OpenRaiser/Intern-Atlas huggingface dataset:
MolecularRNN: Generating realistic molecular graphs with optimized properties
Venue: arXiv cs.LG 2019
Year: 2019

Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development. There is a growing need for de-novo design methods that would address this problem. We present MolecularRNN, the graph recurrent generative model for molecular structures. Our model generates diverse realistic molecular graphs after likelihood pretraining on a big database of molecules. We perform an analysis of our pretrained models on large-scale generated datasets of 1 million samples. Further, the model is tuned with policy gradient algorithm, provided a critic that estimates the reward for the property of interest. We show a significant distribution shift to the desired range for lipophilicity, drug-likeness, and melting point outperforming state-of-the-art works. With the use of rejection sampling based on valency constraints, our model yields 100% validity. Moreover, we show that invalid molecules provide a rich signal to the model through the use of structure penalty in our reinforcement learning pipeline.
Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm
Venue: arXiv cs.CV 2019
Year: 2019

Deep 3-dimensional (3D) Convolutional Network (ConvNet) has shown promising performance on video recognition tasks because of its powerful spatio-temporal information fusion ability. However, the extremely intensive requirements on memory access and computing power prohibit it from being used in resource-constrained scenarios, such as portable and edge devices. So in this paper, we first propose a two-stage Fully Separable Block (FSB) to significantly compress the model sizes of 3D ConvNets. Then a feature enhancement approach named Temporal Residual Gradient (TRG) is developed to improve the performance of compressed model on video tasks, which provides higher accuracy, faster convergency and better robustness. Moreover, in order to further decrease the computing workload, we propose a hybrid Fast Algorithm (hFA) to drastically reduce the computation complexity of convolutions. These methods are effectively combined to design a light-weight and efficient ConvNet for video recognition tasks. Experiments on the popular dataset report 2.3x compression rate, 3.6x workload reduction, and 6.3% top-1 accuracy gain, over the state-of-the-art SlowFast model, which is already a highly compact model. The proposed methods also show good adaptability on traditional 3D ConvNet, demonstrating 7.4x more compact model, 11.0x less workload, and 3.0% higher accuracy
Rethinking Table Recognition using Graph Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning problems has not been reflected in document structure analysis since conventional neural networks are not well suited to the input structure of the problem. In this paper, we propose an architecture based on graph networks as a better alternative to standard neural networks for table recognition. We argue that graph networks are a more natural choice for these problems, and explore two gradient-based graph neural networks. Our proposed architecture combines the benefits of convolutional neural networks for visual feature extraction and graph networks for dealing with the problem structure. We empirically demonstrate that our method outperforms the baseline by a significant margin. In addition, we identify the lack of large scale datasets as a major hindrance for deep learning research for structure analysis and present a new large scale synthetic dataset for the problem of table recognition. Finally, we open-source our implementation of dataset generation and the training framework of our graph networks to promote reproducible research in this direction.
Fast and Agile Vision-Based Flight with Teleoperation and Collision Avoidance on a Multirotor
Venue: arXiv cs.RO 2019
Year: 2019

We present a multirotor architecture capable of aggressive autonomous flight and collision-free teleoperation in unstructured, GPS-denied environments. The proposed system enables aggressive and safe autonomous flight around clutter by integrating recent advancements in visual-inertial state estimation and teleoperation. Our teleoperation framework maps user inputs onto smooth and dynamically feasible motion primitives. Collision-free trajectories are ensured by querying a locally consistent map that is incrementally constructed from forward-facing depth observations. Our system enables a non-expert operator to safely navigate a multirotor around obstacles at speeds of 10 m/s. We achieve autonomous flights at speeds exceeding 12 m/s and accelerations exceeding 12 m/s^2 in a series of outdoor field experiments that validate our approach.
Majorisation-minimisation algorithms for minimising the difference between lattice submodular functions
Venue: arXiv cs.DS 2019
Year: 2019

We consider the problem of minimising functions represented as a difference of lattice submodular functions. We propose analogues to the SupSub, SubSup and ModMod routines for lattice submodular functions. We show that our majorisation-minimisation algorithms produce iterates that monotonically decrease, and that we converge to a local minimum. We also extend additive hardness results, and show that a broad range of functions can be expressed as the difference of submodular functions.
Synthesizing Reactive Systems from Hyperproperties
Venue: arXiv cs.LO 2019
Year: 2019

We study the reactive synthesis problem for hyperproperties given as formulas of the temporal logic HyperLTL. Hyperproperties generalize trace properties, i.e., sets of traces, to sets of sets of traces. Typical examples are information-flow policies like noninterference, which stipulate that no sensitive data must leak into the public domain. Such properties cannot be expressed in standard linear or branching-time temporal logics like LTL, CTL, or CTL$^*$. We show that, while the synthesis problem is undecidable for full HyperLTL, it remains decidable for the $\exists^*$, $\exists^*\forall^1$, and the $\mathit{linear}\;\forall^*$ fragments. Beyond these fragments, the synthesis problem immediately becomes undecidable. For universal HyperLTL, we present a semi-decision procedure that constructs implementations and counterexamples up to a given bound. We report encouraging experimental results obtained with a prototype implementation on example specifications with hyperproperties like symmetric responses, secrecy, and information-flow.
Foundations of Digital Archæoludology
Venue: arXiv cs.AI 2019
Year: 2019

Digital Archaeoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines - computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. - to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project.
Constraint-Based Monitoring of Hyperproperties
Venue: arXiv cs.LO 2019
Year: 2019

Verifying hyperproperties at runtime is a challenging problem as hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other. It is necessary to store previously seen traces, because every new incoming trace needs to be compatible with every run of the system observed so far. Furthermore, the new incoming trace poses requirements on future traces. In our monitoring approach, we focus on those requirements by rewriting a hyperproperty in the temporal logic HyperLTL to a Boolean constraint system. A hyperproperty is then violated by multiple runs of the system if the constraint system becomes unsatisfiable. We compare our implementation, which utilizes either BDDs or a SAT solver to store and evaluate constraints, to the automata-based monitoring tool RVHyper.
Taming Combinatorial Challenges in Optimal Clutter Removal Tasks
Venue: arXiv cs.RO 2019
Year: 2019

We examine an important combinatorial challenge in clearing clutter using a mobile robot equipped with a manipulator, seeking to compute an optimal object removal sequence for minimizing the task completion time, assuming that each object is grasped once and then subsequently removed. On the structural side, we establish that such an optimal sequence can be NP-hard to compute, even when no two objects to be removed have any overlap. Then, we construct asymptotically optimal and heuristic algorithms for clutter removal. Employing dynamic programming, our optimal algorithm scales to 40 objects. On the other hand, for random clutter, fast greedy algorithms tend to produce solutions comparable to these generated by the optimal algorithm.
Cyber Warfare: Terms, Issues, Laws and Controversies
Venue: arXiv cs.CR 2019
Year: 2019

Recent years have shown us the importance of cybersecurity. Especially, when the matter is national security, it is even more essential and crucial. Increasing cyber attacks, especially between countries in governmental level, created a new term cyber warfare. Creating some rules and regulations for this kind of war is necessary therefore international justice systems are working on it continuously. In this paper, we mentioned fundamental terms of cybersecurity, cyber capabilities of some countries, some important cyber attacks in near past, and finally, globally applied cyber warfare law for this attacks.
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework
Venue: arXiv cs.LG 2019
Year: 2019

Although reinforcement learning (RL) can provide reliable solutions in many settings, practitioners are often wary of the discrepancies between the RL solution and their status quo procedures. Therefore, they may be reluctant to adapt to the novel way of executing tasks proposed by RL. On the other hand, many real-world problems require relatively small adjustments from the status quo policies to achieve improved performance. Therefore, we propose a student-teacher RL mechanism in which the RL (the "student") learns to maximize its reward, subject to a constraint that bounds the difference between the RL policy and the "teacher" policy. The teacher can be another RL policy (e.g., trained under a slightly different setting), the status quo policy, or any other exogenous policy. We formulate this problem using a stochastic optimization model and solve it using a primal-dual policy gradient algorithm. We prove that the policy is asymptotically optimal. However, a naive implementation suffers from high variance and convergence to a stochastic optimal policy. With a few practical adjustments to address these issues, our numerical experiments confirm the effectiveness of our proposed method in multiple GridWorld scenarios.
System Administrators Prefer Command Line Interfaces, Don't They? An Exploratory Study of Firewall Interfaces
Venue: arXiv cs.HC 2019
Year: 2019

A graphical user interface (GUI) represents the most common option for interacting with computer systems. However, according to the literature system administrators often favor command line interfaces (CLIs). The goal of our work is to investigate which interfaces system administrators prefer, and which they actually utilize in their daily tasks. We collected experiences and opinions from 300 system administrators with the help of an online survey. All our respondents are system administrators, who work or have worked with firewalls. Our results show that only 32% of the respondents prefer CLIs for managing firewalls, while the corresponding figure is 60% for GUIs. We report the mentioned strengths and limitations of each interface and the tasks for which they are utilized by the system administrators. Based on these results, we provide design recommendations for firewall interfaces.
When Full Duplex Wireless Meets Non-Orthogonal Multiple Access: Opportunities and Challenges
Venue: arXiv cs.IT 2019
Year: 2019

Non-orthogonal multiple access (NOMA) is a promising radio access technology for the 5G wireless systems. The core of NOMA is to support multiple users in the same resource block via power or code domain multiplexing, which provides great enhancement in spectrum efficiency and connectivity. Meanwhile, with the recent advance in self-interference (SI) cancelation techniques, full duplex (FD) wireless communication has become a feasible technology enabling radios to receive and transmit simultaneously. This article aims to investigate the combination of these two emerging technologies. At first, several typical scenarios and protocols are presented to illustrate the application of FD technique in NOMA systems. Then, a novel NOMA system with FD base stations (BSs) based on centralized radio access networks (C-RAN) is proposed. Furthermore, power allocation policies are discussed for the proposed scheme, and simulation results are provided to demonstrate its superiority. Finally, challenges and research opportunities of FD NOMA systems are also identified to stimulate the future research.
L0 Regularization Based Neural Network Design and Compression
Venue: arXiv cs.LG 2019
Year: 2019

We consider complexity of Deep Neural Networks (DNNs) and their associated massive over-parameterization. Such over-parametrization may entail susceptibility to adversarial attacks, loss of interpretability and adverse Size, Weight and Power - Cost (SWaP-C) considerations. We ask if there are methodical ways (regularization) to reduce complexity and how can we interpret trade-off between desired metric and complexity of DNN. Reducing complexity is directly applicable to scaling of AI applications to real world problems (especially for off-the-cloud applications). We show that presence and evaluation of the knee of the tradeoff curve. We apply a form of L0 regularization to MNIST data and signal modulation classifications. We show that such regularization captures saliency in the input space as well.
Investigating an Effective Character-level Embedding in Korean Sentence Classification
Venue: arXiv cs.CL 2019
Year: 2019

Different from the writing systems of many Romance and Germanic languages, some languages or language families show complex conjunct forms in character composition. For such cases where the conjuncts consist of the components representing consonant(s) and vowel, various character encoding schemes can be adopted beyond merely making up a one-hot vector. However, there has been little work done on intra-language comparison regarding performances using each representation. In this study, utilizing the Korean language which is character-rich and agglutinative, we investigate an encoding scheme that is the most effective among Jamo-level one-hot, character-level one-hot, character-level dense, and character-level multi-hot. Classification performance with each scheme is evaluated on two corpora: one on binary sentiment analysis of movie reviews, and the other on multi-class identification of intention types. The result displays that the character-level features show higher performance in general, although the Jamo-level features may show compatibility with the attention-based models if guaranteed adequate parameter set size.
On the Interaction of Object-Oriented Design Patterns and Programming Languages
Venue: arXiv cs.PL 2019
Year: 2019

Design patterns are distilled from many real systems to catalog common programming practice. However, some object-oriented design patterns are distorted or overly complicated because of the lack of supporting programming language constructs or mechanisms. For this paper, we have analyzed several published design patterns looking for idiomatic ways of working around constraints of the implementation language. From this analysis, we lay a groundwork of general-purpose language constructs and mechanisms that, if provided by a statically typed, object-oriented language, would better support the implementation of design patterns and, transitively, benefit the construction of many real systems. In particular, our catalog of language constructs includes subtyping separate from inheritance, lexically scoped closure objects independent of classes, and multimethod dispatch. The proposed constructs and mechanisms are not radically new, but rather are adopted from a variety of languages and programming language research and combined in a new, orthogonal manner. We argue that by describing design patterns in terms of the proposed constructs and mechanisms, pattern descriptions become simpler and, therefore, accessible to a larger number of language communities. Constructs and mechanisms lacking in a particular language can be implemented using paradigmatic idioms.
2.5D Image based Robotic Grasping
Venue: arXiv cs.RO 2019
Year: 2019

We consider the problem of robotic grasping using depth + RGB information sampling from a real sensor. we design an encoder-decoder neural network to predict grasp policy in real time. This method can fuse the advantage of depth image and RGB image at the same time and is robust for grasp and observation height.We evaluate our method in a physical robotic system and propose an open-loop algorithm to realize robotic grasp operation. We analyze the result of experiment from multi-perspective and the result shows that our method is competitive with the state-of-the-art in grasp performance, real-time and model size. The video is available in https://youtu.be/Wxw_r5a8qV0
Multimodal Joint Emotion and Game Context Recognition in League of Legends Livestreams
Venue: arXiv cs.CV 2019
Year: 2019

Video game streaming provides the viewer with a rich set of audio-visual data, conveying information both with regards to the game itself, through game footage and audio, as well as the streamer's emotional state and behaviour via webcam footage and audio. Analysing player behaviour and discovering correlations with game context is crucial for modelling and understanding important aspects of livestreams, but comes with a significant set of challenges - such as fusing multimodal data captured by different sensors in uncontrolled ('in-the-wild') conditions. Firstly, we present, to our knowledge, the first data set of League of Legends livestreams, annotated for both streamer affect and game context. Secondly, we propose a method that exploits tensor decompositions for high-order fusion of multimodal representations. The proposed method is evaluated on the problem of jointly predicting game context and player affect, compared with a set of baseline fusion approaches such as late and early fusion.
Reference Capabilities for Safe Parallel Array Programming
Venue: arXiv cs.PL 2019
Year: 2019

The array is a fundamental data structure that provides an efficient way to store and retrieve non-sparse data contiguous in memory. Arrays are important for the performance of many memory-intensive applications due to the design of modern memory hierarchies: contiguous storage facilitates spatial locality and predictive access patterns which enables prefetching. Operations on large arrays often lend themselves well to parallelisation, such as a fork-join style divide-and-conquer algorithm for sorting. For parallel operations on arrays to be deterministic, data-race freedom must be guaranteed. For operations on arrays of primitive data, data-race freedom is obtained by coordinating accesses so that no two threads operate on the same array indices. This is however not enough for arrays of non-primitives due to aliasing: accesses of separate array elements may return pointers to the same object, or overlapping structures. Reference capabilities have been used successfully in the past to statically guarantee the absence of data-races in object-oriented programs. This paper presents the first extension of reference capabilities -- called array capabilities -- that support concurrent and parallel operations on arrays of both primitive and non-primitive values. In addition to element access, array capabilities support the abstract manipulation of arrays, logical splitting of arrays into subarrays, and merging subarrays. These operations allow expressing a wide range of array use cases. This paper presents the array capability design space and show how it applies to a number of array use cases. The core ideas are formalised and proven sound in a simple calculus, along with a proof that shows that well-typed programs with array capabilities are free from data-races.
An Efficient Detection of Malware by Naive Bayes Classifier Using GPGPU
Venue: arXiv cs.CR 2019
Year: 2019

Due to continuous increase in the number of malware (according to AV-Test institute total ~8 x 10^8 malware are already known, and every day they register ~2.5 x 10^4 malware) and files in the computational devices, it is very important to design a system which not only effectively but can also efficiently detect the new or previously unseen malware to prevent/minimize the damages. Therefore, this paper presents a novel group-wise approach for the efficient detection of malware by parallelizing the classification using the power of GPGPU and shown that by using the Naive Bayes classifier the detection speed-up can be boosted up to 200x. The investigation also shows that the classification time increases significantly with the number of features.
Human-Usable Password Schemas: Beyond Information-Theoretic Security
Venue: arXiv cs.CR 2019
Year: 2019

Password users frequently employ passwords that are too simple, or they just reuse passwords for multiple websites. A common complaint is that utilizing secure passwords is too difficult. One possible solution to this problem is to use a password schema. Password schemas are deterministic functions which map challenges (typically the website name) to responses (passwords). Previous work has been done on developing and analyzing publishable schemas, but these analyses have been information-theoretic, not complexity-theoretic; they consider an adversary with infinite computing power. We perform an analysis with respect to adversaries having currently achievable computing capabilities, assessing the realistic practical security of such schemas. We prove for several specific schemas that a computer is no worse off than an infinite adversary and that it can successfully extract all information from leaked challenges and their respective responses, known as challenge-response pairs. We also show that any schema that hopes to be secure against adversaries with bounded computation should obscure information in a very specific way, by introducing many possible constraints with each challenge-response pair. These surprising results put the analyses of password schemas on a more solid and practical footing.