Reviewerly/exHarmony huggingface dataset:
The Battle Against Online Harmful Information

Social media have given the opportunity to users to express their opinions online in a fast and easy way. The ease of generating content online and the anonymity that social media provide have increased the amount of harmful content that is published. This tutorial will focus on the topic of online harmful information. First, we will analyse and explain the different types of online harmful information with a particular focus on fake news and hate speech. In addition, we will explain the different computational approaches proposed in the literature for the detection of fake news and hate speech. Next, we will present details regarding the evaluation process, datasets and shared tasks and finally, we will discuss future directions in the field of online harmful information detection.
What the Coronavirus Disease 2019 (COVID-19) Pandemic Has Reinforced: The Need for Accurate Data

The COVID-19 pandemic has challenged the United States' existing national public health informatics infrastructure. This report details the factors that have contributed to COVID-19 data inaccuracies and reporting delays and their effect on the modeling and monitoring of the COVID-19 pandemic.
Debiasing knowledge graph embeddings

It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers. As graph embeddings begin to be used more widely in NLP pipelines, there is a need to develop training methods which remove such biases. Previous approaches to this problem both significantly increase the training time, by a factor of eight or more, and decrease the accuracy of the model substantially. We present a novel approach, in which all embeddings are trained to be neutral to sensitive attributes such as gender by default using an adversarial loss. We then add sensitive attributes back on in whitelisted cases. Training time only marginally increases over a baseline model, and the debiased embeddings perform almost as accurately in the triple prediction task as their non-debiased counterparts.
A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning

Structured belief states are crucial for user goal tracking and database query in task-oriented dialog systems.However, training belief trackers often requires expensive turn-level annotations of every user utterance.In this paper we aim at alleviating the reliance on belief state labels in building end-to-end dialog systems, by leveraging unlabeled dialog data towards semi-supervised learning.We propose a probabilistic dialog model, called the LAtent BElief State (LABES) model, where belief states are represented as discrete latent variables and jointly modeled with system responses given user inputs.Such latent variable modeling enables us to develop semi-supervised learning under the principled variational learning framework.Furthermore, we introduce LABES-S2S, which is a copyaugmented Seq2Seq model instantiation of LABES 1 .In supervised experiments, LABES-S2S obtains strong results on three benchmark datasets of different scales.In utilizing unlabeled dialog data, semi-supervised LABES-S2S significantly outperforms both supervisedonly and semi-supervised baselines.Remarkably, we can reduce the annotation demands to 50% without performance loss on MultiWOZ.
Subspace benchmarking high-fidelity entangling operations with trapped ions

The authors propose and demonstrate a new method for measuring the quality of an entangling operations in trapped-ion quantum computers. They use the method to tune up an entangling operation rivaling current world-record error rates, while providing a fidelity characterization for a gate of this quality.
Convergence Rates for Quantum Evolution and Entropic Continuity Bounds in Infinite Dimensions

Abstract By extending the concept of energy-constrained diamond norms, we obtain continuity bounds on the dynamics of both closed and open quantum systems in infinite dimensions, which are stronger than previously known bounds. We extensively discuss applications of our theory to quantum speed limits, attenuator and amplifier channels, the quantum Boltzmann equation, and quantum Brownian motion. Next, we obtain explicit log-Lipschitz continuity bounds for entropies of infinite-dimensional quantum systems, and classical capacities of infinite-dimensional quantum channels under energy-constraints. These bounds are determined by the high energy spectrum of the underlying Hamiltonian and can be evaluated using Weyl’s law.
Learning as the unsupervised alignment of conceptual systems

Concept induction requires the extraction and naming of concepts from noisy perceptual experience. For supervised approaches, as the number of concepts grows, so does the number of required training examples. Philosophers, psychologists, and computer scientists, have long recognized that children can learn to label objects without being explicitly taught. In a series of computational experiments, we highlight how information in the environment can be used to build and align conceptual systems. Unlike supervised learning, the learning problem becomes easier the more concepts and systems there are to master. The key insight is that each concept has a unique signature within one conceptual system (e.g., images) that is recapitulated in other systems (e.g., text or audio). As predicted, children's early concepts form readily aligned systems.
Impact of information technologies and social networks on knowledge management processes in Middle Eastern audit and consulting companies

Purpose The purpose of this study is to examine the impact of information technologies and technology-enabled social networks on the efficiency of knowledge management processes in the Middle Eastern audit and consulting companies. Design/methodology/approach Scientific literature analysis, structural equation modeling and expert evaluation (structured questionnaire) were used to develop the research model, collect data from the audit and consulting companies’ experts and test the research hypotheses. Findings The empirical results of this research supported the hypotheses stating that information technologies and social networks positively affect the knowledge management cycle, including five processes (acquisition, creation, storage, sharing and application) within the Middle Eastern audit and consulting companies. Research limitations/implications The research results were generated from the Middle Eastern audit and consulting companies, which form a limitation concerning the geographical area and the business sector. Practical implications From the results of this study, audit and consulting companies, as well as organizations and society broadly, would benefit via the positive effect of information technologies and technology-enabled social networks on the whole knowledge management cycle, which has a further impact on organizational performance. These practical implications are related to a more open, sharing culture that drives organizational performance to the members and stakeholders of organizations, which, in turn, benefits society. Originality/value This research analyzes information technologies and technology-enabled social networks’ impact on knowledge management processes, particularly in the context of the Middle Eastern audit and consulting companies. While the phenomena have received some attention in the prior scientific research, the studied context so far remained under-researched, where a gap is found in studying the knowledge management cycle as a whole.
Two-local qubit Hamiltonians: when are they stoquastic?

We examine the problem of determining if a 2-local Hamiltonian is stoquastic by local basis changes. We analyze this problem for two-qubit Hamiltonians, presenting some basic tools and giving a concrete example where using unitaries beyond Clifford rotations is required in order to decide stoquasticity. We report on simple results for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-qubit Hamiltonians with identical 2-local terms on bipartite graphs. Our most significant result is that we give an efficient algorithm to determine whether an arbitrary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-qubit XYZ Heisenberg Hamiltonian is stoquastic by local basis changes.
Adaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications

In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals. It is often challenging for the prior art of frequency tracking to i)achieve a high accuracy under noisy conditions, ii)detect and track multiple frequency components efficiently, or iii)strike a good trade-off of the processing delay versus the resilience and the accuracy of tracking. To address these issues, we propose Adaptive Multi-Trace Carving (AMTC), a unified approach for detecting and tracking one or more subtle frequency components under very low signal-to-noise ratio (SNR) conditions and in near real time. AMTC takes as input a time-frequency representation of the system's preprocessing results (such as the spectrogram), and identifies frequency components through iterative dynamic programming and adaptive trace compensation. The proposed algorithm considers relatively high energy traces sustaining over a certain duration as an indicator of the presence of frequency/oscillation components of interest and track their time-varying trend. Extensive experiments using both synthetic data and real-world forensic data of power signatures and physiological monitoring reveal that the proposed method outperforms representative prior art under low SNR conditions, and can be implemented in near real-time settings. The proposed AMTC algorithm can empower the development of new information forensic technologies that harness very small signals.
Diabolical points in coupled active cavities with quantum emitters

Abstract In single microdisks, embedded active emitters intrinsically affect the cavity modes of the microdisks, resulting in trivial symmetric backscattering and low controllability. Here we demonstrate macroscopic control of the backscattering direction by optimizing the cavity size. The signature of the positive and negative backscattering directions in each single microdisk is confirmed with two strongly coupled microdisks. Furthermore, diabolical points are achieved at the resonance of the two microdisks, which agrees well with theoretical calculations considering the backscattering directions. Diabolical points in active optical structures pave the way for an implementation of quantum information processing with geometric phase in quantum photonic networks.
k-stretchability of entanglement, and the duality of k-separability and k-producibility

The notions of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-separability and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-producibility are useful and expressive tools for the characterization of entanglement in multipartite quantum systems, when a more detailed analysis would be infeasible or simply needless. In this work we reveal a partial duality between them, which is valid also for their correlation counterparts. This duality can be seen from a much wider perspective, when we consider the entanglement and correlation properties which are invariant under the permutations of the subsystems. These properties are labeled by Young diagrams, which we endow with a refinement-like partial order, to build up their classification scheme. This general treatment reveals a new property, which we call <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-stretchability, being sensitive in a balanced way to both the maximal size of correlated (or entangled) subsystems and the minimal number of subsystems uncorrelated with (or separable from) one another.
Understanding Neural Abstractive Summarization Models via Uncertainty

An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior.In this work, we analyze summarization decoders in both blackbox and whitebox ways by studying on the entropy, or uncertainty, of the model's token-level predictions.For two strong pretrained models, PEGASUS (Zhang et al., 2020) and BART (Lewis et al., 2020) on two summarization datasets, we find a strong correlation between low prediction entropy and where the model copies tokens rather than generating novel text.The decoder's uncertainty also connects to factors like sentence position and syntactic distance between adjacent pairs of tokens, giving a sense of what factors make a context particularly selective for the model's next output token.Finally, we study the relationship of decoder uncertainty and attention behavior to understand how attention gives rise to these observed effects in the model.We show that uncertainty is a useful perspective for analyzing summarization and text generation models more broadly. 1
Statically verified refinements for multiparty protocols

With distributed computing becoming ubiquitous in the modern era, safe distributed programming is an open challenge. To address this, multiparty session types (MPST) provide a typing discipline for message-passing concurrency, guaranteeing communication safety properties such as deadlock freedom. While originally MPST focus on the communication aspects, and employ a simple typing system for communication payloads, communication protocols in the real world usually contain constraints on the payload. We introduce refined multiparty session types (RMPST), an extension of MPST, that express data dependent protocols via refinement types on the data types. We provide an implementation of RMPST, in a toolchain called Session*, using Scribble, a toolchain for multiparty protocols, and targeting F*, a verification-oriented functional programming language. Users can describe a protocol in Scribble and implement the endpoints in F* using refinement-typed APIs generated from the protocol. The F* compiler can then statically verify the refinements. Moreover, we use a novel approach of callback-styled API generation, providing static linearity guarantees with the inversion of control. We evaluate our approach with real world examples and show that it has little overhead compared to a naive implementation, while guaranteeing safety properties from the underlying theory.
Non-convergence of stochastic gradient descent in the training of deep neural networks

Deep neural networks have successfully been trained in various application areas with stochastic gradient descent. However, there exists no rigorous mathematical explanation why this works so well. The training of neural networks with stochastic gradient descent has four different discretization parameters: (i) the network architecture; (ii) the amount of training data; (iii) the number of gradient steps; and (iv) the number of randomly initialized gradient trajectories. While it can be shown that the approximation error converges to zero if all four parameters are sent to infinity in the right order, we demonstrate in this paper that stochastic gradient descent fails to converge for ReLU networks if their depth is much larger than their width and the number of random initializations does not increase to infinity fast enough.
Understanding virtual reality continuance: an extended perspective of perceived value

Purpose The purpose of this paper is to develop an integrated research model that can explain current users' continuance intention regarding the adoption of virtual reality (VR). Design/methodology/approach This study builds a new research model based on the perceived value theory. The model and the hypotheses formulated are tested using partial least squares analysis with 295 respondents. Findings Both utilitarian value and hedonic value have positive impacts on VR users' continuance intention. Among the four constructs reflecting VR device and service-related attributes, content quality and ease of use have significant impacts on the utilitarian value and hedonic value. Visual attractiveness is positively associated with hedonic value while portability affected only utilitarian value. Practical implications To increase users' retention of VR, VR service providers should focus developing high-quality VR content, and VR device manufacturers should consider both ease of use and visual attractiveness of VR devices. Originality/value This study is the first empirical study to investigate user adoption of VR devices and services synthetically, as most of prior studies related to VR adoption mainly focused on VR devices or specific services.
Iterative label cleaning for transductive and semi-supervised few-shot learning

Few-shot learning amounts to learning representations and acquiring knowledge such that novel tasks may be solved with both supervision and data being limited. Improved performance is possible by transductive inference, where the entire test set is available concurrently, and semi-supervised learning, where more unlabeled data is available.Focusing on these two settings, we introduce a new algorithm that leverages the manifold structure of the labeled and unlabeled data distribution to predict pseudo-labels, while balancing over classes and using the loss value distribution of a limited-capacity classifier to select the cleanest labels, iteratively improving the quality of pseudo-labels. Our solution surpasses or matches the state of the art results on four benchmark datasets, namely miniImageNet, tieredImageNet, CUB and CIFAR-FS, while being robust over feature space pre-processing and the quantity of available data. The publicly available source code can be found in https://github.com/MichalisLazarou/iLPC
Breaking symmetries of the reservoir equations in echo state networks

Reservoir computing has repeatedly been shown to be extremely successful in the prediction of nonlinear time-series. However, there is no complete understanding of the proper design of a reservoir yet. We find that the simplest popular setup has a harmful symmetry, which leads to the prediction of what we call mirror-attractor. We prove this analytically. Similar problems can arise in a general context, and we use them to explain the success or failure of some designs. The symmetry is a direct consequence of the hyperbolic tangent activation function. Furthermore, four ways to break the symmetry are compared numerically: A bias in the output, a shift in the input, a quadratic term in the readout, and a mixture of even and odd activation functions. First, we test their susceptibility to the mirror-attractor. Second, we evaluate their performance on the task of predicting Lorenz data with the mean shifted to zero. The short-time prediction is measured with the forecast horizon while the largest Lyapunov exponent and the correlation dimension are used to represent the climate. Finally, the same analysis is repeated on a combined dataset of the Lorenz attractor and the Halvorsen attractor, which we designed to reveal potential problems with symmetry. We find that all methods except the output bias are able to fully break the symmetry with input shift and quadratic readout performing the best overall.
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning

The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese word segmentation aims to capture various annotation criteria among datasets and leverage their common underlying knowledge. In this paper, we propose a domain adaptive segmenter to exploit diverse criteria of various datasets. Our model is based on Bidirectional Encoder Representations from Transformers (BERT), which is responsible for introducing open-domain knowledge. Private and shared projection layers are proposed to capture domain-specific knowledge and common knowledge, respectively. We also optimize computational efficiency via distillation, quantization, and compiler optimization. Experiments show that our segmenter outperforms the previous state of the art (SOTA) models on 10 CWS datasets with superior efficiency.
A2 Chain: A Blockchain-Based Decentralized Authentication Scheme for 5G-Enabled IoT

The fifth-generation mobile communication technology (5G) provides high-bandwidth and low-latency data channels for massive IoT terminals to access the core business network. At the same time, it also brings higher security threats and challenges. Terminal identity authentication is an important security mechanism to ensure the core business network; however, most of the existing solutions adopt a centralized authentication model. Once the number of authentication requests exceeds the processing capacity of the authentication center service, it will cause authentication request congestion or deadlock. The decentralized authentication model can effectively solve the above problems. This article proposes a decentralized IoT authentication scheme called A2 Chain. First, A2 Chain uses edge computing to decentralize the processing of authentication requests and eliminate the burden on authentication services and the network. Second, to implement cross-domain identity verification of IoT devices, A2 Chain uses blockchain, and sidechain technologies are used to securely share the identity verification information of IoT devices. Additionally, A2 Chain replaces public key infrastructure (PKI) algorithm with identity-based cryptography (IBC) algorithm to eliminate the management overhead caused by centralized authentication model.
MCNet: Multi-level Correction Network for thermal image semantic segmentation of nighttime driving scene

Current state-of-the-art image segmentation methods are mainly based on visible spectrum images. However, it remains challenging under adverse environmental conditions (e.g., nighttime, foggy, snowy). Thermal images can be captured in darkness and deployed in any weather and time. But the research focus on thermal images semantic segmentation remains underexploited since the lack of thermal images segmentation dataset of nighttime driving scene. On the other hand, existing methods do not adequately capture context between related pixels and edge details in thermal images. In this work, we focus on thermal image semantic segmentation of nighttime driving scene. First, we propose a multi-level correction network (MCNet) with a multi-level attention module (MAM) and a multi-level edge enhancement module (MEEM). Specifically, MAM selectively captures the inter-class and intra-class contextual dependencies by a multi-level correction process. In addition, MEEM continuously combines precise context information and edge prior knowledge in each level to correct the final feature representation. Second, we present a new dataset called SCUT-Seg which contains 2010 thermal images recorded from various road scenes with 10 manually annotated semantic region labels. Extensive experiments demonstrate that the proposed method performs favorably against the state-of-the-art methods on SCUT-Seg and the public MFNet dataset. The source code and dataset are available at https://github.com/haitaobiyao/MCNet.