Reviewerly/exHarmony huggingface dataset:
Theory and Design of Joint Time-Vertex Nonsubsampled Filter Banks

Graph signal processing (GSP) is a field that deals with data residing on irregular domains, i.e. graph signals. In this field, the graph filter bank is one of the most important developments, owing to its ability to provide multiresolution analysis of graph signals. However, most of the current research on graph filter bank focuses on static graph signals. The research does not exploit the temporal correlations of time-varying signals in real-world applications, such as in wireless sensor networks. In this paper, the theory and design of joint time-vertex nonsubsampled filter bank are developed, using a generalized product graph framework. Several methods are proposed to design the filter bank with perfect reconstruction, while still achieving filters with good spectral characteristics. A notable feature of the designed filter bank is that it can be completely realized in a distributed manner. The subband filters are either of polynomial type or defined implicitly via iterative equations. In either case, implementing the subband filters requires only the exchange of information between neighboring nodes. The filter banks are therefore of low implementation complexity and suitable for processing large time-varying datasets. Numerical examples will demonstrate the effectiveness of the proposed designed methods. Application in time-varying graph signal denoising will show the superiority of joint time-vertex filter bank over other methods.
Leveraging Automated Mixed-Low-Precision Quantization for Tiny Edge Microcontrollers

The severe on-chip memory limitations are currently preventing the deployment of the most accurate Deep Neural Network (DNN) models on tiny MicroController Units (MCUs), even if leveraging an effective 8-bit quantization scheme. To tackle this issue, in this paper we present an automated mixed-precision quantization flow based on the HAQ framework but tailored for the memory and computational characteristics of MCU devices. Specifically, a Reinforcement Learning agent searches for the best uniform quantization levels, among 2, 4, 8 bits, of individual weight and activation tensors, under the tight constraints on RAM and FLASH embedded memory sizes. We conduct an experimental analysis on MobileNetV1, MobileNetV2 and MNasNet models for Imagenet classification. Concerning the quantization policy search, the RL agent selects quantization policies that maximize the memory utilization. Given an MCU-class memory bound of 2 MB for weight-only quantization, the compressed models produced by the mixed-precision engine result as accurate as the state-of-the-art solutions quantized with a non-uniform function, which is not tailored for CPUs featuring integer-only arithmetic. This denotes the viability of uniform quantization, required for MCU deployments, for deep weights compression. When also limiting the activation memory budget to 512 kB, the best MobileNetV1 model scores up to 68.4% on Imagenet thanks to the found quantization policy, resulting to be 4% more accurate than the other 8-bit networks fitting the same memory constraints.
Speeding up quantum perceptron via shortcuts to adiabaticity

Abstract The quantum perceptron is a fundamental building block for quantum machine learning. This is a multidisciplinary field that incorporates abilities of quantum computing, such as state superposition and entanglement, to classical machine learning schemes. Motivated by the techniques of shortcuts to adiabaticity, we propose a speed-up quantum perceptron where a control field on the perceptron is inversely engineered leading to a rapid nonlinear response with a sigmoid activation function. This results in faster overall perceptron performance compared to quasi-adiabatic protocols, as well as in enhanced robustness against imperfections in the controls.
Strategies to manage quality requirements in agile software development: a multiple case study

Abstract Agile methods can deliver software that fulfills customer needs rapidly and continuously. Quality requirements (QRs) are important in this regard; however, detailed studies on how companies applying agile methods to manage QRs are limited, as are studies on the rationale for choosing specific QR management practices and related challenges. The aim of this study was to address why practitioners manage QRs as they do and what challenges they face. We also analyzed how existing practices mitigate some of the found challenges. Lastly, we connect the contextual elements of the companies with their practices and challenges. We conducted 36 interviews with practitioners from four companies of varying sizes. Since each company operates in different domains, comparing QR management strategies and related challenges in different contexts was possible. We found that the companies apply proactive, reactive, and interactive strategies to manage QRs. Additionally, our study revealed 40 challenges in six categories that companies applying agile methods may face in QR management. We also identified nine contextual elements that affect QR management practice choices and which, importantly, can explain many related challenges. Based on these findings, we constructed a theoretical model about the connection between context, QR management practices, and challenges. Practitioners in similar contexts can learn from the practices identified in this study. Our preliminary theoretical model can help other practitioners identify what challenges they can expect to face in QR management in different developmental contexts as well as which practices to apply to mitigate these challenges.
The Scams Among Us: Who Falls Prey and Why

Not a week goes by without stories about scams appearing in popular media outlets. Given the ease with which scams can be circulated, they have become one of the most common crimes globally, inflicting high emotional, financial, and psychological tolls on millions of individuals. Despite their profound and pervasive impact, researchers know relatively little about why some individuals fall victim to scams but others remain immune to the techniques utilized by scammers to lure potential victims. For example, research thus far provides mixed results about the impact of demographic characteristics (e.g., age) as well as personality variables (e.g., risk taking) on individuals’ susceptibility to scams. Even less is known about how the nature or type of scam affects an individual’s susceptibility. Gaining a deeper understanding of these issues is the key to being able to develop preventive programs and reduce the prevalence of victimization. Here, we discuss some promising directions, existing gaps in current knowledge, and the need for decision scientists to address this important problem.
Massively-multiplexed generation of Bell-type entanglement using a quantum memory

Abstract High-rate generation of hybrid photon-matter entanglement remains a fundamental building block of quantum network architectures enabling protocols such as quantum secure communication or quantum distributed computing. While a tremendous effort has been made to overcome technological constraints limiting the efficiency and coherence times of current systems, an important complementary approach is to employ parallel and multiplexed architectures. Here we follow this approach experimentally demonstrating the generation of bipartite polarization-entangled photonic states across more than 500 modes, with a programmable delay for the second photon enabled by qubit storage in a wavevector-multiplexed cold-atomic quantum memory. We demonstrate Clauser, Horne, Shimony, Holt inequality violation by over 3 standard deviations, lasting for at least 45 μ s storage time for half of the modes. The ability to shape hybrid entanglement between the polarization and wavevector degrees of freedom provides not only multiplexing capabilities but also brings prospects for novel protocols.
Recontextualising the news: How antisemitic discourses are constructed in extreme far-right alternative media

Abstract This study explores how an extreme far-right alternative media site uses content from professional media to convey uncivil news with an antisemitic message. Analytically, it rests on a critical discourse analysis of 231 news items, originating from established national and international news sources, published on Frihetskamp from 2011–2018. In the study, we explore how news items are recontextualised to portray both overt and covert antisemitic discourses, and we identify four antisemitic representations that are reinforced through the selection and adjustment of news: Jews as powerful, as intolerant and anti-liberal, as exploiters of victimhood, and as inferior. These conspiratorial and exclusionary ideas, also known from historical Nazi propaganda, are thus reproduced by linking them to contemporary societal and political contexts and the current news agenda. We argue that this kind of recontextualised, uncivil news can be difficult to detect in a digital public sphere.
A Formal Analysis of the FIDO UAF Protocol

The FIDO protocol suite aims at allowing users to log in to remote services with a local and trusted authenticator.With FIDO, relying services do not need to store user-chosen secrets or their hashes, which eliminates a major attack surface for e-business.Given its increasing popularity, it is imperative to formally analyze whether the security promises of FIDO hold.In this paper, we present a comprehensive and formal verification of the FIDO UAF protocol by formalizing its security assumptions and goals and modeling the protocol under different scenarios in ProVerif.Our analysis identifies the minimal security assumptions required for each of the security goals of FIDO UAF to hold.We confirm previously manually discovered vulnerabilities in an automated way and disclose several new attacks.Guided by the formal verification results we also discovered 2 practical attacks on 2 popular Android FIDO apps, which we responsibly disclosed to the vendors.In addition, we offer several concrete recommendations to fix the identified problems and weaknesses in the protocol.
Active Learning for Entity Alignment

In this work, we propose a novel framework for labeling entity alignments in knowledge graph datasets. Different strategies to select informative instances for the human labeler build the core of our framework. We illustrate how the labeling of entity alignments is different from assigning class labels to single instances and how these differences affect the labeling efficiency. Based on these considerations, we propose and evaluate different active and passive learning strategies. One of our main findings is that passive learning approaches, which can be efficiently precomputed, and deployed more easily, achieve performance comparable to the active learning strategies. In the spirit of reproducible research, we make our code available at https://github.com/mberr/ea_active_learning .
Quantum simulation of Cayley-tree Ising Hamiltonians with three-dimensional Rydberg atoms

Significant efforts are being directed toward developing a quantum simulator capable of solving combinatorial optimization problems. The challenges are Hamiltonian programming in terms of high-dimensional qubit connectivities and large-scale implementations. Here, we report a quantum simulation demonstration of Ising Hamiltonians with up to N=22 spins mapped on various Cayley-tree graphs. For this, we use three-dimensional arrangements of Rydberg single atoms arranged in such a way that their Rydberg atoms and blockaded strong couplings respectively represent the vertices and edges of each graph. Three different Cayley-tree graphs of Z=3 neighbors and of up to S=4 shells are constructed, and their many-body ground states and Néel's order formations are experimentally probed. The antiferromagnetic phase in regular Cayley trees and frustrated competing ground states in a dual-center Cayley tree are directly observed, demonstrating the possibilities of high-dimensional qubit connections in quantum simulators.Received 17 November 2020Accepted 17 March 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013286Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum controlQuantum simulationPhysical SystemsRydberg atoms & moleculesQuantum Information
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration

Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities.We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel admission to discharge task with four common outcome prediction targets: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction.The ideal system should infer outcomes based on symptoms, pre-conditions and risk factors of a patient.We evaluate the effectiveness of language models to handle this scenario and propose clinical outcome pretraining to integrate knowledge about patient outcomes from multiple public sources.We further present a simple method to incorporate ICD code hierarchy into the models.We show that our approach improves performance on the outcome tasks against several baselines.A detailed analysis reveals further strengths of the model, including transferability, but also weaknesses such as handling of vital values and inconsistencies in the underlying data.PRESENT ILLNESS: 58yo man w/ hx of hypertension, AFib on coumadin and NIDDM presented to ED with the worst headache of his life.He had a syncopal episode and was intubated by EMS.Medication on admission: 1mg IV ativan x 1.
How Certain is Your Transformer?

In this work, we consider the problem of uncertainty estimation for Transformer-based models.We investigate the applicability of uncertainty estimates based on dropout usage at the inference stage (Monte Carlo dropout).The series of experiments on natural language understanding tasks shows that the resulting uncertainty estimates improve the quality of detection of error-prone instances.Special attention is paid to the construction of computationally inexpensive estimates via Monte Carlo dropout and Determinantal Point Processes.
Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs

The rise of personal assistants has made conversational question answering (ConvQA) a very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA over knowledge graphs (KGs) can only learn from crisp question-answer pairs found in popular benchmarks. In reality, however, such training data is hard to come by: users would rarely mark answers explicitly as correct or wrong. In this work, we take a step towards a more natural learning paradigm - from noisy and implicit feedback via question reformulations. A reformulation is likely to be triggered by an incorrect system response, whereas a new follow-up question could be a positive signal on the previous turn's answer. We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations. CONQUER models the answering process as multiple agents walking in parallel on the KG, where the walks are determined by actions sampled using a policy network. This policy network takes the question along with the conversational context as inputs and is trained via noisy rewards obtained from the reformulation likelihood. To evaluate CONQUER, we create and release ConvRef, a benchmark with about 11k natural conversations containing around 205k reformulations. Experiments show that CONQUER successfully learns from noisy reward signals, significantly improving over a state-of-the-art baseline.
Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer

In this paper, it is proposed to understand the opinion of the public regarding the policy of demonetization that is implemented recently in India through Aspect-based Sentiment Analysis (ABSA) that predicts the sentiment of specific aspects present in the text. The major aim is to identify the relevant contexts for various aspects. Most of the conventional techniques have adopted attention mechanisms and deep learning concepts that decrease the prediction accuracy and generate huge noise. Another major disadvantage with the attention mechanisms is that the sentiment related to few context words alters with various aspects, and hence it cannot be concluded from itself alone. This paper adopts the optimized deep learning concept for performing the ABSA for demonetization tweets. The proposed model involves various phases such as pre-processing, aspect extraction, polarity feature extraction, and sentiment classification. Initially, the different demonetization tweets collected from the Kaggle dataset are taken. Pre-processing is done with the help of four phases like stop words removal, punctuation removal, lower case conversion, and stemming from minimizing the data to its reduced format. This pre-processed data is further performed with aspect extraction to extract the opinion words. These extracted aspect words are converted to the features with the help of polarity score computation and Word2vec. The weight of the polarity scores is optimized using hybridization of two meta-heuristic algorithms like FireFly Algorithm (FF), and Multi-Verse Optimization (MVO), and the new algorithm is termed as Fire Fly-oriented Multi-Verse Optimizer (FF-MVO). Further, combined features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). As a modification to the existing RNN, the hidden neurons are optimized by the hybrid FF-MVO, FF-MVO-RNN classifies the positive and negative sentiments. Finally, the comparative analysis of different machine learning algorithms proves the competent performance of the proposed model.
Conservative culture, innovative culture, and innovative performance: a multi-group analysis of the moderating role of the job type

Purpose The purpose of this paper is to identify the effect of organisational culture by its two dimensions (innovative organisational culture and conservative organisational culture) on innovative performance and also investigate the moderating role of the job type on innovative performance in the relationship between innovative organisational culture and conservative organisational culture. Design/methodology/approach For this study’s purposes, 321 questionnaires from employees of Jordanian private universities were analysed. Convergent validity and discriminant validity tests were performed. Data reliability was confirmed. A bootstrapping technique was used to analyse the data. The multi-group analysis was performed to investigate the moderating role of job type. Findings The results of this study indicate that the innovative organisational culture will improve innovative performance and that a conservative culture reduces innovation opportunities. Moreover, innovative organisational culture promotes innovative performance for all employees regardless of who holds administrative positions. Research limitations/implications This cross-sectional study provides a snapshot at a given moment in time, a methodological limitation that affects the generalisation of its results. Moreover, this study adopted subjective measures and the results are limited to one country, Jordan. Practical implications This paper contributes to drawing the attention of senior management to the importance of innovation culture in the performance of innovation at Jordanian private universities surveyed, in particular, and other organisations in general. Social implications The social impact of this study is to respond to the challenge of building an innovation-based culture and to limit the effects of a conservative culture that limits the response to innovation. Originality/value This study has important implications for leaders in general. It also highlights the need for organisations to develop an orientation towards innovative organisational culture instead of the classic approach based on the conservative culture of all its members (both administrative and non-administrative employees) and to invest in training that supports this trend, thus increasing their innovative performance, which contributes to raising their capabilities to excel, compete and achieve high levels of performance.
Application of Semi-Empirical Models Based on Satellite Images for Estimating Solar Irradiance in Korea

The application of solar energy as a renewable energy source has significantly escalated owing to its abundance and availability worldwide. However, the intermittent behavior of solar irradiance is a serious disadvantage for electricity grids using photovoltaic (PV) systems. Thus, reliable solar irradiance data are vital to achieve consistent energy production. Geostationary satellite images have become a solution to this issue, as they represent a database for solar irradiance on a massive spatiotemporal scale. The estimation of global horizontal irradiance (GHI) using satellite images has been developed based on physical and semi-empirical models, but only a few studies have been dedicated to modeling GHI using semi-empirical models in Korea. Therefore, this study conducted a comparative analysis to determine the most suitable semi-empirical model of GHI in Korea. Considering their applicability, the Beyer, Rigollier, Hammer, and Perez, models were selected to estimate the GHI over Seoul, Korea. After a comparative evaluation, the Hammer model was determined to be the best model. This study also introduced a hybrid model and applied a long short-term memory (LSTM) model in order to improve prediction accuracy. The hybrid model exhibited a smaller root-mean-square error (RMSE), 97.08 W/m2, than that of the Hammer model, 103.92 W/m2, while producing a comparable mean-bias error. Meanwhile, the LSTM model showed the potential to further reduce the RMSE by 11.2%, compared to the hybrid model.