OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating computational models of infant phonetic learning across languages
Venue: arXiv cs.CL 2020
Year: 2020

In the first year of life, infants' speech perception becomes attuned to the sounds of their native language. Many accounts of this early phonetic learning exist, but computational models predicting the attunement patterns observed in infants from the speech input they hear have been lacking. A recent study presented the first such model, drawing on algorithms proposed for unsupervised learning from naturalistic speech, and tested it on a single phone contrast. Here we study five such algorithms, selected for their potential cognitive relevance. We simulate phonetic learning with each algorithm and perform tests on three phone contrasts from different languages, comparing the results to infants' discrimination patterns. The five models display varying degrees of agreement with empirical observations, showing that our approach can help decide between candidate mechanisms for early phonetic learning, and providing insight into which aspects of the models are critical for capturing infants' perceptual development.
An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying
Venue: arXiv cs.CV 2020
Year: 2020

A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally, a novel distance function called Weighted Hamming applicable to QUICCI images is proposed for retrieval applications. The effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million QUICCI images derived from the SHREC2017 dataset, while the clutter resistance of QUICCI is shown using the clutterbox experiment.
A Historical Account of My Early Research Interests
Venue: arXiv cs.LO 2020
Year: 2020

This paper presents a brief account of some of the my early research interests. This historical account starts from my laurea thesis on Signal Theory and my master thesis on Computation Theory. It recalls some results in Combinatory Logic and Term Rewriting Systems. Some other results concern Program Transformation, Parallel Computation, Theory of Concurrency, and Proof of Program Properties. My early research activity has been mainly done in cooperation with Andrzej Skowron, Anna Labella, and Maurizio Proietti.
From Big-Step to Small-Step Semantics and Back with Interpreter Specialisation
Venue: arXiv cs.PL 2020
Year: 2020

We investigate representations of imperative programs as constrained Horn clauses. Starting from operational semantics transition rules, we proceed by writing interpreters as constrained Horn clause programs directly encoding the rules. We then specialise an interpreter with respect to a given source program to achieve a compilation of the source language to Horn clauses (an instance of the first Futamura projection). The process is described in detail for an interpreter for a subset of C, directly encoding the rules of big-step operational semantics for C. A similar translation based on small-step semantics could be carried out, but we show an approach to obtaining a small-step representation using a linear interpreter for big-step Horn clauses. This interpreter is again specialised to achieve the translation from big-step to small-step style. The linear small-step program can be transformed back to a big-step non-linear program using a third interpreter. A regular path expression is computed for the linear program using Tarjan's algorithm, and this regular expression then guides an interpreter to compute a program path. The transformation is realised by specialisation of the path interpreter. In all of the transformation phases, we use an established partial evaluator and exploit standard logic program transformation to remove redundant data structures and arguments in predicates and rename predicates to make clear their link to statements in the original source program.
An Experiment Combining Specialization with Abstract Interpretation
Venue: arXiv cs.PL 2020
Year: 2020

It was previously shown that control-flow refinement can be achieved by a program specializer incorporating property-based abstraction, to improve termination and complexity analysis tools. We now show that this purpose-built specializer can be reconstructed in a more modular way, and that the previous results can be achieved using an off-the-shelf partial evaluation tool, applied to an abstract interpreter. The key feature of the abstract interpreter is the abstract domain, which is the product of the property-based abstract domain with the concrete domain. This language-independent framework provides a practical approach to implementing a variety of powerful specializers, and contributes to a stream of research on using interpreters and specialization to achieve program transformations.
A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset
Venue: arXiv cs.CV 2020
Year: 2020

Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets. However, do we really need large-scale training set for salient object detection (SOD)? In this paper, we provide a deeper insight into the interrelationship between the SOD performances and the training sets. To alleviate the conventional demands for large-scale training data, we provide a feasible way to construct a novel small-scale training set, which only contains 4K images. Moreover, we propose a novel bi-stream network to take full advantage of our proposed small training set, which is consisted of two feature backbones with different structures, achieving complementary semantical saliency fusion via the proposed gate control unit. To our best knowledge, this is the first attempt to use a small-scale training set to outperform state-of-the-art models which are trained on large-scale training sets; nevertheless, our method can still achieve the leading state-of-the-art performance on five benchmark datasets.
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
Venue: arXiv cs.SE 2020
Year: 2020

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings associated with logistic regression yielded an AUC value of about 0.8 in predicting patch correctness on a deduplicated dataset of 1000 labeled patches. Our study shows that learned representations can lead to reasonable performance when comparing against the state-of-the-art, PATCH-SIM, which relies on dynamic information. These representations may further be complementary to features that were carefully (manually) engineered in the literature.
Which Kind Is Better in Open-domain Multi-turn Dialog,Hierarchical or Non-hierarchical Models? An Empirical Study
Venue: arXiv cs.CL 2020
Year: 2020

Currently, open-domain generative dialog systems have attracted considerable attention in academia and industry. Despite the success of single-turn dialog generation, multi-turn dialog generation is still a big challenge. So far, there are two kinds of models for open-domain multi-turn dialog generation: hierarchical and non-hierarchical models. Recently, some works have shown that the hierarchical models are better than non-hierarchical models under their experimental settings; meanwhile, some works also demonstrate the opposite conclusion. Due to the lack of adequate comparisons, it's not clear which kind of models are better in open-domain multi-turn dialog generation. Thus, in this paper, we will measure systematically nearly all representative hierarchical and non-hierarchical models over the same experimental settings to check which kind is better. Through extensive experiments, we have the following three important conclusions: (1) Nearly all hierarchical models are worse than non-hierarchical models in open-domain multi-turn dialog generation, except for the HRAN model. Through further analysis, the excellent performance of HRAN mainly depends on its word-level attention mechanism; (2) The performance of other hierarchical models will also obtain a great improvement if integrating the word-level attention mechanism into these models. The modified hierarchical models even significantly outperform the non-hierarchical models; (3) The reason why the word-level attention mechanism is so powerful for hierarchical models is because it can leverage context information more effectively, especially the fine-grained information. Besides, we have implemented all of the models and already released the codes.
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

The current main stream methods formulate their video saliency mainly from two independent venues, i.e., the spatial and temporal branches. As a complementary component, the main task for the temporal branch is to intermittently focus the spatial branch on those regions with salient movements. In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter. Thus, the key factor to improve the overall video saliency is how to further boost the performance of these branches efficiently. In this paper, we propose a novel spatiotemporal network to achieve such improvement in a full interactive fashion. We integrate a lightweight temporal model into the spatial branch to coarsely locate those spatially salient regions which are correlated with trustworthy salient movements. Meanwhile, the spatial branch itself is able to recurrently refine the temporal model in a multi-scale manner. In this way, both the spatial and temporal branches are able to interact with each other, achieving the mutual performance improvement. Our method is easy to implement yet effective, achieving high quality video saliency detection in real-time speed with 50 FPS.
Textual Description for Mathematical Equations
Venue: arXiv cs.CV 2020
Year: 2020

Reading of mathematical expression or equation in the document images is very challenging due to the large variability of mathematical symbols and expressions. In this paper, we pose reading of mathematical equation as a task of generation of the textual description which interprets the internal meaning of this equation. Inspired by the natural image captioning problem in computer vision, we present a mathematical equation description (MED) model, a novel end-to-end trainable deep neural network based approach that learns to generate a textual description for reading mathematical equation images. Our MED model consists of a convolution neural network as an encoder that extracts features of input mathematical equation images and a recurrent neural network with attention mechanism which generates description related to the input mathematical equation images. Due to the unavailability of mathematical equation image data sets with their textual descriptions, we generate two data sets for experimental purpose. To validate the effectiveness of our MED model, we conduct a real-world experiment to see whether the students are able to write equations by only reading or listening their textual descriptions or not. Experiments conclude that the students are able to write most of the equations correctly by reading their textual descriptions only.
Modelling Multi-Agent Epistemic Planning in ASP
Venue: arXiv cs.AI 2020
Year: 2020

Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in "simple" domains the agents can solely rely on facts about the world, in several contexts, e.g., economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios, epistemic reasoning, i.e., reasoning about agents' beliefs about themselves and about other agents' beliefs, is essential to design winning strategies. This paper addresses the problem of reasoning in multi-agent epistemic settings exploiting declarative programming techniques. In particular, the paper presents an actual implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings, called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP paradigm enables a concise and elegant design of the planner, w.r.t. other imperative implementations, facilitating the development of formal verification of correctness. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature. It is under consideration for acceptance in TPLP.
Energy Communities: From European Law to Numerical Modeling
Venue: arXiv cs.CY 2020
Year: 2020

In 2019, the European Union introduced two new actors in the European energy system: Renewable and Citizen Energy Communities (RECs and CECs). Modelling these two new actors and their effects on the energy system is crucial when implementing the European Legislation, incorporating energy communities (ECs) into the electric grid, planning ECs, and conducting academic research. This paper aims to bridge the gap between the letter of the law and numerical models of ECs. After introducing RECs and CECs, we list elements of the law to be considered by regulators, distribution system operators, EC planners, researchers, and other stakeholders when modelling ECs. Finally, we provide three case studies of EC models that explicitly include elements of the European Law.
A Game-Theoretic Drone-as-a-Service Composition for Delivery
Venue: arXiv cs.NI 2020
Year: 2020

We propose a novel game-theoretic approach for drone service composition considering recharging constraints. We design a non-cooperative game model for drone services. We propose a non-cooperative game algorithm for the selection and composition of optimal drone services. We conduct several experiments on a real drone dataset to demonstrate the efficiency of our proposed approach.
SimPatch: A Nearest Neighbor Similarity Match between Image Patches
Venue: arXiv cs.CV 2020
Year: 2020

Measuring the similarity between patches in images is a fundamental building block in various tasks. Naturally, the patch-size has a major impact on the matching quality, and on the consequent application performance. We try to use large patches instead of relatively small patches so that each patch contains more information. We use different feature extraction mechanisms to extract the features of each individual image patches which forms a feature matrix and find out the nearest neighbor patches in the image. The nearest patches are calculated using two different nearest neighbor algorithms in this paper for a query patch for a given image and the results have been demonstrated in this paper.
Boosting Parallel Influence-Maximization Kernels for Undirected Networks with Fusing and Vectorization
Venue: arXiv cs.DC 2020
Year: 2020

Influence maximization (IM) is the problem of finding a seed vertex set which is expected to incur the maximum influence spread on a graph. It has various applications in practice such as devising an effective and efficient approach to disseminate information, news or ad within a social network. The problem is shown to be NP-hard and approximation algorithms with provable quality guarantees exist in the literature. However, these algorithms are computationally expensive even for medium-scaled graphs. Furthermore, graph algorithms usually suffer from spatial and temporal irregularities during memory accesses, and this adds an extra cost on top of the already expensive IM kernels. In this work, we leverage fused sampling, memoization, and vectorization to restructure, parallelize and boost their performance on undirected networks. The proposed approach employs a pseudo-random function and performs multiple Monte-Carlo simulations in parallel to exploit the SIMD lanes effectively and efficiently. Besides, it significantly reduces the number of edge traversals, hence the amount of data brought from the memory, which is critical for almost all memory-bound graph kernels. We apply the proposed approach to the traditional MixGreedy algorithm and propose Infuser which is more than 3000 times faster than the traditional greedy approaches and can run on large graphs that have been considered as too large in the literature.
On the Distribution of the Sum of Málaga-$\mathcal{M}$ Random Variables and Applications
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, a very accurate approximation method for the statistics of the sum of Málaga-$\mathcal{M}$ random variates with pointing error (MRVs) is proposed. In particular, the probability density function of MRV is approximated by a Fox's $H$-function through the moment-based approach. Then, the respective moment-generating function of the sum of $N$ MRVs is provided, based on which the average symbol error rate is evaluated for an $N $-branch maximal-ratio combining (MRC) receiver. The retrieved results show that the proposed approximate results match accurately with the exact simulated ones. Additionally, the results show that the achievable diversity order increases as a function of the number of MRC diversity branches.
Approximating Constraint Satisfaction Problems Symmetrically
Venue: arXiv cs.LO 2020
Year: 2020

This thesis investigates the extent to which the optimal value of a constraint satisfaction problem (CSP) can be approximated by some sentence of fixed point logic with counting (FPC). It is known that, assuming $\mathsf{P} \neq \mathsf{NP}$ and the Unique Games Conjecture, the best polynomial time approximation algorithm for any CSP is given by solving and rounding a specific semidefinite programming relaxation. We prove an analogue of this result for algorithms that are definable as FPC-interpretations, which holds without the assumption that $\mathsf{P} \neq \mathsf{NP}$. While we are not able to drop (an FPC-version of) the Unique Games Conjecture as an assumption, we do present some partial results toward proving it. Specifically, we give a novel construction which shows that, for all $α> 0$, there exists a positive integer $q = \text{poly}(\frac{1}α)$ such that no there is no FPC-interpretation giving an $α$-approximation of Unique Games on a label set of size $q$.
Adapting Nielsen's Usability Heuristics to the Context of Mobile Augmented Reality
Venue: arXiv cs.HC 2020
Year: 2020

Augmented reality (AR) is an emerging technology in mobile app design during recent years. However, usability challenges in these apps are prominent. There are currently no established guidelines for designing and evaluating interactions in AR as there are in traditional user interfaces. In this work, we aimed to examine the usability of current mobile AR applications and interpreting classic usability heuristics in the context of mobile AR. Particularly, we focused on AR home design apps because of their popularity and ability to incorporate important mobile AR interaction schemas. Our findings indicated that it is important for the designers to consider the unfamiliarity of AR technology to the vast users and to take technological limitations into consideration when designing mobile AR apps. Our work serves as a first step for establishing more general heuristics and guidelines for mobile AR.
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification
Venue: arXiv cs.LG 2020
Year: 2020

Uncertainty quantification in neural networks gained a lot of attention in the past years. The most popular approaches, Bayesian neural networks (BNNs), Monte Carlo dropout, and deep ensembles have one thing in common: they are all based on some kind of mixture model. While the BNNs build infinite mixture models and are derived via variational inference, the latter two build finite mixtures trained with the maximum likelihood method. In this work we investigate the effect of training an infinite mixture distribution with the maximum likelihood method instead of variational inference. We find that the proposed objective leads to stochastic networks with an increased predictive variance, which improves uncertainty based identification of miss-classification and robustness against adversarial attacks in comparison to a standard BNN with equivalent network structure. The new model also displays higher entropy on out-of-distribution data.
Evaluating Snowflake as an Indistinguishable Censorship Circumvention Tool
Venue: arXiv cs.CR 2020
Year: 2020

Tor is the most well-known tool for circumventing censorship. Unfortunately, Tor traffic has been shown to be detectable using deep-packet inspection. WebRTC is a popular web frame-work that enables browser-to-browser connections. Snowflake is a novel pluggable transport that leverages WebRTC to connect Tor clients to the Tor network. In theory, Snowflake was created to be indistinguishable from other WebRTC services. In this paper, we evaluate the indistinguishability of Snowflake. We collect over 6,500 DTLS handshakes from Snowflake, Facebook Messenger, Google Hangouts, and Discord WebRTC connections and show that Snowflake is identifiable among these applications with 100% accuracy. We show that several features, including the extensions offered and the number of packets in the handshake, distinguish Snowflake among these services. Finally, we suggest recommendations for improving identification resistance in Snowflake. We have made the dataset publicly available.
COVID, BLM, and the polarization of US politicians on Twitter
Venue: arXiv cs.SI 2020
Year: 2020

We mapped the tweets of 520 US Congress members, focusing on analyzing their engagement with two broad topics: first, the COVID-19 pandemic, and second, the recent wave of anti-racist protest. We find that, in discussing COVID-19, Democrats frame the issue in terms of public health, while Republicans are more likely to focus on small businesses and the economy. When looking at the discourse around anti-Black violence, we find that Democrats are far more likely to name police brutality as a specific concern. In contrast, Republicans not only discuss the issue far less, but also keep their terms more general, as well as criticizing perceived protest violence.
SafePILCO: a software tool for safe and data-efficient policy synthesis
Venue: arXiv cs.LG 2020
Year: 2020

SafePILCO is a software tool for safe and data-efficient policy search with reinforcement learning. It extends the known PILCO algorithm, originally written in MATLAB, to support safe learning. We provide a Python implementation and leverage existing libraries that allow the codebase to remain short and modular, which is appropriate for wider use by the verification, reinforcement learning, and control communities.