OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Token Systems on Ethereum
Venue: arXiv cs.CR 2018
Year: 2018

We propose and compare two approaches to identify smart contracts as token systems by analyzing their public bytecode. The first approach symbolically executes the code in order to detect token systems by their characteristic behavior of updating internal accounts. The second approach serves as a comparison base and exploits the common interface of ERC-20, the most popular token standard. We present quantitative results for the Ethereum blockchain, and validate the effectiveness of both approaches using a set of curated token systems as ground truth. We observe 100% recall for the second approach. Recall rates of 89% (with well explainable missed detections) indicate that the first approach may also be able to identify "hidden" or undocumented token systems that intentionally do not implement the standard. One possible application of the proposed methods is to facilitate regulator' tasks of monitoring and policing the use of token systems and their underlying platforms.
The complexity of matrix multiplication: developments since 2014. Extended abstract of 2018 Oberwolfach Complexity meeting plenary lecture
Venue: arXiv cs.CC 2018
Year: 2018

This is an overview of recent developments regarding the complexity of matrix multiplication, with an emphasis on the uses of algebraic geometry and representation theory in complexity theory.
Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview
Venue: arXiv cs.LG 2018
Year: 2018

Quantifying and managing uncertainties that occur when data-driven models such as those provided by AI and machine learning methods are applied is crucial. This whitepaper provides a brief motivation and first overview of the state of the art in identifying and quantifying sources of uncertainty for data-driven components as well as means for analyzing their impact.
Few-Shot Generalization Across Dialogue Tasks
Venue: arXiv cs.CL 2018
Year: 2018

Machine-learning based dialogue managers are able to learn complex behaviors in order to complete a task, but it is not straightforward to extend their capabilities to new domains. We investigate different policies' ability to handle uncooperative user behavior, and how well expertise in completing one task (such as restaurant reservations) can be reapplied when learning a new one (e.g. booking a hotel). We introduce the Recurrent Embedding Dialogue Policy (REDP), which embeds system actions and dialogue states in the same vector space. REDP contains a memory component and attention mechanism based on a modified Neural Turing Machine, and significantly outperforms a baseline LSTM classifier on this task. We also show that both our architecture and baseline solve the bAbI dialogue task, achieving 100% test accuracy.
Online External Beam Radiation Treatment Simulator
Venue: arXiv cs.GR 2018
Year: 2018

Radiation therapy is an effective and widely accepted form of treatment for many types of cancer that requires extensive computerized planning. Unfortunately, current treatment planning systems have limited or no visual aid that combines patient volumetric models extracted from patient-specific CT data with the treatment device geometry in a 3D interactive simulation. We illustrate the potential of 3D simulation in radiation therapy with a web-based interactive system that combines novel standards and technologies. We discuss related research efforts in this area and present in detail several components of the simulator. An objective assessment of the accuracy of the simulator and a usability study prove the potential of such a system for simulation and training.
Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
Venue: arXiv cs.CV 2018
Year: 2018

In this work we adapt multi-person pose estimation architecture to use it on edge devices. We follow the bottom-up approach from OpenPose, the winner of COCO 2016 Keypoints Challenge, because of its decent quality and robustness to number of people inside the frame. With proposed network design and optimized post-processing code the full solution runs at 28 frames per second (fps) on Intel$\unicode{xAE}$ NUC 6i7KYB mini PC and 26 fps on Core$^{TM}$ i7-6850K CPU. The network model has 4.1M parameters and 9 billions floating-point operations (GFLOPs) complexity, which is just ~15% of the baseline 2-stage OpenPose with almost the same quality. The code and model are available as a part of Intel$\unicode{xAE}$ OpenVINO$^{TM}$ Toolkit.
An Equivalence Class for Orthogonal Vectors
Venue: arXiv cs.DS 2018
Year: 2018

The Orthogonal Vectors problem ($\textsf{OV}$) asks: given $n$ vectors in $\{0,1\}^{O(\log n)}$, are two of them orthogonal? $\textsf{OV}$ is easily solved in $O(n^2 \log n)$ time, and it is a central problem in fine-grained complexity: dozens of conditional lower bounds are based on the popular hypothesis that $\textsf{OV}$ cannot be solved in (say) $n^{1.99}$ time. However, unlike the APSP problem, few other problems are known to be non-trivially equivalent to $\textsf{OV}$. We show $\textsf{OV}$ is truly-subquadratic equivalent to several fundamental problems, all of which (a priori) look harder than $\textsf{OV}$. A partial list is given below: ($\textsf{Min-IP}/\textsf{Max-IP}$) Find a red-blue pair of vectors with minimum (respectively, maximum) inner product, among $n$ vectors in $\{0,1\}^{O(\log n)}$. ($\textsf{Exact-IP}$) Find a red-blue pair of vectors with inner product equal to a given target integer, among $n$ vectors in $\{0,1\}^{O(\log n)}$. ($\textsf{Apx-Min-IP}/\textsf{Apx-Max-IP}$) Find a red-blue pair of vectors that is a 100-approximation to the minimum (resp. maximum) inner product, among $n$ vectors in $\{0,1\}^{O(\log n)}$. (Approx. $\textsf{Bichrom.-$\ell_p$-Closest-Pair}$) Compute a $(1 + Ω(1))$-approximation to the $\ell_p$-closest red-blue pair (for a constant $p \in [1,2]$), among $n$ points in $\mathbb{R}^d$, $d \le n^{o(1)}$. (Approx. $\textsf{$\ell_p$-Furthest-Pair}$) Compute a $(1 + Ω(1))$-approximation to the $\ell_p$-furthest pair (for a constant $p \in [1,2]$), among $n$ points in $\mathbb{R}^d$, $d \le n^{o(1)}$. We also show that there is a $\text{poly}(n)$ space, $n^{1-ε}$ query time data structure for Partial Match with vectors from $\{0,1\}^{O(\log n)}$ if and only if such a data structure exists for $1+Ω(1)$ Approximate Nearest Neighbor Search in Euclidean space.
Target Control of Directed Networks based on Network Flow Problems
Venue: arXiv cs.SY 2018
Year: 2018

Target control of directed networks, which aims to control only a target subset instead of the entire set of nodes in large natural and technological networks, is an outstanding challenge faced in various real world applications. We address one fundamental issue regarding this challenge, i.e., for a given target subset, how to allocate a minimum number of control sources which provide input signals to the network nodes. This issue remains open in general networks with loops. We show that the issue is essentially a path cover problem and can be further converted into a maximum network flow problem. A method termed `Maximum Flow based Target Path-cover' (MFTP) with complexity $O(|V|^{1/2}|E|)$ in which $|V|$ and $|E|$ denote the number of network nodes and edges is proposed. It is also rigorously proven to provide the minimum number of control sources on arbitrary directed networks, whether loops exist or not. We anticipate that this work would serve wide applications in target control of real-life networks, as well as counter control of various complex systems which may contribute to enhancing system robustness and resilience.
Gathering Problems for Autonomous Mobile Robots with Lights
Venue: arXiv cs.DC 2018
Year: 2018

We study the Gathering problem for n autonomous mobile robots in semi-synchronous settings with persistent memory called light. It is well known that Gathering is impossible in a basic model when robots have no lights, if the system is semi-synchronous or even centralized (only one robot is active in each time). On the other hand, Rendezvous (Gathering when n = 2) is possible if robots have lights of various types with a constant number of colors. In this paper, we extend the model of robots with lights so that Gathering algorithms can be discussed properly. Then we show Gathering algorithms with three types of lights in the semi-synchronous settings and reveal relationship between the power of lights and other additional assumptions. The most algorithms shown here are optimal in the number of colors they use.
Managed Forgetting to Support Information Management and Knowledge Work
Venue: arXiv cs.CY 2018
Year: 2018

Trends like digital transformation even intensify the already overwhelming mass of information knowledge workers face in their daily life. To counter this, we have been investigating knowledge work and information management support measures inspired by human forgetting. In this paper, we give an overview of solutions we have found during the last five years as well as challenges that still need to be tackled. Additionally, we share experiences gained with the prototype of a first forgetful information system used 24/7 in our daily work for the last three years. We also address the untapped potential of more explicated user context as well as features inspired by Memory Inhibition, which is our current focus of research.
Parallel Protein Community Detection in Large-scale PPI Networks Based on Multi-source Learning
Venue: arXiv cs.SI 2018
Year: 2018

Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is essential to understand the principles of cellular organization and explore the causes of various diseases. It is critical to integrate multiple data resources to identify reliable protein communities that have biological significance and improve the performance of community detection methods for large-scale PPI networks. In this paper, we propose a Multi-source Learning based Protein Community Detection (MLPCD) algorithm by integrating Gene Expression Data (GED) and a parallel solution of MLPCD using cloud computing technology. To effectively discover the biological functions of proteins that participating in different cellular processes, GED under different conditions is integrated with the original PPI network to reconstruct a Weighted-PPI (WPPI) network. To flexibly identify protein communities of different scales, we define community modularity and functional cohesion measurements and detect protein communities from WPPI using an agglomerative method. In addition, we respectively compare the detected communities with known protein complexes and evaluate the functional enrichment of protein function modules using Gene Ontology annotations. Moreover, we implement a parallel version of the MLPCD algorithm on the Apache Spark platform to enhance the performance of the algorithm for large-scale realistic PPI networks. Extensive experimental results indicate the superiority and notable advantages of the MLPCD algorithm over the relevant algorithms in terms of accuracy and performance.
Effective Resistance-based Germination of Seed Sets for Community Detection
Venue: arXiv cs.SI 2018
Year: 2018

Community detection is, at its core, an attempt to attach an interpretable function to an otherwise indecipherable form. The importance of labeling communities has obvious implications for identifying clusters in social networks, but it has a number of equally relevant applications in product recommendations, biological systems, and many forms of classification. The local variety of community detection starts with a small set of labeled seed nodes, and aims to estimate the community containing these nodes. One of the most ubiquitous methods - due to its simplicity and efficiency - is personalized PageRank. The most obvious bottleneck for deploying this form of PageRank successfully is the quality of the seeds. We introduce a "germination" stage for these seeds, where an effective resistance-based approach is used to increase the quality and number of seeds from which a community is detected. By breaking seed set expansion into a two-step process, we aim to utilize two distinct random walk-based approaches in the regimes in which they excel. In synthetic and real network data, a simple, greedy algorithm which minimizes the effective resistance diameter combined with PageRank achieves clear improvements in precision and recall over a standalone PageRank procedure.
UFANS: U-shaped Fully-Parallel Acoustic Neural Structure For Statistical Parametric Speech Synthesis With 20X Faster
Venue: arXiv cs.SD 2018
Year: 2018

Neural networks with Auto-regressive structures, such as Recurrent Neural Networks (RNNs), have become the most appealing structures for acoustic modeling of parametric text to speech synthesis (TTS) in ecent studies. Despite the prominent capacity to capture long-term dependency, these models consist of massive sequential computations that cannot be fully parallel. In this paper, we propose a U-shaped Fully-parallel Acoustic Neural Structure (UFANS), which is a deconvolutional alternative of RNNs for Statistical Parametric Speech Synthesis (SPSS). The experiments verify that our proposed model is over 20 times faster than RNN based acoustic model, both training and inference on GPU with comparable speech quality. Furthermore, We also investigate that how long information dependence really matters to synthesized speech quality.
Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence
Venue: arXiv cs.LG 2018
Year: 2018

Adherence can be defined as "the extent to which patients take their medications as prescribed by their healthcare providers"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only about 50% of patients with chronic diseases correctly follow their treatments. This severely compromises the efficiency of long-term therapy and increases the cost of health services. We propose in this paper different models of patient drug consumption in breast cancer treatments. The aim of these different approaches is to predict medication non-adherence while giving insights to doctors of the underlying reasons of these illegitimate drop-outs. Working with oncologists, we show the interest of Machine- Learning algorithms fined tune by the feedback of experts to estimate a risk score of a patient's non-adherence and thus improve support throughout their care path.
The polytope of legal sequences
Venue: arXiv cs.DM 2018
Year: 2018

A sequence of vertices in a graph is called a \emph{(total) legal dominating sequence} if every vertex in the sequence (total) dominates at least one vertex not dominated by those ones that precede it, and at the end all vertices of the graph are (totally) dominated. The \emph{Grundy (total) domination number} of a graph is the size of the largest (total) legal dominating sequence. In this work, we address the problems of determining these two parameters by introducing a generalized version of them. We explicitly calculate the corresponding (general) parameter for paths and web graphs. We propose integer programming formulations for the new problem and we study the polytope associated to one of them. We find families of valid inequalities and derive conditions under which they are facet-defining. Finally, we perform computational experiments to compare the formulations as well as to test valid inequalities as cuts in a B\&C framework.
(Un)Encrypted Computing and Indistinguishability Obfuscation
Venue: arXiv cs.CR 2018
Year: 2018

This paper first describes an `obfuscating' compiler technology developed for encrypted computing, then examines if the trivial case without encryption produces much-sought indistinguishability obfuscation.
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics. A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one. Inertial flow sculpting can be formally defined as an inverse problem, where one identifies a sequence of pillars (chosen, with replacement, from a finite set of pillars, each of which produce a specific transformation) whose composite transformation results in a user-defined desired transformation. Endemic to most such problems in engineering, inverse problems are usually quite computationally intractable, with most traditional approaches based on search and optimization strategies. In this paper, we pose this inverse problem as a Reinforcement Learning (RL) problem. We train a DoubleDQN agent to learn from this environment. The results suggest that learning is possible using a DoubleDQN model with the success frequency reaching 90% in 200,000 episodes and the rewards converging. While most of the results are obtained by fixing a particular target flow shape to simplify the learning problem, we later demonstrate how to transfer the learning of an agent based on one target shape to another, i.e. from one design to another and thus be useful for a generic design of a flow shape.
A rule-based system proposal to aid in the evaluation and decision-making in external beam radiation treatment planning
Venue: arXiv cs.SE 2018
Year: 2018

As part of a plan launched by the Ministry of Health of Brazil to increase the availability of linear accelerators for radiotherapy treatment for the whole country, for which Varian Medical Systems company has won the bidding, a technical cooperation agreement was signed inviting Brazilian Scientific and Technological Institutions to participate in a technology transfer program. As a result, jointly, the Eldorado Research Institute and the Center for Biomedical Engineering of the University of Campinas presents in this work, the concepts behind of a proposed rule engine to aid in the evaluation and decision-making in radiotherapy treatment planning. Normally, the determination of the radiation dose for a given patient is a complex and intensive procedure, which requires a lot of domain knowledge and subjective experience from the oncologists' team. In order to help them in this complex task, and additionally, provide an auxiliary tool for less experienced oncologists, it is presented a project conception of a software system that will make use of a hybrid data-oriented approach. The proposed rule engine will apply both inference mechanism and expression evaluation to verify and accredit the quality of an external beam radiation treatment plan by considering, at first, the 3D-conformal radiotherapy (3DCRT) technique.
Smoothed Analysis of Multi-Item Auctions with Correlated Values
Venue: arXiv cs.GT 2018
Year: 2018

Consider a seller with m heterogeneous items for sale to a single additive buyer whose values for the items are arbitrarily correlated. It was previously shown that, in such settings, distributions exist for which the seller's optimal revenue is infinite, but the best "simple" mechanism achieves revenue at most one ([Briest et. al 15], [Hart and Nisan 13]), even when $m=2$. This result has long served as a cautionary tale discouraging the study of multi-item auctions without some notion of "independent items". In this work we initiate a smoothed analysis of such multi-item auction settings. We consider a buyer whose item values are drawn from an arbitrarily correlated multi-dimensional distribution then randomly perturbed with magnitude $δ$ under several natural perturbation models. On one hand, we prove that the ([Briest et. al 15], [Hart and Nisan 13]) construction is surprisingly robust to certain natural perturbations of this form, and the infinite gap remains. On the other hand, we provide a smoothed model such that the approximation guarantee of simple mechanisms is smoothed-finite. We show that when the perturbation has magnitude $δ$, pricing only the grand bundle guarantees an $O(1/δ)$-approximation to the optimal revenue. That is, no matter the (worst-case) initially correlated distribution, these tiny perturbations suffice to bring the gap down from infinite to finite. We further show that the same guarantees hold when $n$ buyers have values drawn from an arbitrarily correlated $mn$-dimensional distribution (without any dependence on $n$). Taken together, these analyses further pin down key properties of correlated distributions that result in large gaps between simplicity and optimality.
Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications
Venue: arXiv cs.HC 2018
Year: 2018

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.
Playing Soccer without Colors in the SPL: A Convolutional Neural Network Approach
Venue: arXiv cs.CV 2018
Year: 2018

The goal of this paper is to propose a vision system for humanoid robotic soccer that does not use any color information. The main features of this system are: (i) real-time operation in the NAO robot, and (ii) the ability to detect the ball, the robots, their orientations, the lines and key field features robustly. Our ball detector, robot detector, and robot's orientation detector obtain the highest reported detection rates. The proposed vision system is tested in a SPL field with several NAO robots under realistic and highly demanding conditions. The obtained results are: robot detection rate of 94.90%, ball detection rate of 97.10%, and a completely perceived orientation rate of 99.88% when the observed robot is static, and 95.52% when the observed robot is moving.