OpenRaiser/Intern-Atlas huggingface dataset:
Investigation of Sentiment Controllable Chatbot
Venue: arXiv cs.CL 2020
Year: 2020

Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. In this paper, we investigate four models to scale or adjust the sentiment of the chatbot response: a persona-based model, reinforcement learning, a plug and play model, and CycleGAN, all based on the seq2seq model. We also develop machine-evaluated metrics to estimate whether the responses are reasonable given the input. These metrics, together with human evaluation, are used to analyze the performance of the four models in terms of different aspects; reinforcement learning and CycleGAN are shown to be very attractive.
AutoRec: An Automated Recommender System
Venue: arXiv cs.IR 2020
Year: 2020

Realistic recommender systems are often required to adapt to ever-changing data and tasks or to explore different models systematically. To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models. AutoRec also supports a highly flexible pipeline that accommodates both sparse and dense inputs, rating prediction and click-through rate (CTR) prediction tasks, and an array of recommendation models. Lastly, AutoRec provides a simple, user-friendly API. Experiments conducted on the benchmark datasets reveal AutoRec is reliable and can identify models which resemble the best model without prior knowledge.
Model Checking Branching Properties on Petri Nets with Transits (Full Version)
Venue: arXiv cs.LO 2020
Year: 2020

To model check concurrent systems, it is convenient to distinguish between the data flow and the control. Correctness is specified on the level of data flow whereas the system is configured on the level of control. Petri nets with transits and Flow-LTL are a corresponding formalism. In Flow-LTL, both the correctness of the data flow and assumptions on fairness and maximality for the control are expressed in linear time. So far, branching behavior cannot be specified for Petri nets with transits. In this paper, we introduce Flow-CTL* to express the intended branching behavior of the data flow while maintaining LTL for fairness and maximality assumptions on the control. We encode physical access control with policy updates as Petri nets with transits and give standard requirements in Flow-CTL*. For model checking, we reduce the model checking problem of Petri nets with transits against Flow-CTL* via automata constructions to the model checking problem of Petri nets against LTL. Thereby, physical access control with policy updates under fairness assumptions for an unbounded number of people can be verified.
Transformer-XL Based Music Generation with Multiple Sequences of Time-valued Notes
Venue: arXiv cs.SD 2020
Year: 2020

Current state-of-the-art AI based classical music creation algorithms such as Music Transformer are trained by employing single sequence of notes with time-shifts. The major drawback of absolute time interval expression is the difficulty of similarity computing of notes that share the same note value yet different tempos, in one or among MIDI files. In addition, the usage of single sequence restricts the model to separately and effectively learn music information such as harmony and rhythm. In this paper, we propose a framework with two novel methods to respectively track these two shortages, one is the construction of time-valued note sequences that liberate note values from tempos and the other is the separated usage of four sequences, namely, former note on to current note on, note on to note off, pitch, and velocity, for jointly training of four Transformer-XL networks. Through training on a 23-hour piano MIDI dataset, our framework generates significantly better and hour-level longer music than three state-of-the-art baselines, namely Music Transformer, DeepJ, and single sequence-based Transformer-XL, evaluated automatically and manually.
Using Holographically Compressed Embeddings in Question Answering
Venue: arXiv cs.CL 2020
Year: 2020

Word vector representations are central to deep learning natural language processing models. Many forms of these vectors, known as embeddings, exist, including word2vec and GloVe. Embeddings are trained on large corpora and learn the word's usage in context, capturing the semantic relationship between words. However, the semantics from such training are at the level of distinct words (known as word types), and can be ambiguous when, for example, a word type can be either a noun or a verb. In question answering, parts-of-speech and named entity types are important, but encoding these attributes in neural models expands the size of the input. This research employs holographic compression of pre-trained embeddings, to represent a token, its part-of-speech, and named entity type, in the same dimension as representing only the token. The implementation, in a modified question answering recurrent deep learning network, shows that semantic relationships are preserved, and yields strong performance.
Deep learning models for representing out-of-vocabulary words
Venue: arXiv cs.CL 2020
Year: 2020

Communication has become increasingly dynamic with the popularization of social networks and applications that allow people to express themselves and communicate instantly. In this scenario, distributed representation models have their quality impacted by new words that appear frequently or that are derived from spelling errors. These words that are unknown by the models, known as out-of-vocabulary (OOV) words, need to be properly handled to not degrade the quality of the natural language processing (NLP) applications, which depend on the appropriate vector representation of the texts. To better understand this problem and finding the best techniques to handle OOV words, in this study, we present a comprehensive performance evaluation of deep learning models for representing OOV words. We performed an intrinsic evaluation using a benchmark dataset and an extrinsic evaluation using different NLP tasks: text categorization, named entity recognition, and part-of-speech tagging. Although the results indicated that the best technique for handling OOV words is different for each task, Comick, a deep learning method that infers the embedding based on the context and the morphological structure of the OOV word, obtained promising results.
Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners
Venue: arXiv cs.CL 2020
Year: 2020

Advanced machine learning and natural language techniques enable attackers to launch sophisticated and targeted social engineering-based attacks. To counter the active attacker issue, researchers have since resorted to proactive methods of detection. Email masquerading using targeted emails to fool the victim is an advanced attack method. However automatic text generation requires controlling the context and coherency of the generated content, which has been identified as an increasingly difficult problem. The method used leverages a hierarchical deep neural model which uses a learned representation of the sentences in the input document to generate structured written emails. We demonstrate the generation of short and targeted text messages using the deep model. The global coherency of the synthesized text is evaluated using a qualitative study as well as multiple quantitative measures.
Hardware Acceleration of Monte-Carlo Sampling for Energy Efficient Robust Robot Manipulation
Venue: arXiv cs.RO 2020
Year: 2020

Algorithms based on Monte-Carlo sampling have been widely adapted in robotics and other areas of engineering due to their performance robustness. However, these sampling-based approaches have high computational requirements, making them unsuitable for real-time applications with tight energy constraints. In this paper, we investigate 6 degree-of-freedom (6DoF) pose estimation for robot manipulation using this method, which uses rendering combined with sequential Monte-Carlo sampling. While potentially very accurate, the significant computational complexity of the algorithm makes it less attractive for mobile robots, where runtime and energy consumption are tightly constrained. To address these challenges, we develop a novel hardware implementation of Monte-Carlo sampling on an FPGA with lower computational complexity and memory usage, while achieving high parallelism and modularization. Our results show 12X-21X improvements in energy efficiency over low-power and high-end GPU implementations, respectively. Moreover, we achieve real time performance without compromising accuracy.
Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform
Venue: arXiv cs.CL 2020
Year: 2020

Geoparsing is an important task in geographic information retrieval. A geoparsing system, known as a geoparser, takes some texts as the input and outputs the recognized place mentions and their location coordinates. In June 2019, a geoparsing competition, Toponym Resolution in Scientific Papers, was held as one of the SemEval 2019 tasks. The winning teams developed neural network based geoparsers that achieved outstanding performances (over 90% precision, recall, and F1 score for toponym recognition). This exciting result brings the question "are we there yet?", namely have we achieved high enough performances to possibly consider the problem of geoparsing as solved? One limitation of this competition is that the developed geoparsers were tested on only one dataset which has 45 research articles collected from the particular domain of Bio-medicine. It is known that the same geoparser can have very different performances on different datasets. Thus, this work performs a systematic evaluation of these state-of-the-art geoparsers using our recently developed benchmarking platform EUPEG that has eight annotated datasets, nine baseline geoparsers, and eight performance metrics. The evaluation result suggests that these new geoparsers indeed improve the performances of geoparsing on multiple datasets although some challenges remain.
A General Family of Stochastic Proximal Gradient Methods for Deep Learning
Venue: arXiv cs.LG 2020
Year: 2020

We study the training of regularized neural networks where the regularizer can be non-smooth and non-convex. We propose a unified framework for stochastic proximal gradient descent, which we term ProxGen, that allows for arbitrary positive preconditioners and lower semi-continuous regularizers. Our framework encompasses standard stochastic proximal gradient methods without preconditioners as special cases, which have been extensively studied in various settings. Not only that, we present two important update rules beyond the well-known standard methods as a byproduct of our approach: (i) the first closed-form proximal mappings of $\ell_q$ regularization ($0 \leq q \leq 1$) for adaptive stochastic gradient methods, and (ii) a revised version of ProxQuant that fixes a caveat of the original approach for quantization-specific regularizers. We analyze the convergence of ProxGen and show that the whole family of ProxGen enjoys the same convergence rate as stochastic proximal gradient descent without preconditioners. We also empirically show the superiority of proximal methods compared to subgradient-based approaches via extensive experiments. Interestingly, our results indicate that proximal methods with non-convex regularizers are more effective than those with convex regularizers.
The Moral-IT Deck: A Tool for Ethics by Design
Venue: arXiv cs.CY 2020
Year: 2020

This paper presents the design process and empirical evaluation of a new tool for enabling ethics by design: The Moral-IT Cards. Better tools are needed to support the role of technologists in addressing ethical issues during system design. These physical cards support reflection by technologists on normative aspects of technology development, specifically on emerging risks, appropriate safeguards and challenges of implementing these in the system. We discuss how the cards were developed and tested within 5 workshops with 20 participants from both research and commercial settings. We consider the role of technologists in ethics from different EU/UK policymaking initiatives and disciplinary perspectives (i.e. Science and Technology Studies (STS), IT Law, Human Computer Interaction (HCI), Computer/Engineering Ethics). We then examine existing ethics by design tools, and other cards based tools before arguing why cards can be a useful medium for addressing complex ethical issues. We present the development process for the Moral-IT cards, document key features of our card design, background on the content, the impact assessment board process for using them and how this was formulated. We discuss our study design and methodology before examining key findings which are clustered around three overarching themes. These are: the value of our cards as a tool, their impact on the technology design process and how they structure ethical reflection practices. We conclude with key lessons and concepts such as how they level the playing field for debate; enable ethical clustering, sorting and comparison; provide appropriate anchors for discussion and highlighted the intertwined nature of ethics.
Learning to Parse Wireframes in Images of Man-Made Environments
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a learning-based approach to the task of automatically extracting a "wireframe" representation for images of cluttered man-made environments. The wireframe (see Fig. 1) contains all salient straight lines and their junctions of the scene that encode efficiently and accurately large-scale geometry and object shapes. To this end, we have built a very large new dataset of over 5,000 images with wireframes thoroughly labelled by humans. We have proposed two convolutional neural networks that are suitable for extracting junctions and lines with large spatial support, respectively. The networks trained on our dataset have achieved significantly better performance than state-of-the-art methods for junction detection and line segment detection, respectively. We have conducted extensive experiments to evaluate quantitatively and qualitatively the wireframes obtained by our method, and have convincingly shown that effectively and efficiently parsing wireframes for images of man-made environments is a feasible goal within reach. Such wireframes could benefit many important visual tasks such as feature correspondence, 3D reconstruction, vision-based mapping, localization, and navigation. The data and source code are available at https://github.com/huangkuns/wireframe.
Learning Invariances for Interpretability using Supervised VAE
Venue: arXiv cs.LG 2020
Year: 2020

We propose to learn model invariances as a means of interpreting a model. This is motivated by a reverse engineering principle. If we understand a problem, we may introduce inductive biases in our model in the form of invariances. Conversely, when interpreting a complex supervised model, we can study its invariances to understand how that model solves a problem. To this end we propose a supervised form of variational auto-encoders (VAEs). Crucially, only a subset of the dimensions in the latent space contributes to the supervised task, allowing the remaining dimensions to act as nuisance parameters. By sampling solely the nuisance dimensions, we are able to generate samples that have undergone transformations that leave the classification unchanged, revealing the invariances of the model. Our experimental results show the capability of our proposed model both in terms of classification, and generation of invariantly transformed samples. Finally we show how combining our model with feature attribution methods it is possible to reach a more fine-grained understanding about the decision process of the model.
Learning Multiplicative Interactions with Bayesian Neural Networks for Visual-Inertial Odometry
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents an end-to-end multi-modal learning approach for monocular Visual-Inertial Odometry (VIO), which is specifically designed to exploit sensor complementarity in the light of sensor degradation scenarios. The proposed network makes use of a multi-head self-attention mechanism that learns multiplicative interactions between multiple streams of information. Another design feature of our approach is the incorporation of the model uncertainty using scalable Laplace Approximation. We evaluate the performance of the proposed approach by comparing it against the end-to-end state-of-the-art methods on the KITTI dataset and show that it achieves superior performance. Importantly, our work thereby provides an empirical evidence that learning multiplicative interactions can result in a powerful inductive bias for increased robustness to sensor failures.
LinSBFT: Linear-Communication One-Step BFT Protocol for Public Blockchains
Venue: arXiv cs.DC 2020
Year: 2020

This paper presents LinSBFT, a Byzantine Fault Tolerance (BFT) protocol with the capacity of processing over 2000 smart contract transactions per second in production. LinSBFT applies to a permissionless, public blockchain system, in which there is no public-key infrastructure, based on the classic PBFT with 4 improvements: (\romannumeral1) LinSBFT achieves $O(n)$ worst-case communication volume, in contract to PBFT's $O(n^4)$; (\romannumeral2) LinSBFT rotates the leader of protocol randomly to reduce the risk of denial-of-service attacks on leader; and (\romannumeral3) each run of LinSBFT finalizes one block, which is robust against participants that are honest in one run of the protocol, and dishonest in another, and the set of participants is dynamic, which is update periodically. (\romannumeral4) LinSBFT helps the delayed nodes to catch up via a synchronization mechanism to promise the liveness. Further, in the ordinary case, LinSBFT involves only a single round of voting instead of two in PBFT, which reduces both communication overhead and confirmation time, and employs the \emph{proof-of-stake} scheme to reward all participants. Extensive experiments using data obtained from the Ethereum demonstrate that LinSBFT consistently and significantly outperforms existing in-production BFT protocols for blockchains.
A Multilingual Parallel Corpora Collection Effort for Indian Languages
Venue: arXiv cs.CL 2020
Year: 2020

We present sentence aligned parallel corpora across 10 Indian Languages - Hindi, Telugu, Tamil, Malayalam, Gujarati, Urdu, Bengali, Oriya, Marathi, Punjabi, and English - many of which are categorized as low resource. The corpora are compiled from online sources which have content shared across languages. The corpora presented significantly extends present resources that are either not large enough or are restricted to a specific domain (such as health). We also provide a separate test corpus compiled from an independent online source that can be independently used for validating the performance in 10 Indian languages. Alongside, we report on the methods of constructing such corpora using tools enabled by recent advances in machine translation and cross-lingual retrieval using deep neural network based methods.
P$^{2}$Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation
Venue: arXiv cs.CV 2020
Year: 2020

This paper tackles the unsupervised depth estimation task in indoor environments. The task is extremely challenging because of the vast areas of non-texture regions in these scenes. These areas could overwhelm the optimization process in the commonly used unsupervised depth estimation framework proposed for outdoor environments. However, even when those regions are masked out, the performance is still unsatisfactory. In this paper, we argue that the poor performance suffers from the non-discriminative point-based matching. To this end, we propose P$^2$Net. We first extract points with large local gradients and adopt patches centered at each point as its representation. Multiview consistency loss is then defined over patches. This operation significantly improves the robustness of the network training. Furthermore, because those textureless regions in indoor scenes (e.g., wall, floor, roof, \etc) usually correspond to planar regions, we propose to leverage superpixels as a plane prior. We enforce the predicted depth to be well fitted by a plane within each superpixel. Extensive experiments on NYUv2 and ScanNet show that our P$^2$Net outperforms existing approaches by a large margin. Code is available at \url{https://github.com/svip-lab/Indoor-SfMLearner}.
Human $\neq$ AGI
Venue: arXiv cs.CY 2020
Year: 2020

Terms Artificial General Intelligence (AGI) and Human-Level Artificial Intelligence (HLAI) have been used interchangeably to refer to the Holy Grail of Artificial Intelligence (AI) research, creation of a machine capable of achieving goals in a wide range of environments. However, widespread implicit assumption of equivalence between capabilities of AGI and HLAI appears to be unjustified, as humans are not general intelligences. In this paper, we will prove this distinction.
On Indexing and Compressing Finite Automata
Venue: arXiv cs.DS 2020
Year: 2020

An index for a finite automaton is a powerful data structure that supports locating paths labeled with a query pattern, thus solving pattern matching on the underlying regular language. In this paper, we solve the long-standing problem of indexing arbitrary finite automata. Our solution consists in finding a partial co-lexicographic order of the states and proving, as in the total order case, that states reached by a given string form one interval on the partial order, thus enabling indexing. We provide a lower bound stating that such an interval requires $O(p)$ words to be represented, $p$ being the order's width (i.e. the size of its largest antichain). Indeed, we show that $p$ determines the complexity of several fundamental problems on finite automata: (i) Letting $σ$ be the alphabet size, we provide an encoding for NFAs using $\lceil\log σ\rceil + 2\lceil\log p\rceil + 2$ bits per transition and a smaller encoding for DFAs using $\lceil\log σ\rceil + \lceil\log p\rceil + 2$ bits per transition. This is achieved by generalizing the Burrows-Wheeler transform to arbitrary automata. (ii) We show that indexed pattern matching can be solved in $\tilde O(m\cdot p^2)$ query time on NFAs. (iii) We provide a polynomial-time algorithm to index DFAs, while matching the optimal value for $ p $. On the other hand, we prove that the problem is NP-hard on NFAs. (iv) We show that, in the worst case, the classic powerset construction algorithm for NFA determinization generates an equivalent DFA of size $2^p(n-p+1)-1$, where $n$ is the number of NFA's states.
Dual Past and Future for Neural Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

Though remarkable successes have been achieved by Neural Machine Translation (NMT) in recent years, it still suffers from the inadequate-translation problem. Previous studies show that explicitly modeling the Past and Future contents of the source sentence is beneficial for translation performance. However, it is not clear whether the commonly used heuristic objective is good enough to guide the Past and Future. In this paper, we present a novel dual framework that leverages both source-to-target and target-to-source NMT models to provide a more direct and accurate supervision signal for the Past and Future modules. Experimental results demonstrate that our proposed method significantly improves the adequacy of NMT predictions and surpasses previous methods in two well-studied translation tasks.