OpenRaiser/Intern-Atlas huggingface dataset:
Local topological features of robust supply networks
Venue: arXiv cs.SI 2021
Year: 2021

The design of robust supply and distribution systems is one of the fundamental challenges at the interface of network science and logistics. Given the multitude of performance criteria, real-world constraints, and external influences acting upon such a system, even formulating an appropriate research question to address this topic is non-trivial. Here we present an abstraction of a supply and distribution system leading to a minimal model, which only retains stylized facts of the systemic function and, in this way, allows us to investigate the generic properties of robust supply networks. On this level of abstraction, a supply and distribution system is the strategic use of transportation to eliminate mismatches between production patterns (i.e., the amounts of goods produced at each production site of a company) and demand patterns (i.e., the amount of goods consumed at each location). When creating networks based on this paradigm and furthermore requiring the robustness of the system with respect to the loss of transportation routes (edge of the network) we see that robust networks are built from specific sets of subgraphs, while vulnerable networks display a markedly different subgraph composition. Our findings confirm a long-standing hypothesis in the field of network science, namely, that network motifs -- statistically over-represented small subgraphs -- are informative about the robust functioning of a network. Also, our findings offer a blueprint for enhancing the robustness of real-world supply and distribution systems.
Efficient Pressure: Improving efficiency for signalized intersections
Venue: arXiv cs.LG 2021
Year: 2021

Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC) problem. However, existing RL-based methods are rarely deployed considering that they are neither cost-effective in terms of computing resources nor more robust than traditional approaches, which raises a critical research question: how to construct an adaptive controller for TSC with less training and reduced complexity based on RL-based approach? To address this question, in this paper, we (1) innovatively specify the traffic movement representation as a simple but efficient pressure of vehicle queues in a traffic network, namely efficient pressure (EP); (2) build a traffic signal settings protocol, including phase duration, signal phase number and EP for TSC; (3) design a TSC approach based on the traditional max pressure (MP) approach, namely efficient max pressure (Efficient-MP) using the EP to capture the traffic state; and (4) develop a general RL-based TSC algorithm template: efficient Xlight (Efficient-XLight) under EP. Through comprehensive experiments on multiple real-world datasets in our traffic signal settings' protocol for TSC, we demonstrate that efficient pressure is complementary to traditional and RL-based modeling to design better TSC methods. Our code is released on Github.
3rd Place: A Global and Local Dual Retrieval Solution to Facebook AI Image Similarity Challenge
Venue: arXiv cs.CV 2021
Year: 2021

As a basic task of computer vision, image similarity retrieval is facing the challenge of large-scale data and image copy attacks. This paper presents our 3rd place solution to the matching track of Image Similarity Challenge (ISC) 2021 organized by Facebook AI. We propose a multi-branch retrieval method of combining global descriptors and local descriptors to cover all attack cases. Specifically, we attempt many strategies to optimize global descriptors, including abundant data augmentations, self-supervised learning with a single Transformer model, overlay detection preprocessing. Moreover, we introduce the robust SIFT feature and GPU Faiss for local retrieval which makes up for the shortcomings of the global retrieval. Finally, KNN-matching algorithm is used to judge the match and merge scores. We show some ablation experiments of our method, which reveals the complementary advantages of global and local features.
Invalidation-Based Protocols for Replicated Datastores
Venue: arXiv cs.DC 2021
Year: 2021

Distributed in-memory datastores underpin cloud applications that run within a datacenter and demand high performance, strong consistency, and availability. A key feature of datastores is data replication. The data are replicated across servers because a single server often cannot handle the request load. Replication is also necessary to guarantee that a server or link failure does not render a portion of the dataset inaccessible. A replication protocol is responsible for ensuring strong consistency between the replicas of a datastore, even when faults occur, by determining the actions necessary to access and manipulate the data. Consequently, a replication protocol also drives the datastore's performance. Existing strongly consistent replication protocols deliver fault tolerance but fall short in terms of performance. Meanwhile, the opposite occurs in the world of multiprocessors, where data are replicated across the private caches of different cores. The multiprocessor regime uses invalidations to afford strongly consistent replication with high performance but neglects fault tolerance. Although handling failures in the datacenter is critical for data availability, we observe that the common operation is fault-free and far exceeds the operation during faults. In other words, the common operating environment inside a datacenter closely resembles that of a multiprocessor. Based on this insight, we draw inspiration from the multiprocessor for high-performance, strongly consistent replication in the datacenter. The primary contribution of this thesis is in adapting invalidating protocols to the nuances of replicated datastores, which include skewed data accesses, fault tolerance, and distributed transactions.
PointCLIP: Point Cloud Understanding by CLIP
Venue: arXiv cs.CV 2021
Year: 2021

Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training (CLIP) have shown inspirational performance on 2D visual recognition, which learns to match images with their corresponding texts in open-vocabulary settings. However, it remains under explored that whether CLIP, pre-trained by large-scale image-text pairs in 2D, can be generalized to 3D recognition. In this paper, we identify such a setting is feasible by proposing PointCLIP, which conducts alignment between CLIP-encoded point cloud and 3D category texts. Specifically, we encode a point cloud by projecting it into multi-view depth maps without rendering, and aggregate the view-wise zero-shot prediction to achieve knowledge transfer from 2D to 3D. On top of that, we design an inter-view adapter to better extract the global feature and adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in 2D. By just fine-tuning the lightweight adapter in the few-shot settings, the performance of PointCLIP could be largely improved. In addition, we observe the complementary property between PointCLIP and classical 3D-supervised networks. By simple ensembling, PointCLIP boosts baseline's performance and even surpasses state-of-the-art models. Therefore, PointCLIP is a promising alternative for effective 3D point cloud understanding via CLIP under low resource cost and data regime. We conduct thorough experiments on widely-adopted ModelNet10, ModelNet40 and the challenging ScanObjectNN to demonstrate the effectiveness of PointCLIP. The code is released at https://github.com/ZrrSkywalker/PointCLIP.
Predicting Bandwidth Utilization on Network Links Using Machine Learning
Venue: arXiv cs.NI 2021
Year: 2021

Predicting the bandwidth utilization on network links can be extremely useful for detecting congestion in order to correct them before they occur. In this paper, we present a solution to predict the bandwidth utilization between different network links with a very high accuracy. A simulated network is created to collect data related to the performance of the network links on every interface. These data are processed and expanded with feature engineering in order to create a training set. We evaluate and compare three types of machine learning algorithms, namely ARIMA (AutoRegressive Integrated Moving Average), MLP (Multi Layer Perceptron) and LSTM (Long Short-Term Memory), in order to predict the future bandwidth consumption. The LSTM outperforms ARIMA and MLP with very accurate predictions, rarely exceeding a 3\% error (40\% for ARIMA and 20\% for the MLP). We then show that the proposed solution can be used in real time with a reaction managed by a Software-Defined Networking (SDN) platform.
SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction
Venue: arXiv cs.CV 2021
Year: 2021

Pedestrian trajectory prediction is an important technique of autonomous driving, which has become a research hot-spot in recent years. Previous methods mainly rely on the position relationship of pedestrians to model social interaction, which is obviously not enough to represent the complex cases in real situations. In addition, most of existing work usually introduce the scene interaction module as an independent branch and embed the social interaction features in the process of trajectory generation, rather than simultaneously carrying out the social interaction and scene interaction, which may undermine the rationality of trajectory prediction. In this paper, we propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN) which aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. In detail, when modeling social interaction, we propose a new \emph{social soft attention function}, which fully considers various interaction factors among pedestrians. And it can distinguish the influence of pedestrians around the agent based on different factors under various situations. For the physical interaction, we propose one new \emph{sequential scene sharing mechanism}. The influence of the scene on one agent at each moment can be shared with other neighbors through social soft attention, therefore the influence of the scene is expanded both in spatial and temporal dimension. With the help of these improvements, we successfully obtain socially and physically acceptable predicted trajectories. The experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
Grappling with the Scale of Born-Digital Government Publications: Toward Pipelines for Processing and Searching Millions of PDFs
Venue: arXiv cs.DL 2021
Year: 2021

Official government publications are key sources for understanding the history of societies. Web publishing has fundamentally changed the scale and processes by which governments produce and disseminate information. Significantly, a range of web archiving programs have captured massive troves of government publications. For example, hundreds of millions of unique U.S. Government documents posted to the web in PDF form have been archived by libraries to date. Yet, these PDFs remain largely unutilized and understudied in part due to the challenges surrounding the development of scalable pipelines for searching and analyzing them. This paper utilizes a Library of Congress dataset of 1,000 government PDFs in order to offer initial approaches for searching and analyzing these PDFs at scale. In addition to demonstrating the utility of PDF metadata, this paper offers computationally-efficient machine learning approaches to search and discovery that utilize the PDFs' textual and visual features as well. We conclude by detailing how these methods can be operationalized at scale in order to support systems for navigating millions of PDFs.
Deblurring via Stochastic Refinement
Venue: arXiv cs.CV 2021
Year: 2021

Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input. This leads to a significant improvement in perceptual quality over existing state-of-the-art methods across multiple standard benchmarks. Our predict-and-refine approach also enables much more efficient sampling compared to typical diffusion models. Combined with a carefully tuned network architecture and inference procedure, our method is competitive in terms of distortion metrics such as PSNR. These results show clear benefits of our diffusion-based method for deblurring and challenge the widely used strategy of producing a single, deterministic reconstruction.
Adaptive Channel Encoding for Point Cloud Analysis
Venue: arXiv cs.CV 2021
Year: 2021

Attention mechanism plays a more and more important role in point cloud analysis and channel attention is one of the hotspots. With so much channel information, it is difficult for neural networks to screen useful channel information. Thus, an adaptive channel encoding mechanism is proposed to capture channel relationships in this paper. It improves the quality of the representation generated by the network by explicitly encoding the interdependence between the channels of its features. Specifically, a channel-wise convolution (Channel-Conv) is proposed to adaptively learn the relationship between coordinates and features, so as to encode the channel. Different from the popular attention weight schemes, the Channel-Conv proposed in this paper realizes adaptability in convolution operation, rather than simply assigning different weights for channels. Extensive experiments on existing benchmarks verify our method achieves the state of the arts.
The Linear Arrangement Library. A new tool for research on syntactic dependency structures
Venue: arXiv cs.CL 2021
Year: 2021

The new and growing field of Quantitative Dependency Syntax has emerged at the crossroads between Dependency Syntax and Quantitative Linguistics. One of the main concerns in this field is the statistical patterns of syntactic dependency structures. These structures, grouped in treebanks, are the source for statistical analyses in these and related areas; dozens of scores devised over the years are the tools of a new industry to search for patterns and perform other sorts of analyses. The plethora of such metrics and their increasing complexity require sharing the source code of the programs used to perform such analyses. However, such code is not often shared with the scientific community or is tested following unknown standards. Here we present a new open-source tool, the Linear Arrangement Library (LAL), which caters to the needs of, especially, inexperienced programmers. This tool enables the calculation of these metrics on single syntactic dependency structures, treebanks, and collection of treebanks, grounded on ease of use and yet with great flexibility. LAL has been designed to be efficient, easy to use (while satisfying the needs of all levels of programming expertise), reliable (thanks to thorough testing), and to unite research from different traditions, geographic areas, and research fields.
Inf-CP: A Reliable Channel Pruning based on Channel Influence
Venue: arXiv cs.LG 2021
Year: 2021

One of the most effective methods of channel pruning is to trim on the basis of the importance of each neuron. However, measuring the importance of each neuron is an NP-hard problem. Previous works have proposed to trim by considering the statistics of a single layer or a plurality of successive layers of neurons. These works cannot eliminate the influence of different data on the model in the reconstruction error, and currently, there is no work to prove that the absolute values of the parameters can be directly used as the basis for judging the importance of the weights. A more reasonable approach is to eliminate the difference between batch data that accurately measures the weight of influence. In this paper, we propose to use ensemble learning to train a model for different batches of data and use the influence function (a classic technique from robust statistics) to learn the algorithm to track the model's prediction and return its training parameter gradient, so that we can determine the responsibility for each parameter, which we call "influence", in the prediction process. In addition, we theoretically prove that the back-propagation of the deep network is a first-order Taylor approximation of the influence function of the weights. We perform extensive experiments to prove that pruning based on the influence function using the idea of ensemble learning will be much more effective than just focusing on error reconstruction. Experiments on CIFAR shows that the influence pruning achieves the state-of-the-art result.
STSM: Spatio-Temporal Shift Module for Efficient Action Recognition
Venue: arXiv cs.CV 2021
Year: 2021

The modeling, computational cost, and accuracy of traditional Spatio-temporal networks are the three most concentrated research topics in video action recognition. The traditional 2D convolution has a low computational cost, but it cannot capture the time relationship; the convolutional neural networks (CNNs) model based on 3D convolution can obtain good performance, but its computational cost is high, and the amount of parameters is large. In this paper, we propose a plug-and-play Spatio-temporal Shift Module (STSM), which is a generic module that is both effective and high-performance. Specifically, after STSM is inserted into other networks, the performance of the network can be improved without increasing the number of calculations and parameters. In particular, when the network is 2D CNNs, our STSM module allows the network to learn efficient Spatio-temporal features. We conducted extensive evaluations of the proposed module, conducted numerous experiments to study its effectiveness in video action recognition, and achieved state-of-the-art results on the kinetics-400 and Something-Something V2 datasets.
Exploring and Mitigating Gender Bias in Recommender Systems with Explicit Feedback
Venue: arXiv cs.IR 2021
Year: 2021

Recommender systems are indispensable because they influence our day-to-day behavior and decisions by giving us personalized suggestions. Services like Kindle, Youtube, and Netflix depend heavily on the performance of their recommender systems to ensure that their users have a good experience and to increase revenues. Despite their popularity, it has been shown that recommender systems reproduce and amplify the bias present in the real world. The resulting feedback creates a self-perpetuating loop that deteriorates the user experience and results in homogenizing recommendations over time. Further, biased recommendations can also reinforce stereotypes based on gender or ethnicity, thus reinforcing the filter bubbles that we live in. In this paper, we address the problem of gender bias in recommender systems with explicit feedback. We propose a model to quantify the gender bias present in book rating datasets and in the recommendations produced by the recommender systems. Our main contribution is to provide a principled approach to mitigate the bias being produced in the recommendations. We theoretically show that the proposed approach provides unbiased recommendations despite biased data. Through empirical evaluation on publicly available book rating datasets, we further show that the proposed model can significantly reduce bias without significant impact on accuracy. Our method is model agnostic and can be applied to any recommender system. To demonstrate the performance of our model, we present the results on four recommender algorithms, two from the K-nearest neighbors family, UserKNN and ItemKNN, and the other two from the matrix factorization family, Alternating least square and Singular value decomposition.
On Impact of Semantically Similar Apps in Android Malware Datasets
Venue: arXiv cs.CR 2021
Year: 2021

Malware authors reuse the same program segments found in other applications for performing the similar kind of malicious activities such as information stealing, sending SMS and so on. Hence, there may exist several semantically similar malware samples in a family/dataset. Many researchers unaware about these semantically similar apps and use their features in their ML models for evaluation. Hence, the performance measures might be seriously affected by these similar kinds of apps. In this paper, we study the impact of semantically similar applications in the performance measures of ML based Android malware detectors. For this, we propose a novel opcode subsequence based malware clustering algorithm to identify the semantically similar malware and goodware apps. For studying the impact of semantically similar apps in the performance measures, we tested the performance of distinct ML models based on API call and permission features of malware and goodware application with/without semantically similar apps. In our experimentation with Drebin dataset, we found that, after removing the exact duplicate apps from the dataset (? = 0) the malware detection rate (TPR) of API call based ML models is dropped from 0.95 to 0.91 and permission based model is dropped from 0.94 to 0.90. In order to overcome this issue, we advise the research community to use our clustering algorithm to get rid of semantically similar apps before evaluating their malware detection mechanism.
The Complexity of Data-Driven Norm Synthesis and Revision
Venue: arXiv cs.CC 2021
Year: 2021

Norms have been widely proposed as a way of coordinating and controlling the activities of agents in a multi-agent system (MAS). A norm specifies the behaviour an agent should follow in order to achieve the objective of the MAS. However, designing norms to achieve a particular system objective can be difficult, particularly when there is no direct link between the language in which the system objective is stated and the language in which the norms can be expressed. In this paper, we consider the problem of synthesising a norm from traces of agent behaviour, where each trace is labelled with whether the behaviour satisfies the system objective. We show that the norm synthesis problem is NP-complete.
SCMCI: Secured Click and Mortar Commercial Interaction
Venue: arXiv cs.CR 2021
Year: 2021

The wide spread of click-and-mortar model offers an opportunity to consider a universal commercial interaction method. However, issues like privacy protection resist the widespread acceptance. Traditional SET and SSL protocols are designed using Public Key Infrastructure (PKI) where extensive computations are carried out. Our aim is here to design a protocol to secure any type of commercial interaction for online platform, which also considers mobile platform. Therefore, our focus is on reducing heavy computations and making the overall procedure faster. A Secured Click and Mortar Commercial Interaction (SCMCI) protocol is proposed here to improve the performance of the commercial interaction procedure through replacing time consuming public key encryption and decryption algorithms by hybrid logic including the use of symmetric key. Comparative analysis has been done with traditional SET protocol using cryptool to prove the efficiency of the protocol.