OpenRaiser/Intern-Atlas huggingface dataset:
Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
Venue: arXiv cs.DC 2020
Year: 2020

Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles. We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX~2.
Structural Equivalences for Reversible Calculi of Communicating Systems (Oral communication)
Venue: arXiv cs.FL 2020
Year: 2020

The formalization of process algebras usually starts with a minimal core of operators and rules for its transition system, and then relax the system to improve its usability and ease the proofs. In the calculus of communicating systems (CCS), the structural congruence plays this role by making e.g. parallel composition commutative and associative: without it, the system would be cumbersome to use and reason about, and it can be proven that this change is innocuous in a precise technical sense. For the two reversible calculi extending CCS, the situation is less clear: CCS with Communication Keys (CCSK) was first defined without any structural congruence, and then was endowed with a fragment of CCS's congruence. Reversible CCS (RCCS) made the choice of "backing in" the structural equivalence, that became part of the "minimal core" of the system. In this short oral communication, we would like to re-consider the status and role of the structural congruence in general, to question its role in RCCS in particular, and to ask the more general question of the structural equivalences legitimacy.
An Adaptive Networks Model to Simulate Consensus Formation Driven by Social Identity Recognition
Venue: arXiv cs.SI 2020
Year: 2020

Models of the consensus of the individual state in social systems have been the subject of recent researches in the physics literature. We investigate how network structures coevolve with the individual state under the framework of social identity theory. And we propose an adaptive network model to achieve state consensus or local structural adjustment of individuals by evaluating the homogeneity among them. Specifically, the similarity threshold significantly affects the evolution of the network with different initial conditions, and thus there emerges obvious community structure and polarization. More importantly, there exists a critical point of phase transition, at which the network may evolve into a significant community structure and state-consistent group.
Parallel Minimum Spanning Tree Algorithms and Evaluation
Venue: arXiv cs.DC 2020
Year: 2020

Minimum Spanning Tree (MST) is an important graph algorithm that has wide ranging applications in the areas of computer networks, VLSI routing, wireless communications among others. Today virtually every computer is built out of multi-core processors. Hence it is important to take advantage of such parallel computing power by parallelizing existing algorithms and applications. Most of the earlier work on parallelizing MST focused on algorithms for PRAM models. There are two limitations to such studies. First, PRAM models assume infinite memory bandwidth which is unrealistic. Second, PRAM model based algorithms require at least O(n) processors where n being total number of vertices. For large graphs this is infeasible. There are very few implementations which target real systems. In this paper I present and evaluate two new parallel MST algorithms that are a variant of Parallel Boruvka algorithms: i) First algorithm uses lock variables without spin-locks ii) Second algorithm uses only atomic compare-and-swap (CAS) primitive. I evaluated the performance of these algorithms on a six-core, 12-thread Intel system on various input graphs of sizes up to 1 million vertices. First algorithm showed a speedup of up to1.94 over an un-optimized sequential algorithm and a speedup of up to 1.4 over an optimized sequential algorithm. Second algorithm showed a speedup of up to 2.03 over an un-optimized sequential algorithm and a speedup of up to 1.403 over an optimized sequential algorithm. When second algorithm using CAS is compared with the first algorithm second algorithm is found to be up to 1.15 times better than the first algorithm at four threads.
NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to tackle Internet Humor
Venue: arXiv cs.CL 2020
Year: 2020

The paper describes the systems submitted to SemEval-2020 Task 8: Memotion by the `NIT-Agartala-NLP-Team'. A dataset of 8879 memes was made available by the task organizers to train and test our models. Our systems include a Logistic Regression baseline, a BiLSTM + Attention-based learner and a transfer learning approach with BERT. For the three sub-tasks A, B and C, we attained ranks 24/33, 11/29 and 15/26, respectively. We highlight our difficulties in harnessing image information as well as some techniques and handcrafted features we employ to overcome these issues. We also discuss various modelling issues and theorize possible solutions and reasons as to why these problems persist.
The Hyper360 toolset for enriched 360$^\circ$ video
Venue: arXiv cs.MM 2020
Year: 2020

360$^\circ$ video is a novel media format, rapidly becoming adopted in media production and consumption as part of todays ongoing virtual reality revolution. Due to its novelty, there is a lack of tools for producing highly engaging 360$^\circ$ video for consumption on a multitude of platforms. In this work, we describe the work done so far in the Hyper360 project on tools for 360$^\circ$ video. Furthermore, the first pilots which have been produced with the Hyper360 tools are presented.
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Venue: arXiv cs.CV 2020
Year: 2020

Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at night and occlusion. In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instances. In order to fuse the two modalities, we make use of a linear feature-wise fusion module that carries out an affine transformation on visual and auditory features. Finally, we conduct extensive experiments using the proposed dataset and approach. Experimental results show that introducing auditory information can benefit crowd counting under different illumination, noise, and occlusion conditions. The dataset and code will be released. Code and data have been made available
A Secure Fog Based Architecture for Industrial Internet of Things and Industry 4.0
Venue: arXiv cs.CR 2020
Year: 2020

The advent of Industrial IoT (IIoT) along with Cloud computing has brought a huge paradigm shift in manufacturing industries resulting in yet another industrial revolution, Industry 4.0. Huge amounts of delay-sensitive data of diverse nature are being generated which needs to be locally processed and secured due to its sensitivity. But, the low-end IoT devices are unable to handle huge computational overheads. Also, the semi-trusted nature of Cloud introduces several security concerns. To address these issues, this work proposes a secure Fog-based IIoT architecture by suitably plugging a number of security features into it and by offloading some of the tasks judiciously to fog nodes. These features secure the system alongside reducing the trust and burden on the cloud and resource-constrained devices respectively. We validate our proposed architecture through both theoretical overhead analysis and practical experimentation including simulation study and testbed implementation.
Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search
Venue: arXiv cs.AI 2020
Year: 2020

Advances in intelligent game playing agents have led to successes in perfect information games like Go and imperfect information games like Poker. The Information Set Monte Carlo Tree Search (ISMCTS) family of algorithms outperforms previous algorithms using Monte Carlo methods in imperfect information games. In this paper, Single Observer Information Set Monte Carlo Tree Search (SO-ISMCTS) is applied to Secret Hitler, a popular social deduction board game that combines traditional hidden role mechanics with the randomness of a card deck. This combination leads to a more complex information model than the hidden role and card deck mechanics alone. It is shown in 10108 simulated games that SO-ISMCTS plays as well as simpler rule based agents, and demonstrates the potential of ISMCTS algorithms in complicated information set domains.
On the Outage Performance of Ambient Backscatter Communications
Venue: arXiv cs.IT 2020
Year: 2020

Ambient backscatter communications (AmBackComs) have been recognized as a spectrum- and energy-efficient technology for Internet of Things, as it allows passive backscatter devices (BDs) to modulate their information into the legacy signals, e.g., cellular signals, and reflect them to their associated receivers while harvesting energy from the legacy signals to power their circuit operation. {\color{black} However, the co-channel interference between the backscatter link and the legacy link and the non-linear behavior of energy harvesters at the BDs have largely been ignored in the performance analysis of AmBackComs. Taking these two aspects, this paper provides a comprehensive outage performance analysis for an AmBackCom system with multiple backscatter links}, where one of the backscatter links is opportunistically selected to leverage the legacy signals transmitted in a given resource block. For any selected backscatter link, we propose an adaptive reflection coefficient (RC), which is adapted to the non-linear energy harvesting (EH) model and the location of the selected backscatter link, to minimize the outage probability of the backscatter link. In order to study the impact of co-channel interference on both backscatter and legacy links, for a selected backscatter link, we derive the outage probabilities for the legacy link and the backscatter link. Furthermore, we study the best and worst outage performances for the backscatter system where the selected backscatter link maximizes or minimizes the signal-to-interference-plus noise ratio (SINR) at the backscatter receiver. We also study the best and worst outage performances for the legacy link where the selected backscatter link results in the lowest and highest co-channel interference to the legacy receiver, respectively.
Low Cost High Integrity Platform
Venue: arXiv cs.SE 2020
Year: 2020

Developing safety critical applications often require rare human resources to complete successfully while off-the-shelf block solutions appear difficult to adapt especially during short-term projects. The CLEARSY Safety Platform fulfils a need for a technical solution to overcome the difficulties to develop SIL3/SIL4 system with its technology based on a double-processor and a formal method with proof to ensure safety at the highest level. The formal method, namely the B method, has been heavily used in the railways industry for decades. Using its IDE, Atelier B, to program the CLEARSY Safety Platform ensures a higherlevel of confidence on the software generated. This paper presents this platform aimed at revolutionising the development of safety critical systems, developed through the FUI project LCHIP (Low Cost High Integrity Platform).
Evolved Explainable Classifications for Lymph Node Metastases
Venue: arXiv cs.CV 2020
Year: 2020

A novel evolutionary approach for Explainable Artificial Intelligence is presented: the "Evolved Explanations" model (EvEx). This methodology consists in combining Local Interpretable Model Agnostic Explanations (LIME) with Multi-Objective Genetic Algorithms to allow for automated segmentation parameter tuning in image classification tasks. In this case, the dataset studied is Patch-Camelyon, comprised of patches from pathology whole slide images. A publicly available Convolutional Neural Network (CNN) was trained on this dataset to provide a binary classification for presence/absence of lymph node metastatic tissue. In turn, the classifications are explained by means of evolving segmentations, seeking to optimize three evaluation goals simultaneously. The final explanation is computed as the mean of all explanations generated by Pareto front individuals, evolved by the developed genetic algorithm. To enhance reproducibility and traceability of the explanations, each of them was generated from several different seeds, randomly chosen. The observed results show remarkable agreement between different seeds. Despite the stochastic nature of LIME explanations, regions of high explanation weights proved to have good agreement in the heat maps, as computed by pixel-wise relative standard deviations. The found heat maps coincide with expert medical segmentations, which demonstrates that this methodology can find high quality explanations (according to the evaluation metrics), with the novel advantage of automated parameter fine tuning. These results give additional insight into the inner workings of neural network black box decision making for medical data.
MagicPairing: Apple's Take on Securing Bluetooth Peripherals
Venue: arXiv cs.CR 2020
Year: 2020

Device pairing in large Internet of Things (IoT) deployments is a challenge for device manufacturers and users. Bluetooth offers a comparably smooth trust on first use pairing experience. Bluetooth, though, is well-known for security flaws in the pairing process. In this paper, we analyze how Apple improves the security of Bluetooth pairing while still maintaining its usability and specification compliance. The proprietary protocol that resides on top of Bluetooth is called MagicPairing. It enables the user to pair a device once with Apple's ecosystem and then seamlessly use it with all their other Apple devices. We analyze both, the security properties provided by this protocol, as well as its implementations. In general, MagicPairing could be adapted by other IoT vendors to improve Bluetooth security. Even though the overall protocol is well-designed, we identified multiple vulnerabilities within Apple's implementations with over-the-air and in-process fuzzing.
New Frontiers in IoT: Networking, Systems, Reliability, and Security Challenges
Venue: arXiv cs.DC 2020
Year: 2020

The field of IoT has blossomed and is positively influencing many application domains. In this paper, we bring out the unique challenges this field poses to research in computer systems and networking. The unique challenges arise from the unique characteristics of IoT systems such as the diversity of application domains where they are used and the increasingly demanding protocols they are being called upon to run (such as, video and LIDAR processing) on constrained resources (on-node and network). We show how these open challenges can benefit from foundations laid in other areas, such as, 5G cellular protocols, ML model reduction, and device-edge-cloud offloading. We then discuss the unique challenges for reliability, security, and privacy posed by IoT systems due to their salient characteristics which include heterogeneity of devices and protocols, dependence on the physical environment, and the close coupling with humans. We again show how the open research challenges benefit from reliability, security, and privacy advancements in other areas. We conclude by providing a vision for a desirable end state for IoT systems.
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness
Venue: arXiv cs.LG 2020
Year: 2020

As a certified defensive technique, randomized smoothing has received considerable attention due to its scalability to large datasets and neural networks. However, several important questions remain unanswered, such as (i) whether the Gaussian mechanism is an appropriate option for certifying $\ell_2$-norm robustness, and (ii) whether there is an appropriate randomized (smoothing) mechanism to certify $\ell_\infty$-norm robustness. To shed light on these questions, we argue that the main difficulty is how to assess the appropriateness of each randomized mechanism. In this paper, we propose a generic framework that connects the existing frameworks in \cite{lecuyer2018certified, li2019certified}, to assess randomized mechanisms. Under our framework, for a randomized mechanism that can certify a certain extent of robustness, we define the magnitude of its required additive noise as the metric for assessing its appropriateness. We also prove lower bounds on this metric for the $\ell_2$-norm and $\ell_\infty$-norm cases as the criteria for assessment. Based on our framework, we assess the Gaussian and Exponential mechanisms by comparing the magnitude of additive noise required by these mechanisms and the lower bounds (criteria). We first conclude that the Gaussian mechanism is indeed an appropriate option to certify $\ell_2$-norm robustness. Surprisingly, we show that the Gaussian mechanism is also an appropriate option for certifying $\ell_\infty$-norm robustness, instead of the Exponential mechanism. Finally, we generalize our framework to $\ell_p$-norm for any $p\geq2$. Our theoretical findings are verified by evaluations on CIFAR10 and ImageNet.
A chatbot architecture for promoting youth resilience
Venue: arXiv cs.CL 2020
Year: 2020

E-health technologies have the potential to provide scalable and accessible interventions for youth mental health. As part of a developing an ecosystem of e-screening and e-therapy tools for New Zealand young people, a dialog agent, Headstrong, has been designed to promote resilience with methods grounded in cognitive behavioral therapy and positive psychology. This paper describes the architecture underlying the chatbot. The architecture supports a range of over 20 activities delivered in a 4-week program by relatable personas. The architecture provides a visual authoring interface to its content management system. In addition to supporting the original adolescent resilience chatbot, the architecture has been reused to create a 3-week 'stress-detox' intervention for undergraduates, and subsequently for a chatbot to support young people with the impacts of the COVID-19 pandemic, with all three systems having been used in field trials. The Headstrong architecture illustrates the feasibility of creating a domain-focused authoring environment in the context of e-therapy that supports non-technical expert input and rapid deployment.
Mimicking Full-Duplex Secure Communications for Buffer-Aided Multi-Relay Systems
Venue: arXiv cs.IT 2020
Year: 2020

This paper considers secure communication in buffer-aided cooperative wireless networks in the presence of one eavesdropper, which can intercept the data transmission from both the source and relay nodes. A new max-ratio relaying protocol is proposed, in which different relays are chosen for reception and transmission according to the ratio of the legitimate channels to the eavesdropper channels, so that the relay selected for reception and the relay selected for transmission can receive and transmit at the same time. It is worth noting that the relay employs a randomize-and-forward (RF) strategy such that the eavesdropper can only decode the signals received in the two hops independently. Theoretical analysis of the secrecy throughput of the proposed scheme is provided and the approximate closed-form expressions are derived, which are verified by simulations. Through numerical results, it is shown that the proposed scheme achieves a significant improvement in secrecy throughput compared with existing relay selection policies.
Tight Approximation Algorithms for p-Mean Welfare Under Subadditive Valuations
Venue: arXiv cs.GT 2020
Year: 2020

We develop polynomial-time algorithms for the fair and efficient allocation of indivisible goods among $n$ agents that have subadditive valuations over the goods. We first consider the Nash social welfare as our objective and design a polynomial-time algorithm that, in the value oracle model, finds an $8n$-approximation to the Nash optimal allocation. Subadditive valuations include XOS (fractionally subadditive) and submodular valuations as special cases. Our result, even for the special case of submodular valuations, improves upon the previously best known $O(n \log n)$-approximation ratio of Garg et al. (2020). More generally, we study maximization of $p$-mean welfare. The $p$-mean welfare is parameterized by an exponent term $p \in (-\infty, 1]$ and encompasses a range of welfare functions, such as social welfare ($p = 1$), Nash social welfare ($p \to 0$), and egalitarian welfare ($p \to -\infty$). We give an algorithm that, for subadditive valuations and any given $p \in (-\infty, 1]$, computes (in the value oracle model and in polynomial time) an allocation with $p$-mean welfare at least $\frac{1}{8n}$ times the optimal. Further, we show that our approximation guarantees are essentially tight for XOS and, hence, subadditive valuations. We adapt a result of Dobzinski et al. (2010) to show that, under XOS valuations, an $O \left(n^{1-\varepsilon} \right)$ approximation for the $p$-mean welfare for any $p \in (-\infty,1]$ (including the Nash social welfare) requires exponentially many value queries; here, $\varepsilon>0$ is any fixed constant.