OpenRaiser/Intern-Atlas huggingface dataset:
Fluidic Fabric Muscle Sheets for Wearable and Soft Robotics
Venue: arXiv cs.RO 2019
Year: 2019

Conformable robotic systems are attractive for applications in which they can be used to actuate structures with large surface areas, to provide forces through wearable garments, or to realize autonomous robotic systems. We present a new family of soft actuators that we refer to as Fluidic Fabric Muscle Sheets (FFMS). They are composite fabric structures that integrate fluidic transmissions based on arrays of elastic tubes. These sheet-like actuators can strain, squeeze, bend, and conform to hard or soft objects of arbitrary shapes or sizes, including the human body. We show how to design and fabricate FFMS actuators via facile apparel engineering methods, including computerized sewing techniques. Together, these determine the distributions of stresses and strains that can be generated by the FFMS. We present a simple mathematical model that proves effective for predicting their performance. FFMS can operate at frequencies of 5 Hertz or more, achieve engineering strains exceeding 100%, and exert forces greater than 115 times their own weight. They can be safely used in intimate contact with the human body even when delivering stresses exceeding 10$^\text{6}$ Pascals. We demonstrate their versatility for actuating a variety of bodies or structures, and in configurations that perform multi-axis actuation, including bending and shape change. As we also show, FFMS can be used to exert forces on body tissues for wearable and biomedical applications. We demonstrate several potential use cases, including a miniature steerable robot, a glove for grasp assistance, garments for applying compression to the extremities, and devices for actuating small body regions or tissues via localized skin stretch.
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems
Venue: arXiv cs.CL 2019
Year: 2019

With the advent of conversational assistants, like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial setting. These systems typically consist of Spoken Language understanding component which, in turn, consists of two tasks - Intent Classification (IC) and Slot Labeling (SL). Generally, these two tasks are modeled together jointly to achieve best performance. However, this joint modeling adds to model obfuscation. In this work, we first design framework for a modularization of joint IC-SL task to enhance architecture transparency. Then, we explore a number of self-attention, convolutional, and recurrent models, contributing a large-scale analysis of modeling paradigms for IC+SL across two datasets. Finally, using this framework, we propose a class of 'label-recurrent' models that otherwise non-recurrent, with a 10-dimensional representation of the label history, and show that our proposed systems are easy to interpret, highly accurate (achieving over 30% error reduction in SL over the state-of-the-art on the Snips dataset), as well as fast, at 2x the inference and 2/3 to 1/2 the training time of comparable recurrent models, thus giving an edge in critical real-world systems.
Sundials in the Shade: An Internet-wide Perspective on ICMP Timestamps
Venue: arXiv cs.NI 2019
Year: 2019

ICMP timestamp request and response packets have been standardized for nearly 40 years, but have no modern practical application, having been superseded by NTP. However, ICMP timestamps are not deprecated, suggesting that while hosts must support them, little attention is paid to their implementation and use. In this work, we perform active measurements and find 2.2 million hosts on the Internet responding to ICMP timestamp requests from over 42,500 unique autonomous systems. We develop a methodology to classify timestamp responses, and find 13 distinct classes of behavior. Not only do these behaviors enable a new fingerprinting vector, some behaviors leak important information about the host e.g., OS, kernel version, and local timezone.
On the Robustness of Deep K-Nearest Neighbors
Venue: arXiv cs.CR 2019
Year: 2019

Despite a large amount of attention on adversarial examples, very few works have demonstrated an effective defense against this threat. We examine Deep k-Nearest Neighbor (DkNN), a proposed defense that combines k-Nearest Neighbor (kNN) and deep learning to improve the model's robustness to adversarial examples. It is challenging to evaluate the robustness of this scheme due to a lack of efficient algorithm for attacking kNN classifiers with large k and high-dimensional data. We propose a heuristic attack that allows us to use gradient descent to find adversarial examples for kNN classifiers, and then apply it to attack the DkNN defense as well. Results suggest that our attack is moderately stronger than any naive attack on kNN and significantly outperforms other attacks on DkNN.
Any Finite Distributive Lattice is Isomorphic to the Minimizer Set of an ${\rm M}^{\natural}$-Concave Set Function
Venue: arXiv cs.DM 2019
Year: 2019

Submodularity is an important concept in combinatorial optimization, and it is often regarded as a discrete analog of convexity. It is a fundamental fact that the set of minimizers of any submodular function forms a distributive lattice. Conversely, it is also known that any finite distributive lattice is isomorphic to the minimizer set of a submodular function, through the celebrated Birkhoff's representation theorem. ${\rm M}^{\natural}$-concavity is a key concept in discrete convex analysis. It is known for set functions that the class of ${\rm M}^{\natural}$-concavity is a proper subclass of submodularity. Thus, the minimizer set of an ${\rm M}^{\natural}$-concave function forms a distributive lattice. It is natural to ask if any finite distributive lattice appears as the minimizer set of an ${\rm M}^{\natural}$-concave function. This paper affirmatively answers the question.
Extracting Frequent Gradual Patterns Using Constraints Modeling
Venue: arXiv cs.AI 2019
Year: 2019

In this paper, we propose a constraint-based modeling approach for the problem of discovering frequent gradual patterns in a numerical dataset. This SAT-based declarative approach offers an additional possibility to benefit from the recent progress in satisfiability testing and to exploit the efficiency of modern SAT solvers for enumerating all frequent gradual patterns in a numerical dataset. Our approach can easily be extended with extra constraints, such as temporal constraints in order to extract more specific patterns in a broad range of gradual patterns mining applications. We show the practical feasibility of our SAT model by running experiments on two real world datasets.
Three-dimensional Segmentation of Trees Through a Flexible Multi-Class Graph Cut Algorithm (MCGC)
Venue: arXiv cs.CV 2019
Year: 2019

Developing a robust algorithm for automatic individual tree crown (ITC) detection from laser scanning datasets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here we describe a Multi-Class Graph Cut (MCGC) approach to tree crown delineation. This uses local three-dimensional geometry and density information, alongside knowledge of crown allometries, to segment individual tree crowns from LiDAR point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognise small trees. From these three-dimensional crowns, we are able to measure individual tree biomass. Comparing these estimates to those from permanent inventory plots, our algorithm is able to produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of three-dimensional data, such as structure from motion datasets.
EVMFuzz: Differential Fuzz Testing of Ethereum Virtual Machine
Venue: arXiv cs.SE 2019
Year: 2019

Ethereum Virtual Machine (EVM) is the run-time environment for smart contracts and its vulnerabilities may lead to serious problems to the Ethereum ecology. With lots of techniques being developed for the validation of smart contracts, the security problems of EVM have not been well-studied. In this paper, we propose EVMFuzz, aiming to detect vulnerabilities of EVMs with differential fuzz testing. The core idea of EVMFuzz is to continuously generate seed contracts for different EVMs' execution, so as to find as many inconsistencies among execution results as possible, eventually discover vulnerabilities with output cross-referencing. First, we present the evaluation metric for the internal inconsistency indicator, such as the opcode sequence executed and gas used. Then, we construct seed contracts via a set of predefined mutators and employ dynamic priority scheduling algorithm to guide seed contracts selection and maximize the inconsistency. Finally, we leverage different EVMs as crossreferencing oracles to avoid manual checking of the execution output. For evaluation, we conducted large-scale mutation on 36,295 real-world smart contracts and generated 253,153 smart contracts. Among them, 66.2% showed differential performance, including 1,596 variant contracts triggered inconsistent output among EVMs. Accompanied by manual root cause analysis, we found 5 previously unknown security bugs in four widely used EVMs, and all had been included in Common Vulnerabilities and Exposures (CVE) database.
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype
Venue: arXiv cs.NE 2019
Year: 2019

Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use of complex functions such as random number generators, that are expensive to compute on standard general purpose hardware. The prototype chip of the 2nd generation SpiNNaker system is designed to overcome this problem. Low-power ARM processors equipped with a random number generator and an exponential function accelerator enable the efficient execution of brain-inspired algorithms. We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. The numerical simulation of the model requires to update the synapse variables in each time step including an explorative random term. To the best of our knowledge, this is the most complex synapse model implemented so far on the SpiNNaker system. By making efficient use of the hardware accelerators and numerical optimizations the computation time of one plasticity update is reduced by a factor of 2. This, combined with fitting the model into to the local SRAM, leads to 62% energy reduction compared to the case without accelerators and the use of external DRAM. The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. The hardware-software system presented in this work paves the way for power-efficient mobile and biomedical applications with biologically plausible brain-inspired algorithms.
Preference rules for label ranking: Mining patterns in multi-target relations
Venue: arXiv cs.LG 2019
Year: 2019

In this paper we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.
Ontology of Card Sleights
Venue: arXiv cs.AI 2019
Year: 2019

We present a machine-readable movement writing for sleight-of-hand moves with cards -- a "Labanotation of card magic." This scheme of movement writing contains 440 categories of motion, and appears to taxonomize all card sleights that have appeared in over 1500 publications. The movement writing is axiomatized in $\mathcal{SROIQ}$(D) Description Logic, and collected formally as an Ontology of Card Sleights, a computational ontology that extends the Basic Formal Ontology and the Information Artifact Ontology. The Ontology of Card Sleights is implemented in OWL DL, a Description Logic fragment of the Web Ontology Language. While ontologies have historically been used to classify at a less granular level, the algorithmic nature of card tricks allows us to transcribe a performer's actions step by step. We conclude by discussing design criteria we have used to ensure the ontology can be accessed and modified with a simple click-and-drag interface. This may allow database searches and performance transcriptions by users with card magic knowledge, but no ontology background.
Prioritized Process Test: An Alternative to Current Process Testing Strategies
Venue: arXiv cs.SE 2019
Year: 2019

Testing processes and workflows in information and Internet of Things systems is a major part of the typical software testing effort. Consistent and efficient path-based test cases are desired to support these tests. Because certain parts of software system workflows have a higher business priority than others, this fact has to be involved in the generation of test cases. In this paper, we propose a Prioritized Process Test (PPT), which is a model-based test case generation algorithm that represents an alternative to currently established algorithms that use directed graphs and test requirements to model the system under test. The PPT accepts a directed multigraph as a model to express priorities, and edge weights are used instead of test requirements. To determine the test-coverage level of test cases, a test-depth-level concept is used. We compared the presented PPT with five alternatives (i.e., the Process Cycle Test, a naive reduction of test set created by the Process Cycle Test, Brute Force algorithm, Set-covering Based Solution and Matching-based Prefix Graph Solution) for edge coverage and edge-pair coverage. To assess the optimality of the path-based test cases produced by these strategies, we used fourteen metrics based on the properties of these test cases and 59 models that were created for three real-world systems. For all edge coverage, the PPT produced more optimal test cases than the alternatives in terms of the majority of the metrics. For edge-pair coverage, the PPT strategy yielded similar results to those of the alternatives. Thus, the PPT strategy is an applicable alternative, as it reflects both the required test coverage level and the business priority in parallel.
Identifying Maximal Non-Redundant Integer Cone Generators
Venue: arXiv cs.LO 2019
Year: 2019

A non-redundant integer cone generator (NICG) of dimension $d$ is a set $S$ of vectors from $\{0,1\}^d$ whose vector sum cannot be generated as a positive integer linear combination of a proper subset of $S$. The largest possible cardinality of NICG of a dimension $d$, denoted by $N(d)$, provides an upper bound on the sparsity of systems of integer equations with a large number of integer variables. A better estimate of $N(d)$ means that we can consider smaller sub-systems of integer equations when solving systems with many integer variables. Furthermore, given that we can reduce constraints on set algebra expressions to constraints on cardinalities of Venn regions, tighter upper bound on $N(d)$ yields a more efficient decision procedure for a logic of sets with cardinality constraints (BAPA), which has been applied in software verification. Previous attempts to compute $N(d)$ using SAT solvers have not succeeded even for $d=3$. The only known values were computed manually: $N(d)=d$ for $d < 4$ and $N(4) > 4$. We provide the first exact values for $d > 3$, namely, $N(4)=5$, $N(5)=7$, and $N(6)=9$, which is a significant improvement of the known asymptotic bound (which would give only e.g. $N(6) \le 29$, making a decision procedure impractical for $d=6$). We also give lower bounds for $N(7)$, $N(8)$, $N(9)$, and $N(10)$, which are: $11$, $13$, $14$, and $16$, respectively. We describe increasingly sophisticated specialized search algorithms that we used to explore the space of non-redundant generators and obtain these results.
Deep Learning for the Degraded Broadcast Channel
Venue: arXiv cs.IT 2019
Year: 2019

Machine learning has shown promising results for communications system problems. We present results on the use of deep auto-encoders in order to learn a transceiver for the multiuser degraded broadcast channel, and see that the auto encoder is able to learn to communicate on this channel using superposition coding. Additionally, the deep neural net is able to determine a bit labeling and optimize the per user power allocation that depends on the per user SNR.
Contextual Bandits with Random Projection
Venue: arXiv cs.LG 2019
Year: 2019

Contextual bandits with linear payoffs, which are also known as linear bandits, provide a powerful alternative for solving practical problems of sequential decisions, e.g., online advertisements. In the era of big data, contextual data usually tend to be high-dimensional, which leads to new challenges for traditional linear bandits mostly designed for the setting of low-dimensional contextual data. Due to the curse of dimensionality, there are two challenges in most of the current bandit algorithms: the first is high time-complexity; and the second is extreme large upper regret bounds with high-dimensional data. In this paper, in order to attack the above two challenges effectively, we develop an algorithm of Contextual Bandits via RAndom Projection (\texttt{CBRAP}) in the setting of linear payoffs, which works especially for high-dimensional contextual data. The proposed \texttt{CBRAP} algorithm is time-efficient and flexible, because it enables players to choose an arm in a low-dimensional space, and relaxes the sparsity assumption of constant number of non-zero components in previous work. Besides, we provide a linear upper regret bound for the proposed algorithm, which is associated with reduced dimensions.
Numerical Algorithmic Science and Engineering within Computer Science: Rationale, Foundations and Organization
Venue: arXiv cs.NA 2019
Year: 2019

A re-calibration is proposed for "numerical analysis" as it arises specifically within the broader, embracing field of modern computer science (CS). This would facilitate research into theoretical and practicable models of real-number computation at the foundations of CS, and it would also advance the instructional objectives of the CS field. Our approach is premised on the key observation that the great "watershed" in numerical computation is much more between finite- and infinite-dimensional numerical problems than it is between discrete and continuous numerical problems. A revitalized discipline for numerical computation within modern CS can more accurately be defined as "numerical algorithmic science & engineering (NAS&E), or more compactly, as "numerical algorithmics," its focus being the algorithmic solution of numerical problems that are either discrete, or continuous over a space of finite dimension, or a combination of the two. It is the counterpart within modern CS of the numerical analysis discipline, whose primary focus is the algorithmic solution of continuous, infinite-dimensional numerical problems and their finite-dimensional approximates, and whose specialists today have largely been repatriated to departments of mathematics. Our detailed overview of NAS&E from the viewpoints of rationale, foundations, and organization is preceded by a recounting of the role played by numerical analysts in the evolution of academic departments of computer science, in order to provide background for NAS&E and place the newly-emerging discipline within its larger historical context.
A Review of Sensing and Communication, Human Factors, and Controller Aspects for Information-Aware Connected and Automated Vehicles
Venue: arXiv cs.NI 2019
Year: 2019

Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to its potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies, human factors, and information-aware controller design. First, different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factor issues, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.
Russian Language Datasets in the Digitial Humanities Domain and Their Evaluation with Word Embeddings
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we present Russian language datasets in the digital humanities domain for the evaluation of word embedding techniques or similar language modeling and feature learning algorithms. The datasets are split into two task types, word intrusion and word analogy, and contain 31362 task units in total. The characteristics of the tasks and datasets are that they build upon small, domain-specific corpora, and that the datasets contain a high number of named entities. The datasets were created manually for two fantasy novel book series ("A Song of Ice and Fire" and "Harry Potter"). We provide baseline evaluations with popular word embedding models trained on the book corpora for the given tasks, both for the Russian and English language versions of the datasets. Finally, we compare and analyze the results and discuss specifics of Russian language with regards to the problem setting.