OpenRaiser/Intern-Atlas huggingface dataset:
Influence-guided Data Augmentation for Neural Tensor Completion
Venue: arXiv cs.LG 2021
Year: 2021

How can we predict missing values in multi-dimensional data (or tensors) more accurately? The task of tensor completion is crucial in many applications such as personalized recommendation, image and video restoration, and link prediction in social networks. Many tensor factorization and neural network-based tensor completion algorithms have been developed to predict missing entries in partially observed tensors. However, they can produce inaccurate estimations as real-world tensors are very sparse, and these methods tend to overfit on the small amount of data. Here, we overcome these shortcomings by presenting a data augmentation technique for tensors. In this paper, we propose DAIN, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion methods. Specifically, DAIN first trains a neural model and finds tensor cell importances with influence functions. After that, DAIN aggregates the cell importance to calculate the importance of each entity (i.e., an index of a dimension). Finally, DAIN augments the tensor by weighted sampling of entity importances and a value predictor. Extensive experimental results show that DAIN outperforms all data augmentation baselines in terms of enhancing imputation accuracy of neural tensor completion on four diverse real-world tensors. Ablation studies of DAIN substantiate the effectiveness of each component of DAIN. Furthermore, we show that DAIN scales near linearly to large datasets.
Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search
Venue: arXiv cs.LG 2021
Year: 2021

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs. In this paper, we propose a procedure for generating other feasible solutions of the mixed integer linear program by searching the neighbor of output chemical graph in a search space. The procedure is combined in the framework as a new building block. The results of our computational experiments suggest that the proposed method can generate an additional number of new chemical graphs with up to 50 non-hydrogen atoms.
A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing
Venue: arXiv cs.NI 2021
Year: 2021

Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes; however, these methods suffer from high processing delay, overhead, and False-Positive Rate (FPR). We propose herein Greenshield's traffic model-based fog computing scheme called Fog-based Rogue Nodes Detection (F-RouND), which dynamically utilizes the On-Board Units (OBUs) of all vehicles in the region for rogue node detection. We aim to reduce the data processing delays and FPR in detecting rogue nodes at high vehicle densities. The performance of the F-RouND framework was evaluated via simulations. Results show that the F-RouND framework ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at the urban scenario compared to the existing rogue node detection schemes even when the number of rogue nodes increases by up to 40% in the region.
Network control by a constrained external agent as a continuous optimization problem
Venue: arXiv cs.SI 2021
Year: 2021

Social science studies dealing with control in networks typically resort to heuristics or describing the static control distribution. Optimal policies, however, require interventions that optimize control over a socioeconomic network subject to real-world constraints. We integrate optimisation tools from deep-learning with network science into a framework that is able to optimize such interventions in real-world networks. We demonstrate the framework in the context of corporate control, where it allows to characterize the vulnerability of strategically important corporate networks to sensitive takeovers, an important contemporaneous policy challenge. The framework produces insights that are relevant for governing real-world socioeconomic networks, and opens up new research avenues for improving our understanding and control of such complex systems.
VizLinter: A Linter and Fixer Framework for Data Visualization
Venue: arXiv cs.HC 2021
Year: 2021

Despite the rising popularity of automated visualization tools, existing systems tend to provide direct results which do not always fit the input data or meet visualization requirements. Therefore, additional specification adjustments are still required in real-world use cases. However, manual adjustments are difficult since most users do not necessarily possess adequate skills or visualization knowledge. Even experienced users might create imperfect visualizations that involve chart construction errors. We present a framework, VizLinter, to help users detect flaws and rectify already-built but defective visualizations. The framework consists of two components, (1) a visualization linter, which applies well-recognized principles to inspect the legitimacy of rendered visualizations, and (2) a visualization fixer, which automatically corrects the detected violations according to the linter. We implement the framework into an online editor prototype based on Vega-Lite specifications. To further evaluate the system, we conduct an in-lab user study. The results prove its effectiveness and efficiency in identifying and fixing errors for data visualizations.
Marine vessel tracking using a monocular camera
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, a new technique for camera calibration using only GPS data is presented. A new way of tracking objects that move on a plane in a video is achieved by using the location and size of the bounding box to estimate the distance, achieving an average prediction error of 5.55m per 100m distance from the camera. This solution can be run in real-time at the edge, achieving efficient inference in a low-powered IoT environment while also being able to track multiple different vessels.
On The (In)Effectiveness of Static Logic Bomb Detector for Android Apps
Venue: arXiv cs.CR 2021
Year: 2021

Android is present in more than 85% of mobile devices, making it a prime target for malware. Malicious code is becoming increasingly sophisticated and relies on logic bombs to hide itself from dynamic analysis. In this paper, we perform a large scale study of TSOPEN, our open-source implementation of the state-of-the-art static logic bomb scanner TRIGGERSCOPE, on more than 500k Android applications. Results indicate that the approach scales. Moreover, we investigate the discrepancies and show that the approach can reach a very low false-positive rate, 0.3%, but at a particular cost, e.g., removing 90% of sensitive methods. Therefore, it might not be realistic to rely on such an approach to automatically detect all logic bombs in large datasets. However, it could be used to speed up the location of malicious code, for instance, while reverse engineering applications. We also present TRIGDB a database of 68 Android applications containing trigger-based behavior as a ground-truth to the research community.
Predicting Vehicles' Longitudinal Trajectories and Lane Changes on Highway On-Ramps
Venue: arXiv cs.LG 2021
Year: 2021

Vehicles on highway on-ramps are one of the leading contributors to congestion. In this paper, we propose a prediction framework that predicts the longitudinal trajectories and lane changes (LCs) of vehicles on highway on-ramps and tapers. Specifically, our framework adopts a combination of prediction models that inputs a 4 seconds duration of a trajectory to output a forecast of the longitudinal trajectories and LCs up to 15 seconds ahead. Training and Validation based on next generation simulation (NGSIM) data show that the prediction power of the developed model and its accuracy outperforms a traditional long-short term memory (LSTM) model. Ultimately, the work presented here can alleviate the congestion experienced on on-ramps, improve safety, and guide effective traffic control strategies.
StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm
Venue: arXiv cs.LG 2021
Year: 2021

Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main memory, which severely limits the use of KRR in contexts where data size far exceeds the memory size. Such applications are increasingly common in data mining, bioinformatics, and control. A powerful paradigm for computing on data sets that are too large for memory is the streaming model of computation, where we process one data sample at a time, discarding each sample before moving on to the next one. In this paper, we propose StreaMRAK - a streaming version of KRR. StreaMRAK improves on existing KRR schemes by dividing the problem into several levels of resolution, which allows continual refinement to the predictions. The algorithm reduces the memory requirement by continuously and efficiently integrating new samples into the training model. With a novel sub-sampling scheme, StreaMRAK reduces memory and computational complexities by creating a sketch of the original data, where the sub-sampling density is adapted to the bandwidth of the kernel and the local dimensionality of the data. We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum. The results show that the proposed algorithm is fast and accurate.
Supernodes
Venue: arXiv cs.DM 2021
Year: 2021

In this paper, we present two new concepts related to subgraph counting where the focus is not on the number of subgraphs that are isomorphic to some fixed graph $H$, but on the frequency with which a vertex or an edge belongs to such subgraphs. In particular, we are interested in the case where $H$ is a complete graph. These new concepts are termed vertex participation and edge participation respectively. We combine these concepts with that of the rich-club to identify what we call a Super rich-club and rich edge-club. We show that the concept of vertex participation is a generalisation of the rich-club. We present experimental results on randomised Erdös Rényi and Watts-Strogatz small-world networks. We further demonstrate both concepts on a complex brain network and compare our results to the rich-club of the brain.
Language Transformations in the Classroom
Venue: arXiv cs.PL 2021
Year: 2021

Language transformations are algorithms that take a language specification in input, and return the language specification modified. Language transformations are useful for automatically adding features such as subtyping to programming languages (PLs), and for automatically deriving abstract machines. In this paper, we set forth the thesis that teaching programming languages features with the help of language transformations, in addition to the planned material, can be beneficial for students to help them deepen their understanding of the features being taught. We have conducted a study on integrating language transformations into an undergraduate PL course. We describe our study, the material that we have taught, and the exam submitted to students, and we present the results from this study. Although we refrain from drawing general conclusions on the effectiveness of language transformations, this paper offers encouraging data. We also offer this paper to inspire similar studies.
Small Object Detection Based on Modified FSSD and Model Compression
Venue: arXiv cs.CV 2021
Year: 2021

Small objects have relatively low resolution, the unobvious visual features which are difficult to be extracted, so the existing object detection methods cannot effectively detect small objects, and the detection speed and stability are poor. Thus, this paper proposes a small object detection algorithm based on FSSD, meanwhile, in order to reduce the computational cost and storage space, pruning is carried out to achieve model compression. Firstly, the semantic information contained in the features of different layers can be used to detect different scale objects, and the feature fusion method is improved to obtain more information beneficial to small objects; secondly, batch normalization layer is introduced to accelerate the training of neural network and make the model sparse; finally, the model is pruned by scaling factor to get the corresponding compressed model. The experimental results show that the average accuracy (mAP) of the algorithm can reach 80.4% on PASCAL VOC and the speed is 59.5 FPS on GTX1080ti. After pruning, the compressed model can reach 79.9% mAP, and 79.5 FPS in detection speed. On MS COCO, the best detection accuracy (APs) is 12.1%, and the overall detection accuracy is 49.8% AP when IoU is 0.5. The algorithm can not only improve the detection accuracy of small objects, but also greatly improves the detection speed, which reaches a balance between speed and accuracy.
Making RDBMSs Efficient on Graph Workloads Through Predefined Joins
Venue: arXiv cs.DB 2021
Year: 2021

Joins in native graph database management systems (GDBMSs) are predefined to the system as edges, which are indexed in adjacency list indices and serve as pointers. This contrasts with and can be more performant than value-based joins in RDBMSs and has lead researchers to investigate ways to integrate predefined joins directly into RDBMSs. Existing approaches adopt a strict separation of graph and relational data and processors, where a graph-specific processor uses left-deep and index nested loop joins for a subset of joins. This may be suboptimal, and may lead to non-sequential scans of data in some queries. We propose a purely relational approach to integrate predefined joins in columnar RDBMSs that uses row IDs (RIDs) of tuples as pointers. Users can predefine equality joins between any two tables, which leads to materializing RIDs in extended tables and optionally in RID indices. Instead of using the RID index to perform the join directly, we use it primarily in hash joins to generate semi-join filters that can be passed to scans using sideways information passing, ensuring sequential scans. In some settings, we also use RID indices to reduce the number of joins in query plans. Our approach does not introduce any graph-specific system components, can execute predefined joins on any join plan, and can improve performance on any workload that contains equality joins that can be predefined. We integrated our approach to DuckDB and call the resulting system {\em GRainDB}. We demonstrate that GRainDB far improves the performance of DuckDB on relational and graph workloads with large many-to-many joins, making it competitive with a state-of-the-art GDBMS, and incurs no major overheads otherwise.
Joint Learning Architecture for Multiple Object Tracking and Trajectory Forecasting
Venue: arXiv cs.CV 2021
Year: 2021

This paper introduces a joint learning architecture (JLA) for multiple object tracking (MOT) and trajectory forecasting in which the goal is to predict objects' current and future trajectories simultaneously. Motion prediction is widely used in several state of the art MOT methods to refine predictions in the form of bounding boxes. Typically, a Kalman Filter provides short-term estimations to help trackers correctly predict objects' locations in the current frame. However, the Kalman Filter-based approaches cannot predict non-linear trajectories. We propose to jointly train a tracking and trajectory forecasting model and use the predicted trajectory forecasts for short-term motion estimates in lieu of linear motion prediction methods such as the Kalman filter. We evaluate our JLA on the MOTChallenge benchmark. Evaluations result show that JLA performs better for short-term motion prediction and reduces ID switches by 33%, 31%, and 47% in the MOT16, MOT17, and MOT20 datasets, respectively, in comparison to FairMOT.
Rotation-constrained optical see-through headset calibration withbare-hand alignment
Venue: arXiv cs.GT 2021
Year: 2021

The inaccessibility of user-perceived reality remains an open issue in pursuing the accurate calibration of optical see-through (OST) head-mounted displays (HMDs). Manual user alignment is usually required to collect a set of virtual-to-real correspondences, so that a default or an offline display calibration can be updated to account for the user's eye position(s). Current alignment-based calibration procedures usually require point-wise alignments between rendered image point(s) and associated physical landmark(s) of a target calibration tool. As each alignment can only provide one or a few correspondences, repeated alignments are required to ensure calibration quality. This work presents an accurate and tool-less online OST calibration method to update an offline-calibrated eye-display model. The user's bare hand is markerlessly tracked by a commercial RGBD camera anchored to the OST headset to generate a user-specific cursor for correspondence collection. The required alignment is object-wise, and can provide thousands of unordered corresponding points in tracked space. The collected correspondences are registered by a proposed rotation-constrained iterative closest point (rcICP) method to optimise the viewpoint-related calibration parameters. We implemented such a method for the Microsoft HoloLens 1. The resiliency of the proposed procedure to noisy data was evaluated through simulated tests and real experiments performed with an eye-replacement camera. According to the simulation test, the rcICP registration is robust against possible user-induced rotational misalignment. With a single alignment, our method achieves 8.81 arcmin (1.37 mm) positional error and 1.76 degree rotational error by camera-based tests in the arm-reach distance, and 10.79 arcmin (7.71 pixels) reprojection error by user tests.
Outdoor Position Recovery from HeterogeneousTelco Cellular Data
Venue: arXiv cs.NI 2021
Year: 2021

Recent years have witnessed unprecedented amounts of data generated by telecommunication (Telco) cellular networks. For example, measurement records (MRs) are generated to report the connection states between mobile devices and Telco networks, e.g., received signal strength. MR data have been widely used to localize outdoor mobile devices for human mobility analysis, urban planning, and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture spatio-temporal locality in underlying mobility patterns for lower localization errors. The HMM approaches typically assume stable mobility patterns of the underlying mobile devices. Yet real MR datasets exhibit heterogeneous mobility patterns due to mixed transportation modes of the underlying mobile devices and uneven distribution of the positions associated with MR samples. Thus, the existing solutions cannot handle these heterogeneous mobility patterns. we propose a multi-task learning-based deep neural network (DNN) framework, namely PRNet+, to incorporate outdoor position recovery and transportation mode detection. To make sure the framework work, PRNet+ develops a feature extraction module to precisely learn local-, short- and long-term spatio-temporal locality from heterogeneous MR samples. Extensive evaluation on eight datasets collected at three representative areas in Shanghai indicates that PRNet+ greatly outperforms state-of-the-arts.
Towards Predictive Replica Placement for Distributed Data Stores in Fog Environments
Venue: arXiv cs.DC 2021
Year: 2021

Mobile clients that consume and produce data are abundant in fog environments. Low latency access to this data can only be achieved by storing it in close physical proximity to the clients. Current data store systems fall short as they do not replicate data based on client movement. We propose an approach to predictive replica placement that autonomously and proactively replicates data close to likely client locations.
A Random Geometric Model of Blockages in Vehicular Networks
Venue: arXiv cs.IT 2021
Year: 2021

This paper presents a novel spatially consistent approach for modeling line-of-sight (LOS) paths in vehicular networks. We use stochastic geometry to model transmitters, obstacles, and receivers located in three parallel lines, respectively. Their geometric interactions are leveraged to characterize the existence of LOS paths. Specifically, the proposed approach focuses on the role of obstacles in blocking one or more LOS paths, which has been overlooked in most statistical models for blockage. Under the proposed framework, we derive the probability that a typical vehicle is in LOS with respect to transmitters with received signal-to-noise ratios greater than a threshold. The proposed framework and LOS coverage analysis are instrumental to the analysis of LOS-critical applications such as positioning or mmWave communications in vehicular networks.
Sharing Practices for Datasets Related to Accessibility and Aging
Venue: arXiv cs.HC 2021
Year: 2021

Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.
Measuring Wikipedia Article Quality in One Dimension by Extending ORES with Ordinal Regression
Venue: arXiv cs.CL 2021
Year: 2021

Organizing complex peer production projects and advancing scientific knowledge of open collaboration each depend on the ability to measure quality. Article quality ratings on English language Wikipedia have been widely used by both Wikipedia community members and academic researchers for purposes like tracking knowledge gaps and studying how political polarization shapes collaboration. Even so, measuring quality presents many methodological challenges. The most widely used systems use labels on discrete ordinal scales when assessing quality, but such labels can be inconvenient for statistics and machine learning. Prior work handles this by assuming that different levels of quality are "evenly spaced" from one another. This assumption runs counter to intuitions about the relative degrees of effort needed to raise Wikipedia encyclopedia articles to different quality levels. Furthermore, models from prior work are fit to datasets that oversample high-quality articles. This limits their accuracy for representative samples of articles or revisions. I describe a technique extending the Wikimedia Foundations' ORES article quality model to address these limitations. My method uses weighted ordinal regression models to construct one-dimensional continuous measures of quality. While scores from my technique and from prior approaches are correlated, my approach improves accuracy for research datasets and provides evidence that the "evenly spaced" assumption is unfounded in practice on English Wikipedia. I conclude with recommendations for using quality scores in future research and include the full code, data, and models.