OpenRaiser/Intern-Atlas huggingface dataset:
Quantum Identity-Based Encryption from the Learning with Errors Problem
Venue: arXiv cs.CR 2021
Year: 2021

In order to prevent eavesdropping and tampering, the network security protocols use a handshake with an asymmetric cipher to establish a session-specific shared key with which further communication is encrypted using a symmetric cipher. The commonly used asymmetric algorithms include public key encryption, key exchange and identity-based encryption(IBE). However, the network security protocols based on classic identity-based encryption do not have perfect forward security. To solve the problem, we construct the first quantum IBE (QIBE) scheme based on the learning with errors problem, and prove that our scheme is fully secure under the random oracle. Moreover, we construct the quantum circuit of our QIBE scheme and give an estimate of the quantum resource of our circuit including the numbers of Hadamard gate, phase gate, T gate, CNOT gate and the total qubits used in the circuit, and conclude that the quantum resources required by our scheme increase linearly with the number of bits of the encrypted quantum plaintext. Our scheme exhibits the following advantages: (i) The classic key generation center (KGC) system still can be used for our QIBE scheme to generate and distribute the secret identity keys so that the cost can be reduced when the scheme is implemented. The reason why the classic KGC can be used is that the public and private keys are in the form of classic bits. (ii) The network security protocols using a handshake with our QIBE scheme can provide perfect forward security. In our scheme, the ciphertext is transmitted in the form of a quantum state that is unknown to the adversary and therefore cannot be copied and stored. Thus, in the network security protocols based on our QIBE construction, the adversary cannot decrypt the previous quantum ciphertext to threat the previous session keys even if the identity secret key is threatened.
Linear Differential Games for Cooperative Behavior Planning of Autonomous Vehicles Using Mixed-Integer Programming
Venue: arXiv cs.RO 2021
Year: 2021

Cooperatively planning for multiple agents has been proposed as a promising method for strategic and motion planning for automated vehicles. By taking into account the intent of every agent, the ego agent can incorporate future interactions with human-driven vehicles into its planning. The problem is often formulated as a multi-agent game and solved using iterative algorithms operating on a discretized action or state space. Even if converging to a Nash equilibrium, the result will often be only sub-optimal. In this paper, we define a linear differential game for a set of interacting agents and solve it to optimality using mixed-integer programming. A disjunctive formulation of the orientation allows us to formulate linear constraints to prevent agent-to-agent collision while preserving the non-holonomic motion properties of the vehicle model. Soft constraints account for prediction errors. We then define a joint cost function, where a cooperation factor can adapt between altruistic, cooperative, and egoistic behavior. We study the influence of the cooperation factor to solve scenarios, where interaction between the agents is necessary to solve them successfully. The approach is then evaluated in a racing scenario, where we show the applicability of the formulation in a closed-loop receding horizon replanning fashion. By accounting for inaccuracies in the cooperative assumption and the actual behavior, we can indeed successfully plan an optimal control strategy interacting closely with other agents.
MCS Adaptation within the Cellular V2X Sidelink
Venue: arXiv cs.NI 2021
Year: 2021

Adaptation of the Modulation and Coding Scheme (MCS) within the Cellular Vehicle-To-Everything (C-V2X) sidelink has the potential for a wide range of applications including congestion control, support of variable packet sizes, density aware rate adaptation and improved support of unicast transmissions. However, the practical implementation of MCS adaptation presents a wide range of implications for the C-V2X radio resources, bandwidth, computation of power levels and operation of the scheduling mechanism. This paper presents the first study that provides a detailed analysis and an implemented model highlighting the implications of MCS adaptation on the operation of the Sensing-Based Semi-Persistent Scheduling (SB-SPS) mechanism within the C-V2X sidelink. To showcase the use of MCS adaptation for a particular purpose, a detailed analysis of its performance for distributed congestion control is undertaken, while considering different vehicular densities. The results indicate that MCS adaptation can be useful to reduce channel congestion by decreasing resource occupation but may not improve the overall packet delivery rate unless subchannel occupation is reduced. Finally, this study provides the foundation for other applications of MCS adaptation within the C-V2X sidelink.
Fly Out The Window: Exploiting Discrete-Time Flatness for Fast Vision-Based Multirotor Flight
Venue: arXiv cs.RO 2021
Year: 2021

Current control design for fast vision-based flight tends to rely on high-rate, high-dimensional and perfect state estimation. This is challenging in real-world environments due to imperfect sensing and state estimation drift and noise. In this letter, we present an alternative control design that bypasses the need for a state estimate by exploiting discrete-time flatness. To the best of our knowledge, this is the first work to demonstrate that discrete-time flatness holds for the Euler discretization of multirotor dynamics. This allows us to design a controller using only a window of input and output information. We highlight in simulation how exploiting this property in control design can provide robustness to noisy output measurements (where estimating higher-order derivatives and the full state can be challenging). Fast vision-based navigation requires high performance flight despite possibly noisy high-rate real-time position estimation. In outdoor experiments, we show the application of discrete-time flatness to vision-based flight at speeds up to 10 m/s and how it can outperform controllers that hinge on accurate state estimation.
NPS-AntiClone: Identity Cloning Detection based on Non-Privacy-Sensitive User Profile Data
Venue: arXiv cs.SI 2021
Year: 2021

Social sensing is a paradigm that allows crowdsourcing data from humans and devices. This sensed data (e.g. social network posts) can be hosted in social-sensor clouds (i.e. social networks) and delivered as social-sensor cloud services (SocSen services). These services can be identified by their providers' social network accounts. Attackers intrude social-sensor clouds by cloning SocSen service providers' user profiles to deceive social-sensor cloud users. We propose a novel unsupervised SocSen service provider identity cloning detection approach, NPS-AntiClone, to prevent the detrimental outcomes caused by such identity deception. This approach leverages non-privacy-sensitive user profile data gathered from social networks to perform cloned identity detection. It consists of three main components: 1) a multi-view account representation model, 2) an embedding learning model and 3) a prediction model. The multi-view account representation model forms three different views for a given identity, namely a post view, a network view and a profile attribute view. The embedding learning model learns a single embedding from the generated multi-view representation using Weighted Generalized Canonical Correlation Analysis. Finally, NPS-AntiClone calculates the cosine similarity between two accounts' embedding to predict whether these two accounts contain a cloned account and its victim. We evaluated our proposed approach using a real-world dataset. The results showed that NPS-AntiClone significantly outperforms the existing state-of-the-art identity cloning detection techniques and machine learning approaches.
Multilingual AMR Parsing with Noisy Knowledge Distillation
Venue: arXiv cs.CL 2021
Year: 2021

We study multilingual AMR parsing from the perspective of knowledge distillation, where the aim is to learn and improve a multilingual AMR parser by using an existing English parser as its teacher. We constrain our exploration in a strict multilingual setting: there is but one model to parse all different languages including English. We identify that noisy input and precise output are the key to successful distillation. Together with extensive pre-training, we obtain an AMR parser whose performances surpass all previously published results on four different foreign languages, including German, Spanish, Italian, and Chinese, by large margins (up to 18.8 \textsc{Smatch} points on Chinese and on average 11.3 \textsc{Smatch} points). Our parser also achieves comparable performance on English to the latest state-of-the-art English-only parser.
Latent Network Embedding via Adversarial Auto-encoders
Venue: arXiv cs.LG 2021
Year: 2021

Graph auto-encoders have proved to be useful in network embedding task. However, current models only consider explicit structures and fail to explore the informative latent structures cohered in networks. To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders. Under this framework, the problem of discovering latent structures is formulated as inferring the latent ties from partial observations. A latent transmission matrix that describes the strengths of existing edges and latent ties is derived based on influence cascades sampled by simulating diffusion processes over networks. Besides, since the inference process may bring extra noises, we introduce an adversarial training that works as regularization to dislodge noises and improve the model robustness. Extensive experiments on link prediction and node classification tasks show that the proposed model achieves superior results compared with baseline models.
Simulation-based multi-criteria comparison of mono-articular and bi-articular exoskeletons during walking with and without load
Venue: arXiv cs.RO 2021
Year: 2021

Developing exoskeletons that can reduce the metabolic cost of assisted subjects is challenging since a systematic design approach is required to capture the effects of device dynamics and the assistance torques on human performance. Design studies that rely on musculoskeletal models hold high promise in providing effective design guidelines, as the effect of various devices and different assistance torque profiles on metabolic cost can be studied systematically. In this paper, we present a simulation-based multi-criteria design approach to systematically study the effect of different device kinematics and corresponding optimal assistive torque profiles under actuator saturation on the metabolic cost, muscle activation, and joint reaction forces of subjects walking under different loading conditions. For the multi-criteria comparison of exoskeletons, we introduce a Pareto optimization approach to simultaneously optimize the exoskeleton power consumption and the human metabolic rate reduction during walking, under different loading conditions. We further superpose the effects of device inertia and electrical regeneration on the metabolic rate and power consumption, respectively. Our results explain the effects of heavy loads on the optimal assistance profiles of the exoskeletons and provide guidelines on choosing optimal device configurations under actuator torque limitations, device inertia, and regeneration effects. The multi-criteria comparison of devices indicates that despite the similar assistance levels of both devices, mono-articular exoskeletons show better performance on reducing the peak reaction forces, while the power consumption of bi-articular devices is less sensitive to the loading. Furthermore, for the bi-articular exoskeletons, the device inertia has lower detrimental effects on the metabolic cost of subjects and does not affect the Pareto-optimality of solutions.
Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series
Venue: arXiv cs.LG 2021
Year: 2021

Statistic modeling and data-driven learning are the two vital fields that attract many attentions. Statistic models intend to capture and interpret the relationships among variables, while data-based learning attempt to extract information directly from the data without pre-processing through complex models. Given the extensive studies in both fields, a subtle issue is how to properly integrate data based methods with existing knowledge or models. In this paper, based on the time series data, we propose two different directions to integrate the two, a decomposition-based method and a method exploiting the statistic extraction of data features. The first one decomposes the data into linear stable, nonlinear stable and unstable parts, where suitable statistical models are used for the linear stable and nonlinear stable parts while the appropriate machine learning tools are used for the unstable parts. The second one applies statistic models to extract statistics features of data and feed them as additional inputs into the machine learning platform for training. The most critical and challenging thing is how to determine and extract the valuable information from mathematical or statistical models to boost the performance of machine learning algorithms. We evaluate the proposal using time series data with varying degrees of stability. Performance results show that both methods can outperform existing schemes that use models and learning separately, and the improvements can be over 60%. Both our proposed methods are promising in bridging the gap between model-based and data-driven schemes and integrating the two to provide an overall higher learning performance.
Is my agent good enough? Evaluating Embodied Conversational Agents with Long and Short-term interactions
Venue: arXiv cs.HC 2021
Year: 2021

The use of digital resources has been increasing in every instance of todays society, being it in business or even ludic purposes. Despite such ever increasing use of technologies as interfaces, in all fields, it seems that it lacks the importance of users perception in this context. This work aims to present a case study about the evaluation of ECAs. We propose a Long-Term Interaction (LTI) to evaluate our conversational agent effectiveness through the user perception and compare it with Short-Term Interactions (STIs), performed by three users. Results show that many different aspects of users perception about the chosen ECA (i.e. Arthur) could be evaluated in our case study, in particular that LTI and STI are both important in order to have a better understanding of ECA impact in UX.
Emergence of Theory of Mind Collaboration in Multiagent Systems
Venue: arXiv cs.MA 2021
Year: 2021

Currently, in the study of multiagent systems, the intentions of agents are usually ignored. Nonetheless, as pointed out by Theory of Mind (ToM), people regularly reason about other's mental states, including beliefs, goals, and intentions, to obtain performance advantage in competition, cooperation or coalition. However, due to its intrinsic recursion and intractable modeling of distribution over belief, integrating ToM in multiagent planning and decision making is still a challenge. In this paper, we incorporate ToM in multiagent partially observable Markov decision process (POMDP) and propose an adaptive training algorithm to develop effective collaboration between agents with ToM. We evaluate our algorithms with two games, where our algorithm surpasses all previous decentralized execution algorithms without modeling ToM.
Deep Connection: Making Virtual Reality Artworks with Medical Scan Data
Venue: arXiv cs.MM 2021
Year: 2021

Deep Connection is an installation and virtual reality artwork made using full body 3D and 4D magnetic resonance scan datasets. When the user enters Deep Connection, they see a scanned body lying prone in mid-air. The user can walk around the body and inspect it. The user can dive inside and see its inner workings, its lungs, spine, brain. The user can take hold of the figure's outstretched hand: holding the hand triggers the 4D dataset, making the heart beat and the lungs breathe. When the user lets go of the hand, the heart stops beating and the lungs stop breathing. Deep Connection creates a scenario where an embodied human becomes the companion for a virtual body. This paper maps the conceptual and theoretical framework for Deep Connection such as virtual intimacy and digital mediated companionship. It also reflects on working with scanned bodies more generally in virtual reality by discussing transparency, the cyberbody versus the data body, as well as data privacy and data ethics. The paper also explains the technical and procedural aspects of the Deep Connection project with respect to acquiring scan data for the creation of virtual reality artworks.
Towards Generalised Half-Duplex Systems
Venue: arXiv cs.DC 2021
Year: 2021

FIFO automata are finite state machines communicating through FIFO queues. They can be used for instance to model distributed protocols. Due to the unboundedness of the FIFO queues, several verification problems are undecidable for these systems. In order to model-check such systems, one may look for decidable subclasses of FIFO systems. Binary half-duplex systems are systems of two FIFO automata exchanging over a half-duplex channel. They were studied by Cece and Finkel who established the decidability in polynomial time of several properties. These authors also identified some problems in generalising half-duplex systems to multi-party communications. We introduce greedy systems, as a candidate to generalise binary half-duplex systems. We show that greedy systems retain the same good properties as binary half-duplex systems, and that, in the setting of mailbox communications, greedy systems are quite closely related to a multiparty generalisation of half-duplex systems.
Decomposing Monolithic Processes in a Process Algebra with Multi-actions
Venue: arXiv cs.LO 2021
Year: 2021

A monolithic process is a single recursive equation with data parameters, which only uses non-determinism, action prefixing, and recursion. We present a technique that decomposes such a monolithic process into multiple processes where each process defines behaviour for a subset of the parameters of the monolithic process. For this decomposition we can show that a composition of these processes is strongly bisimilar to the monolithic process under a suitable synchronisation context. Minimising the resulting processes before determining their composition can be used to derive a state space that is smaller than the one obtained by a monolithic exploration. We apply the decomposition technique to several specifications to show that this works in practice. Finally, we prove that state invariants can be used to further improve the effectiveness of this decomposition technique.
Under the Microscope: Interpreting Readability Assessment Models for Filipino
Venue: arXiv cs.CL 2021
Year: 2021

Readability assessment is the process of identifying the level of ease or difficulty of a certain piece of text for its intended audience. Approaches have evolved from the use of arithmetic formulas to more complex pattern-recognizing models trained using machine learning algorithms. While using these approaches provide competitive results, limited work is done on analyzing how linguistic variables affect model inference quantitatively. In this work, we dissect machine learning-based readability assessment models in Filipino by performing global and local model interpretation to understand the contributions of varying linguistic features and discuss its implications in the context of the Filipino language. Results show that using a model trained with top features from global interpretation obtained higher performance than the ones using features selected by Spearman correlation. Likewise, we also empirically observed local feature weight boundaries for discriminating reading difficulty at an extremely fine-grained level and their corresponding effects if values are perturbed.
What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence
Venue: arXiv cs.IT 2021
Year: 2021

In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this, the main theme of wireless system design up until 5G was the data rate maximization. In his theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between IoT and AI. By broadening the scope of the classic framework, in this article we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities, human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two, the main theme of the article, represent the paradigm shift in communication and computing. H2M SemCom refers to semantic techniques for conveying meanings understandable by both humans and machines so that they can interact. M2M SemCom refers to effectiveness techniques for efficiently connecting machines such that they can effectively execute a specific computation task in a wireless network. The first part of the article introduces SemCom principles including encoding, system architecture, and layer-coupling and end-to-end design approaches. The second part focuses on specific techniques for application areas of H2M (human and AI symbiosis, recommendation, etc.) and M2M SemCom (distributed learning, split inference, etc.) Finally, we discuss the knowledge graphs approach for designing SemCom systems. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in 6G featuring connected intelligence and integrated sensing, computing, communication, and control.
Simulation-based Evaluation of a Synchronous Transaction Model for Time-Sensitive Software-Defined Networks
Venue: arXiv cs.NI 2021
Year: 2021

Real-time networks based on Ethernet require robust quality-of-service for time-critical traffic. The Time-Sensitive Networking (TSN) collection of standards enables this in real-time environments like vehicle on-board networks. Runtime reconfigurations in TSN must respect the deadlines of real-time traffic. Software-Defined Networking (SDN) moves the control plane of network devices to the SDN controller, making these networks programmable. This allows reconfigurations from a central point in the network. In this work, we present a transactional model for network reconfigurations that are synchronously executed in all network devices. We evaluate its performance in a case study against nontransactional reconfigurations and show that synchronous transactions enable consistency for reconfigurations in TSN without increased latencies for real-time frames.