Sentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes. Applications of sentiment analysis are wide, ranging from recommendation systems, and marketing to customer satisfaction. Recent approaches evaluate textual content using Machine Learning techniques that are trained over large corpora. However, as social media grown, other data types emerged in large quantities, such as images. Sentiment analysis in images has shown to be a valuable complement to textual data since it enables the inference of the underlying message polarity by creating context and connections. Multimodal sentiment analysis approaches intend to leverage information of both textual and image content to perform an evaluation. Despite recent advances, current solutions still flounder in combining both image and textual information to classify social media data, mainly due to subjectivity, inter-class homogeneity and fusion data differences. In this paper, we propose a method that combines both textual and image individual sentiment analysis into a final fused classification based on AutoML, that performs a random search to find the best model. Our method achieved state-of-the-art performance in the B-T4SA dataset, with 95.19% accuracy.

Title to be confirmed Speaker: Fabio Petroni Organised by: Stanford MLSys Join the email list to find out how to register for each seminar. Title to be confirmed Speaker: Chad Jenkins (University of Michigan) Organised by: Robotics Today Watch the seminar here. Title to be confirmed Speaker: Samory K. Kpotufe Organised by: London School of Economics and Political Science Register here.

Co-evolving time series appears in a multitude of applications such as environmental monitoring, financial analysis, and smart transportation. This paper aims to address the following challenges, including (C1) how to incorporate explicit relationship networks of the time series; (C2) how to model the implicit relationship of the temporal dynamics. We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN). TGCN tackles the first challenge by generalizing Graph Convolutional Network (GCN) for flat graphs to tensor graphs, which captures the synergy between multiple graphs associated with the tensors. TRNN leverages tensor decomposition to model the implicit relationships among co-evolving time series. The experimental results on five real-world datasets demonstrate the efficacy of the proposed method.

Early detection of fish diseases and identifying the underlying causes are crucial for farmers to take necessary steps to mitigate the potential outbreak, and thus to avert financial losses with apparent negative implications to national economy. Typically, fish diseases are caused by virus and bacteria; according to biochemical studies, the presence of certain bacteria and virus may affect the level of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N in water, resulting in the death of fishes. Besides, natural processes, e.g., photosynthesis, respiration, and decomposition also contribute to the alteration of water quality that adversely affects fish health. Being motivated by the recent successes of machine learning techniques in complex relational data analyses in accurate classification and decision-making tasks, a state-of-art machine learning algorithm has been adopted in this paper to detect and predict the degradation of water quality timely and accurately, thus it helps taking pre-emptive steps against potential fish diseases. The experimental results show a high accuracy in detecting fish diseases particular to specific water quality based on the algorithm with real datasets.

We give an efficient algorithm for learning a binary function in a given class C of bounded VC dimension, with training data distributed according to P and test data according to Q, where P and Q may be arbitrary distributions over X. This is the generic form of what is called covariate shift, which is impossible in general as arbitrary P and Q may not even overlap. However, recently guarantees were given in a model called PQ-learning (Goldwasser et al., 2020) where the learner has: (a) access to unlabeled test examples from Q (in addition to labeled samples from P, i.e., semi-supervised learning); and (b) the option to reject any example and abstain from classifying it (i.e., selective classification). The algorithm of Goldwasser et al. (2020) requires an (agnostic) noise tolerant learner for C. The present work gives a polynomial-time PQ-learning algorithm that uses an oracle to a "reliable" learner for C, where reliable learning (Kalai et al., 2012) is a model of learning with one-sided noise. Furthermore, our reduction is optimal in the sense that we show the equivalence of reliable and PQ learning.

Graphs are one of the most efficacious structures for representing datapoints and their relations, and they have been largely exploited for different applications. Previously, the higher-order relations between the nodes have been modeled by a generalization of graphs known as hypergraphs. In hypergraphs, the edges are defined by a set of nodes i.e., hyperedges to demonstrate the higher order relationships between the data. However, there is no explicit higher-order generalization for nodes themselves. In this work, we introduce a novel generalization of graphs i.e., K-Nearest Hyperplanes graph (KNH) where the nodes are defined by higher order Euclidean subspaces for multi-view modeling of the nodes. In fact, in KNH, nodes are hyperplanes or more precisely m-flats instead of datapoints. We experimentally evaluate the KNH graph on two multi-aspect datasets for misinformation detection. The experimental results suggest that multi-view modeling of articles using KNH graph outperforms the classic KNN graph in terms of classification performance.

Can the look and the feel of a website give information about the trustworthiness of an article? In this paper, we propose to use a promising, yet neglected aspect in detecting the misinformativeness: the overall look of the domain webpage. To capture this overall look, we take screenshots of news articles served by either misinformative or trustworthy web domains and leverage a tensor decomposition based semi-supervised classification technique. The proposed approach i.e., VizFake is insensitive to a number of image transformations such as converting the image to grayscale, vectorizing the image and losing some parts of the screenshots. VizFake leverages a very small amount of known labels, mirroring realistic and practical scenarios, where labels (especially for known misinformative articles), are scarce and quickly become dated. The F1 score of VizFake on a dataset of 50k screenshots of news articles spanning more than 500 domains is roughly 85% using only 5% of ground truth labels. Furthermore, tensor representations of VizFake, obtained in an unsupervised manner, allow for exploratory analysis of the data that provides valuable insights into the problem. Finally, we compare VizFake with deep transfer learning, since it is a very popular black-box approach for image classification and also well-known text text-based methods. VizFake achieves competitive accuracy with deep transfer learning models while being two orders of magnitude faster and not requiring laborious hyper-parameter tuning.

Even though Afaan Oromo is the most widely spoken language in the Cushitic family by more than fifty million people in the Horn and East Africa, it is surprisingly resource-scarce from a technological point of view. The increasing amount of various useful documents written in English language brings to investigate the machine that can translate those documents and make it easily accessible for local language. The paper deals with implementing a translation of English to Afaan Oromo and vice versa using Neural Machine Translation. But the implementation is not very well explored due to the limited amount and diversity of the corpus. However, using a bilingual corpus of just over 40k sentence pairs we have collected, this study showed a promising result. About a quarter of this corpus is collected via Community Engagement Platform (CEP) that was implemented to enrich the parallel corpus through crowdsourcing translations.

A viral video shows a young woman conducting an exercise class on a roundabout in the Burmese capital, Nyapyidaw. Behind her a military convoy approaches a checkpoint to go conduct arrests at the Parliament building. Has she inadvertently filmed a coup? The video later became a viral meme, but for the first days, online amateur sleuths debated if it was green-screened or otherwise manipulated, often using the jargon of verification and image forensics. Yet claims of audiovisual manipulation are increasingly being used to make people wonder if what is real is a fake.

The arrival of the New Year brings us to think in many areas. What does 2021 hold in store for artificial intelligence? Here are 10 Artificial Intelligence predictions, from academic research to capital markets to regulation. We will take stock in December 2021 to assess the results. Autonomous vehicle developers like Waymo and Cruise have ongoing and massive cash flow needs.