OpenRaiser/Intern-Atlas huggingface dataset:
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems
Venue: arXiv cs.LG 2021
Year: 2021

We study combinatorial problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and planning. This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e.g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process. Our solution is quite generic, scalable, and leverages distributional knowledge of the problem parameters. We frame the solution process as an MDP, and take a Deep Q-Learning approach wherein states are represented as graphs, thereby allowing our trained policies to deal with arbitrary changes in a principled manner. Though learned policies work well in expectation, small deviations can have substantial negative effects in combinatorial settings. We mitigate these drawbacks by employing our graph-convolutional policies as non-optimal heuristics in a compatible search algorithm, Monte Carlo Tree Search, to significantly improve overall performance. We demonstrate our method on two problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our method outperforms custom-tailored mathematical solvers, state of the art learning-based algorithms, and common heuristics, both in computation time and performance.
Probabilistic Collision Constraint for Motion Planning in Dynamic Environments
Venue: arXiv cs.RO 2021
Year: 2021

Online generation of collision free trajectories is of prime importance for autonomous navigation. Dynamic environments, robot motion and sensing uncertainties adds further challenges to collision avoidance systems. This paper presents an approach for collision avoidance in dynamic environments, incorporating robot and obstacle state uncertainties. We derive a tight upper bound for collision probability between robot and obstacle and formulate it as a motion planning constraint which is solvable in real time. The proposed approach is tested in simulation considering mobile robots as well as quadrotors to demonstrate that successful collision avoidance is achieved in real time application. We also provide a comparison of our approach with several state-of-the-art methods.
LAGraph: Linear Algebra, Network Analysis Libraries, and the Study of Graph Algorithms
Venue: arXiv cs.MS 2021
Year: 2021

Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a library of low-level building blocks for such algorithms that targets algorithm developers. LAGraph builds on top of the GraphBLAS to target users of graph algorithms with high-level algorithms common in network analysis. In this paper, we describe the first release of the LAGraph library, the design decisions behind the library, and performance using the GAP benchmark suite. LAGraph, however, is much more than a library. It is also a project to document and analyze the full range of algorithms enabled by the GraphBLAS. To that end, we have developed a compact and intuitive notation for describing these algorithms. In this paper, we present that notation with examples from the GAP benchmark suite.
Understanding Equity, Diversity and Inclusion Challenges Within the Research Software Community
Venue: arXiv cs.SE 2021
Year: 2021

Research software -- specialist software used to support or undertake research -- is of huge importance to researchers. It contributes to significant advances in the wider world and requires collaboration between people with diverse skills and backgrounds. Analysis of recent survey data provides evidence for a lack of diversity in the Research Software Engineer community. We identify interventions which could address challenges in the wider research software community and highlight areas where the community is becoming more diverse. There are also lessons that are applicable, more generally, to the field of software development around recruitment from other disciplines and the importance of welcoming communities.
Training Deep Neural Networks via Branch-and-Bound
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose BPGrad, a novel approximate algorithm for deep nueral network training, based on adaptive estimates of feasible region via branch-and-bound. The method is based on the assumption of Lipschitz continuity in objective function, and as a result, it can adaptively determine the step size for the current gradient given the history of previous updates. We prove that, by repeating such a branch-and-pruning procedure, it can achieve the optimal solution within finite iterations. A computationally efficient solver based on BPGrad has been proposed to train the deep neural networks. Empirical results demonstrate that BPGrad solver works well in practice and compares favorably to other stochastic optimization methods in the tasks of object recognition, detection, and segmentation. The code is available at \url{https://github.com/RyanCV/BPGrad}.
Predicting Mergers and Acquisitions using Graph-based Deep Learning
Venue: arXiv cs.LG 2021
Year: 2021

The graph data structure is a staple in mathematics, yet graph-based machine learning is a relatively green field within the domain of data science. Recent advances in graph-based ML and open source implementations of relevant algorithms are allowing researchers to apply methods created in academia to real-world datasets. The goal of this project was to utilize a popular graph machine learning framework, GraphSAGE, to predict mergers and acquisitions (M&A) of enterprise companies. The results were promising, as the model predicted with 81.79% accuracy on a validation dataset. Given the abundance of data sources and algorithmic decision making within financial data science, graph-based machine learning offers a performant, yet non-traditional approach to generating alpha.
Predicting Crash Fault Residence via Simplified Deep Forest Based on A Reduced Feature Set
Venue: arXiv cs.SE 2021
Year: 2021

The software inevitably encounters the crash, which will take developers a large amount of effort to find the fault causing the crash (short for crashing fault). Developing automatic methods to identify the residence of the crashing fault is a crucial activity for software quality assurance. Researchers have proposed methods to predict whether the crashing fault resides in the stack trace based on the features collected from the stack trace and faulty code, aiming at saving the debugging effort for developers. However, previous work usually neglected the feature preprocessing operation towards the crash data and only used traditional classification models. In this paper, we propose a novel crashing fault residence prediction framework, called ConDF, which consists of a consistency based feature subset selection method and a state-of-the-art deep forest model. More specifically, first, the feature selection method is used to obtain an optimal feature subset and reduce the feature dimension by reserving the representative features. Then, a simplified deep forest model is employed to build the classification model on the reduced feature set. The experiments on seven open source software projects show that our ConDF method performs significantly better than 17 baseline methods on three performance indicators.
StarGAN-based Emotional Voice Conversion for Japanese Phrases
Venue: arXiv cs.SD 2021
Year: 2021

This paper shows that StarGAN-VC, a spectral envelope transformation method for non-parallel many-to-many voice conversion (VC), is capable of emotional VC (EVC). Although StarGAN-VC has been shown to enable speaker identity conversion, its capability for EVC for Japanese phrases has not been clarified. In this paper, we describe the direct application of StarGAN-VC to an EVC task with minimal fundamental frequency and aperiodicity processing. Through subjective evaluation experiments, we evaluated the performance of our StarGAN-EVC system in terms of its ability to achieve EVC for Japanese phrases. The subjective evaluation is conducted in terms of subjective classification and mean opinion score of neutrality and similarity. In addition, the interdependence between the source and target emotional domains was investigated from the perspective of the quality of EVC.
When Can Liquid Democracy Unveil the Truth?
Venue: arXiv cs.GT 2021
Year: 2021

In this paper, we investigate the so-called ODP-problem that has been formulated by Caragiannis and Micha [10]. Here, we are in a setting with two election alternatives out of which one is assumed to be correct. In ODP, the goal is to organise the delegations in the social network in order to maximize the probability that the correct alternative, referred to as ground truth, is elected. While the problem is known to be computationally hard, we strengthen existing hardness results by providing a novel strong approximation hardness result: For any positive constant $C$, we prove that, unless $P=NP$, there is no polynomial-time algorithm for ODP that achieves an approximation guarantee of $α\ge (\ln n)^{-C}$, where $n$ is the number of voters. The reduction designed for this result uses poorly connected social networks in which some voters suffer from misinformation. Interestingly, under some hypothesis on either the accuracies of voters or the connectivity of the network, we obtain a polynomial-time $1/2$-approximation algorithm. This observation proves formally that the connectivity of the social network is a key feature for the efficiency of the liquid democracy paradigm. Lastly, we run extensive simulations and observe that simple algorithms (working either in a centralized or decentralized way) outperform direct democracy on a large class of instances. Overall, our contributions yield new insights on the question in which situations liquid democracy can be beneficial.
Skyeye Team at MBZIRC 2020: A team of aerial and ground robots for GPS-denied autonomous fire extinguishing in an urban building scenario
Venue: arXiv cs.RO 2021
Year: 2021

The paper presents a framework for fire extinguishing in an urban scenario by a team of aerial and ground robots. The system was developed to address Challenge 3 of the 2020Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The challenge required to autonomously detect, locate and extinguish fires on different floors of a building, as well as in its surroundings. The multi-robot system developed consists of a heterogeneous robot team of up to three Unmanned Aerial Vehicles (UAV) and one Unmanned Ground Vehicle (UGV). We describe the main hardware and software components for UAV and UGVplatforms and also present the main algorithmic components of the system: a 3D LIDAR-based mapping and localization module able to work in GPS-denied scenarios; a global planner and a fast local re-planning system for robot navigation; infrared-based perception and robot actuation control for fire extinguishing; and a mission executive and coordination module based on Behavior Trees. The paper finally describes the results obtained during the competition, where the system worked fully autonomously and scored in all the trials performed. The presented system ended in 7th position out of 20 teams in the Challenge3 competition and in 5th position (out of 17 teams) in the Challenge 3 entry to the Grand Finale (Grand Challenge) of MBZIRC 2020 competition.
Advances In Malware Detection- An Overview
Venue: arXiv cs.CR 2021
Year: 2021

Malware has become a widely used means in cyber attacks in recent decades because of various new obfuscation techniques used by malwares. In order to protect the systems, data and information, detection of malware is needed as early as possible. There are various studies on malware detection techniques that have been done but there is no method which can detect the malware completely and make malware detection problematic. Static Malware analysis is very effective for known malwares but it does not work for zero day malware which leads to the need of dynamic malware detection and the behaviour based malware detection is comparatively good among all detection techniques like signature based, deep learning based, mobile/IOT and cloud based detection but still it is not able to detect all zero day malware which shows the malware detection is very challenging task and need more techniques for malware detection. This paper describes a literature review of various methods of malware detection. A short description of each method is provided and discusses various studies already done in the advanced malware detection field and their comparison based on the detection method used, accuracy and other parameters. Apart from this we will discuss various malware detection tools, dataset and their sources which can be used in further study. This paper gives you the detailed knowledge of advanced malwares, its detection methods, how you can protect your devices and data from malware attacks and it gives the comparison of different studies on malware detection.
Tokenising behaviour change: optimising blockchain technology for sustainable transport interventions
Venue: arXiv cs.CY 2021
Year: 2021

Transport makes an impact across SDGs, encompassing climate change, health, inequality and sustainability. It is also an area in which individuals are able to make decisions which have potential to collectively contribute to significant and wide-ranging benefits. Governments and authorities need citizens to make changes towards adopting sustainable transport behaviours and behaviour change interventions are being used as tools to foster changes in travel choices, towards more sustainable modes. Blockchain technology has the potential to bring new levels of scale to transport behaviour change interventions, but a rigorous approach to token design is required. This paper uses a survey of research projects and use cases to analyse current applications of blockchain technology in transport behaviour change interventions, and identifies barriers and limitations to achieving targeted change at scale. The paper draws upon these findings to outline a research agenda that brings a focus on correlating specific Behaviour Change Techniques (BCTs) to token design, and defines processes for standardising token designs in behaviour change tools. The paper further outlines architecture and operational considerations for blockchain-based platforms in behaviour change interventions, such that design choices do not compromise opportunities or wider environmental goals.
Direction-Based Jamming Detection and Suppression in mmWave Massive MIMO Networks
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we study the problem of physical layer security in the uplink of millimeter-wave massive multiple-input multiple-output (MIMO) networks and propose a jamming detection and suppression method. The proposed method is based on directional information of the received signals at the base station antenna array. The proposed jamming detection method can accurately detect both the existence and direction of the jammer using the received pilot signals in the training phase. The obtained information is then exploited to develop a channel estimator that excludes the jammer's angular subspace from received training signals. The estimated channel information is then used for designing a combiner at the base station that is able to effectively cancel out the deliberate interference of the jammer. By numerical simulations, we evaluate the performance of the proposed jamming detection method in terms of correct detection probability and false alarm probability and show its effectiveness when the jammer's power is substantially lower than the user's power. Also, our results show that the proposed jamming suppression method can achieve a very close spectral efficiency as the case of no jamming in the network
Composing Graph Theory and Deep Neural Networks to Evaluate SEU Type Soft Error Effects
Venue: arXiv cs.AR 2021
Year: 2021

Rapidly shrinking technology node and voltage scaling increase the susceptibility of Soft Errors in digital circuits. Soft Errors are radiation-induced effects while the radiation particles such as Alpha, Neutrons or Heavy Ions, interact with sensitive regions of microelectronic devices/circuits. The particle hit could be a glancing blow or a penetrating strike. A well apprehended and characterized way of analyzing soft error effects is the fault-injection campaign, but that typically acknowledged as time and resource-consuming simulation strategy. As an alternative to traditional fault injection-based methodologies and to explore the applicability of modern graph based neural network algorithms in the field of reliability modeling, this paper proposes a systematic framework that explores gate-level abstractions to extract and exploit relevant feature representations at low-dimensional vector space. The framework allows the extensive prediction analysis of SEU type soft error effects in a given circuit. A scalable and inductive type representation learning algorithm on graphs called GraphSAGE has been utilized for efficiently extracting structural features of the gate-level netlist, providing a valuable database to exercise a downstream machine learning or deep learning algorithm aiming at predicting fault propagation metrics. Functional Failure Rate (FFR): the predicted fault propagating metric of SEU type fault within the gate-level circuit abstraction of the 10-Gigabit Ethernet MAC (IEEE 802.3) standard circuit.
AuTO: A Framework for Automatic differentiation in Topology Optimization
Venue: arXiv cs.MS 2021
Year: 2021

A critical step in topology optimization (TO) is finding sensitivities. Manual derivation and implementation of the sensitivities can be quite laborious and error-prone, especially for non-trivial objectives, constraints and material models. An alternate approach is to utilize automatic differentiation (AD). While AD has been around for decades, and has also been applied in TO, wider adoption has largely been absent. In this educational paper, we aim to reintroduce AD for TO, and make it easily accessible through illustrative codes. In particular, we employ JAX, a high-performance Python library for automatically computing sensitivities from a user defined TO problem. The resulting framework, referred to here as AuTO, is illustrated through several examples in compliance minimization, compliant mechanism design and microstructural design.
MeetDurian: A Gameful Mobile App to Prevent COVID-19 Infection
Venue: arXiv cs.HC 2021
Year: 2021

The COVID-19 problem has not gone away with the passing of the seasons. Even though most countries have achieved remarkable results in fighting against epidemic diseases and preventing and controlling viruses, the general public is still far from understanding the new crown virus and lacks imagination on its transmission law. In this paper, we propose MeetDurian: a cross-platform mobile application that exploits a location-based game to improve users' hygiene habits and reduce virus dispersal. We present its main features, its architecture, and its core technologies. Finally, we report a set of experiments that prove the acceptability and usability of MeetDurian. An illustrative demo of the mobile app features is shown in the following video: https://youtu.be/Vqg7nFDQuOU.
Control of a Tail-Sitter VTOL UAV Based on Recurrent Neural Networks
Venue: arXiv cs.RO 2021
Year: 2021

Tail-sitter vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) have the capability of hovering and performing efficient level flight with compact mechanical structures. We present a unified controller design for such UAVs, based on recurrent neural networks. An advantage of this design method is that the various flight modes (i.e., hovering, transition and level flight) of a VTOL UAV are controlled in a unified manner, as opposed to treating them separately and in the runtime switching one from another. The proposed controller consists of an outer-loop position controller and an inner-loop attitude controller. The inner-loop controller is composed of a proportional attitude controller and a loop-shaping linear angular rate controller. For the outer-loop controller, we propose a nonlinear solver to compute the desired attitude and thrust, based on the UAV dynamics and an aerodynamic model, in addition to a cascaded PID controller for the position and velocity tracking. We employ a recurrent neural network (RNN) to approximate the behavior of the nonlinear solver, which suffers from high computational complexity. The proposed RNN has negligible approximation errors, and can be implemented in real-time (e.g., 50 Hz). Moreover, the RNN generates much smoother outputs than the nonlinear solver. We provide an analysis of the stability and robustness of the overall closed-loop system. Simulation and experiments are also presented to demonstrate the effectiveness of the proposed method.
Content-defined Merkle Trees for Efficient Container Delivery
Venue: arXiv cs.DB 2021
Year: 2021

Containerization simplifies the sharing and deployment of applications when environments change in the software delivery chain. To deploy an application, container delivery methods push and pull container images. These methods operate on file and layer (set of files) granularity, and introduce redundant data within a container. Several container operations such as upgrading, installing, and maintaining become inefficient, because of copying and provisioning of redundant data. In this paper, we reestablish recent results that block-level deduplication reduces the size of individual containers, by verifying the result using content-defined chunking. Block-level deduplication, however, does not improve the efficiency of push/pull operations which must determine the specific blocks to transfer. We introduce a content-defined Merkle Tree (\CDMT{}) over deduplicated storage in a container. \CDMT{} indexes deduplicated blocks and determines changes to blocks in logarithmic time on the client. \CDMT{} efficiently pushes and pulls container images from a registry, especially as containers are upgraded and (re-)provisioned on a client. We also describe how a registry can efficiently maintain the \CDMT{} index as new image versions are pushed. We show the scalability of \CDMT{} over Merkle Trees in terms of disk and network I/O savings using 15 container images and 233 image versions from Docker Hub.
Designing Efficient and High-performance AI Accelerators with Customized STT-MRAM
Venue: arXiv cs.AR 2021
Year: 2021

In this paper, we demonstrate the design of efficient and high-performance AI/Deep Learning accelerators with customized STT-MRAM and a reconfigurable core. Based on model-driven detailed design space exploration, we present the design methodology of an innovative scratchpad-assisted on-chip STT-MRAM based buffer system for high-performance accelerators. Using analytically derived expression of memory occupancy time of AI model weights and activation maps, the volatility of STT-MRAM is adjusted with process and temperature variation aware scaling of thermal stability factor to optimize the retention time, energy, read/write latency, and area of STT-MRAM. From the analysis of modern AI workloads and accelerator implementation in 14nm technology, we verify the efficacy of our designed AI accelerator with STT-MRAM STT-AI. Compared to an SRAM-based implementation, the STT-AI accelerator achieves 75% area and 3% power savings at iso-accuracy. Furthermore, with a relaxed bit error rate and negligible AI accuracy trade-off, the designed STT-AI Ultra accelerator achieves 75.4%, and 3.5% savings in area and power, respectively over regular SRAM-based accelerators.