OpenRaiser/Intern-Atlas huggingface dataset:
Scientometric analysis and knowledge mapping of literature-based discovery (1986-2020)
Venue: arXiv cs.DL 2020
Year: 2020

Literature-based discovery (LBD) aims to discover valuable latent relationships between disparate sets of literatures. This paper presents the first inclusive scientometric overview of LBD research. We utilize a comprehensive scientometric approach incorporating CiteSpace to systematically analyze the literature on LBD from the last four decades (1986-2020). After manual cleaning, we have retrieved a total of 409 documents from six bibliographic databases and two preprint servers. The 35 years' history of LBD could be partitioned into three phases according to the published papers per year: incubation (1986-2003), developing (2004-2008), and mature phase (2009-2020). The annual production of publications follows Price's law. The co-authorship network exhibits many subnetworks, indicating that LBD research is composed of many small and medium-sized groups with little collaboration among them. Science mapping reveals that mainstream research in LBD has shifted from baseline co-occurrence approaches to semantic-based methods at the beginning of the new millennium. In the last decade, we can observe the leaning of LBD towards modern network science ideas. In an applied sense, the LBD is increasingly used in predicting adverse drug reactions and drug repurposing. Besides theoretical considerations, the researchers have put a lot of effort into the development of Web-based LBD applications. Nowadays, LBD is becoming increasingly interdisciplinary and involves methods from information science, scientometrics, and machine learning. Unfortunately, LBD is mainly limited to the biomedical domain. The cascading citation expansion announces deep learning and explainable artificial intelligence as emerging topics in LBD. The results indicate that LBD is still growing and evolving.
Solving the Bethe-Salpeter equation on massively parallel architectures
Venue: arXiv cs.CE 2020
Year: 2020

The last ten years have witnessed fast spreading of massively parallel computing clusters, from leading supercomputing facilities down to the average university computing center. Many companies in the private sector have undergone a similar evolution. In this scenario, the seamless integration of software and middleware libraries is a key ingredient to ensure portability of scientific codes and guarantees them an extended lifetime. In this work, we describe the integration of the ChASE library, a modern parallel eigensolver, into an existing legacy code for the first-principles computation of optical properties of materials via solution of the Bethe-Salpeter equation for the optical polarization function. Our numerical tests show that, as a result of integrating ChASE and parallelizing the reading routine, the code experiences a remarkable speedup and greatly improved scaling behavior on both multi- and many-core architectures. We demonstrate that such a modernized BSE code will, by fully exploiting parallel computing architectures and file systems, enable domain scientists to accurately study complex material systems that were not accessible before.
Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge
Venue: arXiv cs.CV 2020
Year: 2020

In today's world, a vast amount of data is being generated by edge devices that can be used as valuable training data to improve the performance of machine learning algorithms in terms of the achieved accuracy or to reduce the compute requirements of the model. However, due to user data privacy concerns as well as storage and communication bandwidth limitations, this data cannot be moved from the device to the data centre for further improvement of the model and subsequent deployment. As such there is a need for increased edge intelligence, where the deployed models can be fine-tuned on the edge, leading to improved accuracy and/or reducing the model's workload as well as its memory and power footprint. In the case of Convolutional Neural Networks (CNNs), both the weights of the network as well as its topology can be tuned to adapt to the data that it processes. This paper provides a first step towards enabling CNN finetuning on an edge device based on structured pruning. It explores the performance gains and costs of doing so and presents an extensible open-source framework that allows the deployment of such approaches on a wide range of network architectures and devices. The results show that on average, data-aware pruning with retraining can provide 10.2pp increased accuracy over a wide range of subsets, networks and pruning levels with a maximum improvement of 42.0pp over pruning and retraining in a manner agnostic to the data being processed by the network.
Is Working From Home The New Norm? An Observational Study Based on a Large Geo-tagged COVID-19 Twitter Dataset
Venue: arXiv cs.SI 2020
Year: 2020

As the COVID-19 pandemic swept over the world, people discussed facts, expressed opinions, and shared sentiments on social media. Since the reaction to COVID-19 in different locations may be tied to local cases, government regulations, healthcare resources and socioeconomic factors, we curated a large geo-tagged Twitter dataset and performed exploratory analysis by location. Specifically, we collected 650,563 unique geo-tagged tweets across the United States (50 states and Washington, D.C.) covering the date range from January 25 to May 10, 2020. Tweet locations enabled us to conduct region-specific studies such as tweeting volumes and sentiment, sometimes in response to local regulations and reported COVID-19 cases. During this period, many people started working from home. The gap between workdays and weekends in hourly tweet volumes inspired us to propose algorithms to estimate work engagement during the COVID-19 crisis. This paper also summarizes themes and topics of tweets in our dataset using both social media exclusive tools (i.e., #hashtags, @mentions) and the latent Dirichlet allocation model. We welcome requests for data sharing and conversations for more insights. Dataset link: http://covid19research.site/geo-tagged_twitter_datasets/
Self-Supervised Domain Mismatch Estimation for Autonomous Perception
Venue: arXiv cs.CV 2020
Year: 2020

Autonomous driving requires self awareness of its perception functions. Technically spoken, this can be realized by observers, which monitor the performance indicators of various perception modules. In this work we choose, exemplarily, a semantic segmentation to be monitored, and propose an autoencoder, trained in a self-supervised fashion on the very same training data as the semantic segmentation to be monitored. While the autoencoder's image reconstruction performance (PSNR) during online inference shows already a good predictive power w.r.t. semantic segmentation performance, we propose a novel domain mismatch metric DM as the earth mover's distance between a pre-stored PSNR distribution on training (source) data, and an online-acquired PSNR distribution on any inference (target) data. We are able to show by experiments that the DM metric has a strong rank order correlation with the semantic segmentation within its functional scope. We also propose a training domain-dependent threshold for the DM metric to define this functional scope.
Does it matter how well I know what you're thinking? Opponent Modelling in an RTS game
Venue: arXiv cs.AI 2020
Year: 2020

Opponent Modelling tries to predict the future actions of opponents, and is required to perform well in multi-player games. There is a deep literature on learning an opponent model, but much less on how accurate such models must be to be useful. We investigate the sensitivity of Monte Carlo Tree Search (MCTS) and a Rolling Horizon Evolutionary Algorithm (RHEA) to the accuracy of their modelling of the opponent in a simple Real-Time Strategy game. We find that in this domain RHEA is much more sensitive to the accuracy of an opponent model than MCTS. MCTS generally does better even with an inaccurate model, while this will degrade RHEA's performance. We show that faced with an unknown opponent and a low computational budget it is better not to use any explicit model with RHEA, and to model the opponent's actions within the tree as part of the MCTS algorithm.
Threats and Countermeasures of Cyber Security in Direct and Remote Vehicle Communication Systems
Venue: arXiv cs.CR 2020
Year: 2020

Traffic management, road safety, and environmental impact are important issues in the modern world. These challenges are addressed by the application of sensing, control and communication methods of intelligent transportation systems (ITS). A part of ITS is a vehicular ad-hoc network (VANET) which means a wireless network of vehicles. However, communication among vehicles in a VANET exposes several security threats which need to be studied and addressed. In this review, firstly, the basic flow of VANET is illustrated focusing on its communication methods, architecture, characteristics, standards, and security facilities. Next, the attacks and threats for VANET are discussed. Moreover, the authentication systems are described by which vehicular networks can be protected from fake messages and malicious nodes. Security threats and counter measures are discussed for different remote vehicle communication methods namely, remote keyless entry system, dedicated short range communication, cellular scheme, Zigbee, Bluetooth, radio frequency identification, WiFi, WiMAX, and different direct vehicle communication methods namely on-board diagnosis and universal serial bus.
Exact and Metaheuristic Approaches for the Production Leveling Problem
Venue: arXiv cs.AI 2020
Year: 2020

In this paper we introduce a new problem in the field of production planning which we call the Production Leveling Problem. The task is to assign orders to production periods such that the load in each period and on each production resource is balanced, capacity limits are not exceeded and the orders' priorities are taken into account. Production Leveling is an important intermediate step between long-term planning and the final scheduling of orders within a production period, as it is responsible for selecting good subsets of orders to be scheduled within each period. A formal model of the problem is proposed and NP-hardness is shown by reduction from Bin Backing. As an exact method for solving moderately sized instances we introduce a MIP formulation. For solving large problem instances, metaheuristic local search is investigated. A greedy heuristic and two neighborhood structures for local search are proposed, in order to apply them using Variable Neighborhood Descent and Simulated Annealing. Regarding exact techniques, the main question of research is, up to which size instances are solvable within a fixed amount of time. For the metaheuristic approaches the aim is to show that they produce near-optimal solutions for smaller instances, but also scale well to very large instances. A set of realistic problem instances from an industrial partner is contributed to the literature, as well as random instance generators. The experimental evaluation conveys that the proposed MIP model works well for instances with up to 250 orders. Out of the investigated metaheuristic approaches, Simulated Annealing achieves the best results. It is shown to produce solutions with less than 3% average optimality gap on small instances and to scale well up to thousands of orders and dozens of periods and products. The presented metaheuristic methods are already being used in the industry.
Comparative Analysis of Economic Instruments in Intersection Operation: A User-Based Perspective
Venue: arXiv cs.CY 2020
Year: 2020

Focusing on different economic instruments implemented in intersection operations under a connected environment, this paper analyzes their advantages and disadvantages from the travelers' perspective. Travelers' concerns revolve around whether a new instrument is easy to learn and operate, whether it can save time or money, and whether it can reduce the rich-poor gap. After a comparative analysis, we found that both credit and free-market schemes can benefit users. Second-price auctions can only benefit high VOT vehicles. From the perspective of technology deployment and adoption, a credit scheme is not easy to learn and operate for travelers.
Learning Smooth and Fair Representations
Venue: arXiv cs.LG 2020
Year: 2020

Organizations that own data face increasing legal liability for its discriminatory use against protected demographic groups, extending to contractual transactions involving third parties access and use of the data. This is problematic, since the original data owner cannot ex-ante anticipate all its future uses by downstream users. This paper explores the upstream ability to preemptively remove the correlations between features and sensitive attributes by mapping features to a fair representation space. Our main result shows that the fairness measured by the demographic parity of the representation distribution can be certified from a finite sample if and only if the chi-squared mutual information between features and representations is finite. Empirically, we find that smoothing the representation distribution provides generalization guarantees of fairness certificates, which improves upon existing fair representation learning approaches. Moreover, we do not observe that smoothing the representation distribution degrades the accuracy of downstream tasks compared to state-of-the-art methods in fair representation learning.
Equilibrium Propagation for Complete Directed Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts. However, the most successful learning algorithm for artificial neural networks, backpropagation, is considered biologically implausible. We contribute to the topic of biologically plausible neuronal learning by building upon and extending the equilibrium propagation learning framework. Specifically, we introduce: a new neuronal dynamics and learning rule for arbitrary network architectures; a sparsity-inducing method able to prune irrelevant connections; a dynamical-systems characterization of the models, using Lyapunov theory.
A Particle Swarm Optimization hyper-heuristic for the Dynamic Vehicle Routing Problem
Venue: arXiv cs.NE 2020
Year: 2020

This paper presents a method for choosing a Particle Swarm Optimization based optimizer for the Dynamic Vehicle Routing Problem on the basis of the initially available data of a given problem instance. The optimization algorithm is chosen on the basis of a prediction made by a linear model trained on that data and the relative results obtained by the optimization algorithms. The achieved results suggest that such a model can be used in a hyper-heuristic approach as it improved the average results, obtained on the set of benchmark instances, by choosing the appropriate algorithm in 82% of significant cases. Two leading multi-swarm Particle Swarm Optimization based algorithms for solving the Dynamic Vehicle Routing Problem are used as the basic optimization algorithms: Khouadjia's et al. Multi-Environmental Multi-Swarm Optimizer and authors' 2--Phase Multiswarm Particle Swarm Optimization.
Explaining reputation assessments
Venue: arXiv cs.AI 2020
Year: 2020

Reputation is crucial to enabling human or software agents to select among alternative providers. Although several effective reputation assessment methods exist, they typically distil reputation into a numerical representation, with no accompanying explanation of the rationale behind the assessment. Such explanations would allow users or clients to make a richer assessment of providers, and tailor selection according to their preferences and current context. In this paper, we propose an approach to explain the rationale behind assessments from quantitative reputation models, by generating arguments that are combined to form explanations. Our approach adapts, extends and combines existing approaches for explaining decisions made using multi-attribute decision models in the context of reputation. We present example argument templates, and describe how to select their parameters using explanation algorithms. Our proposal was evaluated by means of a user study, which followed an existing protocol. Our results give evidence that although explanations present a subset of the information of trust scores, they are sufficient to equally evaluate providers recommended based on their trust score. Moreover, when explanation arguments reveal implicit model information, they are less persuasive than scores.
Quantitatively Assessing the Benefits of Model-driven Development in Agent-based Modeling and Simulation
Venue: arXiv cs.AI 2020
Year: 2020

The agent-based modeling and simulation (ABMS) paradigm has been used to analyze, reproduce, and predict phenomena related to many application areas. Although there are many agent-based platforms that support simulation development, they rely on programming languages that require extensive programming knowledge. Model-driven development (MDD) has been explored to facilitate simulation modeling, by means of high-level modeling languages that provide reusable building blocks that hide computational complexity, and code generation. However, there is still limited knowledge of how MDD approaches to ABMS contribute to increasing development productivity and quality. We thus in this paper present an empirical study that quantitatively compares the use of MDD and ABMS platforms mainly in terms of effort and developer mistakes. Our evaluation was performed using MDD4ABMS-an MDD approach with a core and extensions to two application areas, one of which developed for this study-and NetLogo, a widely used platform. The obtained results show that MDD4ABMS requires less effort to develop simulations with similar (sometimes better) design quality than NetLogo, giving evidence of the benefits that MDD can provide to ABMS.
Index Selection for NoSQL Database with Deep Reinforcement Learning
Venue: arXiv cs.DB 2020
Year: 2020

We propose a new approach of NoSQL database index selection. For different workloads, we select different indexes and their different parameters to optimize the database performance. The approach builds a deep reinforcement learning model to select an optimal index for a given fixed workload and adapts to a changing workload. Experimental results show that, Deep Reinforcement Learning Index Selection Approach (DRLISA) has improved performance to varying degrees according to traditional single index structures.
Edge computing based incentivizing mechanism for mobile blockchain in IOT
Venue: arXiv cs.GT 2020
Year: 2020

Mining in the blockchain requires high computing power to solve the hash puzzle for example proof-of-work puzzle. It takes high cost to achieve the calculation of this problem in devices of IOT, especially the mobile devices of IOT. It consequently restricts the application of blockchain in mobile environment. However, edge computing can be utilized to solve the problem for insufficient computing power of mobile devices in IOT. Edge servers can recruit many mobile devices to contribute computing power together to mining and share the reward of mining with these recruited mobile devices. In this paper, we propose an incentivizing mechanism based on edge computing for mobile blockchain. We design a two-stage Stackelberg Game to jointly optimize the reward of edge servers and recruited mobile devices. The edge server as the leader sets the expected fee for the recruited mobile devices in Stage I. The mobile device as a follower provides its computing power to mine according to the expected fee in Stage. It proves that this game can obtain a uniqueness Nash Equilibrium solution under the same or different expected fee. In the simulation experiment, we obtain a result curve of the profit for the edge server with the different ratio between the computing power from the edge server and mobile devices. In addition, the proposed scheme has been compared with the MDG scheme for the profit of the edge server. The experimental results show that the profit of the proposed scheme is more than that of the MDG scheme under the same total computing power.
Channel Relationship Prediction with Forget-Update Module for Few-shot Classification
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we proposed a pipeline for inferring the relationship of each class in support set and a query sample using forget-update module. We first propose a novel architectural module called "channel vector sequence construction module", which boosts the performance of sequence-prediction-model-based few-shot classification methods by collecting the overall information of all support samples and a query sample. The channel vector sequence generated by this module is organized in a way that each time step of the sequence contains the information from the corresponding channel of all support samples and the query sample to be inferred. Channel vector sequence is obtained by a convolutional neural network and a fully connected network, and the spliced channel vector sequence is spliced of the corresponding channel vectors of support samples and a query sample in the original channel order. Also, we propose a forget-update module consisting of stacked forget-update blocks. The forget block modify the original information with the learned weights and the update block establishes a dense connection for the model. The proposed pipeline, which consists of channel vector sequence construction module and forget-update module, can infer the relationship between the query sample and support samples in few-shot classification scenario. Experimental results show that the pipeline can achieve state-of-the-art results on miniImagenet, CUB dataset, and cross-domain scenario.
Global Feature Aggregation for Accident Anticipation
Venue: arXiv cs.CV 2020
Year: 2020

Anticipation of accidents ahead of time in autonomous and non-autonomous vehicles aids in accident avoidance. In order to recognize abnormal events such as traffic accidents in a video sequence, it is important that the network takes into account interactions of objects in a given frame. We propose a novel Feature Aggregation (FA) block that refines each object's features by computing a weighted sum of the features of all objects in a frame. We use FA block along with Long Short Term Memory (LSTM) network to anticipate accidents in the video sequences. We report mean Average Precision (mAP) and Average Time-to-Accident (ATTA) on Street Accident (SA) dataset. Our proposed method achieves the highest score for risk anticipation by predicting accidents 0.32 sec and 0.75 sec earlier compared to the best results with Adaptive Loss and dynamic parameter prediction based methods respectively.