OpenRaiser/Intern-Atlas huggingface dataset:
Framework for an Integrated Learning Block with CDIO-led Engineering Education
Venue: arXiv cs.CY 2020
Year: 2020

As a CDIO collaborating member, the School of Mechanical and Electrical Engineering of Sheridan maintains a curriculum that is deeply rooted in skills-based learning, experiential learning, and engineering design. To ensure our graduates are agile and ready for the workforce, we are taking proactive measures to further improve their learning experiences. An important challenge still impeding our students knowledge acquisition is the perception that program courses have disjointed learning outcomes. The course map of programs is carefully designed in such a way that technical skills acquired in particular courses gradually build on each other. Despite the traditional existence of prerequisites and co-requisites, the inaccurate view that courses function independently persists among students and, occasionally, among faculty members. One feasible approach to tackle this pedagogical challenge is to combine various courses into an integrated learning block (ILB) having a unified mission and objective. At Sheridan's School of MEET, we are applying an ILB with three engineering courses offered within the same semester for all our B.Eng. degree programs. The ILB deliverables are based on the design of a chosen engineering system or subunit in a project-based learning (PBL) environment. The rationale of this paper is to share our framework for implementing an ILB in engineering programs and to examine the opportunities and challenges related to this type of curriculum design. In particular, we will discuss the methodology by which courses are selected to form an ILB while taking into account their appropriateness for an industry-driven PBL. This will be followed up with some of the strategies that are proposed to evaluate the performance of students in an ILB through formative and summative assessments based on CDIO competencies.
Unsupervised clustering of Roman pottery profiles from their SSAE representation
Venue: arXiv cs.CV 2020
Year: 2020

In this paper we introduce the ROman COmmonware POTtery (ROCOPOT) database, which comprises of more than 2000 black and white imaging profiles of pottery shapes extracted from 11 Roman catalogues and related to different excavation sites. The partiality and the handcrafted variance of the shape fragments within this new database make their unsupervised clustering a very challenging problem: profile similarities are thus explored via the hierarchical clustering of non-linear features learned in the latent representation space of a stacked sparse autoencoder (SSAE) network, unveiling new profile matches. Results are commented both from a mathematical and archaeological perspective so as to unlock new research directions in the respective communities.
Continuous Transfer Learning with Label-informed Distribution Alignment
Venue: arXiv cs.LG 2020
Year: 2020

Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little is devoted to modeling the time evolving target domain, such as the online reviews for movies. To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain. One major challenge associated with continuous transfer learning is the potential occurrence of negative transfer as the target domain evolves over time. To address this challenge, we propose a novel label-informed C-divergence between the source and target domains in order to measure the shift of data distributions as well as to identify potential negative transfer. We then derive the error bound for the target domain using the empirical estimate of our proposed C-divergence. Furthermore, we propose a generic adversarial Variational Auto-encoder framework named TransLATE by minimizing the classification error and C-divergence of the target domain between consecutive time stamps in a latent feature space. In addition, we define a transfer signature for characterizing the negative transfer based on C-divergence, which indicates that larger C-divergence implies a higher probability of negative transfer in real scenarios. Extensive experiments on synthetic and real data sets demonstrate the effectiveness of our TransLATE framework.
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices
Venue: arXiv cs.CV 2020
Year: 2020

Recently, deep learning has represented an important research trend in human activity recognition (HAR). In particular, deep convolutional neural networks (CNNs) have achieved state-of-the-art performance on various HAR datasets. For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations. A high number of operations in deep leaning increases computational cost and is not suitable for real-time HAR using mobile and wearable sensors. Though shallow learning techniques often are lightweight, they could not achieve good performance. Therefore, deep learning methods that can balance the trade-off between accuracy and computation cost is highly needed, which to our knowledge has seldom been researched. In this paper, we for the first time propose a computation efficient CNN using conditionally parametrized convolution for real-time HAR on mobile and wearable devices. We evaluate the proposed method on four public benchmark HAR datasets consisting of WISDM dataset, PAMAP2 dataset, UNIMIB-SHAR dataset, and OPPORTUNITY dataset, achieving state-of-the-art accuracy without compromising computation cost. Various ablation experiments are performed to show how such a network with large capacity is clearly preferable to baseline while requiring a similar amount of operations. The method can be used as a drop-in replacement for the existing deep HAR architectures and easily deployed onto mobile and wearable devices for real-time HAR applications.
Explaining Autonomous Driving by Learning End-to-End Visual Attention
Venue: arXiv cs.CV 2020
Year: 2020

Current deep learning based autonomous driving approaches yield impressive results also leading to in-production deployment in certain controlled scenarios. One of the most popular and fascinating approaches relies on learning vehicle controls directly from data perceived by sensors. This end-to-end learning paradigm can be applied both in classical supervised settings and using reinforcement learning. Nonetheless the main drawback of this approach as also in other learning problems is the lack of explainability. Indeed, a deep network will act as a black-box outputting predictions depending on previously seen driving patterns without giving any feedback on why such decisions were taken. While to obtain optimal performance it is not critical to obtain explainable outputs from a learned agent, especially in such a safety critical field, it is of paramount importance to understand how the network behaves. This is particularly relevant to interpret failures of such systems. In this work we propose to train an imitation learning based agent equipped with an attention model. The attention model allows us to understand what part of the image has been deemed most important. Interestingly, the use of attention also leads to superior performance in a standard benchmark using the CARLA driving simulator.
Topic Detection from Conversational Dialogue Corpus with Parallel Dirichlet Allocation Model and Elbow Method
Venue: arXiv cs.IR 2020
Year: 2020

A conversational system needs to know how to switch between topics to continue the conversation for a more extended period. For this topic detection from dialogue corpus has become an important task for a conversation and accurate prediction of conversation topics is important for creating coherent and engaging dialogue systems. In this paper, we proposed a topic detection approach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering a vocabulary of known similar words based on TF-IDF scores and Bag of Words (BOW) technique. In the experiment, we use K-mean clustering with Elbow Method for interpretation and validation of consistency within-cluster analysis to select the optimal number of clusters. We evaluate our approach by comparing it with traditional LDA and clustering technique. The experimental results show that combining PLDA with Elbow method selects the optimal number of clusters and refine the topics for the conversation.
The citation impact of social sciences and humanities upon patentable technology
Venue: arXiv cs.DL 2020
Year: 2020

This paper examines the citation impact of papers published in scientific-scholarly journals upon patentable technology, as reflected in examiner- or inventor-given references in granted patents. It analyses data created by SCImago Research Group, linking PATSTAT's scientific non-patent references (SNPRs) to source documents indexed in Scopus. The frequency of patent citations to journal papers is calculated per discipline, year, institutional sector, journal subject category, and for "top" journals. PATSTAT/Scopus-based statistics are compared to those derived from Web of Science/USPTO linkage. A detailed assessment is presented of the technological impact of research publications in social sciences and humanities (SSH). Several subject fields perform well in terms of the number of citations from patents, especially Library & Information Science, Language & Linguistics, Education, and Law, but many of the most cited journals find themselves in the interface between SSH and biomedical or natural sciences. Analyses of the titles of citing patents and cited papers are presented that shed light upon the cognitive content of patent citations. It is proposed to develop more advanced indicators of citation impact of papers upon patents, and ways to combine citation counts with citation content and context analysis.
From Checking to Inference: Actual Causality Computations as Optimization Problems
Venue: arXiv cs.AI 2020
Year: 2020

Actual causality is increasingly well understood. Recent formal approaches, proposed by Halpern and Pearl, have made this concept mature enough to be amenable to automated reasoning. Actual causality is especially vital for building accountable, explainable systems. Among other reasons, causality reasoning is computationally hard due to the requirements of counterfactuality and the minimality of causes. Previous approaches presented either inefficient or restricted, and domain-specific, solutions to the problem of automating causality reasoning. In this paper, we present a novel approach to formulate different notions of causal reasoning, over binary acyclic models, as optimization problems, based on quantifiable notions within counterfactual computations. We contribute and compare two compact, non-trivial, and sound integer linear programming (ILP) and Maximum Satisfiability (MaxSAT) encodings to check causality. Given a candidate cause, both approaches identify what a minimal cause is. Also, we present an ILP encoding to infer causality without requiring a candidate cause. We show that both notions are efficiently automated. Using models with more than $8000$ variables, checking is computed in a matter of seconds, with MaxSAT outperforming ILP in many cases. In contrast, inference is computed in a matter of minutes.
Single-machine scheduling with an external resource
Venue: arXiv cs.DS 2020
Year: 2020

This paper studies the complexity of single-machine scheduling with an external resource, which is rented for a non-interrupted period. Jobs that need this external resource are executed only when the external resource is available. There is a cost associated with the scheduling of jobs and a cost associated with the duration of the renting period of the external resource. We look at four classes of problems with an external resource: a class of problems where the renting period is budgeted and the scheduling cost needs to be minimized, a class of problems where the scheduling cost is budgeted and the renting period needs to be minimized, a class of two-objective problems where both, the renting period and the scheduling cost, are to be minimized, and a class of problems where a linear combination of the scheduling cost and the renting period is minimized. We provide a thorough complexity analysis (NP-hardness proofs and (pseudo-)polynomial algorithms) for different members of these four classes.
Spread of Influence in Graphs
Venue: arXiv cs.DS 2020
Year: 2020

Consider a graph $G$ and an initial configuration where each node is black or white. Assume that in each round all nodes simultaneously update their color based on a predefined rule. One can think of graph $G$ as a social network, where each black/white node represents an individual who holds a positive/negative opinion regarding a particular topic. In the $r$-threshold (resp. $α$-threshold) model, a node becomes black if at least $r$ of its neighbors (resp. $α$ fraction of its neighbors) are black, and white otherwise. The $r$-monotone (resp. $α$-monotone) model is the same as the $r$-threshold (resp. $α$-threshold) model, except that a black node remains black forever. What is the number of rounds that the process needs to stabilize? How many nodes must be black initially so that black color takes over or survives? Our main goal in the present paper is to address these two questions
Tensorized Transformer for Dynamical Systems Modeling
Venue: arXiv cs.LG 2020
Year: 2020

The identification of nonlinear dynamics from observations is essential for the alignment of the theoretical ideas and experimental data. The last, in turn, is often corrupted by the side effects and noise of different natures, so probabilistic approaches could give a more general picture of the process. At the same time, high-dimensional probabilities modeling is a challenging and data-intensive task. In this paper, we establish a parallel between the dynamical systems modeling and language modeling tasks. We propose a transformer-based model that incorporates geometrical properties of the data and provide an iterative training algorithm allowing the fine-grid approximation of the conditional probabilities of high-dimensional dynamical systems.
Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion
Venue: arXiv cs.LG 2020
Year: 2020

We introduce Wasserstein Adversarial Proximal Policy Optimization (WAPPO), a novel algorithm for visual transfer in Reinforcement Learning that explicitly learns to align the distributions of extracted features between a source and target task. WAPPO approximates and minimizes the Wasserstein-1 distance between the distributions of features from source and target domains via a novel Wasserstein Confusion objective. WAPPO outperforms the prior state-of-the-art in visual transfer and successfully transfers policies across Visual Cartpole and two instantiations of 16 OpenAI Procgen environments.
Runtime Analysis of a Heavy-Tailed $(1+(λ,λ))$ Genetic Algorithm on Jump Functions
Venue: arXiv cs.NE 2020
Year: 2020

It was recently observed that the $(1+(λ,λ))$ genetic algorithm can comparably easily escape the local optimum of the jump functions benchmark. Consequently, this algorithm can optimize the jump function with jump size $k$ in an expected runtime of only $n^{(k + 1)/2}k^{-k/2}e^{O(k)}$ fitness evaluations (Antipov, Doerr, Karavaev (GECCO 2020)). To obtain this performance, however, a non-standard parameter setting depending on the jump size $k$ was used. To overcome this difficulty, we propose to choose two parameters of the $(1+(λ,λ))$ genetic algorithm randomly from a power-law distribution. Via a mathematical runtime analysis, we show that this algorithm with natural instance-independent choices of the distribution parameters on all jump functions with jump size at most $n/4$ has a performance close to what the best instance-specific parameters in the previous work obtained. This price for instance-independence can be made as small as an $O(n\log(n))$ factor. Given the difficulty of the jump problem and the runtime losses from using mildly suboptimal fixed parameters (also discussed in this work), this appears to be a fair price.
Blockchain, Fog and IoT Integrated Framework: Review, Architecture and Evaluation
Venue: arXiv cs.CR 2020
Year: 2020

In the next-generation computing, the role of cloud, internet, and smart devices will be capacious. Nowadays we all are familiar with the word smart. This word is used a number of times in our daily life. The Internet of Things (IoT) will produce remarkable different kinds of information from different resources. It can store and process big data in the cloud. The fogging acts as an interface between cloud and IoT. The IoT nodes are also known as fog nodes, these nodes are able to access anywhere within the range of the network. The blockchain is a novel approach to record the transactions in a sequence securely. Developing new blockchains based integrated framework in the architecture of the IoT is one of the emerging approaches to solving the issue of communication security among the IoT public nodes. This research explores a novel approach to integrate blockchain technology with the fog and IoT networks and provides communication security to the internet of smart devices. The framework is tested and implemented in the IoT network. The results are found positive.
SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Images
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a novel approach to reconstruct 3D human body shapes based on a sparse set of RGBD frames using a single RGBD camera. We specifically focus on the realistic settings where human subjects move freely during the capture. The main challenge is how to robustly fuse these sparse frames into a canonical 3D model, under pose changes and surface occlusions. This is addressed by our new framework consisting of the following steps. First, based on a generative human template, for every two frames having sufficient overlap, an initial pairwise alignment is performed; It is followed by a global non-rigid registration procedure, in which partial results from RGBD frames are collected into a unified 3D shape, under the guidance of correspondences from the pairwise alignment; Finally, the texture map of the reconstructed human model is optimized to deliver a clear and spatially consistent texture. Empirical evaluations on synthetic and real datasets demonstrate both quantitatively and qualitatively the superior performance of our framework in reconstructing complete 3D human models with high fidelity. It is worth noting that our framework is flexible, with potential applications going beyond shape reconstruction. As an example, we showcase its use in reshaping and reposing to a new avatar.
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view, we propose a novel concept of deployment efficiency, measuring the number of distinct data-collection policies that are used during policy learning. We observe that naïvely applying existing model-free offline RL algorithms recursively does not lead to a practical deployment-efficient and sample-efficient algorithm. We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works. Furthermore, the recursive application of BREMEN is able to achieve impressive deployment efficiency while maintaining the same or better sample efficiency, learning successful policies from scratch on simulated robotic environments with only 5-10 deployments, compared to typical values of hundreds to millions in standard RL baselines. Codes and pre-trained models are available at https://github.com/matsuolab/BREMEN .
An Overview of Neural Network Compression
Venue: arXiv cs.LG 2020
Year: 2020

Overparameterized networks trained to convergence have shown impressive performance in domains such as computer vision and natural language processing. Pushing state of the art on salient tasks within these domains corresponds to these models becoming larger and more difficult for machine learning practitioners to use given the increasing memory and storage requirements, not to mention the larger carbon footprint. Thus, in recent years there has been a resurgence in model compression techniques, particularly for deep convolutional neural networks and self-attention based networks such as the Transformer. Hence, this paper provides a timely overview of both old and current compression techniques for deep neural networks, including pruning, quantization, tensor decomposition, knowledge distillation and combinations thereof. We assume a basic familiarity with deep learning architectures\footnote{For an introduction to deep learning, see ~\citet{goodfellow2016deep}}, namely, Recurrent Neural Networks~\citep[(RNNs)][]{rumelhart1985learning,hochreiter1997long}, Convolutional Neural Networks~\citep{fukushima1980neocognitron}~\footnote{For an up to date overview see~\citet{khan2019survey}} and Self-Attention based networks~\citep{vaswani2017attention}\footnote{For a general overview of self-attention networks, see ~\citet{chaudhari2019attentive}.},\footnote{For more detail and their use in natural language processing, see~\citet{hu2019introductory}}. Most of the papers discussed are proposed in the context of at least one of these DNN architectures.
MMSE Bounds Under Kullback-Leibler Divergence Constraints on the Joint Input-Output Distribution
Venue: arXiv cs.IT 2020
Year: 2020

This paper proposes a new family of lower and upper bounds on the minimum mean squared error (MMSE). The key idea is to minimize/maximize the MMSE subject to the constraint that the joint distribution of the input-output statistics lies in a Kullback-Leibler divergence ball centered at some Gaussian reference distribution. Both bounds are tight and are attained by Gaussian distributions whose mean is identical to that of the reference distribution and whose covariance matrix is determined by a scalar parameter that can be obtained by finding the root of a monotonic function. The upper bound corresponds to a minimax optimal estimator and provides performance guarantees under distributional uncertainty. The lower bound provides an alternative to well-known inequalities in estimation theory, such as the Cramér-Rao bound, that is potentially tighter and defined for a larger class of distributions. Examples of applications in signal processing and information theory illustrate the usefulness of the proposed bounds in practice.