pwc-archive/papers-with-abstracts huggingface dataset:
Set-Membership Estimation in Shared Situational Awareness for Automated Vehicles in Occluded Scenarios
Authors: Vandana Narri, Amr Alanwar, Jonas Mårtensson, Christoffer Norén, Laura Dal Col, Karl Henrik Johansson
Date: 2021-03-02 00:00:00

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to appropriately adjust the ego-vehicle speed to maintain a proper safety margin. It is also argued that the framework is suitable for handling sensor failures and false detections in a systematic way. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety.
Neuronal heterogeneity modulates phase-synchronization between unidirectionally coupled populations with excitation-inhibition balance
Authors: Katiele V. P. Brito, Fernanda Selingardi Matias
Date: 2021-03-02 00:00:00
Tasks: Diversity, EEG, Electroencephalogram (EEG)

Several experiments and models have highlighted the importance of neuronal heterogeneity in brain dynamics and function. However, how such a cell-to-cell diversity can affect cortical computation, synchronization, and neuronal communication is still under debate. Previous studies have focused on the effect of neuronal heterogeneity in one neuronal population. Here we are specifically interested in the effect of neuronal variability on the phase relations between two populations, which can be related to different cortical communication hypotheses. It has been recently shown that two spiking neuron populations unidirectionally connected in a sender-receiver configuration can exhibit anticipated synchronization (AS), which is characterized by a negative phase-lag. This phenomenon has been reported in electrophysiological data of non-human primates and human EEG during a visual discrimination cognitive task. In experiments, the unidirectional coupling could be accessed by Granger causality and can be accompanied by both positive or negative phase difference between cortical areas. Here we propose a model of two coupled populations in which the neuronal heterogeneity can determine the dynamical relation between the sender and the receiver and can reproduce phase relations reported in experiments. Depending on the distribution of parameters characterizing the neuronal firing patterns, the system can exhibit both AS and the usual delayed synchronization regime (DS, with positive phase) as well as a zero-lag synchronization regime and phase bistability between AS and DS. Furthermore, we show that our network can present diversity in their phase relations maintaining the excitation-inhibition balance.
No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging
Authors: Shota Imaki, Kentaro Imajo, Katsuya Ito, Kentaro Minami, Kei Nakagawa
Date: 2021-03-02 00:00:00

Deep hedging (Buehler et al. 2019) is a versatile framework to compute the optimal hedging strategy of derivatives in incomplete markets. However, this optimal strategy is hard to train due to action dependence, that is, the appropriate hedging action at the next step depends on the current action. To overcome this issue, we leverage the idea of a no-transaction band strategy, which is an existing technique that gives optimal hedging strategies for European options and the exponential utility. We theoretically prove that this strategy is also optimal for a wider class of utilities and derivatives including exotics. Based on this result, we propose a no-transaction band network, a neural network architecture that facilitates fast training and precise evaluation of the optimal hedging strategy. We experimentally demonstrate that for European and lookback options, our architecture quickly attains a better hedging strategy in comparison to a standard feed-forward network.
IoT-Enabled Social Relationships Meet Artificial Social Intelligence
Authors: Sahraoui Dhelim, Huansheng Ning, Fadi Farha, Liming Chen, Luigi Atzori, Mahmoud Daneshmand
Date: 2021-02-21 00:00:00
Tasks: Management

With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.
A region-based descriptor network for uniformly sampled keypoints
Authors: Kai Lv, Zongqing Lu, Qingmin Liao
Date: 2021-01-26 00:00:00

Matching keypoint pairs of different images is a basic task of computer vision. Most methods require customized extremum point schemes to obtain the coordinates of feature points with high confidence, which often need complex algorithmic design or a network with higher training difficulty and also ignore the possibility that flat regions can be used as candidate regions of matching points. In this paper, we design a region-based descriptor by combining the context features of a deep network. The new descriptor can give a robust representation of a point even in flat regions. By the new descriptor, we can obtain more high confidence matching points without extremum operation. The experimental results show that our proposed method achieves a performance comparable to state-of-the-art.
Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks
Authors: Christoph Raab, Philipp Väth, Peter Meier, Frank-Michael Schleif
Date: 2021-02-25 00:00:00
Tasks: Domain Adaptation

Statistical and adversarial adaptation are currently two extensive categories of neural network architectures in unsupervised deep domain adaptation. The latter has become the new standard due to its good theoretical foundation and empirical performance. However, there are two shortcomings. First, recent studies show that these approaches focus too much on easily transferable features and thus neglect important discriminative information. Second, adversarial networks are challenging to train. We addressed the first issue by the alignment of transferable spectral properties within an adversarial model to balance the focus between the easily transferable features and the necessary discriminatory features, while at the same time limiting the learning of domain-specific semantics by relevance considerations. Second, we stabilized the discriminator networks training procedure by Spectral Normalization employing the Lipschitz continuous gradients. We provide a theoretical and empirical evaluation of our improved approach and show its effectiveness in a performance study on standard benchmark data sets against various other state of the art methods.
Secure Arithmetic Coding with Error Detection Capability
Authors: "Mahnaz Sinaie and Vahid Tabataba Vakili"
Date: 2010-05-23 00:00:00

Recently, arithmetic coding has attracted the attention of many scholars because of its high compression capability. Accordingly,
this paper proposed a Joint Source-Cryptographic-Channel Coding (JSCC) based on Arithmetic Coding (AC). For this purpose,
embedded error detection arithmetic coding, which is known as continuous error detection (CED), is used. In our proposed
method, a random length of forbidden symbol which is produced with a key is used in each recursion. The dummy symbol is
divided into two dummy symbols with a key and then is placed in random positions in order to provide security. Finally, in
addition to producing secure codes, the suggested method reduced the added redundancy to half of the total redundancy added by
CED. It has less complexity than cascades source, channel coding, and encryption while its key space in comparison to other joint
methods has enlarged. Moreover, the coder provides a flexible switch between a standard compression model and a joint model.
Deep Multi Query Image Retrieval
Authors: Cabir Vural; Enver Akbacak
Date: 2020-08-12 00:00:00
Tasks: Deep Hashing, Image Retrieval, Multi-Label Classification, Reranking, Retrieval

There exist few studies investigating the multi-query image retrieval problem. Existing methods are not based on hash codes. As a result, they are not efficient and fast. In this study, we develop an efficient and fast multi-query image retrieval method when the queries are related to more than one semantic. Image hash codes are generated by a deep hashing method. Consequently, the method requires lower storage space, and it is faster compared to the existing methods. The retrieval is based on the Pareto front method. Reranking performed on the retrieved images by using non-binary deep-convolutional features increase retrieval accuracy considerably. Unlike previous studies, the method supports an arbitrary number of queries. It outperforms similar multi-query image retrieval studies in terms of retrieval time and retrieval accuracy.
Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition
Authors: Marvin Chancán, Michael Milford
Date: 2021-03-02 00:00:00
Tasks: Autonomous Driving, Autonomous Navigation, Autonomous Vehicles, Image Retrieval, Motion Estimation, Navigate, Representation Learning, Robot Navigation, Self-Driving Cars, Sequential Image Classification, Sequential Place Learning, Sequential Place Recognition, Simultaneous Localization and Mapping, Visual Localization, Visual Navigation, Visual Place Recognition, Vocal Bursts Intensity Prediction

Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade. However, precision-recall performance of these algorithms dramatically degrades when searching on short temporal window (TW) lengths, while demanding high compute and storage costs on large robotic datasets for autonomous navigation research. Here, influenced by biological systems that robustly navigate spacetime scales even without vision, we develop a joint visual and positional representation learning technique, via a sequential process, and design a learning-based CNN+LSTM architecture, trainable via backpropagation through time, for viewpoint- and appearance-invariant place recognition. Our approach, Sequential Place Learning (SPL), is based on a CNN function that visually encodes an environment from a single traversal, thus reducing storage capacity, while an LSTM temporally fuses each visual embedding with corresponding positional data -- obtained from any source of motion estimation -- for direct sequential inference. Contrary to classical two-stage pipelines, e.g., match-then-temporally-filter, our network directly eliminates false-positive rates while jointly learning sequence matching from a single monocular image sequence, even using short TWs. Hence, we demonstrate that our model outperforms 15 classical methods while setting new state-of-the-art performance standards on 4 challenging benchmark datasets, where one of them can be considered solved with recall rates of 100% at 100% precision, correctly matching all places under extreme sunlight-darkness changes. In addition, we show that SPL can be up to 70x faster to deploy than classical methods on a 729 km route comprising 35,768 consecutive frames. Extensive experiments demonstrate the... Baseline code available at https://github.com/mchancan/deepseqslam
GLAMOUR: Graph Learning over Macromolecule Representations
Authors: Somesh Mohapatra, Joyce An, Rafael Gómez-Bombarelli
Date: 2021-03-03 00:00:00
Tasks: Decision Making, Diversity, Graph Learning

The near-infinite chemical diversity of natural and artificial macromolecules arises from the vast range of possible component monomers, linkages, and polymers topologies. This enormous variety contributes to the ubiquity and indispensability of macromolecules but hinders the development of general machine learning methods with macromolecules as input. To address this, we developed GLAMOUR, a framework for chemistry-informed graph representation of macromolecules that enables quantifying structural similarity, and interpretable supervised learning for macromolecules.
Design and Analysis of a Modified Remote Attestation Protocol
Authors: Santra, Monika Peddoju, Sateesh K. Bhattacharjee, A.K. Khan, Arindam
Date: 2017-09-11 00:00:00

Secure interaction amongst system components is inherent to ensure the trustworthiness of the applications. In a distributed system, the attester should know whether the communicating client as well as the user who is using that client system is trustworthy. This can be achieved by a technique called remote attestation. This study presents a three-phase remote attestation protocol which provides relaxation over the low extensibility and low openness problem of binary remote attestation method, using the support of the SELinux module. It also analyses the performance of different existing and related binary remote attestation methods with the proposed approach which shows that the suggested remote attestation method is efficient. The results of the formal analysis are achieved using ProVerif tool which proves that the proposed remote attestation protocol satisfies several security properties such as secrecy, authenticity, indistinguishability and unlinkability.
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control
Authors: Jacopo Panerati, Hehui Zheng, SiQi Zhou, James Xu, Amanda Prorok, Angela P. Schoellig
Date: 2021-03-03 00:00:00
Tasks: Benchmarking, Multi-agent Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications. Reinforcement learning environments -- simple simulations coupled with a problem specification in the form of a reward function -- are also important to standardize the development (and benchmarking) of learning algorithms. Yet, full-scale simulators typically lack portability and parallelizability. Vice versa, many reinforcement learning environments trade-off realism for high sample throughputs in toy-like problems. While public data sets have greatly benefited deep learning and computer vision, we still lack the software tools to simultaneously develop -- and fairly compare -- control theory and reinforcement learning approaches. In this paper, we propose an open-source OpenAI Gym-like environment for multiple quadcopters based on the Bullet physics engine. Its multi-agent and vision based reinforcement learning interfaces, as well as the support of realistic collisions and aerodynamic effects, make it, to the best of our knowledge, a first of its kind. We demonstrate its use through several examples, either for control (trajectory tracking with PID control, multi-robot flight with downwash, etc.) or reinforcement learning (single and multi-agent stabilization tasks), hoping to inspire future research that combines control theory and machine learning.
Cooking Object's State Identification Without Using Pretrained Model
Authors: Md Sadman Sakib
Date: 2021-03-03 00:00:00
Tasks: model, Object, Object Recognition, Robotic Grasping

Recently, Robotic Cooking has been a very promising field. To execute a recipe, a robot has to recognize different objects and their states. Contrary to object recognition, state identification has not been explored that much. But it is very important because different recipe might require different state of an object. Moreover, robotic grasping depends on the state. Pretrained model usually perform very well in this type of tests. Our challenge was to handle this problem without using any pretrained model. In this paper, we have proposed a CNN and trained it from scratch. The model is trained and tested on the dataset from cooking state recognition challenge. We have also evaluated the performance of our network from various perspective. Our model achieves 65.8% accuracy on the unseen test dataset.
Self-Distribution Binary Neural Networks
Authors: Ping Xue, Yang Lu, Jingfei Chang, Xing Wei, Zhen Wei
Date: 2021-03-03 00:00:00
Tasks: Quantization

In this work, we study the binary neural networks (BNNs) of which both the weights and activations are binary (i.e., 1-bit representation). Feature representation is critical for deep neural networks, while in BNNs, the features only differ in signs. Prior work introduces scaling factors into binary weights and activations to reduce the quantization error and effectively improves the classification accuracy of BNNs. However, the scaling factors not only increase the computational complexity of networks, but also make no sense to the signs of binary features. To this end, Self-Distribution Binary Neural Network (SD-BNN) is proposed. Firstly, we utilize Activation Self Distribution (ASD) to adaptively adjust the sign distribution of activations, thereby improve the sign differences of the outputs of the convolution. Secondly, we adjust the sign distribution of weights through Weight Self Distribution (WSD) and then fine-tune the sign distribution of the outputs of the convolution. Extensive experiments on CIFAR-10 and ImageNet datasets with various network structures show that the proposed SD-BNN consistently outperforms the state-of-the-art (SOTA) BNNs (e.g., achieves 92.5% on CIFAR-10 and 66.5% on ImageNet with ResNet-18) with less computation cost. Code is available at https://github.com/ pingxue-hfut/SD-BNN.
Energy-Based Learning for Scene Graph Generation
Authors: Mohammed Suhail, Abhay Mittal, Behjat Siddiquie, Chris Broaddus, Jayan Eledath, Gerard Medioni, Leonid Sigal
Date: 2021-03-03 00:00:00
Proceeding: CVPR 2021 1
Tasks: Graph Generation, Inductive Bias, Scene Graph Classification, Scene Graph Detection, Scene Graph Generation, Structured Prediction

Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores the structure in the output space, in an inherently structured prediction problem. In this work, we introduce a novel energy-based learning framework for generating scene graphs. The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space. This additional constraint in the learning framework acts as an inductive bias and allows models to learn efficiently from a small number of labels. We use the proposed energy-based framework to train existing state-of-the-art models and obtain a significant performance improvement, of up to 21% and 27%, on the Visual Genome and GQA benchmark datasets, respectively. Furthermore, we showcase the learning efficiency of the proposed framework by demonstrating superior performance in the zero- and few-shot settings where data is scarce.
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
Authors: Yisheng He, Haibin Huang, Haoqiang Fan, Qifeng Chen, Jian Sun
Date: 2021-03-03 00:00:00
Proceeding: CVPR 2021 1
Tasks: 6D Pose Estimation, Pose Estimation, Representation Learning

In this work, we present FFB6D, a Full Flow Bidirectional fusion network designed for 6D pose estimation from a single RGBD image. Our key insight is that appearance information in the RGB image and geometry information from the depth image are two complementary data sources, and it still remains unknown how to fully leverage them. Towards this end, we propose FFB6D, which learns to combine appearance and geometry information for representation learning as well as output representation selection. Specifically, at the representation learning stage, we build bidirectional fusion modules in the full flow of the two networks, where fusion is applied to each encoding and decoding layer. In this way, the two networks can leverage local and global complementary information from the other one to obtain better representations. Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation. Experimental results show that our method outperforms the state-of-the-art by large margins on several benchmarks. Code and video are available at \url{https://github.com/ethnhe/FFB6D.git}.
外卖骑士联盟电报Telegram群，群号@gonghao51
Authors: 外卖骑士联盟电报Telegram群
Date: 2021-04-02 00:00:00

外卖骑士联盟电报Telegram群，群号@gonghao51
电报群：外卖骑士联盟电报Telegram群，群号@gonghao51。为了进一步组织外卖骑士，进一步进行权益维护，建立了官方Telegram电报群！同时也是为了声援外卖江湖盟主，用实际行动支持盟主，利用一切可能的方式和途径向身边的骑手和工友们扩散消息！为了进一步组织外卖骑士，进一步进行权益维护，建立了官方Telegram电报群！同时也是为了声援外卖江湖盟主，用实际行动支持盟主，利用一切可能的方式和途径向身边的骑手和工友们扩散消息！
外卖骑士联盟盟主熊焰无故被捕，网友建立电报群声援营救盟主群 @gonghao51
Authors: 外卖骑士联盟盟主熊焰
Date: 2021-03-04 00:00:00

外卖骑士联盟盟主熊焰无故被捕，网友建立电报群声援营救盟主群 @gonghao51。官方维权Telegram电报群！声援外卖江湖盟主与外卖员集体维权群，用实际行动支持盟主，利用一切可能的方式和途径向身边的骑手和工友们扩散消息！ https://t. me/gonghao51 各位朋友，近期舆论封锁疯狂，继公众号平台文章屡屡被封后，今天编辑小何的微信号也因为声援外卖江湖盟主被抓遭遇毒手。为此，我们建了电报群，用于保持联系和长期保存文章。 请加声援外卖江湖盟主与外卖员集体官方维权群 https://t. me/gonghao51。外卖骑士联盟盟主熊焰无故被捕，网友建立电报群声援营救盟主