Reviewerly/exHarmony huggingface dataset:
Sentiment analysis using fuzzy logic: A comprehensive literature review

Abstract Understanding and comprehending humans' views, beliefs, attitudes, or opinions toward a particular entity is sentiment analysis (SA). Advancements in e‐commerce platforms has led to an abundance of the real‐time and free forms of opinions floating on social media platforms. This real‐world data are imprecise and vague hence fuzzy logic is required to deal with such subjective data. Since opinions can be fuzzy in nature and definitions of opinion words can be elucidated differently; fuzzy logic has witnessed itself as an effective method to capture the expression of opinions. The study presents an elaborate review of the around 170 published research works for SA using fuzzy logic. The primary emphasis is focused on text‐based SA, audio‐based SA, and fusion of text‐audio features‐based SA. This article discusses the various novel ways of classifying fuzzy logic‐based SA research articles, which have not been accomplished by any other review article till date. The article puts forward the importance of SA tasks and identifies how fuzzy logic adds to this importance. Finally, the article outlines a taxonomy for sentiment classification based on the technique‐supervised and unsupervised in the SA models and comprehensively reviews the SA approaches specific to their task. Prominently, this study highlights the suitability of fuzzy‐based SA approaches into five different classes vis‐a‐vis (a) Sentiment Cognition from Words using fuzzy logic, (b) Sentiment Cognition from Phrases using fuzzy logic, (c) Fuzzy‐rule based SA, (d) Neuro‐fuzzy network‐based SA, and (e) Fuzzy Emotion Recognition. This article is categorized under: Algorithmic Development &gt; Text Mining Fundamental Concepts of Data and Knowledge &gt; Motivation and Emergence of Data Mining
Hierarchy of Multipartite Nonlocality and Device-Independent Effect Witnesses

According to recent new definitions, a multiparty behavior is genuinely multipartite nonlocal (GMNL) if it cannot be modeled by measurements on an underlying network of bipartite-only nonlocal resources, possibly supplemented with local (classical) resources shared by all parties. The new definitions differ on whether to allow entangled measurements upon, and/or superquantum behaviors among, the underlying bipartite resources. Here, we categorize the full hierarchy of these new candidate definitions of GMNL in three-party quantum networks, highlighting the intimate link to device-independent witnesses of network effects. A key finding is the existence of a behavior in the simplest nontrivial multipartite measurement scenario (three parties, two measurement settings, and two outcomes) that cannot be simulated in a bipartite network prohibiting entangled measurements and superquantum resources---thus witnessing the most general form of GMNL---but can be simulated with bipartite-only quantum states with an entangled measurement, indicating an approach to device-independent certification of entangled measurements with fewer settings than in previous protocols. Surprisingly, we also find that this (3,2,2) behavior, as well as the others previously studied as device-independent witnesses of entangled measurements, can all be simulated at a higher echelon of the GMNL hierarchy that allows superquantum bipartite resources while still prohibiting entangled measurements. This poses a challenge to a theory-independent understanding of entangled measurements as an observable phenomenon distinct from bipartite nonlocality.
QISampling: An Effective Sampling Strategy for Event-Based Sign Language Recognition

Event cameras are innovative neuromorphic sensors that detect changes in brightness and generate a stream of events, thus providing high temporal resolution and dynamic range advantages. With its ability to perceive motion information, event data is well-suited for sign language recognition (SLR). Existing methods of event-based SLR rely on a uniform sampling strategy, which may result in redundant and indiscriminate information being captured when segments are randomly sampled. In this letter, we propose an effective sampling strategy called Quantity-inspired Sampling (QISampling) that takes advantage of the quantity feature of event distribution to sample key segments containing more discriminative and remarkable motion features from a sign. These segments are then converted into frame-like representations, which serve as input to subsequent Deep Neural Networks (DNNs). In addition, we introduce a synthetic event-based sign language dataset N-WLASL to the community. We apply our strategy to DNNs and conduct experiments on this synthetic dataset and another real-world dataset. The results demonstrate that our QISampling strategy could accelerate training and make event data provide superior performance for the SLR task.
A Case for Accelerating Standards to Achieve the FAIR Principles of Environmental Health Research Experimental Data

Background: Funding agencies, publishers, and other stakeholders are pushing environmental health science investigators to improve data sharing; to promote the findable, accessible, interoperable, and reusable (FAIR) principles; and to increase the rigor and reproducibility of the data collected. Accomplishing these goals will require significant cultural shifts surrounding data management and strategies to develop robust and reliable resources that bridge the technical challenges and gaps in expertise. Objective: In this commentary, we examine the current state of managing data and metadata—referred to collectively as (meta)data—in the experimental environmental health sciences. We introduce new tools and resources based on in&nbsp;vivo experiments to serve as examples for the broader field. Methods: We discuss previous and ongoing efforts to improve (meta)data collection and curation. These include global efforts by the Functional Genomics Data Society to develop metadata collection tools such as the Investigation, Study, Assay (ISA) framework, and the Center for Expanded Data Annotation and Retrieval. We also conduct a case study of in&nbsp;vivo data deposited in the Gene Expression Omnibus that demonstrates the current state of in&nbsp;vivo environmental health data and highlights the value of using the tools we propose to support data deposition. Discussion: The environmental health science community has played a key role in efforts to achieve the goals of the FAIR guiding principles and is well positioned to advance them further. We present a proposed framework to further promote these objectives and minimize the obstacles between data producers and data scientists to maximize the return on research investments. https://doi.org/10.1289/EHP11484
Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy.
Speech Act Theory and Ethics of Speech Processing as Distinct Stages: the ethics of collecting, contextualizing and the releasing of (speech) data

Using speech act theory from the Philosophy of Language, this paper attempts to develop an ethical framework for the phenomenon of speech processing. We use the concepts of the illocutionary force and the illocutionary content of a speech act to explain the ethics of speech processing. By emphasizing the different stages involved in speech processing, we explore the distinct ethical issues that arise in relation to each stage. Input, processing, and output are the different ethically relevant stages under which a spoken item or a speech navigates within the range of speech-processing modules. Employing the illocutionary force-content distinction, we specify and characterize the input-related ethical issues, the output-related ethical issues, and the processing-related ethical issues involved in speech processing. Together with illocutionary force-content distinction, we employ the data-information distinction to characterize the stage-wise ethical issues in the phenomenon of speech processing as the ethics of collecting (speech) data, the ethics of contextualizing (speech) data/information, and the ethics of releasing the con-textualized information (processed speech). Immediate ethical issues that arise from the range of speech processing modules are distinguished from distant ethical issues. We also indicate the nature of ethical issues that arise from Speaker Independent speech technologies.
Stealing Keys From Hardware Wallets: A Single Trace Side-Channel Attack on Elliptic Curve Scalar Multiplication Without Profiling

Over the past decade, decentralized cryptocurrencies have received attention in industry and academia. Hardware wallets are dedicated devices that manage cryptocurrencies safely without entrusting cryptographic keys to a third party. Side-channel attacks have been widely studied in cryptanalysis and have already been proven threatening, but analysis on hardware wallets still needs to be researched. Although the previous work demonstrated several side-channel vulnerabilities, their attacks require a finely controlled environment or a learning phase of target devices’ physical properties before the attacks. This paper proposes a side-channel attack on hardware wallets extracting private keys. The proposed attack needs a single power trace measured when wallets process elliptic curve scalar multiplication with private keys. Our attack is reasonable since we do not damage the device under attack and do not target a specific device but an algorithm; it is widely applicable to wallets using that algorithm or analogous ones. It also presents the attack results conducted with three datasets: simulation, ChipWhisperer, and actual dataset collected from the Trezor Model One, the first and representative hardware wallets which comply with the de facto standard of hardware wallets.
E-commerce Price Comparison Website using Web Scraping

Price comparison sites are designed to compare the price of goods and services from a range of providers, which will help consumers in making decision to choose products that will save their money through online. Considering the customers’ busy lifestyle especially those who are living in the city area, most of the consumers prefer to buy their needs through the internet because it saves their time. Besides, consumers always go for the cheaper price in purchasing products therefore by using price comparison website, customers do not have to travel from shop to shop only to survey the price offered by different shops for the same product. They can just check it from the price comparison website itself and decide where they should buy the products they need. This project, named as Price comparison website using web scrapping is the place where shoppers could find the great deals on the products. The best deals will be clearly highlighted. To obtain best deals from Price comparison websites web scrapping techniques are used to fetch detailed information. This way, paper aims to provide solution for online customers to buy products at good deal and save their valuable time, effort, and money.
Instance Reweighting Adversarial Training Based on Confused Label

Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks, which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights. The probability margin (PM) method is a promising approach to continuously and path-independently measuring such closeness between the example and decision boundary. However, the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories, where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation. To tackle this problem, this paper proposed an improved PM criterion, called confused-label-based PM (CL-PM), to measure the closeness mentioned above and reweight adversarial examples during training. Specifically, a confused label (CL) is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example. Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does, we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label. CL-PM shares a negative correlation with data vulnerability: data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight. Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories, and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.
AI-ASSISTED DIGITALISATION OF HISTORICAL DOCUMENTS

Abstract. Preserving historical archival heritage involves not only physical measures to safeguard these valuable texts but also providing for their digital preservation. However, merely digitising manuscripts and codexes is not enough. A further step is needed: the digitalisation of their content, i.e. the verbatim transcription of scanned texts. This process enables the accurate preservation of their textual content, making it easier to search for information and conduct further analyses. With the help of artificial intelligence, particularly Deep Neural Networks (DNNs), automatic handwriting recognition can be performed. In this study, we employed a Convolutional Recurrent Neural Network (CRNN), an established type of DNN, to determine the minimum amount of labelled data required to automatically transcribe five different historical datasets that vary in language and time period. The results show that a Character Error Rate (CER) lower than 10% can be achieved with just a few hundred labelled text lines in almost all cases.
Exploiting subspace constraints and ab initio variational methods for quantum chemistry

Variational methods offer a highly promising route to exploiting quantum computers for chemistry tasks. Here we employ methods described in a sister paper to the present report, entitled ab initio machine synthesis of quantum circuits, in order to solve problems using adaptively evolving quantum circuits. Consistent with prior authors we find that this approach can outperform human-designed circuits such as the coupled-cluster or hardware-efficient ans\"atze, and we make comparisons for larger instances up to 14 qubits. Moreover we introduce a novel approach to constraining the circuit evolution in the physically relevant subspace, finding that this greatly improves performance and compactness of the circuits. We consider both static and dynamics properties of molecular systems. The emulation environments used is QuESTlink; all resources are open source and linked from this paper.
On the Effectiveness of Parameter-Efficient Fine-Tuning

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new model for each task. Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters shared across different tasks. These methods achieve surprisingly good performance and are shown to be more stable than their corresponding fully fine-tuned counterparts. However, such kind of methods is still not well understood. Some natural questions arise: How does the parameter sparsity lead to promising performance? Why is the model more stable than the fully fine-tuned models? How to choose the tunable parameters? In this paper, we first categorize the existing methods into random approaches, rule-based approaches, and projection-based approaches based on how they choose which parameters to tune. Then, we show that all of the methods are actually sparse fine-tuned models and conduct a novel theoretical analysis of them. We indicate that the sparsity is actually imposing a regularization on the original model by controlling the upper bound of the stability. Such stability leads to better generalization capability which has been empirically observed in a lot of recent research works. Despite the effectiveness of sparsity grounded by our theory, it still remains an open problem of how to choose the tunable parameters. Currently, the random and rule-based methods do not utilize task-specific data information while the projection-based approaches suffer from the projection discontinuity problem. To better choose the tunable parameters, we propose a novel Second-order Approximation Method (SAM) which approximates the original problem with an analytically solvable optimization function. The tunable parameters are determined by directly optimizing the approximation function. We conduct extensive experiments on several tasks. The experimental results show that our proposed SAM model outperforms many strong baseline models and it also verifies our theoretical analysis. The source code of this paper can be obtained from https://github.com/fuzihaofzh/AnalyzeParameterEff\/icientFinetune .
Data Collection for Classification of Radio Signals Using SDR Transceivers

The paper deals with the problem of automatic classification of telecommunication signal modulation types using machine-learning methods. The task is actual in the field radio electronic, with the advent of integrated telecommunication systems, new modulation, and coding methods, it became necessary to search and develop simple, efficient, reliable methods for processing signals in channels with interference. The type of modulation is an important parameter on which the signal processing circuit of the receiving or transmitting device depends. It is impossible to extract useful information from the received signal without knowing the type of modulation. A database for the problem solution was collected through the experiments with software-defined radio. During the experiments the signals at a frequency of 900 MHz, with modulations: BPSK, GMSK, NBFM, OFDM were collected. The most popular of the contemporary methods for classification are evaluated and compared. As the result of this study, a method for collecting data using an SDR transceiver was considered, and a comparison was made of the accuracy of algorithms trained on the collected dataset.
An Equivalence Analysis of Binary Quantification Methods

Quantification (or prevalence estimation) algorithms aim at predicting the class distribution of unseen sets (or bags) of examples. These methods are useful for two main tasks: 1) quantification applications, for instance when we need to track the proportions of several groups of interest over time, and 2) domain adaptation problems, in which we usually need to adapt a previously trained classifier to a different --albeit related-- target distribution according to the estimated prevalences. This paper analyzes several binary quantification algorithms showing that not only do they share a common framework but are, in fact, equivalent. Inspired by this study, we propose a new method that extends one of the approaches analyzed. After an empirical evaluation of all these methods using synthetic and benchmark datasets, the paper concludes recommending three of them due to their precision, efficiency, and diversity.
Discrete-time quantum walk dispersion control through long-range correlations

We investigate the evolution dynamics of inhomogeneous discrete-time one-dimensional quantum walks displaying long-range correlations in both space and time. The associated quantum coin operators of internal states are built to exhibit random inhomogeneity distribution of long-range correlations embedded in the time evolution protocol through a fractional Brownian motion with spectrum following a power-law behavior, $S(k)\ensuremath{\sim}1/{k}^{\ensuremath{\nu}}$. From extensive numerical simulations with averages over a large number of independent realizations of the phases of quantum coins, the power-law correlated disorder encoded in the coin phases is shown to give rise to a wide variety of spreading patterns of the qubit states, from localized to subdiffusive, diffusive, and superdiffusive (including ballistic) behavior, depending on the relative strength of the parameters driving the correlation degree. Dispersion control is possible in one-dimensional discrete-time quantum walks by tuning the long-range correlation properties assigned to the inhomogeneous quantum coin operator.
Establishing a new workflow in the study of core reduction intensity and distribution

New methodological approaches focused on studying the reduction and use-life of stone tools have emerged in recent years, enabling researchers to move beyond strict technical and technological characterizations and explore specific aspects of occupation dynamics and economic management of resources. Previous studies have shown the importance of reduction distributions of individual measurements rather than averaged values. In this sense, survival analysis, and more specifically Weibull distributions, are one of the main inferential tools used in reduction studies. However, the resolution of Weibull distribution obtained from different methods has not been tested experimentally. In this paper, we present an evaluation of some of the main methods used in the study of core reduction intensity, such as the Volumetric Reconstruction Method, the Scar Density Index, and the non-cortical surface percentage. Our results show 1) strong and positive correlations between these approaches and actual reduction intensity, 2) similar Weibull distributions for non-cortical surface percentage, Volumetric Reconstruction Method, and logarithmic transformation of Scar Density Index. In addition, 3) the results from each method show a similar intra-assemblage variation, with a high percentage of agreement between them.&#x0D; As a result, all the evaluated proposals are useful and reliable methods for estimating the degree of reduction. Finally, a workflow is proposed for approaching reduction in archaeological assemblages by integrating different methods in the same study.