OpenRaiser/Intern-Atlas huggingface dataset:
Deep Social Force
Venue: arXiv cs.LG 2021
Year: 2021

The Social Force model introduced by Helbing and Molnar in 1995 is a cornerstone of pedestrian simulation. This paper introduces a differentiable simulation of the Social Force model where the assumptions on the shapes of interaction potentials are relaxed with the use of universal function approximators in the form of neural networks. Classical force-based pedestrian simulations suffer from unnatural locking behavior on head-on collision paths. In addition, they cannot model the bias of pedestrians to avoid each other on the right or left depending on the geographic region. My experiments with more general interaction potentials show that potentials with a sharp tip in the front avoid locking. In addition, asymmetric interaction potentials lead to a left or right bias when pedestrians avoid each other.
Invited Paper: Failure is (literally) an Option: Atomic Commitment vs Optionality in Decentralized Finance
Venue: arXiv cs.DC 2021
Year: 2021

Many aspects of blockchain-based decentralized finance can be understood as an extension of classical distributed computing. In this paper, we trace the evolution of two interrelated notions: failure and fault-tolerance. In classical distributed computing, a failure to complete a multi-party protocol is typically attributed to hardware malfunctions. A fault-tolerant protocol is one that responds to such failures by rolling the system back to an earlier consistent state. In the presence of Byzantine failures, a failure may be the result of an attack, and a fault-tolerant protocol is one that ensures that attackers will be punished and victims compensated. In modern decentralized finance however, failure to complete a protocol can be considered a legitimate option, not a transgression. A fault-tolerant protocol is one that ensures that the party offering the option cannot renege, and the party purchasing the option provides fair compensation (in the form of a fee) to the offering party. We sketch the evolution of such protocols, starting with two-phase commit, and finishing with timed hashlocked smart contracts.
Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations
Venue: arXiv cs.CL 2021
Year: 2021

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long and noisy transcripts, and high target summary variability. In this paper, we explore the feasibility of using pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. We show that fluent and adequate summaries can be generated with limited training data by fine-tuning BART on a specially constructed dataset. The resulting models greatly surpass the performance of an average human annotator and the quality of previous published work for the task. We evaluate multiple methods for handling long conversations, comparing them to the obvious baseline of truncating the conversation to fit the pretrained model length limit. We introduce a multistage approach that tackles the task by learning two fine-tuned models: one for summarizing conversation chunks into partial summaries, followed by one for rewriting the collection of partial summaries into a complete summary. Using a carefully chosen fine-tuning dataset, this method is shown to be effective at handling longer conversations, improving the quality of generated summaries. We conduct both an automatic evaluation (through ROUGE and two concept-based metrics focusing on medical findings) and a human evaluation (through qualitative examples from literature, assessing hallucination, generalization, fluency, and general quality of the generated summaries).
Toolbox Release: A WiFi-Based Relative Bearing Sensor for Robotics
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open source C++ framework. It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an opensource tool. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for localization, ad-hoc robot networks, and security in multi-robot teams, amongst others. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase. Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees. This leads to a median error of 0.5m and 0.9m for localization in LOS and NLOS settings respectively, in a hardware deployment in an indoor office environment.
Finding Taint-Style Vulnerabilities in Linux-based Embedded Firmware with SSE-based Alias Analysis
Venue: arXiv cs.CR 2021
Year: 2021

Although the importance of using static analysis to detect taint-style vulnerabilities in Linux-based embedded firmware is widely recognized, existing approaches are plagued by three major limitations. (a) Approaches based on symbolic execution may miss alias information and therefore suffer from a high false-negative rate. (b) Approaches based on VSA (value set analysis) often provide an over-approximate pointer range. As a result, many false positives could be produced. (c) Existing work for detecting taint-style vulnerability does not consider indirect call resolution, whereas indirect calls are frequently used in Internet-facing embedded devices. As a result, many false negatives could be produced. In this work, we propose a precise demand-driven flow-, context- and field-sensitive alias analysis approach. Based on this new approach, we also design a novel indirect call resolution scheme. Combined with sanitization rule checking, our solution discovers taint-style vulnerabilities by static taint analysis. We implemented our idea with a prototype called EmTaint and evaluated it against 35 real-world embedded firmware samples from six popular vendors. EmTaint discovered at least 192 bugs, including 41 n-day bugs and 151 0-day bugs. At least 115 CVE/PSV numbers have been allocated from a subset of the reported vulnerabilities at the time of writing. Compared to state-of-the-art tools such as KARONTE and SaTC, EmTaint found significantly more bugs on the same dataset in less time.
Style Control for Schema-Guided Natural Language Generation
Venue: arXiv cs.CL 2021
Year: 2021

Natural Language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are crucial for a successful dialogue, it is also desirable to simultaneously accomplish specific stylistic goals, such as response length, point-of-view, descriptiveness, sentiment, formality, and empathy. In this work, we focus on stylistic control and evaluation for schema-guided NLG, with joint goals of achieving both semantic and stylistic control. We experiment in detail with various controlled generation methods for large pretrained language models: specifically, conditional training, guided fine-tuning, and guided decoding. We discuss their advantages and limitations, and evaluate them with a broad range of automatic and human evaluation metrics. Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods. The results also suggest that methods that are more scalable (with less hyper-parameters tuning) and that disentangle content generation and stylistic variations are more effective at achieving semantic correctness and style accuracy.
Low Cost Bin Picking Solution for E-Commerce Warehouse Fulfillment Centers
Venue: arXiv cs.RO 2021
Year: 2021

In recent years, the throughput requirements of e-commerce fulfillment warehouses have seen a steep increase. This has resulted in various automation solutions being developed for item picking and movement. In this paper, we address the problem of manipulators picking heterogeneous items placed randomly in a bin. Traditional solutions require that the items be picked to be placed in an orderly manner in the bin and that the exact dimensions of the items be known beforehand. Such solutions do not perform well in the real world since the items in a bin are seldom placed in an orderly manner and new products are added almost every day by e-commerce suppliers. We propose a cost-effective solution that handles both the aforementioned challenges. Our solution comprises of a dual sensor system comprising of a regular RGB camera and a 3D ToF depth sensor. We propose a novel algorithm that fuses data from both these sensors to improve object segmentation while maintaining the accuracy of pose estimation, especially in occluded environments and tightly packed bins. We experimentally verify the performance of our system by picking boxes using an ABB IRB 1200 robot. We also show that our system maintains a high level of accuracy in pose estimation that is independent of the dimensions of the box, texture, occlusion or orientation. We further show that our system is computationally less expensive and maintains a consistent detection time of 1 second. We also discuss how this approach can be easily extended to objects of all shapes.
MC$^2$-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

With the hardware development of mobile devices, it is possible to build the recommendation models on the mobile side to utilize the fine-grained features and the real-time feedbacks. Compared to the straightforward mobile-based modeling appended to the cloud-based modeling, we propose a Slow-Fast learning mechanism to make the Mobile-Cloud Collaborative recommendation (MC$^2$-SF) mutual benefit. Specially, in our MC$^2$-SF, the cloud-based model and the mobile-based model are respectively treated as the slow component and the fast component, according to their interaction frequency in real-world scenarios. During training and serving, they will communicate the prior/privileged knowledge to each other to help better capture the user interests about the candidates, resembling the role of System I and System II in the human cognition. We conduct the extensive experiments on three benchmark datasets and demonstrate the proposed MC$^2$-SF outperforms several state-of-the-art methods.
Performance Analyses of TAS/Alamouti-MRC NOMA in Dual-Hop Full-Duplex AF Relaying Networks
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, the performance of a power domain downlink multiple-input multiple-output non-orthogonal multiple access system in dual-hop full-duplex (FD) relaying networks is investigated over Nakagami-$m$ fading channels by considering the channel estimation error and feedback delay. Particularly, in the investigated system, the base station equipped with multiple antennas transmits information to all mobile users by applying conventional transmit antenna selection/Alamouti-space-time block coding scheme with the help of a dedicated FD amplify-and-forward relay. The received signals at mobile users are combined according to maximal-ratio combining technique to exploit benefits of receive diversity. In order to demonstrate the superiority of the proposed system, outage probability (OP) is investigated and tight lower bound expressions are derived for the obtained OP. Moreover, asymptotic analyses are also conducted for ideal and practical conditions to provide further insights about the outage behavior in the high signal-to-noise ratio region. Finally, theoretical analyses are validated via Monte Carlo simulations and software defined radio based test-bed implementation.
Predicting Hidden Links and Missing Nodes in Scale-Free Networks with Artificial Neural Networks
Venue: arXiv cs.SI 2021
Year: 2021

There are many networks in real life which exist as form of Scale-free networks such as World Wide Web, protein-protein inter action network, semantic networks, airline networks, interbank payment networks, etc. If we want to analyze these networks, it is really necessary to understand the properties of scale-free networks. By using the properties of scale free networks, we can identify any type of anomalies in those networks. In this research, we proposed a methodology in a form of an algorithm to predict hidden links and missing nodes in scale-free networks where we combined a generator of random networks as a source of train data, on one hand, with artificial neural networks for supervised classification, on the other, we aimed at training the neural networks to discriminate between different subtypes of scale-free networks and predicted the missing nodes and hidden links among (present and missing) nodes in a given scale-free network. We chose Bela Bollobas's directed scale-free random graph generation algorithm as a generator of random networks to generate a large set of scale-free network's data.
TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition
Venue: arXiv cs.LG 2021
Year: 2021

There has been a surge of recent interest in Machine Learning (ML), particularly Deep Neural Network (DNN)-based models, to decode muscle activities from surface Electromyography (sEMG) signals for myoelectric control of neurorobotic systems. DNN-based models, however, require large training sets and, typically, have high structural complexity, i.e., they depend on a large number of trainable parameters. To address these issues, we developed a framework based on the Transformer architecture for processing sEMG signals. We propose a novel Vision Transformer (ViT)-based neural network architecture (referred to as the TEMGNet) to classify and recognize upperlimb hand gestures from sEMG to be used for myocontrol of prostheses. The proposed TEMGNet architecture is trained with a small dataset without the need for pre-training or fine-tuning. To evaluate the efficacy, following the-recent literature, the second subset (exercise B) of the NinaPro DB2 dataset was utilized, where the proposed TEMGNet framework achieved a recognition accuracy of 82.93% and 82.05% for window sizes of 300ms and 200ms, respectively, outperforming its state-of-the-art counterparts. Moreover, the proposed TEMGNet framework is superior in terms of structural capacity while having seven times fewer trainable parameters. These characteristics and the high performance make DNN-based models promising approaches for myoelectric control of neurorobots.
Implementing ECC on Data Link Layer of the OSI Reference Model
Venue: arXiv cs.CR 2021
Year: 2021

The Internet, a rapidly expanding communication infrastructure, poses significant cybersecurity challenges. A few techniques have been developed to provide security in the OSI model's application, presentation, and network layers. Instead of using Media Access Control (MAC), this paper recommends using public key Elliptic Curve Cryptography to serve the Data Link Layer. On a microFourQ-MSP-IAR Embedded Workbench IDE-MSP430 7.12.4, a prototype of the architecture was implemented. When the cost of key generation was compared to the curve, it was shown that they are directly related. The cost of key generation time rises in proportion to the bit length of the curve. Because of the large number of connected devices that may be exposed due to a lack of effective security in the data link layer, significant concerns arise in terms of network privacy, governance, and security. As a result, this research suggests using a public key cryptosystem based on an elliptic curve to secure the data link layer for safe data communication in Internet-connected devices.
Analysing GSM Insecurity
Venue: arXiv cs.NI 2021
Year: 2021

In the 1990s, GSM emerged as a cutting-edge technology that promised improved services, mobility, security, and increase in the revenues of companies through improved, secure communication for business transactions. The practical experience, however, has shown global subscribers and companies that mere technological excellence of GSM has not resulted in its success. This paper has been carried out to analyse the existing GSM subscriber activities in both personal and business worlds by implementing a customized GSM baseband protocol stack on a phone. The paper encompasses the tasks of analysing the prevailing GSM insecurity, establishing a conceptual design towards the findings revealed in the practical personal and business environments and knowledge gathered from the literature.
Defect Prediction Guided Search-Based Software Testing
Venue: arXiv cs.SE 2021
Year: 2021

Today, most automated test generators, such as search-based software testing (SBST) techniques focus on achieving high code coverage. However, high code coverage is not sufficient to maximise the number of bugs found, especially when given a limited testing budget. In this paper, we propose an automated test generation technique that is also guided by the estimated degree of defectiveness of the source code. Parts of the code that are likely to be more defective receive more testing budget than the less defective parts. To measure the degree of defectiveness, we leverage Schwa, a notable defect prediction technique. We implement our approach into EvoSuite, a state of the art SBST tool for Java. Our experiments on the Defects4J benchmark demonstrate the improved efficiency of defect prediction guided test generation and confirm our hypothesis that spending more time budget on likely defective parts increases the number of bugs found in the same time budget.
Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration
Venue: arXiv cs.CV 2021
Year: 2021

Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate, they typically carry a significant computational burden. Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods. In this paper, we attempt to reduce this difference in speed whilst retaining the performance advantage of iterative approaches in DiffIR. We first propose a simple iterative scheme that functionally composes intermediate non-stationary velocity fields to handle large deformations in images whilst guaranteeing diffeomorphisms in the resultant deformation. We then propose a convex optimisation model that uses a regularisation term of arbitrary order to impose smoothness on these velocity fields and solve this model with a fast algorithm that combines Nesterov gradient descent and the alternating direction method of multipliers (ADMM). Finally, we leverage the computational power of GPU to implement this accelerated ADMM solver on a 3D cardiac MRI dataset, further reducing runtime to less than 2 seconds. In addition to producing strictly diffeomorphic deformations, our methods outperform both state-of-the-art deep learning-based and iterative DiffIR approaches in terms of dice and Hausdorff scores, with speed approaching the inference time of deep learning-based methods.
OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts
Venue: arXiv cs.CL 2021
Year: 2021

In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts. However, with the development of multi-modal dialogue learning, the dataset scale gradually becomes a bottleneck. In this report, we release OpenViDial 2.0, a larger-scale open-domain multi-modal dialogue dataset compared to the previous version OpenViDial 1.0. OpenViDial 2.0 contains a total number of 5.6 million dialogue turns extracted from either movies or TV series from different resources, and each dialogue turn is paired with its corresponding visual context. We hope this large-scale dataset can help facilitate future researches on open-domain multi-modal dialog generation, e.g., multi-modal pretraining for dialogue generation.
Rumour Detection via Zero-shot Cross-lingual Transfer Learning
Venue: arXiv cs.CL 2021
Year: 2021

Most rumour detection models for social media are designed for one specific language (mostly English). There are over 40 languages on Twitter and most languages lack annotated resources to build rumour detection models. In this paper we propose a zero-shot cross-lingual transfer learning framework that can adapt a rumour detection model trained for a source language to another target language. Our framework utilises pretrained multilingual language models (e.g.\ multilingual BERT) and a self-training loop to iteratively bootstrap the creation of ''silver labels'' in the target language to adapt the model from the source language to the target language. We evaluate our methodology on English and Chinese rumour datasets and demonstrate that our model substantially outperforms competitive benchmarks in both source and target language rumour detection.
ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSs
Venue: arXiv cs.LG 2021
Year: 2021

The overwhelming abundance of data has created a misinformation crisis. Unverified sensationalism that is designed to grab the readers' short attention span, when crafted with malice, has caused irreparable damage to our society's structure. As a result, determining the reliability of an article has become a crucial task. After various ablation studies, we propose a multi-input model that can effectively leverage both tabular metadata and post content for the task. Applying state-of-the-art finetuning techniques for the pretrained component and training strategies for our complete model, we have achieved a 0.9462 ROC-score on the VLSP private test set.
HTBQueue: A Hierarchical Token Bucket Implementation for the OMNeT++/INET Framework
Venue: arXiv cs.NI 2021
Year: 2021

The hierarchical token bucket (HTB) algorithm allows to specify per-flow bitrate guarantees and enables excess bandwidth sharing between flows of the same class. Additionally, it provides capabilities to prioritize the traffic of specific flows, potentially considering their delay demands. HTB hence constitutes a powerful mechanism to enforce QoS requirements hierarchically and on a fine granular per-flow level, making it an appropriate choice in numerous use-cases. In this paper, we present HTBQueue, our implementation of a compound module for HTB support in the discrete event simulator OMNeT++. We validate HTBQueue's functionality in terms of rate conformance and fair bandwidth sharing behavior between competing flows. We furthermore demonstrate its support for flow prioritization.