This is a study on the potential widespread usage of alternative fuel vehicles, linking them with the socio-economic status of the respective consumers as well as the impact on the resulting air quality index. Research in this area aims to leverage machine learning techniques in order to promote appropriate policies for the proliferation of alternative fuel vehicles such as electric vehicles with due justice to different population groups. Pearson correlation coefficient is deployed in the modeling the relationships between socio-economic data, air quality index and data on alternative fuel vehicles. Linear regression is used to conduct predictive modeling on air quality index as per the adoption of alternative fuel vehicles, based on socio-economic factors. This work exemplifies artificial intelligence for social good.

Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out multi-scenario transfer learning on the basis of the Mixture-of-Expert (MoE) architecture. However, the MoE-based method, which aims to project all information in the same feature space, cannot effectively deal with the complex relationships inherent among various scenarios and tasks, resulting in unsatisfactory performance. To tackle the problem, we propose a Hierarchical information extraction Network (HiNet) for multi-scenario and multi-task recommendation, which achieves hierarchical extraction based on coarse-to-fine knowledge transfer scheme. The multiple extraction layers of the hierarchical network enable the model to enhance the capability of transferring valuable information across scenarios while preserving specific features of scenarios and tasks. Furthermore, a novel scenario-aware attentive network module is proposed to model correlations between scenarios explicitly. Comprehensive experiments conducted on real-world industrial datasets from Meituan Meishi platform demonstrate that HiNet achieves a new state-of-the-art performance and significantly outperforms existing solutions. HiNet is currently fully deployed in two scenarios and has achieved 2.87% and 1.75% order quantity gain respectively.

Since Twitter launched in 2006, the company has acted as a kind of heartbeat for social media conversation. That's partly because it's where media people go to talk about the media, but also because it's been willing to open up its backend to researchers. Academics have used free access to Twitter's API, or application programming interface, in order to access data on the kinds of conversations occurring on the platform, which helps them understand what the online world is talking about. Twitter's API is used by vast numbers of researchers. Since 2020, there have been more than 17,500 academic papers based on the platform's data, giving strength to the argument that Twitter owner Elon Musk has long claimed, that the platform is the "de facto town square."

TikTok and ByteDance officials have since 2019 been negotiating with a group of federal officials, known as the Committee on Foreign Investment in the United States, about which privacy standards and technical safeguards they'd need to adopt to satisfy U.S. national-security concerns. The company finalized its proposal in August and presented it to CFIUS, but it has yet to be approved, and CFIUS officials have declined to explain why.

As demonstrated by [1], Sentiment Analysis (SA) can be studied at three levels: the document level where the task is to identify sentiment polarities (positive, neutral, or negative) that is indicated throughout the entire document. The sentence level is concerned with classifying sentiments relevant to a single sentence. But the document contains many sentences and each sentence may contain multiple aspects with different sentiments, so the document and sentence level sentiment analysis may not be accurate and need another suitable type that makes this fine-grained analysis called ABSA. ABSA was first launched on SemEval-2014 [2], with the introduction of datasets containing annotated restaurant and laptop reviews. ABSA's work was largely replicated at SemEval over the next two years [3, 4] as the task has extended into various domains, languages, and challenges. SemEval-2016 provided 39 datasets in 7 domains and 8 languages for the ABSA task, additionally, the datasets were provided with Support Vector Machine (SVM) as a baseline evaluation procedure.

The free flow of information has been accelerated by the rapid development of social media technology. There has been a significant social and psychological impact on the population due to the outbreak of Coronavirus disease (COVID-19). The COVID-19 pandemic is one of the current events being discussed on social media platforms. In order to safeguard societies from this pandemic, studying people's emotions on social media is crucial. As a result of their particular characteristics, sentiment analysis of texts like tweets remains challenging. Sentiment analysis is a powerful text analysis tool. It automatically detects and analyzes opinions and emotions from unstructured data. Texts from a wide range of sources are examined by a sentiment analysis tool, which extracts meaning from them, including emails, surveys, reviews, social media posts, and web articles. To evaluate sentiments, natural language processing (NLP) and machine learning techniques are used, which assign weights to entities, topics, themes, and categories in sentences or phrases. Machine learning tools learn how to detect sentiment without human intervention by examining examples of emotions in text. In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful in gaining a better understanding of society's needs and predicting future trends. We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models. The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.

ByteDance-owned TikTok wants to convince European governments that it is an industry leader in data protection, rather than a Chinese-owned app that warrants the wave of bans across the continent. The company outlined plans on Wednesday to build three European data centers to store information on TikTok's 150 million users in the region locally with the help of an independent third company that will oversee data access controls. Once operational, the data centers will cost the company €1.2 billion ($1.3 billion) annually. Similar to the company's Project Texas in the U.S., TikTok's Project Clover is meant to assure concerned governments that the Chinese Communist Party cannot access Europeans' data either through the front door, via official legal requests, or back door. It follows the White House endorsement on Tuesday of a bipartisan bill that could give the president authority to ban or force a sale of TikTok. This could be due to a conflict with your ad-blocking or security software.

Today, the web has become a mandatory platform to express users' opinions, emotions and feelings about various events. Every person using his smartphone can give his opinion about the purchase of a product, the occurrence of an accident, the occurrence of a new disease, etc. in blogs and social networks such as (Twitter, WhatsApp, Telegram and Instagram) register. Therefore, millions of comments are recorded daily and it creates a huge volume of unstructured text data that can extract useful knowledge from this type of data by using natural language processing methods. Sentiment analysis is one of the important applications of natural language processing and machine learning, which allows us to analyze the sentiments of comments and other textual information recorded by web users. Therefore, the analysis of sentiments, approaches and challenges in this field will be explained in the following.

While fine-tuning pre-trained language models has become a standard NLP practice, data labeling remains a significant bottleneck in NLP. To alleviate this problem, active learning (AL) methods have been used for various NLP tasks such as sentiment analysis and document classification.

Internet memes are characterised by the interspersing of text amongst visual elements. State-of-the-art multimodal meme classifiers do not account for the relative positions of these elements across the two modalities, despite the latent meaning associated with where text and visual elements are placed. Against two meme sentiment classification datasets, we systematically show performance gains from incorporating the spatial position of visual objects, faces, and text clusters extracted from memes. In addition, we also present facial embedding as an impactful enhancement to image representation in a multimodal meme classifier. Finally, we show that incorporating this spatial information allows our fully automated approaches to outperform their corresponding baselines that rely on additional human validation of OCR-extracted text.