Reviewerly/exHarmony huggingface dataset:
Integrating Noisy Knowledge into Language Representations for E-Commerce Applications

Integrating structured knowledge into language model representations increases recall of domain-specific information useful for downstream tasks. Matching between knowledge graph entities and text entity mentions can be easily performed when entity names are unique or there exists entity linking data. When extending this setting to new domains, newly mined knowledge contains ambiguous and incorrect information, with no explicit linking information. In such settings, we design a framework to robustly link relevant knowledge to input texts as an intermediate modeling step while performing end-to-end domain fine-tuning tasks. This is done by first computing the similarity of the existing task labels with candidate knowledge triplets to generate relevance labels. We use these labels to train a relevance model, which predicts the relevance of the inserted triplets to the original text. This relevance model is integrated within a language model, leading to our Knowledge Relevance BERT (KR-BERT) framework. We test KR-BERT for entity linking tasks on a real-world e-commerce dataset as well as a public linking task, where we show performance improvements over strong baselines.
Design and Application of Multi-objective Optimal Routing Algorithm in English Machine Translation System

In the field of English machine translation, translation is a crucial link. Traditional methods usually require manual operations to process English words, grammar and sentences, etc. However, through the design of English vocabulary and sentence structure based on the multi-objective optimization algorithm, it can not only solve the problem that traditional methods are difficult to deal with complex words and sentence patterns, but also store the text in pages on the server for readers to use to read or download query functions. Based on the multi-objective optimization routing algorithm, this paper designs the basic structure of the English machine translation system and tests the performance of the system. The test results show that the resource occupancy rate of the English machine translation model is generally between 2% and 5%. At the same time, it can find and translate content information that meets the standards in the data, thereby improving efficiency and accuracy. The research findings on multi-objective optimization routing algorithms in English machine translation systems have been conducted. Multi-objective optimization routing algorithms aim to find the optimal routes that satisfy multiple objectives simultaneously, such as minimizing translation errors, maximizing translation quality, and reducing processing time. These algorithms consider the trade-offs among different objectives and attempt to find a set of Pareto-optimal solutions that represent the best compromise among the objectives. The research on multi-objective optimization routing algorithms in English machine translation systems has shown promising results. These algorithms can significantly improve the overall performance of the translation system by considering multiple objectives simultaneously. By finding the optimal routes that balance different objectives, these algorithms can effectively enhance the translation accuracy, fluency, and efficiency.
Machine Learning Approach in Melanoma Cancer Stage Detection

Finding the stage of melanoma cancer is important for cancer research. Because of the lack of awareness of the signs, the number of mortalities has dramatically increased, making early detection of skin cancer essential. Due to its capacity to learn from data and create predictions based on that learning, the machine learning techniques that use deep learning algorithms such as Convolutional Neural Network (CNN) have been widely used in this sector.
Leveraging Deep Learning Model for Image Caption Generation for Scenes Description

The generation of image captions is an important task. with the recent advancements in deal learning paradigm, it is possible to develop models which are based on image feature extraction model and natural language processing to automate generation of image captions for a given image. Image captioning involves recognition of significant objects, features, and their associations with other objects in the given image and generate syntactically and semantically correct sentences. This paper presents detailed survey on recent methods used for automatically generating image captions. The contributions of the work include summarization on various techniques, recommendation of benchmark databases for training and testing image captioning models, implementation of model based on CNN and LSTM. The CNN model helps in the image feature extraction which is fed to the LSTM model to generate text in the natural language. The performance of the model is evaluated on Flickr 8k and model achieves a BLEU-I score of 0.755367.
GPT in Data Science: A Practical Exploration of Model Selection

There is an increasing interest in leveraging Large Language Models (LLMs) for managing structured data and enhancing data science processes. Despite the potential benefits, this integration poses significant questions regarding their reliability and decision-making methodologies. Our objective is to elucidate and express the factors and assumptions guiding GPT-4’s model selection recommendations. It highlights the importance of various factors in the model selection process, including the nature of the data, problem type, performance metrics, computational resources, interpretability vs accuracy, assumptions about data, and ethical considerations. We employ a variability model to depict these factors and use toy datasets to evaluate both the model and the implementation of the identified heuristics. By contrasting these outcomes with heuristics from other platforms, our aim is to determine the effectiveness and distinctiveness of GPT-4’s methodology. This research is committed to advancing our comprehension of AI decision-making processes, especially in the realm of model selection within data science. Our efforts are directed towards creating AI systems that are more transparent and comprehensible, contributing to a more responsible and efficient practice in data science.
Web 3.0: A Decentralized Future Empowered by Blockchain

Web 3.0, marks the third stage in the evolution of the World Wide Web. Its primary aim is to establish a decentralized and all-encompassing system built upon blockchain technology and advancements in the Semantic Web. In this upcoming version of the internet, users will have the capability to maintain ownership of their data, safeguarding their privacy. Consequently, the need for managing separate accounts across various social platforms will become obsolete. Web 3.0 imagines a forthcoming internet that prioritizes decentralization and democracy over the current model. In the future, services won’t be dependent on a single provider or a select group of providers; instead, algorithms will play a key role in hosting websites.
Context-based Fact-checking Using Knowledge Graph

There are many attentions for the fact-checking to prevent the hallucination, malfunction of circulating content on the web such as deception, counterfeit, fake news regardless of whether they are intended or not. For fact-checking, ConFcheKG (Context-based Fact-checking using Knowledge Graph) is proposed based on the Knowledge Graph (KG). In the content including multiple entities, the KG is adopted to check coherence between the entities to determine whether there are semantic conflicts among them. The coherence is based on the temporal, spatial, and logical arrangement of the contents based on the associated relationships among the entities using KG. To do this, it checks the existence of intersected KGs and conflicted KGs through mutual comparison of KGs step by step. According to the verifying process, if is the constructed KT with coherent entities of the content, then the reliability of the content’s coherence is high because it has a high probability of not being false, hallucination, fake-news, and so on. Otherwise, the probability of being false increases. To check the fact, ConFcheKG can be applied based on semantic coherence of the content, to reduce the hallucination, to determine the fake news, to detect deceptions, to prevent counterfeits, to generate the well-composed prompt for the generative AI, and so on.
Multi-View Graph Convolution for Participant Recommendation

Social networks have become essential for people’s lives. The proliferation of web services further expands social networks at an unprecedented scale, leading to immeasurable commercial value for online platforms. Recently, the group buying (GB) business mode is prevalent and also becoming more popular in E-commerce. GB explicitly forms groups of users with similar interests to secure better discounts from the merchants, often operating within social networks. It is a novel way to further unlock the commercial value by explicitly utilizing the online social network in E-commerce. Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate. This paper proposes Multi-View Graph Convolution for Participant Recommendation (MVPRec) to tackle this problem. To differentiate the roles of users (Initiator/Participant) within the GB process, we explicitly reconstruct historical GB data into initiator-view and participant-view graphs. Together with the social graph, we obtain a multi-view user representation with graph encoders. Then MVPRec fuses the GB and social representation with an attention module to obtain the user representation and learns a matching score with the initiator’s social friends via a multi-head attention mechanism. Social friends with the Top-k matching score are recommended for the corresponding GB process. Experiments on three datasets justify the effectiveness of MVPRec in the emerging participant recommendation problem. MVPRec is open-sourced at https://github.com/Xiaolong-Liu-bdsc/MVPRec to inspire further research in the new group buying E-commerce business mode.
Semantic Feature Extraction-Based Twitter Sentiment Analysis Using Atom Search Optimizer and Ensemble Classifier

In modern days, a computer-based technique called natural language processing (NLP) is used to process spoken and written natural language data that is specific to human culture. Numerous NLP research directions and technologies have steadily arisen in the process of mining large amounts of textual data. Furthermore, prior models ignored the combined effect of relative distance and grammatical distance on the aspect words, instead only including the relative or grammatical distance of words into the model. This study examines the use of learning algorithms for Twitter sentiment analysis that is based on emotions like fear, anger, joy, and sadness. Sentiment-140 dataset is used in this research to conduct the analysis. First, a pre-processing step using tokenization, stemming, adding part of speech, and punctuation removal is carried out using the collected datasets. Then, using Bag of Words (BoW), Latent Dirichlet Analysis (LDA), Term Frequency, and Inverse Document Frequency (TF-IDF), the processed data is employed for semantic feature extraction. The next step is to choose the best features for accurate classification using the Atom Search Optimizer (ASO). Finally, the chosen features are divided into various classes using a Voting based Ensemble Classifier (VEC). From the experimental results, it is observed that the proposed VEC has outperformed existing methods by obtaining 98.69% of accuracy on and Sentiment-140 dataset.
Sentiment Analysis of Client Reviews on a French E-Commerce Platform

This research aims to analyze customer reviews on a French e-commerce platform to understand customer perceptions and identify trends. The dataset includes a representative subset of reviews written in French, encompassing a diverse range of products and services. Machine learning techniques, such as recurrent neural networks as well as long short-term memory (LSTM), are used throughout the research to get emotional data via these reviews. The findings will provide valuable insights for businesses looking to improve their products or services by leveraging customer feedback. The research aims to provide valuable insights for businesses seeking to enhance their services.
Semantic Prompt Based Multi-Scale Transformer for Few-Shot Classification

Few-shot learning (FSL) is a machine learning method aimed at learning problem-solving models from a small number of samples. Few-shot classification is a task that utilizes few-shot learning for image classification. However, the existing models have poor classification performance when dealing with datasets with large differences in target scale distribution. In view of the large difference in the scale distribution of the sample target in the few-shot learning dataset, this paper proposes a novel few-shot classification model based on multi-scale Vision Transformer using semantic prompt. This model uses the Transformer block which has more generalization ability and long-range dependencies, and constructs a multi-scale feature fusion model on the large-scale cross-domain and cross-scale dataset Meta-Dataset which consists of 10 datasets, including natural image datasets, handwritten characters, and graffiti datasets. By using an explicit multi-scale feature fusion method, the model can obtain category features with cross scale information, thereby improving the robustness of the model to sample targets of different scales. In addition, the model improved its performance by learning additional semantic prompt, achieving a 2.43% improvement.
Text Segmentation and Transliteration Using Deep Learning

Reading text from images is crucial for understanding image content. This can range from scanned documents with predominant text, video frames with captions, or photos with naturally occurring text. Recognizing text in images can be used in various ways, such as indexing multimedia, assisting drivers by recognizing signs, aiding visually impaired individuals with scene descriptions, or identifying vehicles via license plates. The surge in affordable digital cameras and high-quality mobile phone cameras has intensified the focus on extracting text from photographs in the field of computer vision. While there are many OCR (Optical Character Recognition) systems, the preliminary step is to segment the text from the image. This project aims to extract and segment text from natural images, process it through an OCR, and enhance its usability through transliteration. It aims to enhance image-based text recognition by segmenting text from natural images, processing it with OCR, and amplifying its utility through transliteration, catering to diverse applications like multimedia indexing and aiding the visually impaired.
Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification

Aspect-based meeting transcript summarization aims to produce multiple summaries, each focusing on one aspect of content in a meeting transcript. It is challenging as sentences related to different aspects can mingle together, and those relevant to a specific aspect can be scattered throughout the long transcript of a meeting. The traditional summarization methods produce one summary mixing information of all aspects, which cannot deal with the above challenges of aspect-based meeting transcript summarization. In this paper, we propose a two-stage method for aspect-based meeting transcript summarization. To select the input content related to specific aspects, we train a sentence classifier on a dataset constructed from the AMI corpus with pseudo-labeling. Then we merge the sentences selected for a specific aspect as the input for the summarizer to produce the aspect-based summary. Experimental results on the AMI corpus outperform many strong baselines, which verifies the effectiveness of our proposed method.
A Federated Learning Scheme Based on Lightweight Differential Privacy

With the rapid growth of data and the increasing awareness of privacy protection, data privacy issues have become particularly important in the field of machine learning. Federated learning, as a distributed learning method, achieves collaborative training of models while preserving data privacy by keeping the data stationary and allowing the model to move. However, during the federated learning process, there is still a risk of privacy leakage when aggregating the intermediate parameters of models trained by different data providers. Researchers have found that adding noise to the intermediate parameters of the model using differential privacy can effectively prevent privacy inference on the data contributors. Nevertheless, there exists an inherent trade-off between the accuracy and privacy in federated learning models under differential privacy. Strengthening privacy protection often leads to a decrease in model performance. This trade-off becomes more pronounced in complex deep learning models that require multiple iterations to converge. To address the issues of data privacy, data silos, and the trade-off between data privacy leakage and model availability in deep learning within federated learning, this paper proposes a relaxed differential privacy federated learning approach. It reduces the impact of noise on the final results by selectively perturbing gradients when data providers return intermediate model parameters. Experiments demonstrate that this approach achieves a high level of accuracy while preserving data privacy. Additionally, it exhibits superior performance in terms of computational efficiency, striking a well-balanced compromise between accuracy and privacy.
SST: Semantic and Structural Transformers for Hierarchy-aware Language Models in E-commerce

Hierarchies are common structures used to organize data, such as e-commerce hierarchies associated with product data. With these product hierarchies, we aim to learn hierarchy-aware product text embeddings to improve fine-tuning performance on a variety of downstream e-commerce tasks. Existing methods leverage hierarchies by either aligning the text embeddings to separate hierarchical embeddings or by aligning the hierarchical information implicitly within a unified text Transformer. Although these models optimize to predict hierarchy information, performing further fine-tuning on new tasks is non-trivial. To bridge this gap, we propose a pre-training architecture to implicitly encode the hierarchy within the product text and then directly leverage a sub-set of the pre-training model during fine-tuning. Pre-training is done through Semantic and Structural Transformers (SST) where the Semantic-Transformer first encodes the product text into a contextual embedding, which is then used by the Structural-Transformer to infer the product’s path in the hierarchy. Fine-tuning is done using only the initial Semantic-Transformer, now that hierarchy-aware text embeddings are learned. With this design, we eliminate the need of linking each fine-tuning dataset with corresponding hierarchies. This leads to fine-tuning performance improvements on critical e-commerce downstream tasks over the existing state-of-the-art hierarchy models, even when hierarchy data $is$ available during fine-tuning. Moreover, this improvement is consistent even after augmenting our baseline models to support fine-tuning. We conclude by discussing how such implicit structural encodings can be leveraged beyond the e-commerce domain.
Causal Fairness-Guided Dataset Reweighting using Neural Networks

The importance of achieving fairness in machine learning models cannot be overstated. Recent research has pointed out that fairness should be examined from a causal perspective, and several fairness notions based on the on Pearl’s causal framework have been proposed. In this paper, we construct a reweighting scheme of datasets to address causal fairness. Our approach aims at mitigating bias by considering the causal relationships among variables and incorporating them into the reweighting process. The proposed method adopts two neural networks, whose structures are intentionally used to reflect the structures of a causal graph and of an interventional graph. The two neural networks can approximate the causal model of the data, and the causal model of interventions. Furthermore, reweighting guided by a discriminator is applied to achieve various fairness notions. Experiments on real-world datasets show that our method can achieve causal fairness on the data while remaining close to the original data for downstream tasks.
Cloud-Based Secure Vehicular Navigation System: Ensuring Privacy and Security in Intelligent Transportation

The rapid advancement in intelligent navigation systems has revolutionized the driving experience and significantly bolstered roadway safety. However, one of the fundamental challenges is to preserve the security of the navigation system. Therefore in this paper, we have addressed the vulnerabilities and threats of navigation systems and provided security against potential cryptographic attacks. Our research not only focuses on identifying and addressing these vulnerabilities but also offers insights into the broader implications of enhancing security in this domain. The performance evaluation and effectiveness of the proposed protocol have been evaluated using the AVISPA tool for security analysis, MIRACL libraries for computational cost, and comparative performance evaluations of the proposed protocol with existing works. The outcomes of the proposed protocol with existing related work show that the former offers improved security and computational efficiency.
Transaction Mechanism of Measurement and data Privacy

Metering and data privacy, as a transaction mechanism, means that when consumers purchase goods or receive services, they judge whether they need to provide corresponding information based on the information they obtain. This article aims to explore how to regulate marketing through the analysis of measurement and data. First, this paper introduced theories and model types such as statistical methods and model classification; secondly, taking security algorithms as an example, the concepts, characteristics and application status of quantization and privacy were explained. After that, this paper also designed a transaction mechanism model for data privacy, and tested the performance of all aspects of the mechanism model. The test resulted show that the stability of the model was relatively strong, and could be stabilized between 86% and 91%. In the research of measurement and data privacy transaction mechanisms, the application of privacy protection technologies such as differential privacy, homomorphic encryption, and secure multi-party computing algorithms can effectively protect users’ personal privacy information. In addition, research is also conducted on how to maximize the use of data for quantitative analysis, including data quality assessment, data cleaning, and repair methods, while protecting privacy, in order to improve the availability and value of data. At the same time, it is necessary to balance the relationship between privacy protection and data utility, and adjust the balance between the degree of privacy protection and data availability to meet the needs of different application scenarios. In addition, it is necessary to design appropriate data trading mechanisms and establish incentive mechanisms to encourage data providers to share data and ensure fairness and efficiency of transactions. In the research process, it is also necessary to consider relevant laws, regulations, and policy formulation, and explore their impact and guidance on measurement and data privacy trading mechanisms.