Reviewerly/exHarmony huggingface dataset:
Product Information Extraction using ChatGPT

Structured product data in the form of attribute/value pairs is the foundation of many e-commerce applications such as faceted product search, product comparison, and product recommendation. Product offers often only contain textual descriptions of the product attributes in the form of titles or free text. Hence, extracting attribute/value pairs from textual product descriptions is an essential enabler for e-commerce applications. In order to excel, state-of-the-art product information extraction methods require large quantities of task-specific training data. The methods also struggle with generalizing to out-of-distribution attributes and attribute values that were not a part of the training data. Due to being pre-trained on huge amounts of text as well as due to emergent effects resulting from the model size, Large Language Models like ChatGPT have the potential to address both of these shortcomings. This paper explores the potential of ChatGPT for extracting attribute/value pairs from product descriptions. We experiment with different zero-shot and few-shot prompt designs. Our results show that ChatGPT achieves a performance similar to a pre-trained language model but requires much smaller amounts of training data and computation for fine-tuning.
Mode-pairing quantum key distribution with advantage distillation

Mode-pairing quantum key distribution (MP-QKD) is an easy-to-implement scheme that transcends the Pirandola-Laurenza-Ottaviani-Banchi bound without using quantum repeaters. In this paper, we present an improvement of the performance of MP-QKD using an advantage distillation method. The simulation results demonstrate that the proposed scheme extends the transmission distance significantly with a channel loss exceeding 7.6 dB. Moreover, the scheme tolerates a maximum quantum bit error rate of 8.9%, which is nearly twice that of the original MP-QKD. In particular, as the system misalignment error increases, the expandable distance of the proposed scheme also increases. The proposed system is expected to promote the practical implementation of MP-QKD in a wide range of applications, particularly in scenarios involving high channel losses and system errors.
A Dissection of Hyflex Education in Terms of Equal Opportunities in Education

This chapter examines recent educational advances in light of equal educational opportunities. HyFlex's technology-based teaching was selected. Given the pandemic's impact on digital education, this form of education is nascent. Due to worldwide accessibility regulations and the COVID-19 pandemic, technology-based educational techniques are needed to diversify the student population and digitalize higher education. This chapter addresses the following question: Is the Hyflex model suitable for creating an inclusive and effective learning environment for underprivileged students? This new model's influence on educational equality has been extensively discussed. The HyFlex approach fits students' learning demands due to its flexibility, temporization, and highly organized online and offline learning environment; however, it is not the great equalizer it is portrayed as. HyFlex can improve higher education access for diverse students if the technology employed is UDL-based.
Towards Measuring the Representation of Subjective Global Opinions in Language Models

Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to evaluate whose opinions model-generated responses are more similar to. We first build a dataset, GlobalOpinionQA, comprised of questions and answers from cross-national surveys designed to capture diverse opinions on global issues across different countries. Next, we define a metric that quantifies the similarity between LLM-generated survey responses and human responses, conditioned on country. With our framework, we run three experiments on an LLM trained to be helpful, honest, and harmless with Constitutional AI. By default, LLM responses tend to be more similar to the opinions of certain populations, such as those from the USA, and some European and South American countries, highlighting the potential for biases. When we prompt the model to consider a particular country's perspective, responses shift to be more similar to the opinions of the prompted populations, but can reflect harmful cultural stereotypes. When we translate GlobalOpinionQA questions to a target language, the model's responses do not necessarily become the most similar to the opinions of speakers of those languages. We release our dataset for others to use and build on. Our data is at https://huggingface.co/datasets/Anthropic/llm_global_opinions. We also provide an interactive visualization at https://llmglobalvalues.anthropic.com.
A Conversational Web-Based Chatbot to Disseminate COVID-19 Advisory Information

Chatbots are computer programs which integrate Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies to simulate human conversation. The use of chatbots has significantly been increased during the COVID-19 pandemic to support healthcare procedures and systems. The purpose of this study is to present the design, implementation, and initial evaluation of a web-based conversational chatbot aiming at the immediate and reliable information regarding the COVID-19 disease. The implementation of the chatbot was built on IBM's Watson Assistant. The chatbot "Iris" that was created is highly developed with the ability to support dialogue, since it has a satisfactory understanding of the relevant subject matter. The system was pilot evaluated using the University of Ulster's Chatbot Usability Questionnaire (CUQ). The results confirmed its usability and that Chatbot "Iris" is a pleasant experience for users. Finally, the related study's limitations and future steps are discussed.
Lightweight image de-snowing: A better trade-off between network capacity and performance

The single image de-snowing task is an essential topic in computer vision, as images captured on snowy days degrade the performance of current vision-based intelligent systems. Existing methods build complex network structures with numerous parameters to pursue continuous performance improvement. Nonetheless, they generally ignore the negative impact of large memory consumption in real applications. This paper aims to address the above problem by making a trade-off between network capacity and performance. We propose two novel networks suitable for different application scenarios. For devices with small memory and requiring fast inference speed, we propose an extremely lightweight recursive network (XLRNet). XLRNet is constructed by a single recursive strategy and two novel lightweight modules. For devices with large memory and pursuing better de-snowing performance, we propose a coupled lightweight dual recursive network (CLDRNet). CLDRNet cascades two XLRNets by a novel dual recursive strategy and a novel dual coupled LSTM module (DC-LSTM). Extensive experiments demonstrate the effectiveness and superiority of our two models on three synthetic datasets and real-world datasets.
Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of 1.000 and a standard deviation of 0.000. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is 0.000 shows that there is no variability in the outcomes.
GRU and XGBoost Performance with Hyperparameter Tuning Using GridSearchCV and Bayesian Optimization on an IoT-Based Weather Prediction System

Weather is essential to human life, but it is difficult to forecast due to its diverse nature. We evaluated and compared the accuracy of two machine learning algorithms, GRU and XGBoost, in predicting weather patterns. We used GridSearchCV to tune the hyperparameters for the GRU algorithm and Bayesian optimization for the XGBoost algorithm. We used regression to predict weather sensor data and classification to predict rainfall in the following four days. We then deployed the best-performing model to the cloud server and connected it to the local IoT device with weather sensors in Sedati, Sidoarjo Regency, Indonesia. We conducted tests using data from the BMKG Juanda Sidoarjo and data from the local IoT device. The findings indicated that the XGBoost regression model outperformed the GRU model in the first stage, with an average RMSE of 1.2728125. In comparison, the average RMSE for GRU regression was 1.551666667. In the second stage, however, GRU regression performed better, with an average RMSE of 2.23, while the XGBoost regression had 2.28. In the classification tests, the GRU model had a higher F1 score of 0.88 in the first stage, while the XGBoost classification was 0.86. Both models had the same accuracy of 0.75 when tested with IoT data. However, the GRU classification model was better since it considered the context of the prediction, resulting in a lower likelihood of rain when it was not raining.
Towards a Toolkit for Teaching AI Supported by Robotic-agents: Proposal and First Impressions

Using hardware platforms combined with other paradigms stimulates learning and has become an apparent trend in education. The Multi-Agent Systems domain studies the use of autonomous entities to solve complex problems. When combined with the Belief-Desire-Intention (BDI), a cognitive model close to human thinking, it can contribute to facilitating the teaching of Artificial Intelligence (AI) and Computational Thinking (CT). This work presents a toolkit to support the teaching of AI supported by BDI intelligent agents using a prototyping approach. Four workshops were held at different levels, and a technological acceptance questionnaire was designed to analyze the proposed toolkit.
Optimizing the number of measurements for vibrational structure on quantum computers: coordinates and measurement schemes

One of the primary challenges prohibiting demonstrations of practical quantum advantages for near-term devices amounts to excessive measurement overheads for estimating relevant physical quantities such as ground state energies. However, with major differences between the electronic and vibrational structures of molecules, the question of how the resource requirements of computing anharmonic, vibrational states can be reduced remains relatively unexplored compared to its electronic counterpart. Importantly, bosonic commutation relations, distinguishable Hilbert spaces and vibrational coordinates allow manipulations of the vibrational system that can be exploited to minimize resource requirements. In this work, we investigate the impact of different coordinate systems and measurement schemes on the number of measurements needed to estimate anharmonic, vibrational states for a variety of three-mode (six-mode) molecules. We demonstrate an average of 3-fold (1.5-fold), with up to 7-fold (2.5-fold), reduction in the number of measurements required by employing appropriate coordinate transformations, based on an automized construction of qubit Hamiltonians from a conventional vibrational structure program.
Machine Learning in Cyberbullying Detection from Social-Media Image or Screenshot with Optical Character Recognition

Along with the growth of the Internet, social media usage has drastically expanded. As people share their opinions and ideas more frequently on the Internet and through various social media platforms, there has been a notable rise in the number of consumer phrases that contain sentiment data. According to reports, cyberbullying frequently leads to severe emotional and physical suffering, especially in women and young children. In certain instances, it has even been reported that sufferers attempt suicide. The bully may occasionally attempt to destroy any proof they believe to be on their side. Even if the victim gets the evidence, it will still be a long time before they get justice at that point. This work used OCR, NLP, and machine learning to detect cyberbullying in photos in order to design and execute a practical method to recognize cyberbullying from images. Eight classifier techniques are used to compare the accuracy of these algorithms against the BoW Model and the TF-IDF, two key features. These classifiers are used to understand and recognize bullying behaviors. Based on testing the suggested method on the cyberbullying dataset, it was shown that linear SVC after OCR and logistic regression perform better and achieve the best accuracy of 96 percent. This study aid in providing a good outline that shapes the methods for detecting online bullying from a screenshot with design and implementation details.
Robust and Optimized Algorithm for Detection of Copy-Rotate-Move Tempering

Since the entire world is covered with computerized digital data, information through digital media is not difficult to move, access, and recycle.Currently, advanced image processing tools are used to create tempered pictures.Using these apparatuses, falsifiers presented forged pictures to spread gossip and rumors.Presently, it is quite difficult for scientific divisions to demonstrate the reality of the original picture.Consequently, a new capable copy-rotate-move tempering recognition algorithm is required.To fulfill this requirement, a computationally efficient technique for copy-rotate-move tempering recognition using Zernike moments and ant colony optimization was proposed.Tests were conducted on various datasets to evaluate the effectiveness of the proposed method.The results were compared with those of the previously proposed state-ofthe-art method, using metrics such as F1 score, recall, and precision.It is evident that the proposed method outperforms the currently used state-of-the-art methods.
FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem; however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy.
The War of the Efficiencies: Understanding the Tension between Carbon and Energy Optimization

Major innovations in computing have been driven by scaling up computing infrastructure, while aggressively optimizing operating costs. The result is a network of worldwide datacenters that consume a large amount of energy, mostly in an energy-efficient manner. Since the electric grid powering these datacenters provided a simple and opaque abstraction of an unlimited and reliable power supply, the computing industry remained largely oblivious to the carbon intensity of the electricity it uses. Much like the rest of the society, it generally treated the carbon intensity of the electricity as constant, which was mostly true for a fossil fuel-driven grid. As a result, the cost-driven objective of increasing energy-efficiency --- by doing more work per unit of energy --- has generally been viewed as the most carbon-efficient approach. However, as the electric grid is increasingly powered by clean energy and is exposing its time-varying carbon intensity, the most energy-efficient operation is no longer necessarily the most carbon-efficient operation. There has been a recent focus on exploiting the flexibility of computing's workloads---along temporal, spatial, and resource dimensions---to reduce carbon emissions, which comes at the cost of either performance or energy efficiency. In this paper, we discuss the trade-offs between energy efficiency and carbon efficiency in exploiting computing's flexibility and show that blindly optimizing for energy efficiency is not always the right approach.
WhatsApp Messenger as a Supplementary Tool for School Curriculum Knowledge Transfer and Acquisition During COVID-19 Stricter Lockdown: Educators’ Perceptions

The COVID-19 pandemic was unarguably one of the most disastrous events whose detriment to the normalcy of the education and training sectors will never be forgotten. To salvage the academic year, the Department of Basic Education (DBE) encouraged historically disadvantaged schools to explore rotational learning supplemented by mobile learning (with WhatsApp Messenger as schools’ most preferred application). However, in face of the concerns that were raised by the media, educational commentators, student bodies and teacher unions about the lack of public schools’ readiness for mobile learning, this social constructivist oriented qualitative study (which drew on 12 educators’ diverse and convergent views) adopted a Technology Acceptance Model (TAM) as a lens to investigate educators’ perceptions of WhatsApp messenger as a supplementary mode of curriculum knowledge transfer and acquisition during COVID 19 stricter lockdown at three historically disadvantaged schools in South Africa. Participants generally deposited positive feedback regarding the benefit of using it WhatsApp messenger to engender curriculum knowledge transfer and acquisition. They however explicitly also detailed a few negative aspects of this pursuit, primarily on how for socio-economic related reasons, the process was not completely inclusive as some learners could not partake in it. Also, educators admitted that while going beyond the call of duty was necessary during the pandemic, WhatsApp mediated teaching consumed most of their leisure time. According to them, this was compounded by poor internet connectivity due to the country’s power crisis, which in some instances adversely affected the productivity of WhatsApp messenger mediated curriculum knowledge transfer and acquisition processes during COVID-19 stricter lockdown.
THE IMPACT OF ARTIFICIAL INTELLIGENCE (AI) ON GUEST SATISFACTION IN HOTEL MANAGEMENT: AN EMPIRICAL STUDY OF LUXURY HOTELS

This study examines the impact of artificial intelligence (AI) (Personalized Guest Experience, Chatbots and Virtual Assistants, Revenue Management, Operational Efficiency, Fraud Detection and Security, Predictive Maintenance) on guest satisfaction in luxury hotels from perspective of managers. A quantitative methodology was employed through adopting questionnaire that was self-administered by (89) managers within luxury five stars hotels in Amman – Jordan. SPSS was used to tackle the primary data, results of study was able to accepted study hypotheses arguing that artificial intelligence has the ability to increase guest satisfaction in luxury hotels. Results indicated that AI in hospitality industry has the ability to revolutionize the concept of guest satisfaction through easing the process of services and increase their loyalty as well. Study indicated that the highest agreement was for chatbots and virtual assistants in influence. However, it argued that while AI has the potential to significantly enhance guest satisfaction in luxury hotels, it is essential to balance the use of AI with human interaction. Many guests still value the personal touch and human connection that comes with traditional hospitality. Therefore, it is important for hotels to ensure that AI is used to enhance, rather than replace, human interaction with guests. Study recommended that hotels should be focused on providing personalized experiences, streamlining operations, and enhancing convenience and comfort in order to increase guest satisfaction. Further recommendations were presented in the study. Significance of study lies in the fact that examining the impact of AI in increasing guest satisfaction in luxury hotel play a role in increasing competitive advantage, help luxury hotels save costs by automating tasks and reducing the need for human labor and stay ahead of the curve and embrace new technologies such as AI. Originality of study is in the fact that it help in developing new theories and frameworks for understanding the complex relationship between technology and guest satisfaction in the hospitality industry.
Introduction to Human and Artificial Intelligence

Intelligence can be described in education as the ability to learn or understand new challenges that may arise in any situation, or even deal with them. Broadly speaking, intelligence in psychological terms refers to a person's ability to apply knowledge to manipulate the environment or to think abstractly.
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems

Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems.This paper tested six representative matrix factorization models, using four collaborative filtering datasets.Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity.Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values.To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided.