution. We show that in the limiting case when the entropy order approaches unity, this framework reduces to the conventional Bayesian Cramér-Rao inequality. Further, in the absence of priors, the same framework yields the deterministic Cramér-Rao inequality.