Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for E-commerce Product Reviews: Current Trends and Future Directions

Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today&amp;#039;s business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.
Application of 3D hologram technology combined with reciprocal style to learn some fundamental boxing skills

The use of 3D Hologram Technology (3DHT) in teaching and learning has many advantages, such as attracting students’ attention, reducing cognitive load and self-effort, and improving spatial awareness. In addition, a number of studies have confirmed that the reciprocal teaching style is effective in teaching motor skills. Thus, the current study aimed to investigate the effectiveness of reciprocal style when combined with 3DHT in learning some fundamental boxing skills. A quasi-experimental design was used by creating two experimental and control groups. For the experimental group, the reciprocal style is combined with 3DHT to teach some fundamental boxing skills. In contrast, the control group is taught a program based on a teacher command style. Pretest-posttest designs were made for the two groups. The sample consisted of 40 boxing beginners aged 12 to 14 who were enrolled in the 2022/2023 training season at Port Fouad Sports Club in Port Said, Egypt. The participants were randomly divided into two experimental and control groups. There were homogenized based on age, height, weight, IQ, physical fitness, and skill level. The results showed the experimental group achieved a higher skill level due to the combination of 3DHT and a reciprocal style in compared to the control group, which relied solely on the teacher’s command style,. Due to this, it is imperative to utilize hologram technology as a pedagogical resource to enhance the learning process and combine it with teaching strategies that support active learning.
The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial

In this tutorial paper, we first define mean squared error, variance, covariance, and bias of both random variables and classification/predictor models. Then, we formulate the true and generalization errors of the model for both training and validation/test instances where we make use of the Stein's Unbiased Risk Estimator (SURE). We define overfitting, underfitting, and generalization using the obtained true and generalization errors. We introduce cross validation and two well-known examples which are $K$-fold and leave-one-out cross validations. We briefly introduce generalized cross validation and then move on to regularization where we use the SURE again. We work on both $\ell_2$ and $\ell_1$ norm regularizations. Then, we show that bootstrap aggregating (bagging) reduces the variance of estimation. Boosting, specifically AdaBoost, is introduced and it is explained as both an additive model and a maximum margin model, i.e., Support Vector Machine (SVM). The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting. As examples of regularization, the theory of ridge and lasso regressions, weight decay, noise injection to input/weights, and early stopping are explained. Random forest, dropout, histogram of oriented gradients, and single shot multi-box detector are explained as examples of bagging in machine learning and computer vision. Finally, boosting tree and SVM models are mentioned as examples of boosting.
BiasAsker: Measuring the Bias in Conversational AI System

Powered by advanced Artificial Intelligence (AI) techniques, conversational AI systems, such as ChatGPT and digital assistants like Siri, have been widely deployed in daily life. However, such systems may still produce content containing biases and stereotypes, causing potential social problems. Due to the data-driven, black-box nature of modern AI techniques, comprehensively identifying and measuring biases in conversational systems remains a challenging task. Particularly, it is hard to generate inputs that can comprehensively trigger potential bias due to the lack of data containing both social groups as well as biased properties. In addition, modern conversational systems can produce diverse responses (e.g., chatting and explanation), which makes existing bias detection methods simply based on the sentiment and the toxicity hardly being adopted. In this paper, we propose BiasAsker, an automated framework to identify and measure social bias in conversational AI systems. To obtain social groups and biased properties, we construct a comprehensive social bias dataset, containing a total of 841 groups and 8,110 biased properties. Given the dataset, BiasAsker automatically generates questions and adopts a novel method based on existence measurement to identify two types of biases (i.e., absolute bias and related bias) in conversational systems. Extensive experiments on 8 commercial systems and 2 famous research models, such as ChatGPT and GPT-3, show that 32.83% of the questions generated by BiasAsker can trigger biased behaviors in these widely deployed conversational systems. All the code, data, and experimental results have been released to facilitate future research.
GPT-3.5, GPT-4, or BARD? Evaluating LLMs Reasoning Ability in Zero-Shot Setting and Performance Boosting Through Prompts

Large Language Models (LLMs) have exhibited remarkable performance on various Natural Language Processing (NLP) tasks. However, there is a current hot debate regarding their reasoning capacity. In this paper, we examine the performance of GPT-3.5, GPT-4, and BARD models, by performing a thorough technical evaluation on different reasoning tasks across eleven distinct datasets. Our paper provides empirical evidence showcasing the superior performance of ChatGPT-4 in comparison to both ChatGPT-3.5 and BARD in zero-shot setting throughout almost all evaluated tasks. While the superiority of GPT-4 compared to GPT-3.5 might be explained by its larger size and NLP efficiency, this was not evident for BARD. We also demonstrate that the three models show limited proficiency in Inductive, Mathematical, and Multi-hop Reasoning Tasks. To bolster our findings, we present a detailed and comprehensive analysis of the results from these three models. Furthermore, we propose a set of engineered prompts that enhances the zero-shot setting performance of all three models.
Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training

Automatic text simplification systems help to reduce textual information barriers on the internet. However, for languages other than English, only few parallel data to train these systems exists. We propose a two-step approach to overcome this data scarcity issue. First, we fine-tuned language models on a corpus of German Easy Language, a specific style of German. Then, we used these models as decoders in a sequence-to-sequence simplification task. We show that the language models adapt to the style characteristics of Easy Language and output more accessible texts. Moreover, with the style-specific pre-training, we reduced the number of trainable parameters in text simplification models. Hence, less parallel data is sufficient for training. Our results indicate that pre-training on unaligned data can reduce the required parallel data while improving the performance on downstream tasks.
Deepfake Text Detection in the Wild

Recent advances in large language models have enabled them to reach a level of text generation comparable to that of humans. These models show powerful capabilities across a wide range of content, including news article writing, story generation, and scientific writing. Such capability further narrows the gap between human-authored and machine-generated texts, highlighting the importance of deepfake text detection to avoid potential risks such as fake news propagation and plagiarism. However, previous work has been limited in that they testify methods on testbed of specific domains or certain language models. In practical scenarios, the detector faces texts from various domains or LLMs without knowing their sources. To this end, we build a wild testbed by gathering texts from various human writings and deepfake texts generated by different LLMs. Human annotators are only slightly better than random guessing at identifying machine-generated texts. Empirical results on automatic detection methods further showcase the challenges of deepfake text detection in a wild testbed. In addition, out-of-distribution poses a greater challenge for a detector to be employed in realistic application scenarios. We release our resources at https://github.com/yafuly/DeepfakeTextDetect.
Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights

Text-based collaborative filtering (TCF) has become the mainstream approach for text and news recommendation, utilizing text encoders, also known as language models (LMs), to represent items. However, existing TCF models primarily focus on using small or medium-sized LMs. It remains uncertain what impact replacing the item encoder with one of the largest and most powerful LMs, such as the 175-billion parameter GPT-3 model, would have on recommendation performance. Can we expect unprecedented results? To this end, we conduct an extensive series of experiments aimed at exploring the performance limits of the TCF paradigm. Specifically, we increase the size of item encoders from one hundred million to one hundred billion to reveal the scaling limits of the TCF paradigm. We then examine whether these extremely large LMs could enable a universal item representation for the recommendation task. Furthermore, we compare the performance of the TCF paradigm utilizing the most powerful LMs to the currently dominant ID embedding-based paradigm and investigate the transferability of this TCF paradigm. Finally, we compare TCF with the recently popularized prompt-based recommendation using ChatGPT. Our research findings have not only yielded positive results but also uncovered some surprising and previously unknown negative outcomes, which can inspire deeper reflection and innovative thinking regarding text-based recommender systems. Codes and datasets will be released for further research.
APLIKASI PENGOLAHAN CITRA SEBAGAI MEDIA PENGENALAN BATIK NUSANTARA

In this case the development of clothing from year to year changes in motifs from certain areas. Traditional batik has many types and motifs where each variation contains a meaning that reflects the art of its manufacture, thus leading to a special meaning that is good to see with the many other motifs and variations. This research method uses several sources from the library, references and materials related to this research. The formula is how to design an application that can be used as an interactive batik introduction and the implementation process of this introduction media application can be more interesting. Testing this application using the black box and white box testing methods, the test results are stated that the test has been successful.&#x0D; The results of this study are Image Processing Applications as a Media for Introduction to Batik Nusantara, which helps users in selecting or matching batik cloth models that have been provided by markers easily and attractively.
IMPLEMENTASI WEB SERVICE DENGAN METODE REST API UNTUK INTEGRASI DATA COVID 19 DI SULAWESI SELATAN

Web service is a software that is used as a liaison that allows various systems to communicate with each other without being affected by differences in platforms and not directly connected to the database they have. So the web service will provide existing services on the old system to be able to communicate with the new system without making changes to both. This paper will explain about making web services that can integrate web applications and mobile applications in the COVID-19 Development Information System in South Sulawesi. Using RESTful technology to exchange data in the form of JSON via URI (Uniform Resource Identifier). In this study, the author can produce a centralizedby utilizing websiteweb service the REST APImethod in the data retrieval process which aims to combine or unify the data even though it comes from different databases.
<scp>EVKG</scp>: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

Abstract Over the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV‐centric knowledge graph (EVKG) as a comprehensive, cross‐domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV‐related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision‐making related to EV technology development, infrastructure planning, and policy‐making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV‐relevant ontology modules from existing well‐known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision‐making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV‐related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV‐related knowledge into a single, easily accessible resource, the EVKG supports decision‐makers in making informed choices about EV technology development, infrastructure planning, and policy‐making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.
HAMBATAN PEMANGKU KEBIJAKAN DALAM MENGGUNAKAN DATA RUTIN KESEHATAN IBU DAN ANAK UNTUK PERENCANAAN DAN PENGANGGARAN

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Framework of Interaction Design Method Based on Blockchain System

&lt;p&gt;As the fifth subversive and innovative technology for computing paradigm after the mainframe computer, personal computer, internet and mobile/social network, blockchain technology has entered into a new phase of development. Nevertheless, more intrinsic issues remain to be explored. With the usage of cryptography and a distributed database, once a block is recorded, it can no longer be changed. To defeat these issues, more existing techniques have emerged. The blockchain system mainly features decentralization, but as the existing architectures of the blockchain system are mixed up, it is hard to determine whether they are centralized or decentralized. Such hybrid feature is derived from the impact of uncertainty of human demands. In this study, we intend to explore the scientific perception of the blockchain technology after its integration with human activities by adopting the method of interaction design. Furthermore, we plan to establish a set of mechanisms on the method of blockchain system-based interaction design, so as to identify the impact of human uncertainty factors on the design of the blockchain system. In addition, we aim to provide theoretical guidance for establishing the decentralized blockchain systems in both standardized and mixed categories and the centralized blockchain system with decentralization as central node. Through the methods of the observational results, the article is summarized and assessed the methods of the interaction design based on blockchain system. Moreover, the evaluation process consists of the analysis of expert evaluation, practical evaluation and participatory evaluation ferments an efficient outcome.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Intelligent Chat Bots: An AI Based Chat Bot For Better Banking Applications

A text-based and audio-based chat and voice assistant for a banking application that is powered by artificial intelligence and does conversions that are designed to resemble human conversations. Chat and voice assistants have come a long way from their humble beginnings, with advances in machine learning and natural language processing enabling them to learn from their interactions and hold conversations in a human-like manner. Becoming more human can help with calculating loan interests and checking transaction details and savings, among other things. Chat and voice assistants have come a long way from their humble beginnings. Conversational banking is an improved method for retaining loyal customers because it enables the bank to respond more quickly to the customers’ inquiries. The Stone Age was followed by the Bronze Age, and now we are in the Digital Age thanks to the advancement of technology. When it comes to customer behaviour, the transition from being a service seeker to a game-changer is happening at a very rapid rate. Those days are long gone when a company could still get away with putting the requirements of their customers lower on their priority list. Since the customer is now the person who ultimately decides whether a company will succeed or fail, it has become the top goal of any company to make certain that the needs and desires of the customer are met to the greatest possible extent.
Fine-Grained Redactable Blockchain Using Trapdoor-Hash

The blockchains immutability permits widespread application in untrusted contexts such as digital currency, the Internet of Things, and anti-counterfeiting. This functionality, however, presents difficulties for regulators, developers, and users. For instance, unsuitable data may be permanently retained, blockchains may breach applicable rules and regulations, and smart contracts may contain bugs. To elegantly address a series of issues arising from the immutability of the blockchain, we propose a redactable blockchain that utilizes trapdoor-hash to repair harmful data in the blockchain, allowing for efficient transaction editing, deletion, and smart contract repair without destroying the blockchain structure or security features. Additionally, our solution provides fine-grained control over modification authority, allowing the user to change the transaction he initiated and the regulators to delete the erroneous transaction. Finally, we integrate and test the suggested technique with one of the most influential enterprise-grade permissioned blockchain platforms, Hyperledger Fabric. The results demonstrate that our technique has a low overhead when compared to an immutable blockchain. As compared to other existing redactable blockchains, our technique provides greater permission control flexibility without sacrificing efficiency or performance.
An Architecture That Enables Cross-Chain Interoperability for Next-Gen Blockchain Systems

Blockchain technology is crucial for cutting-edge demands and aligns with the trend toward decentralized architecture. Interoperability between private and public blockchain technology can revolutionize digital record-keeping and enable automation. Traditional database systems saw major developments when application programming interfaces (APIs) and data were used across centralized entities. For blockchain technology, the natural evolution would be to facilitate communication and data exchange between private and public blockchain technologies, potentially revolutionizing digital record-keeping for future automation. Our research tests this interoperability in real-world scenarios and explores smart city elements and use-cases for application in the next generation of blockchain systems.
HOW METAVERSE EVOLVES THE ARCHITECTURAL DESIGN

Architects have long relied on visualization tools to develop their concepts for specific design problems.From the early traditional drawings to the three-dimensional visualizations and virtual environments, all have enabled architects to demonstrate design outputs relatively early in the process.Real-world projects are similar to what architects imagined from the beginning.In other words, the design process has always started by creating the digital representation of a project and then attempting to replicate it in real life.Once the digital representation of design parts is complete, architects prepare their design for construction.However, the final visualization emerges from actual architectural functions, structure constraints, Gravity, materiality, privacy, and physical laws, meaning that architecture evolves the digitally represented visualizations.With the growth of the metaverse, all physical restrictions are being eliminated, and architects can expand the boundaries of how spaces can be represented regardless of being virtual or physical.As a virtual environment on the internet, the metaverse redefines the rules of architecture and offers endless possibilities for architectural innovation.This article aims to explore the role the metaverse plays in designing architecture.It outlines the fundamental concepts of the metaverse to identify significant elements that could influence architecture design.
MIXED REALITY FRAMEWORK FOR ARCHITECTURAL DESIGN EDUCATION

This study introduces a framework to build a new concept of architectural education based on the idea of Mixed Reality (MR) as a technique of merging real and virtual learning environment, studying its impact on the education curricular contents and the course outline especially in design studio courses to provide new possibilities for innovation design.the study aims to achieve strategies to design a model for architectural design education with the focus on the concept of design studio's evolution through the analysis of its basic components, between Mind (Think of the human factor), Reality (spatial factor for the design process), Media (tools used in Design) and the work organization in the studio between students and instructor/s and the Design Processes from the pre-design stage to design development stage.