Reviewerly/exHarmony huggingface dataset:
Principle-Driven Fiber Transmission Model Based on PINN Neural Network

In this paper, a novel principle-driven fiber transmission model based on physical induced neural network (PINN) is proposed. Unlike data-driven models which regard fiber transmission problem as data regression tasks, this model views it as an equation solving problem. Instead of adopting input signals and output signals which are calculated by SSFM algorithm in advance before training, this principle-driven PINN based fiber model adopts frames of time and distance as its inputs and the corresponding real and imaginary parts of NLSE solutions as its outputs. By taking into account of pulses and signals before transmission as initial conditions and fiber physical principles as NLSE in the design of loss functions, this model will progressively learn the transmission rules. Therefore, it can be effectively trained without the data labels, referred as the pre-calculated signals after transmission in data-driven models. Due to this advantage, SSFM algorithm is no longer needed before the training of principle-driven fiber model which can save considerable time consumption. Through numerical demonstration, the results show that this principle-driven PINN based fiber model can handle the prediction tasks of pulse evolution, signal transmission and fiber birefringence for different transmission parameters of fiber telecommunications.
Fundamentals of Neural Networks

The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs). Neural networks are an effort to use the human brain's information processing skills to address challenging real-world AI issues. The evolution of neural networks and their significance are briefly explored. ANNs and BNNs are contrasted, and their qualities, benefits, and disadvantages are discussed. The drawbacks of the perceptron model and their improvement by the sigmoid neuron and ReLU neuron are briefly discussed. In addition, we give a bird's-eye view of the different Neural Network models. We study neural networks (NNs) and highlight the different learning approaches and algorithms used in Machine Learning and Deep Learning. We also discuss different types of NNs and their applications. A brief introduction to Neuro-Fuzzy and its applications with a comprehensive review of NN technological advances is provided.
The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification

Abstract In order to simplify sentences, several rewriting operations can be performed, such as replacing complex words per simpler synonyms, deleting unnecessary information, and splitting long sentences. Despite this multi-operation nature, evaluation of automatic simplification systems relies on metrics that moderately correlate with human judgments on the simplicity achieved by executing specific operations (e.g., simplicity gain based on lexical replacements). In this article, we investigate how well existing metrics can assess sentence-level simplifications where multiple operations may have been applied and which, therefore, require more general simplicity judgments. For that, we first collect a new and more reliable data set for evaluating the correlation of metrics and human judgments of overall simplicity. Second, we conduct the first meta-evaluation of automatic metrics in Text Simplification, using our new data set (and other existing data) to analyze the variation of the correlation between metrics’ scores and human judgments across three dimensions: the perceived simplicity level, the system type, and the set of references used for computation. We show that these three aspects affect the correlations and, in particular, highlight the limitations of commonly used operation-specific metrics. Finally, based on our findings, we propose a set of recommendations for automatic evaluation of multi-operation simplifications, suggesting which metrics to compute and how to interpret their scores.
Learn to synchronize, synchronize to learn

In recent years, the artificial intelligence community has seen a continuous interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks, we have the Reservoir Computing (RC) paradigm characterized by conceptual simplicity and a fast training scheme. Yet, the guiding principles under which RC operates are only partially understood. In this work, we analyze the role played by Generalized Synchronization (GS) when training a RC to solve a generic task. In particular, we show how GS allows the reservoir to correctly encode the system generating the input signal into its dynamics. We also discuss necessary and sufficient conditions for the learning to be feasible in this approach. Moreover, we explore the role that ergodicity plays in this process, showing how its presence allows the learning outcome to apply to multiple input trajectories. Finally, we show that satisfaction of the GS can be measured by means of the mutual false nearest neighbors index, which makes effective to practitioners theoretical derivations.
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

Forecasting the dynamics of large, complex, sparse networks from previous time series data is important in a wide range of contexts. Here we present a machine learning scheme for this task using a parallel architecture that mimics the topology of the network of interest. We demonstrate the utility and scalability of our method implemented using reservoir computing on a chaotic network of oscillators. Two levels of prior knowledge are considered: (i) the network links are known, and (ii) the network links are unknown and inferred via a data-driven approach to approximately optimize prediction.
Stock market reactions to favorable and unfavorable information security events: A systematic literature review

The rapid digital transformations across every industry sector, accelerated partly due to the COVID-19 pandemic, have increased organizations’ use of information systems for operational and strategic purposes. These organizational responses have led to a confluence of digital, biological, and physical technologies that are revolutionizing business practices and workflows. But accompanying the pervasive use of digital technologies and the evolutionary nature of digital assets, is a shifting world of cyberattacks and information security (ISec) cybercrimes. Dynamic cybercrimes make it increasingly difficult for managers and researchers to anticipate the types, magnitude, and severity of future information security (ISec) breaches. Thus, we perform a systematic literature review (SLR) that explores, gathers, and categorizes event studies to examine the influence of favorable and unfavorable ISec events on stock markets. We extend the research conducted by Spanos and Angelis (2016) and provide a comprehensive understanding of the market’s efficiency to process public information released about ISec events, ISec contingency factors, and the influence of ISec events on stock prices and factors other than price. Our systematic search reveals 58 relevant papers that include 80 studies. We find that in 75% of the studies ISec events can significantly affect a company’s stock market performance, and that such effects are primarily exhibited within two days before and after the event day. Further, the magnitude of abnormal returns is higher in studies examining unfavorable ISec events, such as ISec breaches, compared to abnormal returns from favorable events, such as ISec investments and ISec certifications. In the end, our SLR serves as a foundation for ISec and management communities to build upon to keep industry and academia apprised of continually developing trends, new attack vectors and types of data breaches, protective ISec behaviors and programs, and their subsequent influences on stock market values and returns.
Reframing Instructional Prompts to GPTk’s Language

What kinds of instructional prompts are easier to follow for Language Models (LMs)? We study this question by conducting extensive empirical analysis that shed light on important features of successful instructional prompts. Specifically, we study several classes of reframing techniques for manual reformulation of prompts into more effective ones. Some examples include decomposing a complex task instruction into multiple simpler tasks or itemizing instructions into sequential steps. Our experiments compare the zero-shot and few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks across 6 categories. Compared with original instructions, our reframed instructions lead to significant improvements across LMs with different sizes. For example, the same reframed prompts boost few-shot performance of GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all tasks. Furthermore, reframed instructions reduce the number of examples required to prompt LMs in the few-shot setting. We hope these empirically-driven techniques will pave the way towards more effective future prompting algorithms.
Document-level Entity-based Extraction as Template Generation

Document-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains.Most document-level EE systems build extractive models, which struggle to model long-term dependencies among entities at the document level.To address this issue, we propose a generative framework for two document-level EE tasks: role-filler entity extraction (REE) and relation extraction (RE).We first formulate them as a template generation problem, allowing models to efficiently capture crossentity dependencies, exploit label semantics, and avoid the exponential computation complexity of identifying N-ary relations.A novel cross-attention guided copy mechanism, TOPK COPY, is incorporated into a pre-trained sequence-to-sequence model to enhance the capabilities of identifying key information in the input document.Experiments done on the MUC-4 and SCIREX dataset show new stateof-the-art results on REE (+3.26%), binary RE (+4.8%), and 4-ary RE (+2.7%) in F1 score 1 .
PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams

As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptive IoT anomaly detection through IoT data stream analytics. Experiments on two public datasets show the effectiveness of our proposed PWPAE method compared against state-of-the-art methods.
A Strong Baseline for Query Efficient Attacks in a Black Box Setting

Existing black box search methods have achieved high success rate in generating adversarial attacks against NLP models.However, such search methods are inefficient as they do not consider the amount of queries required to generate adversarial attacks.Also, prior attacks do not maintain a consistent search space while comparing different search methods.In this paper, we propose a query efficient attack strategy to generate plausible adversarial examples on text classification and entailment tasks.Our attack jointly leverages attention mechanism and locality sensitive hashing (LSH) to reduce the query count.We demonstrate the efficacy of our approach by comparing our attack with four baselines across three different search spaces.Further, we benchmark our results across the same search space used in prior attacks.In comparison to attacks proposed, on an average, we are able to reduce the query count by 75% across all datasets and target models.We also demonstrate that our attack achieves a higher success rate when compared to prior attacks in a limited query setting.
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes

Empathy is a complex cognitive ability based on the reasoning of others' affective states.In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation.However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding.Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label.Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation.Our method is applicable to any dialogue models with no additional training on the fly.We show our approach improves multiple best performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.
Forging Source

This chapter presents the observation of computer programmers along three axes with clear relevance to craft: materiality, aesthetics, and embodiment. It explains the details of the materiality of “plain text” and source code, the aesthetic considerations of the most common tools of the programmer, and the bodily praxis indexed by the programmer’s primary physical interface: the keyboard. The chapter argues that in applying the label of “craft” to their activity, programmers are expressing a sublimated tension, one created by a pervasive shift in how humans relate to “the built environment” and to the political configurations within which that environment is produced. It presents “text” as the raw material of the programmer, arguing that those aspects of materiality central to the analysis of craft are robustly sustained among digital artisans. The chapter consider two levels of apparatus phenomenologically closest to the programmer him- or herself so as to reveal the ordering of programming as craft: the text editor and the keyboard.
An Extended LBWA Framework in Picture Fuzzy Environment Using Actual Score Measures Application in Social Enterprise Systems

Level-based weight assessment (LBWA) model is a recently introduced algorithm for determining criteria weights for multi-criteria group decision making. In this paper, the authors aim to extend the basic framework of LBWA in the picture fuzzy (PF) environment using actual score (AS) measures of the picture fuzzy numbers (PFN). They apply this extended framework in addressing a real-life problem pertaining to social entrepreneurship or social entrepreneurs (SE) in the context of COVID-19. They endeavor to identify the critical challenging factors of SE in the new normal. They list the challenges as revealed through literature review and take the opinion of a group of SEs using PF linguistic scale. They then apply the proposed framework, actual score-based picture fuzzy LBWA. They notice that ability to withstand disruption risk and show resilience and fund availability and creation of a supporting business ecosystems are the major challenges that SEs face in the new normal. They carry out validity checking and sensitivity analysis, which show reasonable consistency and stability in the result.
A comparative performance of breast cancer classification using hyper-parameterized machine learning models

In most cases, cancer derives its name after the body part in which it emanated therefore, breast cancer is the term for the unstable growth of cells that emanated in the breast tissue.The growth results in a
Logical Relations as Types: Proof-Relevant Parametricity for Program Modules

The theory of program modules is of interest to language designers not only for its practical importance to programming, but also because it lies at the nexus of three fundamental concerns in language design: the phase distinction , computational effects , and type abstraction . We contribute a fresh “synthetic” take on program modules that treats modules as the fundamental constructs, in which the usual suspects of prior module calculi (kinds, constructors, dynamic programs) are rendered as derived notions in terms of a modal type-theoretic account of the phase distinction. We simplify the account of type abstraction (embodied in the generativity of module functors) through a lax modality that encapsulates computational effects, placing projectibility of module expressions on a type-theoretic basis. Our main result is a (significant) proof-relevant and phase-sensitive generalization of the Reynolds abstraction theorem for a calculus of program modules, based on a new kind of logical relation called a parametricity structure . Parametricity structures generalize the proof-irrelevant relations of classical parametricity to proof- relevant families, where there may be non-trivial evidence witnessing the relatedness of two programs—simplifying the metatheory of strong sums over the collection of types, for although there can be no “relation classifying relations,” one easily accommodates a “family classifying small families.” Using the insight that logical relations/parametricity is itself a form of phase distinction between the syntactic and the semantic, we contribute a new synthetic approach to phase separated parametricity based on the slogan logical relations as types , by iterating our modal account of the phase distinction. We axiomatize a dependent type theory of parametricity structures using two pairs of complementary modalities (syntactic, semantic) and (static, dynamic), substantiated using the topos theoretic Artin gluing construction. Then, to construct a simulation between two implementations of an abstract type, one simply programs a third implementation whose type component carries the representation invariant.
Efficient Prediction of the First Just Noticeable Difference Point for JPEG Compressed Images

In this paper, we show that a high-level of correlation exists between a simple image featuremean gradient magnitude and the peak signal-to-noise ratio (PSNR) of the first just noticeable difference point for JPEG image compression.On the basis of this observation, we proposed a method to estimate the JPEG quality factor which represents the effective limit between perceptually lossy and lossless coding as the PSNR of the first just noticeable difference point.The goal is optimal image/video coding, at the lowest compression bit-rate that ensures perceptually lossless output image quality.We also show that this feature can be used to predict higher PSNR just noticeable difference points.
Behavior of quantum discord, local quantum uncertainty, and local quantum Fisher information in two-spin-1/2 Heisenberg chain with DM and KSEA interactions

A two-qubit Heisenberg XYZ model with Dzyaloshinsky–Moriya (DM) and Kaplan–Shekhtman–Entin-Wohlman–Aharony (KSEA) interactions is considered at thermal equilibrium. Analytical formulas are derived for the local quantum uncertainty (LQU) and local quantum Fisher information (LQFI). Using the available expressions for the entropic quantum discord, we perform a comparative study of these measures of nonclassical correlation. Our analysis showed the following: all three measures of quantum correlation have similar qualitative and even quantitative behavior on temperature for different values of system parameters, there are regions in the parameter space correspondent to a local increase of correlations with increasing temperature, and sudden changes in the behavior of quantum correlations occur at certain values of the interaction parameters.
Antibunched <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -photon bundles emitted by a Josephson photonic device

We propose an experimentally feasible scheme for generating antibunched $N$-photon bundles by a dc voltage-biased Josephson junction in series with a superconducting microwave resonator and a charge qubit. Each resonant tunneling Cooper pair leads to the excitation of the charge qubit and resonator with $N$ photons simultaneously via the ac Josephson effect. Meanwhile, the charge qubit with strong anharmonicity is utilized to regulate the tunneling behavior of the Cooper pairs; that is, the presence of $N$ photons in the resonator prevents the next tunnel event. So the resonator contains only $N$ photons or none, and all other possibilities are greatly suppressed. Combined with the system's dissipation, the resonator can emit its energy in antibunched bundles of $N$ strongly correlated photons with high purity and an in situ tunable emission rate. Such a nonclassical source could be useful in applications in the field of quantum information science.
Automatic modulation classification using modulation fingerprint extraction

An automatic method for classifying frequency shift keying (FSK), minimum shift keying (MSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), and orthogonal frequency division multiplexing (OFDM) is proposed by simultaneously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation diagram. Since the extracted features are unique for each modulation, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%. Another advantage of the proposed method is its wide SNR range; such that the probability of classification for 16-QAM at SNR=3 dB is almost 1. The proposed method also provides a database for geometrical features of I-Q constellation diagram. By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal, the processing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low complexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.
Detecting quantum entanglement with unsupervised learning

Abstract Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.