Reviewerly/exHarmony huggingface dataset:
Divergence Radii and the Strong Converse Exponent of Classical-Quantum Channel Coding With Constant Compositions

There are different inequivalent ways to define the Rényi capacity of a channel for a fixed input distribution. In [IEEE Transactions on Information Theory, 41(1):26-34, 1995], Csiszár has shown that for classical discrete memoryless channels there is a distinguished such quantity that has an operational interpretation as a generalized cutoff rate for constant composition channel coding. We show that the analogous notion of Rényi capacity, defined in terms of the sandwiched quantum Rényi divergences, has the same operational interpretation in the strong converse problem of constant composition classical-quantum channel coding.
COVID-19: What does it mean for digital social protection?

COVID-19 has hit a world in which social protection schemes are increasingly augmented with digital measures. Digital identity schemes are especially being adopted to match citizens’ data with social protection entitlements, enabling authentication through demographic and, increasingly, biometric data at the point of access. In this commentary, I discuss three sets of implications that COVID-19 has yielded on digital social protection, whose central trade-off – increasing the probabilities of accurate user identification, at the cost of greater exclusions – has become even more problematic during the crisis. I argue that three forms of data injustice – legal, informational and design-related, previously identified in datafied social protection schemes, will need to be monitored in the post-pandemic scenario. I finally observe that the crisis exposes the long-term need to place digitality within social protection schemes that expand user entitlements rather than constraining them. Implications of such reflections are drawn for the study of data-based social welfare interventions.
Contour Transformer Network for One-Shot Segmentation of Anatomical Structures

Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs). Training the CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. On segmentation tasks of four different anatomies, we demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved to surpass the fully supervised methods.
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer

Unsupervised text style transfer is full of challenges due to the lack of parallel data and difficulties in content preservation.In this paper, we propose a novel neural approach to unsupervised text style transfer which we refer to as Cycle-consistent Adversarial autoEncoders (CAE) trained from non-parallel data.CAE consists of three essential components: (1) LSTM autoencoders that encode a text in one style into its latent representation and decode an encoded representation into its original text or a transferred representation into a style-transferred text, (2) adversarial style transfer networks that use an adversarially trained generator to transform a latent representation in one style into a representation in another style, and (3) a cycle-consistent constraint that enhances the capacity of the adversarial style transfer networks in content preservation.The entire CAE with these three components can be trained end-to-end.Extensive experiments and in-depth analyses on two widely-used public datasets consistently validate the effectiveness of proposed CAE in both style transfer and content preservation against several strong baselines in terms of four automatic evaluation metrics and human evaluation.
On the generalization of learning-based 3D reconstruction

State-of-the-art learning-based monocular 3D reconstruction methods learn priors over object categories on the training set, and as a result struggle to achieve reasonable generalization to object categories unseen during training. In this paper we study the inductive biases encoded in the model architecture that impact the generalization of learning-based 3D reconstruction methods. We find that 3 inductive biases impact performance: the spatial extent of the encoder, the use of the underlying geometry of the scene to describe point features, and the mechanism to aggregate information from multiple views. Additionally, we propose mechanisms to enforce those inductive biases: a point representation that is aware of camera position, and a variance cost to aggregate information across views. Our model achieves state-of-the-art results on the standard ShapeNet 3D reconstruction benchmark in various settings.
Resilience in Multirobot Multitarget Tracking With Unknown Number of Targets Through Reconfiguration

We address the problem of maintaining resource availability in a networked multirobot team performing distributed tracking of an unknown number of targets in a bounded environment. Robots are equipped with sensing and computational resources, enabling them to cooperatively track a set of targets using a distributed probability hypothesis density (PHD) filter. We use the trace of a robot's sensor measurement noise covariance matrix to quantify its sensing quality. While executing the tracking task, if a robot experiences sensor quality degradation, the team's communication network is reconfigured such that the robot with the faulty sensor may share information with other robots to improve the team's target-tracking ability without enforcing a large change in the number of active communication links. A central system monitor executes the network reconfiguration computations. We consider two different PHD fusion methods and propose four different mixed-integer semi-definite programming (MISDP) formulations (two formulations for each PHD fusion method) to accomplish our objective. All MISDP formulations are validated in simulation.
Towards Private Data-driven Control

Control as a Service (CaaS) is becoming a reality- particularly in the case of building automation and smart grid management. Often, the control algorithms in CaaS focus on controlling the client's system directly from input-output data, since the system's model might be private or unavailable. Therefore, large quantities of data collected from the client need to be uploaded to a cloud server. This data can be used by a malevolent cloud service provider to infer sensitive information about the client and mount attacks. In this paper, we co-design a solution that interlaces control and privacy. Our goal is to perform online data-driven control on encrypted input-output data, while maintaining the privacy of the client's uploaded data, desired setpoint and control actions. We design our control algorithm based on results from the behavioral framework, which is more encryption-friendly compared to other classical frameworks. We obtain privacy by using a leveled homomorphic encryption scheme to enable the cloud to perform complex computations on the client's encrypted data. Finally, we achieve efficiency by manipulating the tasks required by the control algorithm such that they only involve arithmetic circuits, as well as by leveraging parallelization and ciphertext packing.
Isolation and Contact Tracing Can Tip the Scale To&amp;nbsp;Containment of COVID-19&amp;nbsp;In Populations with Social Distancing

Background: Novel coronavirus (SARS-CoV-2) has extended its range of transmission in all parts of the world, with substantial variation in rates of transmission
Authorization Mechanism Based on Blockchain Technology for Protecting Museum-Digital Property Rights

In addition to the exhibition, collection, research, and educational functions of the museum, the development of a future museum includes the trend of leisure and sightseeing. Although the museum is a non-profit organization, if it can provide digital exhibits and collections under the premises of “intellectual property rights” and “cultural assets protection”, and licensing and adding value in various fields, it can generate revenue from digital licensing and handle the expenses of museum operations. This will be a new trend in the sustainable development of museum operations. Especially since the outbreak of COVID-19 at the beginning of this year (2020), the American Alliance of Museums (AAM) recently stated that nearly a third of the museums in the United States may be permanently closed since museum operations are facing “extreme financial difficulties.” This research is aimed at museums using the business model of “digital authorization”. It proposes an authorization mechanism based on blockchain technology protecting the museums’ digital rights in the business model and the application of cryptography. The signature and time stamp mechanism achieve non-repudiation and timeless mechanism, which combines blockchain and smart contracts to achieve verifiability, un-forgery, decentralization, and traceability, as well as the non-repudiation of the issue of cash flow with signatures and digital certificates, for the digital rights of museums in business. The business model proposes achievable sustainable development. Museums not only achieve the goal of promoting social education, but also solve their financial problems.
Enabling (End-to-End) Encrypted Cloud Emails With Practical Forward Secrecy

With the widespread use of cloud emails and frequent reports on large-scale email leakage events, a security property so-called forward secrecy becomes desirable and indispensable for both individuals and cloud email service providers to strengthen the security of cloud email systems. Specifically, forward secrecy can guarantee the confidentiality of those previously encrypted emails even if the user’s secret key gets exposed. However, due to the failure to meet the security and practicality requirements of email systems simultaneously, typical methods of achieving forward secrecy, such as Diffie-Hellman key exchange and forward-secure public-key encryption, have not been widely approved and adopted. In this article, to capture forward secrecy of encrypted cloud email systems without sacrificing the practicability, we introduce a new cryptographic primitive named forward-secure puncturable identity-based encryption (fs-PIBE), which enables an email user to perform fine-grained revocation of decryption capacity. In more detail, the user is allowed to preserve the decryption capacity of unreceived encrypted emails, while abolishing that of those received ones. Thus, it provides more practical forward secrecy than typical manners, in which the decryption capacity of received and unreceived encrypted emails is revoked simultaneously. Based on such a primitive, we build a framework of encrypted cloud email systems, and instantiate it with a concrete fs-PIBE construction that has constant size of ciphertext and provable security in the standard model. Furthermore, to improve the security and efficiency of the presented framework, we extend the proposed fs-PIBE scheme to support end-to-end encryption and outsourced decryption, respectively. In addition, as a proof-of-concept of the proposed fs-PIBE scheme, we implement it and produce various experiments to demonstrate its practicability and correctness.
What Do We Mean by Smart Contracts? Open Challenges in Smart Contracts

Contracts regulate most of our professional and personal life: they enable modern society to operate. The term “Smart Contract,” coined in 1994 by Nick Szabo, means different things to different people. This editorial perspective explores the meanings of the term “smart contract” and the challenges about the legality of “smart contracts.” We are familiar with contracts written in natural language, yet our relationships with smart contracts is yet to be defined. The advent of blockchain technology seems to have accelerated the development and the opportunities for the adoption of smart contracts. The purpose of this editorial is to create an interdisciplinary section where computer scientists and members of the legal profession participate in a constructive debate around smart contracts to positively influence future development.
Embedded quantum-error correction and controlled-phase gate for molecular spin qubits

A scalable architecture for quantum computing requires logical units supporting quantum-error correction. In this respect, magnetic molecules are particularly promising, since they allow one to define logical qubits with embedded quantum-error correction by exploiting multiple energy levels of a single molecule. The single-object nature of this encoding is expected to facilitate the implementation of error correction procedures and logical operations. In this work, we make progress in this direction by showing how two-qubit gates between error-protected units can be realised, by means of easily implementable sequences of electro-magnetic pulses.
BCOOL: A Novel Blockchain Congestion Control Architecture Using Dynamic Service Function Chaining and Machine Learning for Next Generation Vehicular Networks

This paper presents the first, novel, dynamic, resilient, and consistent Blockchain COngestion ContrOL (BCOOL) system for vehicular networks that fills the gap of trustworthy Blockchain congestion prediction systems. BCOOL relies on the heterogeneity of Machine Learning, Software-Defined Networks and Network Function Virtualization that is customized in three hybrid cloud/edge-based On/Offchain smart contract modules and ruled by an efficient and reliable communication protocol. BCOOL's first novel module aims at managing message and vehicle trustworthiness using a novel, dynamic and hybrid Blockchain Fog-based Distributed Trust Contract Strategy (FDTCS). The second novel module accurately and proactively predicts the occurrence of congestion, ahead of time, using a novel Hybrid On/Off-Chain Multiple Linear Regression Software-defined Contract Strategy (HOMLRCS). This module presents a virtualization facility layer to the third novel K-means/Random Forest-based On/Off-Chain Dynamic Service Function Chaining Contract Strategy (KRF-ODSFCS) that dynamically, securely and proactively predicts VNF placements and their chaining order in the context of SFCs w.r.t users' dynamic QoS priority demands. BCOOL exhibits a linear complexity and a strong resilience to failures. Simulation results show that BCOOL outperforms the next best comparable strategies by 80% and 100% reliability and efficiency gains in challenging data congestion environments. This yields to fast, reliable and accurate congestion prediction decisions, ahead of time, and optimizes transaction validation processing time. Globally, the Byzantine resilience, complexity and attack mitigation strategies along with simulation results prove that BCOOL securely predicts the congestion and provides real-time monitoring, fast and accurate SFC deployment decisions while lowering both capital and operational expenditures (CAPEX/OPEX) costs.
On The Usefulness of Self-Attention for Automatic Speech Recognition with Transformers

Self-attention models such as Transformers, which can capture temporal relationships without being limited by the distance between events, have given competitive speech recognition results.However, we note the range of the learned context increases from the lower to upper self-attention layers, whilst acoustic events often happen within short time spans in a left-to-right order.This leads to a question: for speech recognition, is a global view of the entire sequence useful for the upper self-attention encoder layers in Transformers?To investigate this, we train models with lower selfattention/upper feed-forward layers encoders on Wall Street Journal and Switchboard.Compared to baseline Transformers, no performance drop but minor gains are observed.We further developed a novel metric of the diagonality of attention matrices and found the learned diagonality indeed increases from the lower to upper encoder self-attention layers.We conclude the global view is unnecessary in training upper encoder layers.
Toward Oblivious Location-Based <i>k</i>-Nearest Neighbor Query in Smart Cities

Enabled by the flourishing Internet-of-Things technology, smart cities can offer a variety of smart services to our daily lives and have received considerable attention in recent years. As a pivotal component of smart cities, location-based services (LBSs) have been deeply exploited by both academia and industry. Meanwhile, since cloud computing can provide reliable and flexible IT resources, many LBS services have been outsourced to the cloud for offering better services. Nevertheless, as the cloud is not fully trusted, privacy preservation becomes an essential requirement for these services. Over the past years, many privacy-preserving location-based k-nearest neighbor ( kNN) query schemes over the cloud have been proposed. However, most of them are subjected to an inevitable design defect, i.e., whenever a user queries twice at the same location, the cloud can identify and return the same query result to the query user, and such information together with third-party data breaches could be exploited by the cloud for some location disclosures. Although some existing schemes can cope with the issue, they are not quite practical, as they will bring heavy overheads on the query user side. In this article, aiming to address the above challenge, we propose a novel oblivious location-based kNN query scheme, in which the cloud cannot link two queries even if they are initiated by query users at the same location. Specifically, based on the modified Paillier cryptosystem, we first present three privacy-preserving protocols, namely, oblivious absolute value calculation, sorting, and top- k extraction. Then, by integrating these three protocols, we propose our novel oblivious location-based kNN query scheme. The detailed security analysis shows that our proposed scheme really enhances the privacy preservation in LBS queries. In addition, extensive performance analysis and experiments are conducted, and the results indicate that our proposed scheme is also efficient for the query user.
Personalized Approximate Pareto-Efficient Recommendation

Real-world recommendation systems usually have different learning objectives and evaluation criteria on accuracy, diversity or novelty. Therefore, multi-objective recommendation (MOR) has been widely explored to jointly model different objectives. Pareto efficiency, where no objective can be further improved without hurting others, is viewed as an optimal situation in multi-objective optimization. Recently, Pareto efficiency model has been introduced to MOR, while all existing scalarization methods only have shared objective weights for all instances. To capture users’ objective-level preferences and enhance personalization in Pareto-efficient recommendation, we propose a novel Personalized Approximate Pareto-Efficient Recommendation (PAPERec) framework for multi-objective recommendation. Specifically, we design an approximate Pareto-efficient learning based on scalarization with KKT conditions that closely mimics Pareto efficiency, where users have personalized weights on different objectives. We propose a Pareto-oriented reinforcement learning module to find appropriate personalized objective weights for each user, with the weighted sum of multiple objectives’ gradients considered in reward. In experiments, we conduct extensive offline and online evaluations on a real-world recommendation system. The significant improvements verify the effectiveness of PAPERec in practice. We have deployed PAPERec on WeChat Top Stories, affecting millions of users. The source codes are released in https://github.com/onepunch-cyber/PAPERec.
Towards Integrated Perception and Motion Planning with Distributionally Robust Risk Constraints

Safely deploying robots in uncertain and dynamic environments requires a systematic accounting of various risks, both within and across layers in an autonomy stack from perception to motion planning and control. Many widely used motion planning algorithms do not adequately incorporate inherent perception and prediction uncertainties, often ignoring them altogether or making questionable assumptions of Gaussianity. We propose a distributionally robust incremental sampling-based motion planning framework that explicitly and coherently incorporates perception and prediction uncertainties. We design output feedback policies and consider moment-based ambiguity sets of distributions to enforce probabilistic collision avoidance constraints under the worst-case distribution in the ambiguity set. Our solution approach, called Output Feedback Distributionally Robust RRT (OFDR-RRT ), produces asymptotically optimal risk-bounded trajectories for robots operating in dynamic, cluttered, and uncertain environments, explicitly incorporating mapping and localization error, stochastic process disturbances, unpredictable obstacle motion, and uncertain obstacle locations. Numerical experiments illustrate the effectiveness of the proposed algorithm.
Road-Map Aided GM-PHD Filter for Multivehicle Tracking With Automotive Radar

Nowadays, accurate and real-time vehicle tracking is critical to ensure the safety of intelligent vehicles. However, tracking in the complex traffic environments still remains a challenging issue. In this article, we present a road-map aided Gaussian mixture probability hypothesis density (RA-GMPHD) filter for multivehicle tracking with automotive radar. Since the road-map is commonly available in traffic scenarios, we focus on leveraging road-map information to enhance the tracking performance. We first model the vehicle dynamics in a 2-D road coordinates, then approximatively map it onto ground coordinates considering map errors. Additionally, we integrate the variable structure interacting multiple model into the RA-GMPHD filter considering both the dynamic uncertainty of targets and the road geographic constraints. Furthermore, we perform extensive simulations and conduct physical testings to demonstrate the superiority of our approaches compared with state-of-the-art method. Experimental results show our methods enhance both the tracking quality and tracking continuity.