OpenRaiser/Intern-Atlas huggingface dataset:
Performance Evaluation of Cooperative NOMA-based Improved Hybrid SWIPT Protocol
Venue: arXiv cs.IT 2021
Year: 2021

This study proposes the integration of a cooperative non-orthogonal multiple access (CNOMA) and improved hybrid simultaneous wireless information and power transfer (IHS SWIPT) protocol (termed as CNOMA-IHS) to enhance the spectral efficiency (SE) of a downlink (DL) CNOMA communication system. CNOMA-IHS scheme can enhance the ergodic sum capacity (ESC) and energy efficiency (EE) of DL CNOMA by transferring additional symbols towards the users and energize the relay operation as well without any additional resources (e.g., time slot/frequency/code). The analytical and simulation results indicate that the proposed CNOMA-IHS scheme outperforms other existing SWIPT-based schemes (e.g., CNOMA with hybrid SWIPT, CNOMA with power-splitting SWIPT, wireless-powered CNOMA, CNOMA with time switching SWIPT, and orthogonal multiple access with IHS SWIPT) in terms of the ESC. Moreover, the CNOMA-IHS scheme also enhances EE compared with other conventional TS-SWIPT-based schemes, which is also illustrated by the simulation results. In addition, the proposed CNOMA-IHS scheme with the considered EE optimization technique outplayed the proposed CNOMA-IHS scheme without EE optimization and other existing TS-SWIPT-based schemes in terms of EE.
Online Handbook of Argumentation for AI: Volume 2
Venue: arXiv cs.AI 2021
Year: 2021

This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.
Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Multiple object tracking faces several challenges that may be alleviated with trajectory information. Knowing the posterior locations of an object helps disambiguating and solving situations such as occlusions, re-identification, and identity switching. In this work, we show that trajectory estimation can become a key factor for tracking, and present TrajE, a trajectory estimator based on recurrent mixture density networks, as a generic module that can be added to existing object trackers. To provide several trajectory hypotheses, our method uses beam search. Also, relying on the same estimated trajectory, we propose to reconstruct a track after an occlusion occurs. We integrate TrajE into two state of the art tracking algorithms, CenterTrack [63] and Tracktor [3]. Their respective performances in the MOTChallenge 2017 test set are boosted 6.3 and 0.3 points in MOTA score, and 1.8 and 3.1 in IDF1, setting a new state of the art for the CenterTrack+TrajE configuration
Wireless Communication Aided by Intelligent Reflecting Surface: Active or Passive?
Venue: arXiv cs.IT 2021
Year: 2021

In this letter, we consider an intelligent reflecting surface (IRS)-aided wireless communication system, where an active or passive IRS is employed to assist the communication between an access point and a user. First, we consider the downlink/uplink communication separately and optimize the IRS placement for rate maximization with an active or passive IRS. We show that the active IRS should be deployed closer to the receiver with the IRS's decreasing amplification power; while in contrast, the passive IRS should be deployed near either the transmitter or receiver. Moreover, with optimized IRS placement, the passive IRS is shown to outperform its active counterpart when the number of reflecting elements is sufficiently large and/or the active-IRS amplification power is too small. Next, we optimize the IRS placement for both active and passive IRSs to maximize the weighted sum-rate of uplink and downlink communications. We show that in this case, the passive IRS is more likely to achieve superior rate performance. This is because the optimal active-IRS placement needs to balance the rate performance in the uplink and downlink, while deploying the passive IRS near the transmitter or receiver is optimal regardless of the uplink or downlink.
Near-Optimal Pool Testing under Urgency Constraints
Venue: arXiv cs.IT 2021
Year: 2021

Detection of rare traits or diseases in a large population is challenging. Pool testing allows covering larger swathes of population at a reduced cost, while simplifying logistics. However, testing precision decreases as it becomes unclear which member of a pool made the global test positive. In this paper we discuss testing strategies that provably approach best-possible strategy - optimal in the sense that no other strategy can give exact results with fewer tests. Our algorithms guarantee that they provide a complete and exact result for every individual, without exceeding $1/0.99$ times the number of tests the optimal strategy would require. This threshold is arbitrary: algorithms closer to the optimal bound can be described, however their complexity increases, making them less practical. Moreover, the way the algorithms process input samples leads to some individuals' status to be known sooner, thus allowing to take urgency into account when assigning individuals to tests.
SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild
Venue: arXiv cs.CV 2021
Year: 2021

Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more. Recognition of hand gestures can be nowadays performed directly from the stream of hand skeletons estimated by software provided by low-cost trackers (Ultraleap) and MR headsets (Hololens, Oculus Quest) or by video processing software modules (e.g. Google Mediapipe). Despite the recent advancements in gesture and action recognition from skeletons, it is unclear how well the current state-of-the-art techniques can perform in a real-world scenario for the recognition of a wide set of heterogeneous gestures, as many benchmarks do not test online recognition and use limited dictionaries. This motivated the proposal of the SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild. For this contest, we created a novel dataset with heterogeneous gestures featuring different types and duration. These gestures have to be found inside sequences in an online recognition scenario. This paper presents the result of the contest, showing the performances of the techniques proposed by four research groups on the challenging task compared with a simple baseline method.
Hard Choices in Artificial Intelligence
Venue: arXiv cs.CY 2021
Year: 2021

As AI systems are integrated into high stakes social domains, researchers now examine how to design and operate them in a safe and ethical manner. However, the criteria for identifying and diagnosing safety risks in complex social contexts remain unclear and contested. In this paper, we examine the vagueness in debates about the safety and ethical behavior of AI systems. We show how this vagueness cannot be resolved through mathematical formalism alone, instead requiring deliberation about the politics of development as well as the context of deployment. Drawing from a new sociotechnical lexicon, we redefine vagueness in terms of distinct design challenges at key stages in AI system development. The resulting framework of Hard Choices in Artificial Intelligence (HCAI) empowers developers by 1) identifying points of overlap between design decisions and major sociotechnical challenges; 2) motivating the creation of stakeholder feedback channels so that safety issues can be exhaustively addressed. As such, HCAI contributes to a timely debate about the status of AI development in democratic societies, arguing that deliberation should be the goal of AI Safety, not just the procedure by which it is ensured.
Come back when you are charged! Self-Organized Charging for Electric Vehicles
Venue: arXiv cs.CY 2021
Year: 2021

Dwindling nonrenewable fuel reserves, progressing severe environmental pollution, and accelerating climate change require society to reevaluate existing transportation concepts. While electric vehicles (EVs) have become more popular and slowly gain widespread adoption, the corresponding battery charging infrastructures still limits EVs' use in our everyday life. This is especially true for EV owners that do not have the option to operate charging hardware, such as wall boxes, at their premises. Charging an EV without an at-home wall box is time-consuming since the owner has to drive to the charger, charge the vehicle while waiting nearby, and finally drive back home. Thus, a convenient and easy-to-use solution is required to overcome the issue and incentivize EVs for daily commuters. Therefore, we propose an ecosystem and a service platform for (semi-)autonomous electric vehicles that allow them to utilize their "free"-time, e.g., at night, to access public and private charging infrastructure, charge their batteries, and get back home before the owner needs the car again. To do so, we utilize the concept of the Machine-to-Everything Economy (M2X Economy) and outline a decentralized ecosystem for smart machines that transact, interact and collaborate via blockchain-based smart contracts to enable a convenient battery charging marketplace for (semi-)autonomous EVs.
Sampling of Shape Expressions
Venue: arXiv cs.CY 2021
Year: 2021

Cyber-physical systems (CPS) are increasingly becoming driven by data, using multiple types of sensors to capture huge amounts of data. Extraction and characterization of useful information from big streams of data is a challenging problem. Shape expressions facilitate formal specification of rich temporal patterns encountered in time series as well as in behaviors of CPS. In this paper, we introduce a method for systematically sampling shape expressions. The proposed approach combines methods for uniform sampling of automata (for exploring qualitative shapes) with hit-and-run Monte Carlo sampling procedures (for exploring multi-dimensional parameter spaces defined by sets of possibly non-linear constraints). We study and implement several possible solutions and evaluate them in the context of visualization and testing applications.
Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification
Venue: arXiv cs.LG 2021
Year: 2021

Data fusion is a well-known technique, becoming more and more popular in the Artificial Intelligence for Earth Observation (AI4EO) domain mainly due to its ability of reinforcing AI4EO applications by combining multiple data sources and thus bringing better results. On the other hand, like other methods for satellite data analysis, data fusion itself is also benefiting and evolving thanks to the integration of Artificial Intelligence (AI). In this letter, four data fusion paradigms, based on Convolutional Neural Networks (CNNs), are analyzed and implemented. The goals are to provide a systematic procedure for choosing the best data fusion framework, resulting in the best classification results, once the basic structure for the CNN has been defined, and to help interested researchers in their work when data fusion applied to remote sensing is involved. The procedure has been validated for land-cover classification but it can be transferred to other cases.
Improving Multi-Modal Learning with Uni-Modal Teachers
Venue: arXiv cs.LG 2021
Year: 2021

Learning multi-modal representations is an essential step towards real-world robotic applications, and various multi-modal fusion models have been developed for this purpose. However, we observe that existing models, whose objectives are mostly based on joint training, often suffer from learning inferior representations of each modality. We name this problem Modality Failure, and hypothesize that the imbalance of modalities and the implicit bias of common objectives in fusion method prevent encoders of each modality from sufficient feature learning. To this end, we propose a new multi-modal learning method, Uni-Modal Teacher, which combines the fusion objective and uni-modal distillation to tackle the modality failure problem. We show that our method not only drastically improves the representation of each modality, but also improves the overall multi-modal task performance. Our method can be effectively generalized to most multi-modal fusion approaches. We achieve more than 3% improvement on the VGGSound audio-visual classification task, as well as improving performance on the NYU depth V2 RGB-D image segmentation task.
Analytically Tractable Bayesian Deep Q-Learning
Venue: arXiv cs.LG 2021
Year: 2021

Reinforcement learning (RL) has gained increasing interest since the demonstration it was able to reach human performance on video game benchmarks using deep Q-learning (DQN). The current consensus for training neural networks on such complex environments is to rely on gradient-based optimization. Although alternative Bayesian deep learning methods exist, most of them still rely on gradient-based optimization, and they typically do not scale on benchmarks such as the Atari game environment. Moreover none of these approaches allow performing the analytical inference for the weights and biases defining the neural network. In this paper, we present how we can adapt the temporal difference Q-learning framework to make it compatible with the tractable approximate Gaussian inference (TAGI), which allows learning the parameters of a neural network using a closed-form analytical method. Throughout the experiments with on- and off-policy reinforcement learning approaches, we demonstrate that TAGI can reach a performance comparable to backpropagation-trained networks while using fewer hyperparameters, and without relying on gradient-based optimization.
A PTAS for $k$-hop MST on the Euclidean plane: Improving Dependency on $k$
Venue: arXiv cs.DS 2021
Year: 2021

For any $ε>0$, Laue and Matijević [CCCG'07, IPL'08] give a PTAS for finding a $(1+ε)$-approximate solution to the $k$-hop MST problem in the Euclidean plane that runs in time $(n/ε)^{O(k/ε)}$. In this paper, we present an algorithm that runs in time $(n/ε)^{O(\log k \cdot(1/ε)^2\cdot\log^2(1/ε))}$. This gives an improvement on the dependency on $k$ on the exponent, while having a worse dependency on $ε$. As in Laue and Matijević, we follow the framework introduced by Arora for Euclidean TSP. Our key ingredients include exponential distance scaling and compression of dynamic programming state tables.
Low-rank Dictionary Learning for Unsupervised Feature Selection
Venue: arXiv cs.LG 2021
Year: 2021

There exist many high-dimensional data in real-world applications such as biology, computer vision, and social networks. Feature selection approaches are devised to confront with high-dimensional data challenges with the aim of efficient learning technologies as well as reduction of models complexity. Due to the hardship of labeling on these datasets, there are a variety of approaches on feature selection process in an unsupervised setting by considering some important characteristics of data. In this paper, we introduce a novel unsupervised feature selection approach by applying dictionary learning ideas in a low-rank representation. Dictionary learning in a low-rank representation not only enables us to provide a new representation, but it also maintains feature correlation. Then, spectral analysis is employed to preserve sample similarities. Finally, a unified objective function for unsupervised feature selection is proposed in a sparse way by an $\ell_{2,1}$-norm regularization. Furthermore, an efficient numerical algorithm is designed to solve the corresponding optimization problem. We demonstrate the performance of the proposed method based on a variety of standard datasets from different applied domains. Our experimental findings reveal that the proposed method outperforms the state-of-the-art algorithm.
The Role of Evolution in Machine Intelligence
Venue: arXiv cs.NE 2021
Year: 2021

Machine intelligence can develop either directly from experience or by inheriting experience through evolution. The bulk of current research efforts focus on algorithms which learn directly from experience. I argue that the alternative, evolution, is important to the development of machine intelligence and underinvested in terms of research allocation. The primary aim of this work is to assess where along the spectrum of evolutionary algorithms to invest in research. My first-order suggestion is to diversify research across a broader spectrum of evolutionary approaches. I also define meta-evolutionary algorithms and argue that they may yield an optimal trade-off between the many factors influencing the development of machine intelligence.
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
Venue: arXiv cs.CV 2021
Year: 2021

Recently, few-shot video classification has received an increasing interest. Current approaches mostly focus on effectively exploiting the temporal dimension in videos to improve learning under low data regimes. However, most works have largely ignored that videos are often accompanied by rich textual descriptions that can also be an essential source of information to handle few-shot recognition cases. In this paper, we propose to leverage these human-provided textual descriptions as privileged information when training a few-shot video classification model. Specifically, we formulate a text-based task conditioner to adapt video features to the few-shot learning task. Furthermore, our model follows a transductive setting to improve the task-adaptation ability of the model by using the support textual descriptions and query instances to update a set of class prototypes. Our model achieves state-of-the-art performance on four challenging benchmarks commonly used to evaluate few-shot video action classification models.
Self-Calibrating Neural-Probabilistic Model for Authorship Verification Under Covariate Shift
Venue: arXiv cs.CL 2021
Year: 2021

We are addressing two fundamental problems in authorship verification (AV): Topic variability and miscalibration. Variations in the topic of two disputed texts are a major cause of error for most AV systems. In addition, it is observed that the underlying probability estimates produced by deep learning AV mechanisms oftentimes do not match the actual case counts in the respective training data. As such, probability estimates are poorly calibrated. We are expanding our framework from PAN 2020 to include Bayes factor scoring (BFS) and an uncertainty adaptation layer (UAL) to address both problems. Experiments with the 2020/21 PAN AV shared task data show that the proposed method significantly reduces sensitivities to topical variations and significantly improves the system's calibration.
Data Optimisation for a Deep Learning Recommender System
Venue: arXiv cs.IR 2021
Year: 2021

This paper advocates privacy preserving requirements on collection of user data for recommender systems. The purpose of our study is twofold. First, we ask if restrictions on data collection will hurt test quality of RNN-based recommendations. We study how validation performance depends on the available amount of training data. We use a combination of top-K accuracy, catalog coverage and novelty for this purpose, since good recommendations for the user is not necessarily captured by a traditional accuracy metric. Second, we ask if we can improve the quality under minimal data by using secondary data sources. We propose knowledge transfer for this purpose and construct a representation to measure similarities between purchase behaviour in data. This to make qualified judgements of which source domain will contribute the most. Our results show that (i) there is a saturation in test performance when training size is increased above a critical point. We also discuss the interplay between different performance metrics, and properties of data. Moreover, we demonstrate that (ii) our representation is meaningful for measuring purchase behaviour. In particular, results show that we can leverage secondary data to improve validation performance if we select a relevant source domain according to our similarly measure.
PHYSFRAME: Type Checking Physical Frames of Reference for Robotic Systems
Venue: arXiv cs.RO 2021
Year: 2021

A robotic system continuously measures its own motions and the external world during operation. Such measurements are with respect to some frame of reference, i.e., a coordinate system. A nontrivial robotic system has a large number of different frames and data have to be translated back-and-forth from a frame to another. The onus is on the developers to get such translation right. However, this is very challenging and error-prone, evidenced by the large number of questions and issues related to frame uses on developers' forum. Since any state variable can be associated with some frame, reference frames can be naturally modeled as variable types. We hence develop a novel type system that can automatically infer variables' frame types and in turn detect any type inconsistencies and violations of frame conventions. The evaluation on a set of 180 publicly available ROS projects shows that our system can detect 190 inconsistencies with 154 true positives. We reported 52 to developers and received 18 responses so far, with 15 fixed/acknowledged. Our technique also finds 45 violations of common practices.
Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations
Venue: arXiv cs.AI 2021
Year: 2021

Involving humans directly for the benefit of AI agents' training is getting traction thanks to several advances in reinforcement learning and human-in-the-loop learning. Humans can provide rewards to the agent, demonstrate tasks, design a curriculum, or act in the environment, but these benefits also come with architectural, functional design and engineering complexities. We present Cogment, a unifying open-source framework that introduces an actor formalism to support a variety of humans-agents collaboration typologies and training approaches. It is also scalable out of the box thanks to a distributed micro service architecture, and offers solutions to the aforementioned complexities.
Content Addressable Parallel Processors on a FPGA
Venue: arXiv cs.AR 2021
Year: 2021

In this short article, we report on the implementation of a Content Addressable Parallel Processor using a FPGA. While Content addressable memories have been implemented in FPGAs, to our knowledge this is the first implementation in FPGA of Caxton C. Foster's vision of parallel processing, particularly the notions of parallel write as well as the combining of output values, which are usually missing in more typical CAM implementations, such as the ones designed for network routing. The resulting CAPP is made accessible to a host computer over a USB/UART interface, using a straightforward serial protocol that is demonstrated using a Python-based driver.