Reviewerly/exHarmony huggingface dataset:
Superposition of two-mode squeezed states for quantum information processing and quantum sensing

We investigate superpositions of two-mode squeezed states (TMSSs), which have potential applications in quantum information processing and quantum sensing. We study some properties of these nonclassical states such as the statistics of each mode and the degree of entanglement between the two modes, which can be higher than that of a TMSS with the same degree of squeezing. The states we consider can be prepared by inducing two-mode Jaynes-Cummings and anti-Jaynes-Cummings interactions in a system of two modes and a spin-$\frac{1}{2}$ particle, for instance, in the trapped ion domain, as described here. We show that when two harmonic oscillators are prepared in a superposition of two TMSSs, each reduced single-mode state can be advantageously employed to sense arbitrary displacements of the mode in phase space. The Wigner function of this reduced state exhibits a symmetrical peak centered at the phase-space origin, which has the convenient peculiarity of getting narrower in both quadratures simultaneously as the average photon number increases. This narrow peak can be used as the pointer of our quantum sensor, with its position in phase space indicating the displacement undergone by the oscillator.
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data

Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. However, MTL must deal with challenges such as: overfitting to low resource tasks, catastrophic forgetting, and negative task transfer, or learning interference. Often, in Natural Language Processing (NLP), a separate model per task is needed to obtain the best performance. However, many fine-tuning approaches are both parameter inefficient, i.e., potentially involving one new model per task, and highly susceptible to losing knowledge acquired during pretraining. We propose a novel Transformer based Adapter consisting of a new conditional attention mechanism as well as a set of task-conditioned modules that facilitate weight sharing. Through this construction, we achieve more efficient parameter sharing and mitigate forgetting by keeping half of the weights of a pretrained model fixed. We also use a new multi-task data sampling strategy to mitigate the negative effects of data imbalance across tasks. Using this approach, we are able to surpass single task fine-tuning methods while being parameter and data efficient (using around 66% of the data). Compared to other BERT Large methods on GLUE, our 8-task model surpasses other Adapter methods by 2.8% and our 24-task model outperforms by 0.7-1.0% models that use MTL and single task fine-tuning. We show that a larger variant of our single multi-task model approach performs competitively across 26 NLP tasks and yields state-of-the-art results on a number of test and development sets.
The Risks of Invariant Risk Minimization

Invariant Causal Prediction (Peters et al., 2016) is a technique for out-of-distribution generalization which assumes that some aspects of the data distribution vary across the training set but that the underlying causal mechanisms remain constant. Recently, Arjovsky et al. (2019) proposed Invariant Risk Minimization (IRM), an objective based on this idea for learning deep, invariant features of data which are a complex function of latent variables; many alternatives have subsequently been suggested. However, formal guarantees for all of these works are severely lacking. In this paper, we present the first analysis of classification under the IRM objective—as well as these recently proposed alternatives—under a fairly natural and general model. In the linear case, we show simple conditions under which the optimal solution succeeds or, more often, fails to recover the optimal invariant predictor. We furthermore present the very first results in the non-linear regime: we demonstrate that IRM can fail catastrophically unless the test data is sufficiently similar to the training distribution—this is precisely the issue that it was intended to solve. Thus, in this setting we find that IRM and its alternatives fundamentally do not improve over standard Empirical Risk Minimization.
Dagstuhl seminar 19461 on conversational search

In the week of November 10--15, 2019, 44 researchers from the fields of information retrieval and Web search, natural language processing, human computer interaction, and dialogue systems met for the Dagstuhl Seminar 19461 "Conversational Search" to share the latest development in the area of conversational search and discuss its research agenda and future directions. The clear signal from the seminar is that research opportunities to advance conversational search are available to many areas and that collaboration in an interdisciplinary community is essential to achieve the goals. This report overviews the program and selected findings of the working groups.
Deep Compositional Spatial Models

Spatial processes with nonstationary and anisotropic covariance structure are often used when modeling, analyzing, and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and isotropic covariance structure on a warped spatial domain. However, the warping function is generally difficult to fit and not constrained to be injective, often resulting in “space-folding.” Here, we propose modeling an injective warping function through a composition of multiple elemental injective functions in a deep-learning framework. We consider two cases; first, when these functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. Inspired by recent methodological and technological advances in deep learning and deep Gaussian processes, we employ approximate Bayesian methods to make inference with these models using graphics processing units. Through simulation studies in one and two dimensions we show that the deep compositional spatial models are quick to fit, and are able to provide better predictions and uncertainty quantification than other deep stochastic models of similar complexity. We also show their remarkable capacity to model nonstationary, anisotropic spatial data using radiances from the MODIS instrument aboard the Aqua satellite.
Catala: a programming language for the law

Law at large underpins modern society, codifying and governing many aspects of citizens' daily lives. Oftentimes, law is subject to interpretation, debate and challenges throughout various courts and jurisdictions. But in some other areas, law leaves little room for interpretation, and essentially aims to rigorously describe a computation, a decision procedure or, simply said, an algorithm. Unfortunately, prose remains a woefully inadequate tool for the job. The lack of formalism leaves room for ambiguities; the structure of legal statutes, with many paragraphs and sub-sections spread across multiple pages, makes it hard to compute the intended outcome of the algorithm underlying a given text; and, as with any other piece of poorly-specified critical software, the use of informal language leaves corner cases unaddressed. We introduce Catala, a new programming language that we specifically designed to allow a straightforward and systematic translation of statutory law into an executable implementation. Catala aims to bring together lawyers and programmers through a shared medium, which together they can understand, edit and evolve, bridging a gap that often results in dramatically incorrect implementations of the law. We have implemented a compiler for Catala, and have proven the correctness of its core compilation steps using the F* proof assistant. We evaluate Catala on several legal texts that are algorithms in disguise, notably section 121 of the US federal income tax and the byzantine French family benefits; in doing so, we uncover a bug in the official implementation. We observe as a consequence of the formalization process that using Catala enables rich interactions between lawyers and programmers, leading to a greater understanding of the original legislative intent, while producing a correct-by-construction executable specification reusable by the greater software ecosystem.
Measure-Resend Semi-Quantum Private Comparison Scheme燯sing GHZ Class States

Quantum private comparison is an important topic in quantum cryptography. Recently, the idea of semi-quantumness has been often used in designing private comparison protocol, which allows some of the participants to remain classical. In this paper, we propose a semi quantum private comparison scheme based on Greenberge-Horne-Zeilinger (GHZ) class states, which allows two classical participants to compare the equality of their private secret with the help of a quantum third party (server). In the proposed protocol, server is semi-honest who will follow the protocol honestly, but he may try to learn additional information from the protocol execution. The classical participants’ activities are restricted to either measuring a quantum state or reflecting it in the classical basis{0,1}. In addition, security and efficiency of the proposed schemes have been discussed.
An efficient hybrid system for anomaly detection in social networks

Abstract Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.
COVID-19 Pandemic: Threat or Opportunity for Blind and Partially Sighted Museum Visitors?

The Coronavirus (COVID-19) pandemic is having a severe impact on museums and the cultural sector. New social distancing rules, one-way navigation systems, and hand sanitising regulations are affecting the embodied practice of visitors inside the museum. These changes potentially pose a threat to the experience of disabled people, in particular blind and partially sighted visitors, as they create new barriers to access the environment and the collection. On the other hand, the development of accessible digital content and access to online collections offered a positive experience during the lockdown periods, as disabled people could socialise and participate in cultural activities from home.Museums are now called upon to identify the long-term positive and negative effects of the pandemic on the physical and digital museum experience of disabled visitors. Museum professionals need to work around the clock to ensure that new embodied and digital practices become long-term opportunities to enhance accessibility and inclusion, rather than another insurmountable barrier for disabled people.
Measuring DNS over TLS from the Edge: Adoption, Reliability, and Response Times

The Domain Name System (DNS) is a cornerstone of communication on the Internet. DNS over TLS (DoT) has been standardized in 2016 as an extension to the DNS protocol, however, its performance has not been extensively studied yet. In the first study that measures DoT from the edge, we leverage 3.2k RIPE Atlas probes deployed in home networks to assess the adoption, reliability, and response times of DoT in comparison with DNS over UDP/53 (Do53). Each probe issues 200 domain name lookups to 15 public resolvers, five of which support DoT, and to the probes’ local resolvers over a period of one week, resulting in 90M DNS measurements in total. We find that the support for DoT among open resolvers has increased by 23.1% after nine months in comparison with previous studies. However, we observe that DoT is still only supported by local resolvers for 0.4% of the RIPE Atlas probes. In terms of reliability, we find failure rates for DoT to be inflated by 0.4–32.2% points (p.p.) when compared to Do53. While Do53 failure rates for most resolvers individually are consistent across continents, DoT failure rates have much higher variation. As for response times, we see high regional differences for DoT and find that nearly all DoT requests take at least 100 ms to return a response (in a large part due to connection and session establishment), showing an inflation in response times of more than 100 ms compared to Do53. Despite the low adoption of DoT among local resolvers, they achieve DoT response times of around 140–150 ms similar to public resolvers (130–230 ms), although local resolvers also exhibit higher failure rates in comparison.
Overview of BioASQ 2020: The Eighth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

In this paper, we present an overview of the eighth edition of the BioASQ challenge, which ran as a lab in the Conference and Labs of the Evaluation Forum (CLEF) 2020. BioASQ is a series of challenges aiming at the promotion of systems and methodologies for large-scale biomedical semantic indexing and question answering. To this end, shared tasks are organized yearly since 2012, where different teams develop systems that compete on the same demanding benchmark datasets that represent the real information needs of experts in the biomedical domain. This year, the challenge has been extended with the introduction of a new task on medical semantic indexing in Spanish. In total, 34 teams with more than 100 systems participated in the three tasks of the challenge. As in previous years, the results of the evaluation reveal that the top-performing systems managed to outperform the strong baselines, which suggests that state-of-the-art systems keep pushing the frontier of research through continuous improvements.
Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization

The Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are stochastic optimization techniques. The PSO is inspired by the simulated behavior of bird flocking whereas GSA is the physics-based heuristic technique that is inspired by the law of mass interaction. In this paper, a new hybridization algorithm CPSOGSA i.e. constriction coefficient based particle swarm optimization and gravitational search algorithm have been proposed. It combines the exploitation and exploration capabilities of PSO and GSA, respectively in order to obtain the best result. The experimental results on 23 standard unimodal and multimodal test functions confirm the better performance of CPSOGSA as compared with classical Particle swarm optimization and Gravitational search algorithm. The efficiency of the hybrid CPSOGSA has been demonstrated through faster intensification rate and avoidance from local minima.
Assembling Accountability: Algorithmic Impact Assessment for the Public Interest

The Algorithmic Impact Assessment is a new concept for regulating algorithmic systems and protecting the public interest. Assembling Accountability: Algorithmic Impact Assessment for the Public Interest is a report that maps the challenges of constructing algorithmic impact assessments (AIAs) and provides a framework for evaluating the effectiveness of current and proposed AIA regimes. This framework is a practical tool for regulators, advocates, public-interest technologists, technology companies, and critical scholars who are identifying, assessing, and acting upon algorithmic harms.First, report authors Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, Madeleine Clare Elish, and Jacob Metcalf analyze the use of impact assessment in other domains, including finance, the environment, human rights, and privacy. Building on this comparative analysis, they then identify common components of existing impact assessment practices in order to provide a framework for evaluating current and proposed AIA regimes. The authors find that a singular, generalized model for AIAs would not be effective due to the variances of governing bodies, specific systems being evaluated, and the range of impacted communities.After illustrating the novel decision points required for the development of effective AIAs, the report specifies ten necessary components that constitute robust impact assessment regimes.
Sky Image-Based Localized, Short-Term Solar Irradiance Forecasting for Multiple PV Sites via Cloud Motion Tracking

Power generation through solar photovoltaics has shown significant growth in recent years. However, high penetration of solar PV creates power system operational issues as a result of solar PV variability and uncertainty. Short-term PV variability mainly occurs due to the intermittency of cloud cover. Therefore, to mitigate the effects of PV variability, a sky-image-based, localized, global horizontal irradiance forecasting model was introduced considering the individual cloud motion, cloud thicknesses, and the elevations of clouds above the ground level. The proposed forecasting model works independently of any historical irradiance measurements. Two inexpensive sky camera systems were developed and placed in two different locations to obtain sky images for cloud tracking and cloud-based heights. Then, irradiance values for onsite and for a PV site located with a distance of 2 km from the main camera were forecasted for 1 minute, 5 minutes, and 15 minutes ahead of real-time. Results show that the three-level cloud categorization and the individual cloud movement tracking method introduced in this paper increase the forecasting accuracy. For partially cloudy and sunny days, the forecasting model for 15 min forecasting time interval achieved a positive skill factor concerning the persistent model. The accuracy of determining the correct irradiance state for a 1 min forecasting time interval using the proposed model is 81%. The average measures of RMSE, MAE, and SF obtained using the proposed method for 15 min forecasting time horizon are 101 Wm-2, 64 Wm-2, and 0.26, respectively. These forecasting accuracy levels are much higher than the other benchmarks considered in this paper.
Transaction fee mechanism design

Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include "on-chain" from the pool of all pending transactions. EIP-1559 is a proposal to make several tightly coupled changes to the Ethereum blockchain's transaction fee mechanism, including the introduction of variable-size blocks and a burned base fee that rises and falls with demand. These changes are slated for deployment in Ethereum's "London fork," scheduled for late summer 2021, at which point it will be the biggest economic change made to a major blockchain to date. This short note provides an overview of recent work by the author that formally investigates and compares the incentive guarantees offered by Ethereum's current transaction fee mechanism and the new mechanism proposed in EIP-1559.
An Integrated Deep Learning and Belief Rule-Based Expert System for Visual Sentiment Analysis under Uncertainty

Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual.
Spatial and temporal learning representation for end-to-end recording device identification

Abstract Deep learning techniques have achieved specific results in recording device source identification. The recording device source features include spatial information and certain temporal information. However, most recording device source identification methods based on deep learning only use spatial representation learning from recording device source features, which cannot make full use of recording device source information. Therefore, in this paper, to fully explore the spatial information and temporal information of recording device source, we propose a new method for recording device source identification based on the fusion of spatial feature information and temporal feature information by using an end-to-end framework. From a feature perspective, we designed two kinds of networks to extract recording device source spatial and temporal information. Afterward, we use the attention mechanism to adaptively assign the weight of spatial information and temporal information to obtain fusion features. From a model perspective, our model uses an end-to-end framework to learn the deep representation from spatial feature and temporal feature and train using deep and shallow loss to joint optimize our network. This method is compared with our previous work and baseline system. The results show that the proposed method is better than our previous work and baseline system under general conditions.