OpenRaiser/Intern-Atlas huggingface dataset:
Solving Zero-Sum One-Sided Partially Observable Stochastic Games
Venue: arXiv cs.GT 2020
Year: 2020

Many security and other real-world situations are dynamic in nature and can be modelled as strictly competitive (or zero-sum) dynamic games. In these domains, agents perform actions to affect the environment and receive observations -- possibly imperfect -- about the situation and the effects of the opponent's actions. Moreover, there is no limitation on the total number of actions an agent can perform -- that is, there is no fixed horizon. These settings can be modelled as partially observable stochastic games (POSGs). However, solving general POSGs is computationally intractable, so we focus on a broad subclass of POSGs called one-sided POSGs. In these games, only one agent has imperfect information while their opponent has full knowledge of the current situation. We provide a full picture for solving one-sided POSGs: we (1) give a theoretical analysis of one-sided POSGs and their value functions, (2) show that a variant of a value-iteration algorithm converges in this setting, (3) adapt the heuristic search value-iteration algorithm for solving one-sided POSGs, (4) describe how to use approximate value functions to derive strategies in the game, and (5) demonstrate that our algorithm can solve one-sided POSGs of non-trivial sizes and analyze the scalability of our algorithm in three different domains: pursuit-evasion, patrolling, and search games.
Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Cellular Systems
Venue: arXiv cs.NI 2020
Year: 2020

Scheduling plays a pivotal role in multi-user wireless communications, since the quality of service of various users largely depends upon the allocated radio resources. In this paper, we propose a novel scheduling algorithm with contiguous frequency-domain resource allocation (FDRA) based on deep reinforcement learning (DRL) that jointly selects users and allocates resource blocks (RBs). The scheduling problem is modeled as a Markov decision process, and a DRL agent determines which user and how many consecutive RBs for that user should be scheduled at each RB allocation step. The state space, action space, and reward function are delicately designed to train the DRL network. More specifically, the originally quasi-continuous action space, which is inherent to contiguous FDRA, is refined into a finite and discrete action space to obtain a trade-off between the inference latency and system performance. Simulation results show that the proposed DRL-based scheduling algorithm outperforms other representative baseline schemes while having lower online computational complexity.
System Design and Control of an Apple Harvesting Robot
Venue: arXiv cs.RO 2020
Year: 2020

There is a growing need for robotic apple harvesting due to decreasing availability and rising cost in labor. Towards the goal of developing a viable robotic system for apple harvesting, this paper presents synergistic mechatronic design and motion control of a robotic apple harvesting prototype, which lays a critical foundation for future advancements. Specifically, we develop a deep learning-based fruit detection and localization system using an RGB-D camera. A three degree-of-freedom manipulator is then designed with a hybrid pneumatic/motor actuation mechanism to achieve fast and dexterous movements. A vacuum-based end-effector is used for apple detaching. These three components are integrated into a robotic apple harvesting prototype with simplicity, compactness, and robustness. Moreover, a nonlinear velocity-based control scheme is developed for the manipulator to achieve accurate and agile motion control. Test experiments are conducted to demonstrate the performance of the developed apple harvesting robot.
Learning to Plan Optimally with Flow-based Motion Planner
Venue: arXiv cs.RO 2020
Year: 2020

Sampling-based motion planning is the predominant paradigm in many real-world robotic applications, but its performance is immensely dependent on the quality of the samples. The majority of traditional planners are inefficient as they use uninformative sampling distributions as opposed to exploiting structures and patterns in the problem to guide better sampling strategies. Moreover, most current learning-based planners are susceptible to posterior collapse or mode collapse due to the sparsity and highly varying nature of C-Space and motion plan configurations. In this work, we introduce a conditional normalising flow based distribution learned through previous experiences to improve sampling of these methods. Our distribution can be conditioned on the current problem instance to provide an informative prior for sampling configurations within promising regions. When we train our sampler with an expert planner, the resulting distribution is often near-optimal, and the planner can find a solution faster, with less invalid samples, and less initial cost. The normalising flow based distribution uses simple invertible transformations that are very computationally efficient, and our optimisation formulation explicitly avoids mode collapse in contrast to other existing learning-based planners. Finally, we provide a formulation and theoretical foundation to efficiently sample from the distribution; and demonstrate experimentally that, by using our normalising flow based distribution, a solution can be found faster, with less samples and better overall runtime performance.
QISTA-Net: DNN Architecture to Solve $\ell_q$-norm Minimization Problem and Image Compressed Sensing
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we reformulate the non-convex $\ell_q$-norm minimization problem with $q\in(0,1)$ into a 2-step problem, which consists of one convex and one non-convex subproblems, and propose a novel iterative algorithm called QISTA ($\ell_q$-ISTA) to solve the $\left(\ell_q\right)$-problem. By taking advantage of deep learning in accelerating optimization algorithms, together with the speedup strategy that using the momentum from all previous layers in the network, we propose a learning-based method, called QISTA-Net-s, to solve the sparse signal reconstruction problem. Extensive experimental comparisons demonstrate that the QISTA-Net-s yield better reconstruction qualities than state-of-the-art $\ell_1$-norm optimization (plus learning) algorithms even if the original sparse signal is noisy. On the other hand, based on the network architecture associated with QISTA, with considering the use of convolution layers, we proposed the QISTA-Net-n for solving the image CS problem, and the performance of the reconstruction still outperforms most of the state-of-the-art natural images reconstruction methods. QISTA-Net-n is designed in unfolding QISTA and adding the convolutional operator as the dictionary. This makes QISTA-Net-s interpretable. We provide complete experimental results that QISTA-Net-s and QISTA-Net-n contribute the better reconstruction performance than the competing.
Energy-Efficient Node Deployment in Heterogeneous Two-Tier Wireless Sensor Networks with Limited Communication Range
Venue: arXiv cs.IT 2020
Year: 2020

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its cost function. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Furthermore, we study the sensor deployment when the communication range is limited for sensors and APs. Simulation results show that our proposed algorithms outperform the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, Divisive Clustering, Particle Swarm Optimization, Relay Node placement in Double-tiered Wireless Sensor Networks, and Improved Relay Node Placement, on average.
Task-Adaptive Feature Transformer for Few-Shot Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable module for few-shot segmentation, the task-adaptive feature transformer (TAFT). TAFT linearly transforms task-specific high-level features to a set of task-agnostic features well-suited to the segmentation job. Using this task-conditioned feature transformation, the model is shown to effectively utilize the semantic information in novel classes to generate tight segmentation masks. The proposed TAFT module can be easily plugged into existing semantic segmentation algorithms to achieve few-shot segmentation capability with only a few added parameters. We combine TAFT with Deeplab V3+, a well-known segmentation architecture; experiments on the PASCAL-$5^i$ dataset confirm that this combination successfully adds few-shot learning capability to the segmentation algorithm, achieving the state-of-the-art few-shot segmentation performance in some key representative cases.
GAN based Unsupervised Segmentation: Should We Match the Exact Number of Objects
Venue: arXiv cs.CV 2020
Year: 2020

The unsupervised segmentation is an increasingly popular topic in biomedical image analysis. The basic idea is to approach the supervised segmentation task as an unsupervised synthesis problem, where the intensity images can be transferred to the annotation domain using cycle-consistent adversarial learning. The previous studies have shown that the macro-level (global distribution level) matching on the number of the objects (e.g., cells, tissues, protrusions etc.) between two domains resulted in better segmentation performance. However, no prior studies have exploited whether the unsupervised segmentation performance would be further improved when matching the exact number of objects at micro-level (mini-batch level). In this paper, we propose a deep learning based unsupervised segmentation method for segmenting highly overlapped and dynamic sub-cellular microvilli. With this challenging task, both micro-level and macro-level matching strategies were evaluated. To match the number of objects at the micro-level, the novel fluorescence-based micro-level matching approach was presented. From the experimental results, the micro-level matching did not improve the segmentation performance, compared with the simpler macro-level matching.
Parallel Tacotron: Non-Autoregressive and Controllable TTS
Venue: arXiv cs.SD 2020
Year: 2020

Although neural end-to-end text-to-speech models can synthesize highly natural speech, there is still room for improvements to its efficiency and naturalness. This paper proposes a non-autoregressive neural text-to-speech model augmented with a variational autoencoder-based residual encoder. This model, called \emph{Parallel Tacotron}, is highly parallelizable during both training and inference, allowing efficient synthesis on modern parallel hardware. The use of the variational autoencoder relaxes the one-to-many mapping nature of the text-to-speech problem and improves naturalness. To further improve the naturalness, we use lightweight convolutions, which can efficiently capture local contexts, and introduce an iterative spectrogram loss inspired by iterative refinement. Experimental results show that Parallel Tacotron matches a strong autoregressive baseline in subjective evaluations with significantly decreased inference time.
Machine Learning-Based Early Detection of IoT Botnets Using Network-Edge Traffic
Venue: arXiv cs.CR 2020
Year: 2020

In this work, we present a lightweight IoT botnet detection solution, EDIMA, which is designed to be deployed at the edge gateway installed in home networks and targets early detection of botnets prior to the launch of an attack. EDIMA includes a novel two-stage Machine Learning (ML)-based detector developed specifically for IoT bot detection at the edge gateway. The ML-based bot detector first employs ML algorithms for aggregate traffic classification and subsequently Autocorrelation Function (ACF)-based tests to detect individual bots. The EDIMA architecture also comprises a malware traffic database, a policy engine, a feature extractor and a traffic parser. Performance evaluation results show that EDIMA achieves high bot scanning and bot-CnC traffic detection accuracies with very low false positive rates. The detection performance is also shown to be robust to an increase in the number of IoT devices connected to the edge gateway where EDIMA is deployed. Further, the runtime performance analysis of a Python implementation of EDIMA deployed on a Raspberry Pi reveals low bot detection delays and low RAM consumption. EDIMA is also shown to outperform existing detection techniques for bot scanning traffic and bot-CnC server communication.
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
Venue: arXiv cs.LG 2020
Year: 2020

To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data representations, as a way to control the trade-off between data utility and vulnerability to inversion attacks. Our method is motivated by the theoretical insights of data separability in neural networking training and results on the hardness of model inversion. Empirically, by adjusting the separability of data representation, we show that there exist sweet-spots for data separability such that it is difficult to recover data during inference while maintaining data utility.
Novel View Synthesis from only a 6-DoF Camera Pose by Two-stage Networks
Venue: arXiv cs.CV 2020
Year: 2020

Novel view synthesis is a challenging problem in computer vision and robotics. Different from the existing works, which need the reference images or 3D models of the scene to generate images under novel views, we propose a novel paradigm to this problem. That is, we synthesize the novel view from only a 6-DoF camera pose directly. Although this setting is the most straightforward way, there are few works addressing it. While, our experiments demonstrate that, with a concise CNN, we could get a meaningful parametric model that could reconstruct the correct scenery images only from the 6-DoF pose. To this end, we propose a two-stage learning strategy, which consists of two consecutive CNNs: GenNet and RefineNet. GenNet generates a coarse image from a camera pose. RefineNet is a generative adversarial network that refines the coarse image. In this way, we decouple the geometric relationship between mapping and texture detail rendering. Extensive experiments conducted on the public datasets prove the effectiveness of our method. We believe this paradigm is of high research and application value and could be an important direction in novel view synthesis.
NightOwl: Robotic Platform for Wheeled Service Robot
Venue: arXiv cs.RO 2020
Year: 2020

NightOwl is a robotic platform designed exclusively for a wheeled service robot. The robot navigates autonomously in omnidirectional fashion movement and equipped with LIDAR to sense the surrounding area. The platform itself was built using the Robot Operating System (ROS) and written in two different programming languages (C++ and Python). NightOwl is composed of several modular programs, namely hardware controller, light detection and ranging (LIDAR), simultaneous localization and mapping (SLAM), world model, path planning, robot control, communication, and behaviour. The programs run in parallel and communicate reciprocally to share various information. This paper explains the role of modular programs in the term of input, process, and output. In addition, NightOwl provides simulation visualized in both Gazebo and RViz. The robot in its environment is visualized by Gazebo. Sensor data from LIDAR and results from SLAM will be visualized by RViz.
Efficient RDF Graph Storage based on Reinforcement Learning
Venue: arXiv cs.DB 2020
Year: 2020

Knowledge graph is an important cornerstone of artificial intelligence. The construction and release of large-scale knowledge graphs in various fields pose new challenges to knowledge graph data management. Due to the maturity and stability, relational database is also suitable for RDF data storage. However, the complex structure of RDF graph brings challenges to storage structure design for RDF graph in the relational database. To address the difficult problem, this paper adopts reinforcement learning (RL) to optimize the storage partition method of RDF graph based on the relational database. We transform the graph storage into a Markov decision process, and develop the reinforcement learning algorithm for graph storage design. For effective RL-based storage design, we propose the data feature extraction method of RDF tables and the query rewriting priority policy during model training. The extensive experimental results demonstrate that our approach outperforms existing RDF storage design methods.
A simulation-based evaluation of a Cargo-Hitching service for E-commerce using mobility-on-demand vehicles
Venue: arXiv cs.MA 2020
Year: 2020

Time-sensitive parcel deliveries, shipments requested for delivery in a day or less, are an increasingly important research subject. It is challenging to deal with these deliveries from a carrier perspective since it entails additional planning constraints, preventing an efficient consolidation of deliveries which is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo-hitching. While there are various possible system designs, it is crucial that such solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of Mobility-On-Demand services to perform same-day parcel deliveries. For this purpose, we use SimMobility, a high-resolution agent-based simulation platform of passenger and freight flows, applied in Singapore. E-commerce demand carrier data are used to characterize simulated parcel delivery demand. Operational scenarios that aim to minimize the adverse effect of fulfilling deliveries with Mobility-On-Demand vehicles on Mobility-On-Demand passenger flows (fulfillment, wait and travel times) are explored. Results indicate that the Mobility-On-Demand services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle-kilometers-travelled without compromising the quality of Mobility On-Demand for passenger travel.
A Simple Methodology for Model-Driven Business Innovation and Low Code Implementation
Venue: arXiv cs.SE 2020
Year: 2020

Low Code platforms, according to Gartner Group, represent one of the more disruptive technologies in the development and maintenance of enterprise applications. The key factor is represented by the central involvement of business people and domain expert, with a substantial disintermediation with respect to technical people. In this paper we propose a methodology conceived to support non-technical people in addressing business process innovation and developing enterprise software application. The proposed methodology, called EasInnova, is solidly rooted in Model-Driven Engineering and adopts a three staged model of an innovation undertaking. The three stages are: AsIs that models the existing business scenario; Transformation that consists in the elaboration of the actual innovation; ToBe that concerns the modeling of new business scenario. The core of EasInnova is represented by a matrix where columns are the three innovation stages and the rows are the three Model-Driven Architecture layers: CIM, PIM, PSM. The cells indicate the steps to be followed in achieving the sought innovation. Finally, the produced models will be transferred onto a BonitaSoft, the Low Code platform selected in our work. The methodology is described by means of a simple example in the domain of home food delivery.
Reducing Unintended Identity Bias in Russian Hate Speech Detection
Venue: arXiv cs.CL 2020
Year: 2020

Toxicity has become a grave problem for many online communities and has been growing across many languages, including Russian. Hate speech creates an environment of intimidation, discrimination, and may even incite some real-world violence. Both researchers and social platforms have been focused on developing models to detect toxicity in online communication for a while now. A common problem of these models is the presence of bias towards some words (e.g. woman, black, jew) that are not toxic, but serve as triggers for the classifier due to model caveats. In this paper, we describe our efforts towards classifying hate speech in Russian, and propose simple techniques of reducing unintended bias, such as generating training data with language models using terms and words related to protected identities as context and applying word dropout to such words.
DPAttack: Diffused Patch Attacks against Universal Object Detection
Venue: arXiv cs.CV 2020
Year: 2020

Recently, deep neural networks (DNNs) have been widely and successfully used in Object Detection, e.g. Faster RCNN, YOLO, CenterNet. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Adversarial attacks against object detection can be divided into two categories, whole-pixel attacks and patch attacks. While these attacks add perturbations to a large number of pixels in images, we proposed a diffused patch attack (\textbf{DPAttack}) to successfully fool object detectors by diffused patches of asteroid-shaped or grid-shape, which only change a small number of pixels. Experiments show that our DPAttack can successfully fool most object detectors with diffused patches and we get the second place in the Alibaba Tianchi competition: Alibaba-Tsinghua Adversarial Challenge on Object Detection. Our code can be obtained from https://github.com/Wu-Shudeng/DPAttack.
DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding
Venue: arXiv cs.CV 2020
Year: 2020

Form understanding depends on both textual contents and organizational structure. Although modern OCR performs well, it is still challenging to realize general form understanding because forms are commonly used and of various formats. The table detection and handcrafted features in previous works cannot apply to all forms because of their requirements on formats. Therefore, we concentrate on the most elementary components, the key-value pairs, and adopt multimodal methods to extract features. We consider the form structure as a tree-like or graph-like hierarchy of text fragments. The parent-child relation corresponds to the key-value pairs in forms. We utilize the state-of-the-art models and design targeted extraction modules to extract multimodal features from semantic contents, layout information, and visual images. A hybrid fusion method of concatenation and feature shifting is designed to fuse the heterogeneous features and provide an informative joint representation. We adopt an asymmetric algorithm and negative sampling in our model as well. We validate our method on two benchmarks, MedForm and FUNSD, and extensive experiments demonstrate the effectiveness of our method.