Reviewerly/exHarmony huggingface dataset:
A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques

Sentiment Analysis (SA) of text reviews is an emerging concern in Natural Language Processing (NLP). It is a broadly active method for analyzing and extracting opinions from text using individual or ensemble learning techniques. This field has unquestionable potential in the digital world and social media platforms. Therefore, we present a systematic survey that organizes and describes the current scenario of the SA and provides a structured overview of proposed approaches from traditional to advance. This work also discusses the SA-related challenges, feature engineering techniques, benchmark datasets, popular publication platforms, and best algorithms to advance the automatic SA. Furthermore, a comparative study has been conducted to assess the performance of bagging and boosting-based ensemble techniques for social network SA. Bagging and Boosting are two major approaches of ensemble learning that contain various ensemble algorithms to classify sentiment polarity. Recent studies recommend that ensemble learning techniques have the potential of applicability for sentiment classification. This analytical study examines the bagging and boosting-based ensemble techniques on four benchmark datasets to provide extensive knowledge regarding ensemble techniques for SA. The efficiency and accuracy of these techniques have been measured in terms of TPR, FPR, Weighted F-Score, Weighted Precision, Weighted Recall, Accuracy, ROC-AUC curve, and Run-Time. Moreover, comparative results reveal that bagging-based ensemble techniques outperformed boosting-based techniques for text classification. This extensive review aims to present benchmark information regarding social network SA that will be helpful for future research in this field.
Realization of a Multi-Output Quantum Pulse Gate for Decoding High-Dimensional Temporal Modes of Single-Photon States

Temporal modes (TMs) of photons provide an appealing high-dimensional encoding basis for quantum information. While techniques to generate TM states have been established, high-dimensional decoding of single-photon TMs remains an open challenge. In this work, we experimentally demonstrate demultiplexing of five-dimensional TMs of single photons with an average fidelity of 0.96±0.01, characterized via measurement tomography. This is achieved with use of a newly developed device, the multi-output quantum pulse gate (MQPG). We demonstrate a proof-of-principle complete decoder based on the MQPG that operates on any basis from a set of six five-dimensional mutually unbiased bases and is therefore suitable as a receiver for high-dimensional quantum key distribution. Furthermore, we confirm the high-quality operation of the MQPG by performing resource-efficient state tomography with an average fidelity of 0.98±0.02.3 MoreReceived 20 September 2022Accepted 15 March 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020306Published 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 AreasIntegrated opticsNonlinear waveguidesOptical quantum information processingQuantum communicationQuantum gatesQuantum opticsQuantum tomographyQuantum Information
Kurdish News Dataset Headlines (KNDH) through multiclass classification

The rapid growth of technology has massively increased the amount of text data. The data can be mined and utilized for numerous natural language processing (NLP) tasks, particularly text classification. The core part of text classification is collecting the data for predicting a good model. This paper collects Kurdish News Dataset Headlines (KNDH) for text classification. The dataset consists of 50000 news headlines which are equally distributed among five classes, with 10000 headlines for each class (Social, Sport, Health, Economic, and Technology). The percentage ratio of getting the channels of headlines is distinct, while the numbers of samples are equal for each category. There are 34 distinct channels that are used to collect the different headlines for each class, such as 8 channels for economics, 14 channels for health, 18 channels for science, 15 channels for social, and 5 channels for sport. The dataset is preprocessed using the Kurdish Language Processing Toolkit (KLPT) for tokenizing, spell-checking, stemming, and preprocessing.
ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning

This paper assesses the accuracy, reliability and bias of the Large Language Model (LLM) ChatGPT-4 on the text analysis task of classifying the political affiliation of a Twitter poster based on the content of a tweet. The LLM is compared to manual annotation by both expert classifiers and crowd workers, generally considered the gold standard for such tasks. We use Twitter messages from United States politicians during the 2020 election, providing a ground truth against which to measure accuracy. The paper finds that ChatGPT-4 has achieves higher accuracy, higher reliability, and equal or lower bias than the human classifiers. The LLM is able to correctly annotate messages that require reasoning on the basis of contextual knowledge, and inferences around the author's intentions - traditionally seen as uniquely human abilities. These findings suggest that LLM will have substantial impact on the use of textual data in the social sciences, by enabling interpretive research at a scale.
Criticality of quantum energy teleportation at phase transition points in quantum field theory

Quantum field theory can be a new medium for communication through quantum energy teleportation. We performed a demonstration of quantum energy teleportation with a relativistic fermionic field theory of self-coupled fermions, called the massive Thirring model. Our results reveal that there is a close relation between the amount of energy teleported and the phase diagram of the theory. In particular, it is shown that the teleported energy peaks near the phase transition points. The results provide new implications for phase diagrams of field theory in terms of quantum communication and quantum computing.
Inference on the Parameters of Zubair-Exponential Distribution with Application to Survival Times of Guinea Pigs

In this paper, we derived a sub-model of Zubair-G familiy of distribution named Zubair-Exponential distribution with two parameters. Simulation of the Estimates of the parameters based on some classical methods are obtained. The likelihood equations and the maximum likelihood estimator as well as asymptotic confidence interval are derived. Bayes estimates with the estimates of the associated greatest posterior density credible interval are derived using squared error Loss (SEL), Linear-Exponential (LINEX) and Generalized Entropy Loss (GEL) functions. Using the Metropolis-Hasting algorithm and the method of Markov Chain Monte Carlo (MCMC), estimates of Bayes are summarized. To determine the performance of the estimates, a Monte Carlo simulation study is carried out and maximum likelihood estimates, their standard errors and measures of fitness using real data on survival times of Guinea pigs are obtained. The proposed distribution has a better fit based on Akaike Information criterion (AIC) and the Bayesian Information criterion (BIC).
Quantifying the Link between Financial Development and Bank Profitability

This study seeks to examine the impact of financial development on bank profitability in Sub-Saharan Africa (SSA).We also investigate how institutional quality, bank stability, financial openness, and competition affect profitability.The researchers employ data from 33 countries covering from 2000 to 2017.Using the generalized method of moments (GMM) technique, the findings show that financial development exerts a negative significant effect on banking sector profitability.This result holds regardless of the indicator of profitability.This indicates that financial development dampens profitability in SSA.The study also reveals that institutional quality and bank stability positively and significantly influence profitability.We further observe that the impact of financial openness and competition on profitability significantly differs depending on the measure of profitability.The study discusses key implications for policy.
Project management in the system of education

The purpose of this study is to identify universal methodological characteristics and general aspects of project management in the field of education (general principles, strategy, approaches, terms). The methodological conditions for the effectiveness of the preparation and use of the project are the relationship and interdependence of three approaches: a systematic approach that focuses on the implementation of the principle of reasonable sufficiency in the distribution of structural components in the organization; a situational approach that takes into account the characteristics of the external environment as a significant factor influencing the direction and content of changes in the organization; a behavioral approach that orients designers to understand the need to create conditions that have a beneficial effect on the behavior of people in organizational structures. Project management in education is a method of purposeful influence on the structure and processes taking place in the education system in order to transfer it to a qualitatively new state through a set of measures and actions. It is established that the project management model in education is a set of sequential actions at all stages, which ensures high efficiency of the process of finding a correspondence between the strategy of the organization in society and its basic components.
Transcending the “Male Code”: Implicit Masculine Biases in NLP Contexts

Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are "coded" into language and the assumption of "male" as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.
Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation‐based methods

All fields have seen an increase in machine-learning techniques. To accurately evaluate the efficacy of novel modeling methods, it is necessary to conduct a critical evaluation of the utilized model metrics, such as sensitivity, specificity, and area under the receiver operator characteristic curve (AUROC). For commonly used model metrics, we proposed the use of analytically derived distributions (ADDs) and compared it with simulation-based approaches.A retrospective cohort study was conducted using the England National Health Services Heart Disease Prediction Cohort. Four machine learning models (XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boost) were used. The distribution of the model metrics and covariate gain statistics were empirically derived using boot-strap simulation (N = 10,000). The ADDs were created from analytic formulas from the covariates to describe the distribution of the model metrics and compared with those of bootstrap simulation.XGBoost had the most optimal model having the highest AUROC and the highest aggregate score considering six other model metrics. Based on the Anderson-Darling test, the distribution of the model metrics created from bootstrap did not significantly deviate from a normal distribution. The variance created from the ADD led to smaller SDs than those derived from bootstrap simulation, whereas the rest of the distribution remained not statistically significantly different.ADD allows for cross study comparison of model metrics, which is usually done with bootstrapping that rely on simulations, which cannot be replicated by the reader.
Deep learning based registration of serial whole-slide histopathology images in different stains

For routine pathology diagnosis and imaging-based biomedical research, Whole-slide image (WSI) analyses have been largely limited to a 2D tissue image space. For a more definitive tissue representation to support fine-resolution spatial and integrative analyses, it is critical to extend such tissue-based investigations to a 3D tissue space with spatially aligned serial tissue WSIs in different stains, such as Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) biomarkers. However, such WSI registration is technically challenged by the overwhelming image scale, the complex histology structure change, and the significant difference in tissue appearances in different stains. The goal of this study is to register serial sections from multi-stain histopathology whole-slide image blocks. We propose a novel translation-based deep learning registration network CGNReg that spatially aligns serial WSIs stained in H&E and by IHC biomarkers without prior deformation information for the model training. First, synthetic IHC images are produced from H&E slides through a robust image synthesis algorithm. Next, the synthetic and the real IHC images are registered through a Fully Convolutional Network with multi-scaled deformable vector fields and a joint loss optimization. We perform the registration at the full image resolution, retaining the tissue details in the results. Evaluated with a dataset of 76 breast cancer patients with 1 H&E and 2 IHC serial WSIs for each patient, CGNReg presents promising performance as compared with multiple state-of-the-art systems in our evaluation. Our results suggest that CGNReg can produce promising registration results with serial WSIs in different stains, enabling integrative 3D tissue-based biomedical investigations.
Partners or Opponents? How Mindset Shapes Consumers’ Attitude Toward Anthropomorphic Artificial Intelligence Service Robots

The use of artificial intelligence (AI) service robots is on the rise. With service frontlines gradually shifting to human–robot interactions, the question of whether the anthropomorphism of robots facilitates or constrains consumers’ experiences has emerged. This article focuses on the individual factor “consumer mindset” (competition vs collaboration) and investigates how it impacts consumers’ attitudes toward anthropomorphic AI robots during service delivery. Across three studies, we confirm our main prediction that competitive mindset consumers respond less favorably to anthropomorphic (vs. non-anthropomorphic) AI robots, whereas collaborative mindset consumers respond more favorably to anthropomorphic (vs. non-anthropomorphic) AI robots. We test the mediating role of perceived psychological closeness and the moderating role of interaction distance to explain the underlying mechanism. Our findings provide theoretical insights into the mixed results of previous studies of service robot anthropomorphism and have practical implications for service agencies using frontline robots.
Quantum identity authentication based on the extension of quantum rotation

Abstract In this work, we propose a bit-oriented QIA protocol based on special properties of quantum rotation and the public key cryptographic framework. The proposed protocol exhibited good resistance to both forward search and measure-resend attacks, whereby its security performance was directly related to the length of the authentication code. From our analysis, it was demonstrated that the protocol has good performance, in terms of quantum bit efficiency. In addition, the protocol is well-expandable. The developed protocol is resource-efficient and can be also applied in quantum computing networks.
Green video complexity analysis for efficient encoding in Adaptive Video Streaming

For adaptive streaming applications, low-complexity and accurate video complexity features are necessary to analyze the video content in real time, which ensures fast and compression-efficient video streaming without disruptions. State-of-the-art video complexity features are Spatial Information (SI) and Temporal Information (TI) features which do not correlate well with the encoding parameters in adaptive streaming applications. To this light, Video Complexity Analyzer (VCA) was introduced, determining the features based on Discrete Cosine Transform (DCT)-energy. This paper presents optimizations on VCA for faster and energy-efficient video complexity analysis. Experimental results show that VCA v2.0, using eight CPU threads, Single Instruction Multiple Data (SIMD), and low-pass DCT optimization, determines seven complexity features of Ultra High Definition 8-bit videos with better accuracy at a speed of up to 292.68 fps and an energy consumption of 97.06% lower than the reference SITI implementation.
Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models

ChatGPT, an AI-powered chatbot developed by OpenAI, has gained immense popularity since its public launch in November 2022. With its ability to write essays, emails, poems, and even computer code, it has become a useful tool for professionals in various fields. However, ChatGPT’s responses are not always rooted in reality and are instead generated by a GAN. This paper aims to build a text classification model for a chatbot using Python. The model is trained on a dataset consisting of customer responses to a survey and their corresponding class labels. Many classifiers are trained and tested, such as Naive Bayes, Random Forest, Extra Trees, and Decision Trees. The results show that the Extra Trees classifier performs the best with an accuracy of 90%. The system demonstrates the importance of text preprocessing and selecting appropriate classifiers for text classification tasks in building an effective chatbot. In this paper, we also explore the capabilities and limitations of ChatGPT and its alternatives in 2023. We present a comprehensive overview of the alternative’s performance. The work here, concludes with a discussion of the future directions of large language models and their impact on society and technology.
Toward surface defect detection in electronics manufacturing by an accurate and lightweight YOLO-style object detector

In electronics manufacturing, surface defect detection is very important for product quality control, and defective products can cause severe customer complaints. At the same time, in the manufacturing process, the cycle time of each product is usually very short. Furthermore, high-resolution input images from high-resolution industrial cameras are necessary to meet the requirements for high quality control standards. Hence, how to design an accurate object detector with real-time inference speed that can accept high-resolution input is an important task. In this work, an accurate YOLO-style object detector was designed, ATT-YOLO, which uses only one self-attention module, many-scale feature extraction and integration in the backbone and feature pyramid, and an improved auto-anchor design to address this problem. There are few datasets for surface detection in electronics manufacturing. Hence, we curated a dataset consisting of 14,478 laptop surface defects, on which ATT-YOLO achieved 92.8% mAP0.5 for the binary-class object detection task. We also further verified our design on the COCO benchmark dataset. Considering both computation costs and the performance of object detectors, ATT-YOLO outperforms several state-of-the-art and lightweight object detectors on the COCO dataset. It achieves a 44.9% mAP score and 21.8 GFLOPs, which is better than the compared models including YOLOv8-small (44.9%, 28.6G), YOLOv7-tiny-SiLU (38.7%, 13.8G), YOLOv6-small (43.1%, 44.2G), pp-YOLOE-small (42.7%, 17.4G), YOLOX-small (39.6%, 26.8G), and YOLOv5-small (36.7%, 17.2G). We hope that this work can serve as a useful reference for the utilization of attention-based networks in real-world situations.
Self-Supervised Monocular Depth Estimation With Self-Reference Distillation and Disparity Offset Refinement

Monocular depth estimation plays a fundamental role in computer vision. Due to the costly acquisition of depth ground truth, self-supervised methods that leverage adjacent frames to establish a supervision signal have emerged as the most promising paradigms. In this work, we propose two novel ideas to improve self-supervised monocular depth estimation: 1) self-reference distillation and 2) disparity offset refinement. Specifically, we use a parameter-optimized model as the teacher updated as the training epochs to provide additional supervision during the training process. The teacher model has the same structure as the student model, with weights inherited from the historical student model. In addition, a multiview check is introduced to filter out the outliers produced by the teacher model. Furthermore, we leverage the contextual consistency between high-level and low-level features to obtain multiscale disparity offsets, which are used to refine the disparity output incrementally by aligning disparity information at different scales. The experimental results on the KITTI and Make3D datasets show that our method outperforms previous state-of-the-art competitors.
Boosting Batch Arguments and RAM Delegation

We show how to generically improve the succinctness of non-interactive publicly verifiable batch argument (BARG) systems. In particular, we show (under a mild additional assumption) how to convert a BARG that generates proofs of length poly (m)· k1−є, where m is the length of a single instance and k is the number of instances being batched, into one that generates proofs of length poly (m, logk), which is the gold standard for succinctness of BARGs. By prior work, such BARGs imply the existence of SNARGs for deterministic time T computation with succinctness poly(logT).
Virtual Reality Applications in Neurorehabilitation: Current Panorama and Challenges

Central Nervous System Diseases are a leading cause of disability worldwide, posing significant social and economic burdens for patients, their families, caregivers, and society as a whole [...].