To have the greatest impact, public health initiatives must be made using evidence-based decision-making. Machine learning Algorithms are created to gather, store, process, and analyse data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning has ended up curious about it as of late. This study uses a variety of machine learning and image processing approaches to detect and forecast the malarial illness. In our research, we discovered the potential of deep learning techniques as smart tools with broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We examine the common confinements of deep learning for computer frameworks and organising, counting need of preparing data, preparing overhead, realtime execution, and explain ability, and uncover future inquire about bearings focusing on these restrictions.

Information seeking is crucial for people's self-care and wellbeing in times of public crises. Extensive research has investigated empirical understandings as well as technical solutions to facilitate information seeking by domestic citizens of affected regions. However, limited knowledge is established to support international migrants who need to survive a crisis in their host countries. The current paper presents an interview study with two cohorts of Chinese migrants living in Japan (N=14) and the United States (N=14). Participants reflected on their information seeking experiences during the COVID pandemic. The reflection was supplemented by two weeks of self-tracking where participants maintained records of their COVIDrelated information seeking practice. Our data indicated that participants often took language detours, or visits to Mandarin resources for information about the COVID outbreak in their host countries. They also made strategic use of the Mandarin information to perform selective reading, cross-checking, and contextualized interpretation of COVID-related information in Japanese or English. While such practices enhanced participants' perceived effectiveness of COVID-related information gathering and sensemaking, they disadvantaged people through sometimes incognizant ways. Further, participants lacked the awareness or preference to review migrant-oriented information that was issued by the host country's public authorities despite its availability. Building upon these findings, we discussed solutions to improve international migrants' COVID-related information seeking in their non-native language and cultural environment. We advocated inclusive crisis infrastructures that would engage people with diverse levels of local language fluency, information literacy, and experience in leveraging public services.

The interwebs, as of late, have been filled with images created by artificial intelligence rendering bots such as DALL-E and Midjourney -- and the humans (I think they're humans) using them as tools. Brooklyn-based artist Zach Katz has used it to reimagine the urban design of cities. A reporter at SFGATE has undertaken a similar project, asking DALL-E 2 to retool some of the city's architecture and infrastructure. In July, the Guardian rounded up four artists to come up with unlikely prompts -- such as "biotech harpy in field at sunset" -- for DALL-E Mini (the free, public version of DALL-E). Naturally, the advent of bots that can create an image out of a simple text command is drawing the scrutiny of illustrators.

Debris from a drone strike in northern Ethiopia's Tigray region has damaged a truck carrying humanitarian aid and belonging to the World Food Programme (WFP) and injured the truck's driver, the United Nations agency said on Monday. The WFP said the drone strike on Sunday hit near an area called Zana Woreda in northwestern Tigray, as two trucks were delivering relief supplies to families displaced by the nearly two-year long conflict. "Flying debris from the strike injured a driver contracted by WFP and caused minor damage to a WFP fleet truck," the spokesperson said, adding it was not possible to say yet whether further distributions would be suspended in the area. "WFP calls on all parties to respect and adhere to international humanitarian laws and to commit to safeguarding humanitarian workers, premises and assets." The WFP truck was delivering food to internally displaced people as hundreds of thousands have been uprooted by renewed fighting since August 24 after a five-month ceasefire broke down.

Unexplainable black-box models create scenarios where anomalies cause deleterious responses, thus creating unacceptable risks. These risks have motivated the field of eXplainable Artificial Intelligence (XAI) to improve trust by evaluating local interpretability in black-box neural networks. Unfortunately, the ground truth is unavailable for the model's decision, so evaluation is limited to qualitative assessment. Further, interpretability may lead to inaccurate conclusions about the model or a false sense of trust. We propose to improve XAI from the vantage point of the user's trust by exploring a black-box model's latent feature space. We present an approach, ProtoShotXAI, that uses a Prototypical few-shot network to explore the contrastive manifold between nonlinear features of different classes. A user explores the manifold by perturbing the input features of a query sample and recording the response for a subset of exemplars from any class. Our approach is the first locally interpretable XAI model that can be extended to, and demonstrated on, few-shot networks. We compare ProtoShotXAI to the state-of-the-art XAI approaches on MNIST, Omniglot, and ImageNet to demonstrate, both quantitatively and qualitatively, that ProtoShotXAI provides more flexibility for model exploration. Finally, ProtoShotXAI also demonstrates novel explainabilty and detectabilty on adversarial samples.

The technical report presents our emotion recognition pipeline for high-dimensional emotion task (A-VB High) in The ACII Affective Vocal Bursts (A-VB) 2022 Workshop \& Competition. Our proposed method contains three stages. Firstly, we extract the latent features from the raw audio signal and its Mel-spectrogram by self-supervised learning methods. Then, the features from the raw signal are fed to the self-relation attention and temporal awareness (SA-TA) module for learning the valuable information between these latent features. Finally, we concatenate all the features and utilize a fully-connected layer to predict each emotion's score. By empirical experiments, our proposed method achieves a mean concordance correlation coefficient (CCC) of 0.7295 on the test set, compared to 0.5686 on the baseline model. The code of our method is available at https://github.com/linhtd812/A-VB2022.

Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve the well-trained clustering models, this paper proposes a novel method by first ranking samples within each cluster based on the confidence in their belonging to the current cluster and then using the ranking to formulate a weighted cross-entropy loss to train the model. For ranking the samples, we developed a method for computing the likelihood of samples belonging to the current clusters based on whether they are situated in densely populated neighborhoods, while for training the model, we give a strategy for weighting the ranked samples. We present extensive experimental results that demonstrate that the new technique can be used to improve the State-of-the-Art image clustering models, achieving accuracy performance gains ranging from $2.1\%$ to $15.9\%$. Performing our method on a variety of datasets from remote sensing, we show that our method can be effectively applied to remote--sensing images.

We discuss C-MP and C-MAPF, generalizations of the classical Motion Planning (MP) and Multi-Agent Path Finding (MAPF) problems on a directed graph G. Namely, we enforce an upper bound on the number of agents that occupy each member of a family of vertex subsets. For instance, this constraint allows maintaining a safety distance between agents. We prove that finding a feasible solution of C-MP and C-MAPF is NP-hard, and we propose a reduction method to convert them to standard MP and MAPF. This reduction method consists in finding a subset of nodes W and a reduced graph G/W, such that a solution of MAPF on G/W provides a solution of C-MAPF on G. Moreover, we study the problem of finding W of maximum cardinality, which is strongly NP-hard.

To create models that are robust across a wide range of test inputs, training datasets should include diverse examples that span numerous phenomena. Dynamic adversarial data collection (DADC), where annotators craft examples that challenge continually improving models, holds promise as an approach for generating such diverse training sets. Prior work has shown that running DADC over 1-3 rounds can help models fix some error types, but it does not necessarily lead to better generalization beyond adversarial test data. We argue that running DADC over many rounds maximizes its training-time benefits, as the different rounds can together cover many of the task-relevant phenomena. We present the first study of longer-term DADC, where we collect 20 rounds of NLI examples for a small set of premise paragraphs, with both adversarial and non-adversarial approaches. Models trained on DADC examples make 26% fewer errors on our expert-curated test set compared to models trained on non-adversarial data. Our analysis shows that DADC yields examples that are more difficult, more lexically and syntactically diverse, and contain fewer annotation artifacts compared to non-adversarial examples.

This thesis investigates share buybacks, specifically share buyback announcements. It addresses how to recognize such announcements, the excess return of share buybacks, and the prediction of returns after a share buyback announcement. We illustrate two NLP approaches for the automated detection of share buyback announcements. Even with very small amounts of training data, we can achieve an accuracy of up to 90%. This thesis utilizes these NLP methods to generate a large dataset consisting of 57,155 share buyback announcements. By analyzing this dataset, this thesis aims to show that most companies, which have a share buyback announced are underperforming the MSCI World. A minority of companies, however, significantly outperform the MSCI World. This significant overperformance leads to a net gain when looking at the averages of all companies. If the benchmark index is adjusted for the respective size of the companies, the average overperformance disappears, and the majority underperforms even greater. However, it was found that companies that announce a share buyback with a volume of at least 1% of their market cap, deliver, on average, a significant overperformance, even when using an adjusted benchmark. It was also found that companies that announce share buybacks in times of crisis emerge better than the overall market. Additionally, the generated dataset was used to train 72 machine learning models. Through this, it was able to find many strategies that could achieve an accuracy of up to 77% and generate great excess returns. A variety of performance indicators could be improved across six different time frames and a significant overperformance was identified. This was achieved by training several models for different tasks and time frames as well as combining these different models, generating significant improvement by fusing weak learners, in order to create one strong learner.