OpenRaiser/Intern-Atlas huggingface dataset:
Device to Remotely Track and Locate the Position of a Child for Safety
Venue: arXiv cs.CY 2020
Year: 2020

Parents are always worried about the wellbeing of their children. As per the Statistics Report 2017 by Missing Children Europe Organization, a child is reported missing every 2 minutes. Due to the imminent threat, parents are prone to buy their children mobile phones to keep in touch with them. However, giving a Mobile phone to a child can cause issues including cyber bullying, improper use of social networks, access to mature age and illicit content on the internet and possibly, phone theft. As an effort to tackle some of those issues, this paper proposes a solution which enables parents to call, locate and track their children using a child-friendly mobile device. The common scenario the device would come to play is in enhancing the safety of a child who would travel alone on a typical route; for instance a child who walks from home to school and back. The device can be calibrated to keep track of a typical route of travel. Then, if the device de-tects some deviation from the usual route, it would trigger a notification to parents. A probability matrix based nov-el algorithm is introduced to detect route deviation. De-sign details of the mobile device, along with the details of the route deviation detection algorithm are presented in this paper.
Theta palindromes in theta conjugates
Venue: arXiv cs.FL 2020
Year: 2020

A DNA string is a Watson-Crick (WK) palindrome when the complement of its reverse is equal to itself. The Watson-Crick mapping $θ$ is an involution that is also an antimorphism. $θ$-conjugates of a word is a generalisation of conjugates of a word that incorporates the notion of WK-involution $θ$. In this paper, we study the distribution of palindromes and Watson-Crick palindromes, also known as $θ$-palindromes among both the set of conjugates and $θ$-conjugates of a word $w$. We also consider some general properties of the set $C_θ(w)$, i.e., the set of $θ$-conjugates of a word $w$, and characterize words $w$ such that $|C_θ(w)|=|w|+1$, i.e., with the maximum number of elements in $C_θ(w)$. We also find the structure of words that have at least one (WK)-palindrome in $C_θ(w)$.
Dividing Bads is Harder than Dividing Goods: On the Complexity of Fair and Efficient Division of Chores
Venue: arXiv cs.GT 2020
Year: 2020

We study the chore division problem where a set of agents needs to divide a set of chores (bads) among themselves fairly and efficiently. We assume that agents have linear disutility (cost) functions. Like for the case of goods, competitive division is known to be arguably the best mechanism for the bads as well. However, unlike goods, there are multiple competitive divisions with very different disutility value profiles in bads. Although all competitive divisions satisfy the standard notions of fairness and efficiency, some divisions are significantly fairer and efficient than the others. This raises two important natural questions: Does there exist a competitive division in which no agent is assigned a chore that she hugely dislikes? Are there simple sufficient conditions for the existence and polynomial-time algorithms assuming them? We investigate both these questions in this paper. We show that the first problem is strongly NP-hard. Further, we derive a simple sufficient condition for the existence, and we show that finding a competitive division is PPAD-hard assuming the condition. These results are in sharp contrast to the case of goods where both problems are strongly polynomial-time solvable. To the best of our knowledge, these are the first hardness results for the chore division problem, and, in fact, for any economic model under linear preferences.
CuttleSys: Data-Driven Resource Management forInteractive Applications on Reconfigurable Multicores
Venue: arXiv cs.AR 2020
Year: 2020

Multi-tenancy for latency-critical applications leads to re-source interference and unpredictable performance. Core reconfiguration opens up more opportunities for colocation,as it allows the hardware to adjust to the dynamic performance and power needs of a specific mix of co-scheduled applications. However, reconfigurability also introduces challenges, as even for a small number of reconfigurable cores, exploring the design space becomes more time- and resource-demanding. We present CuttleSys, a runtime for reconfigurable multi-cores that leverages scalable and lightweight data mining to quickly identify suitable core and cache configurations for a set of co-scheduled applications. The runtime combines collaborative filtering to infer the behavior of each job on every core and cache configuration, with Dynamically Dimensioned Search to efficiently explore the configuration space. We evaluate CuttleSys on multicores with tens of reconfigurable cores and show up to 2.46x and 1.55x performance improvements compared to core-level gating and oracle-like asymmetric multicores respectively, under stringent power constraints.
Vision and Inertial Sensing Fusion for Human Action Recognition : A Review
Venue: arXiv cs.HC 2020
Year: 2020

Human action recognition is used in many applications such as video surveillance, human computer interaction, assistive living, and gaming. Many papers have appeared in the literature showing that the fusion of vision and inertial sensing improves recognition accuracies compared to the situations when each sensing modality is used individually. This paper provides a survey of the papers in which vision and inertial sensing are used simultaneously within a fusion framework in order to perform human action recognition. The surveyed papers are categorized in terms of fusion approaches, features, classifiers, as well as multimodality datasets considered. Challenges as well as possible future directions are also stated for deploying the fusion of these two sensing modalities under realistic conditions.
Blackbox Trojanising of Deep Learning Models : Using non-intrusive network structure and binary alterations
Venue: arXiv cs.CR 2020
Year: 2020

Recent advancements in Artificial Intelligence namely in Deep Learning has heightened its adoption in many applications. Some are playing important roles to the extent that we are heavily dependent on them for our livelihood. However, as with all technologies, there are vulnerabilities that malicious actors could exploit. A form of exploitation is to turn these technologies, intended for good, to become dual-purposed instruments to support deviant acts like malicious software trojans. As part of proactive defense, researchers are proactively identifying such vulnerabilities so that protective measures could be developed subsequently. This research explores a novel blackbox trojanising approach using a simple network structure modification to any deep learning image classification model that would transform a benign model into a deviant one with a simple manipulation of the weights to induce specific types of errors. Propositions to protect the occurrence of such simple exploits are discussed in this research. This research highlights the importance of providing sufficient safeguards to these models so that the intended good of AI innovation and adoption may be protected.
Concentration-Bound Analysis for Probabilistic Programs and Probabilistic Recurrence Relations
Venue: arXiv cs.PL 2020
Year: 2020

Analyzing probabilistic programs and randomized algorithms are classical problems in computer science. The first basic problem in the analysis of stochastic processes is to consider the expectation or mean, and another basic problem is to consider concentration bounds, i.e. showing that large deviations from the mean have small probability. Similarly, in the context of probabilistic programs and randomized algorithms, the analysis of expected termination time/running time and their concentration bounds are fundamental problems.In this work, we focus on concentration bounds for probabilistic programs and probabilistic recurrences of randomized algorithms. For probabilistic programs, the basic technique to achieve concentration bounds is to consider martingales and apply the classical Azuma's inequality. For probabilistic recurrences of randomized algorithms, Karp's classical "cookbook" method, which is similar to the master theorem for recurrences, is the standard approach to obtain concentration bounds. In this work, we propose a novel approach for deriving concentration bounds for probabilistic programs and probabilistic recurrence relations through the synthesis of exponential supermartingales. For probabilistic programs, we present algorithms for synthesis of such supermartingales in several cases. We also show that our approach can derive better concentration bounds than simply applying the classical Azuma's inequality over various probabilistic programs considered in the literature. For probabilistic recurrences, our approach can derive tighter bounds than the Karp's well-established methods on classical algorithms. Moreover, we show that our approach could derive bounds comparable to the optimal bound for quicksort, proposed by McDiarmid and Hayward. We also present a prototype implementation that can automatically infer these bounds
SemEval-2020 Task 5: Counterfactual Recognition
Venue: arXiv cs.CL 2020
Year: 2020

We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequents with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. The data, baseline code, and leaderboard can be found at https://competitions.codalab.org/competitions/21691. The data and baseline code are also available at https://zenodo.org/record/3932442.
Modeling of Personalized Anatomy using Plastic Strains
Venue: arXiv cs.GR 2020
Year: 2020

We give a method for modeling solid objects undergoing large spatially varying and/or anisotropic strains, and use it to reconstruct human anatomy from medical images. Our novel shape deformation method uses plastic strains and the Finite Element Method to successfully model shapes undergoing large and/or anisotropic strains, specified by sparse point constraints on the boundary of the object. We extensively compare our method to standard second-order shape deformation methods, variational methods and surface-based methods and demonstrate that our method avoids the spikiness, wiggliness and other artefacts of previous methods. We demonstrate how to perform such shape deformation both for attached and un-attached ("free flying") objects, using a novel method to solve linear systems with singular matrices with a known nullspace. While our method is applicable to general large-strain shape deformation modeling, we use it to create personalized 3D triangle and volumetric meshes of human organs, based on MRI or CT scans. Given a medically accurate anatomy template of a generic individual, we optimize the geometry of the organ to match the MRI or CT scan of a specific individual. Our examples include human hand muscles, a liver, a hip bone, and a gluteus medius muscle ("hip abductor").
A Combinatorial Design for Cascaded Coded Distributed Computing on General Networks
Venue: arXiv cs.IT 2020
Year: 2020

Coding theoretic approached have been developed to significantly reduce the communication load in modern distributed computing system. In particular, coded distributed computing (CDC) introduced by Li et al. can efficiently trade computation resources to reduce the communication load in MapReduce like computing systems. For the more general cascaded CDC, Map computations are repeated at r nodes to significantly reduce the communication load among nodes tasked with computing Q Reduce functions s times. In this paper, we propose a novel low-complexity combinatorial design for cascaded CDC which 1) determines both input file and output function assignments, 2) requires significantly less number of input files and output functions, and 3) operates on heterogeneous networks where nodes have varying storage and computing capabilities. We provide an analytical characterization of the computation-communication tradeoff, from which we show the proposed scheme can outperform the state-of-the-art scheme proposed by Li et al. for the homogeneous networks. Further, when the network is heterogeneous, we show that the performance of the proposed scheme can be better than its homogeneous counterpart. In addition, the proposed scheme is optimal within a constant factor of the information theoretic converse bound while fixing the input file and the output function assignments.
audioLIME: Listenable Explanations Using Source Separation
Venue: arXiv cs.SD 2020
Year: 2020

Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable. We propose audioLIME, a method based on Local Interpretable Model-agnostic Explanations (LIME) extended by a musical definition of locality. The perturbations used in LIME are created by switching on/off components extracted by source separation which makes our explanations listenable. We validate audioLIME on two different music tagging systems and show that it produces sensible explanations in situations where a competing method cannot.
Deep Photo Cropper and Enhancer
Venue: arXiv cs.CV 2020
Year: 2020

This paper introduces a new type of image enhancement problem. Compared to traditional image enhancement methods, which mostly deal with pixel-wise modifications of a given photo, our proposed task is to crop an image which is embedded within a photo and enhance the quality of the cropped image. We split our proposed approach into two deep networks: deep photo cropper and deep image enhancer. In the photo cropper network, we employ a spatial transformer to extract the embedded image. In the photo enhancer, we employ super-resolution to increase the number of pixels in the embedded image and reduce the effect of stretching and distortion of pixels. We use cosine distance loss between image features and ground truth for the cropper and the mean square loss for the enhancer. Furthermore, we propose a new dataset to train and test the proposed method. Finally, we analyze the proposed method with respect to qualitative and quantitative evaluations.
On the Expressive Power of Higher-Order Pushdown Systems
Venue: arXiv cs.FL 2020
Year: 2020

We show that deterministic collapsible pushdown automata of second order can recognize a language that is not recognizable by any deterministic higher-order pushdown automaton (without collapse) of any order. This implies that there exists a tree generated by a second order collapsible pushdown system (equivalently, by a recursion scheme of second order) that is not generated by any deterministic higher-order pushdown system (without collapse) of any order (equivalently, by any safe recursion scheme of any order). As a side effect, we present a pumping lemma for deterministic higher-order pushdown automata, which potentially can be useful for other applications.
The Subfield Codes of $[q+1, 2, q]$ MDS Codes
Venue: arXiv cs.IT 2020
Year: 2020

Recently, subfield codes of geometric codes over large finite fields $\gf(q)$ with dimension $3$ and $4$ were studied and distance-optimal subfield codes over $\gf(p)$ were obtained, where $q=p^m$. The key idea for obtaining very good subfield codes over small fields is to choose very good linear codes over an extension field with small dimension. This paper first presents a general construction of $[q+1, 2, q]$ MDS codes over $\gf(q)$, and then studies the subfield codes over $\gf(p)$ of some of the $[q+1, 2,q]$ MDS codes over $\gf(q)$. Two families of dimension-optimal codes over $\gf(p)$ are obtained, and several families of nearly optimal codes over $\gf(p)$ are produced. Several open problems are also proposed in this paper.
Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning
Venue: arXiv cs.RO 2020
Year: 2020

We present Learning to Drive (L2D), a low-cost benchmark for real-world reinforcement learning (RL). L2D involves a simple and reproducible experimental setup where an RL agent has to learn to drive a Donkey car around three miniature tracks, given only monocular image observations and speed of the car. The agent has to learn to drive from disengagements, which occurs when it drives off the track. We present and open-source our training pipeline, which makes it straightforward to apply any existing RL algorithm to the task of autonomous driving with a Donkey car. We test imitation learning, state-of-the-art model-free, and model-based algorithms on the proposed L2D benchmark. Our results show that existing RL algorithms can learn to drive the car from scratch in less than five minutes of interaction. We demonstrate that RL algorithms can learn from sparse and noisy disengagement to drive even faster than imitation learning and a human operator.
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
Venue: arXiv cs.LG 2020
Year: 2020

Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias. This causes a newly trained model to act greedily and favor items that have already been engaged by users. This behavior is particularly harmful in personalised ads recommendations, as it can also cause new campaigns to remain unexplored. Exploration aims to address this limitation by providing new information about the environment, which encompasses user preference, and can lead to higher long-term reward. In this work, we formulate a display advertising recommender as a contextual bandit and implement exploration techniques that require sampling from the posterior distribution of click-through-rates in a computationally tractable manner. Traditional large-scale deep learning models do not provide uncertainty estimates by default. We approximate these uncertainty measurements of the predictions by employing a bootstrapped model with multiple heads and dropout units. We benchmark a number of different models in an offline simulation environment using a publicly available dataset of user-ads engagements. We test our proposed deep Bayesian bandits algorithm in the offline simulation and online AB setting with large-scale production traffic, where we demonstrate a positive gain of our exploration model.
Distributed Localization of Wireless Sensor Network Using Communication Wheel
Venue: arXiv cs.DC 2020
Year: 2020

We study the network localization problem, i.e., the problem of determining node positions of a wireless sensor network modeled as a unit disk graph. In an arbitrarily deployed network, positions of all nodes of the network may not be uniquely determined. It is known that even if the network corresponds to a unique solution, no polynomial-time algorithm can solve this problem in the worst case, unless RP = NP. So we are interested in algorithms that efficiently localize the network partially. A widely used technique that can efficiently localize a uniquely localizable portion of the network is trilateration: starting from three anchors (nodes with known positions), nodes having at least three localized neighbors are sequentially localized. However, the performance of trilateration can substantially differ for different choices of the initial three anchors. In this paper, we propose a distributed localization scheme with a theoretical characterization of nodes that are guaranteed to be localized. In particular, our proposed distributed algorithm starts localization from a strongly interior node and provided that the subgraph induced by the strongly interior nodes is connected, it localizes all nodes of the network except some boundary nodes and isolated weakly interior nodes.
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version
Venue: arXiv cs.LG 2020
Year: 2020

Learning-based approaches for solving large sequential decision making problems have become popular in recent years. The resulting agents perform differently and their characteristics depend on those of the underlying learning approach. Here, we consider a benchmark planning problem from the reinforcement learning domain, the Racetrack, to investigate the properties of agents derived from different deep (reinforcement) learning approaches. We compare the performance of deep supervised learning, in particular imitation learning, to reinforcement learning for the Racetrack model. We find that imitation learning yields agents that follow more risky paths. In contrast, the decisions of deep reinforcement learning are more foresighted, i.e., avoid states in which fatal decisions are more likely. Our evaluations show that for this sequential decision making problem, deep reinforcement learning performs best in many aspects even though for imitation learning optimal decisions are considered.
DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal
Venue: arXiv cs.CV 2020
Year: 2020

Rain removal is an important but challenging computer vision task as rain streaks can severely degrade the visibility of images that may make other visions or multimedia tasks fail to work. Previous works mainly focused on feature extraction and processing or neural network structure, while the current rain removal methods can already achieve remarkable results, training based on single network structure without considering the cross-scale relationship may cause information drop-out. In this paper, we explore the cross-scale manner between networks and inner-scale fusion operation to solve the image rain removal task. Specifically, to learn features with different scales, we propose a multi-sub-networks structure, where these sub-networks are fused via a crossscale manner by Gate Recurrent Unit to inner-learn and make full use of information at different scales in these sub-networks. Further, we design an inner-scale connection block to utilize the multi-scale information and features fusion way between different scales to improve rain representation ability and we introduce the dense block with skip connection to inner-connect these blocks. Experimental results on both synthetic and real-world datasets have demonstrated the superiority of our proposed method, which outperforms over the state-of-the-art methods. The source code will be available at https://supercong94.wixsite.com/supercong94.
Fluid Composition of Intermittent IoT Energy Services
Venue: arXiv cs.DC 2020
Year: 2020

We propose a novel fluid composition approach of wireless energy services in a crowdsourced IoT environment. The proposed approach selects an optimal set of dynamic energy services according to the consumer's requirements. We leverage the mobility patterns of the crowd in confined areas to capture the intermittent behavior of IoT energy services. We model the IoT energy services based on their mobility patterns to propose a knapsack-based heuristic for the fluid composition. Experimental results demonstrate the efficiency of the proposed approach.