We propose a scheme for supervised image classification that uses privileged information, in the form of keypoint annotations for the training data, to learn strong models from small and/or biased training sets. Our main motivation is the recognition of animal species for ecological applications such as biodiversity modelling, which is challenging because of long-tailed species distributions due to rare species, and strong dataset biases such as repetitive scene background in camera traps. To counteract these challenges, we propose a visual attention mechanism that is supervised via keypoint annotations that highlight important object parts. This privileged information, implemented as a novel privileged pooling operation, is only required during training and helps the model to focus on regions that are discriminative. In experiments with three different animal species datasets, we show that deep networks with privileged pooling can use small training sets more efficiently and generalize better.

Over the past few months, shiny metallic orbs have materialized cities around the world, from New York to Berlin to Tokyo. Its detractors slam them as invasive, dystopian and exploitative. Welcome to the rollout of Worldcoin, an AI-meets-crypto project from OpenAI founder Sam Altman that has stirred endless controversy. The startup uses orbs to scan people's eyes in exchange for a digital ID and possibly some cryptocurrency, depending on what country they live in. Altman and his co-founder Alex Blania hope that Worldcoin will provide a new solution to online identity in a digital landscape rife with scams, bots and even AI imposters.

The European Space Agency said Thursday that it is using artificial intelligence for satellite navigation. The engineering teams of the agency's NAVISP program are working with European industry and academics to "invent the future of navigation," it said, resulting in a growing portfolio of services to improve space and Earth weather forecasting, enhance autonomous car and boat performance and help to identify rogue drones in sensitive airspace. The program aims to improve "satnav" performance by combining Global Navigation Satellite Systems (GNSS) with AI. "AI comprises all techniques enabling computers to mimic intelligence, whether they be data analysis systems or the embedded intelligence overseeing an autonomous vehicle," Rafael Lucas Rodriguez, the head of the NAVISP Technical Programme Office, said in a statement. A picture taken on February 7, 2020, shows the logo of the European Space Agency (ESA) at its European Space Operations Centre (ESOC) in Darmstadt, western Germany. "What AI is very good at, through so-called Machine Learning, ML, is extracting meaningful information to identify useful patterns that would otherwise have gone unseen. Satellite navigation is among the fields yielding large amounts of data, so within our sector AI could also serve as the basis of novel approaches and services," he noted.

Emergent chain-of-thought (CoT) reasoning capabilities promise to improve performance and explainability of large language models (LLMs). However, uncertainties remain about how reasoning strategies formulated for previous model generations generalize to new model generations and different datasets. In this small-scale study, we compare different reasoning strategies induced by zero-shot prompting across six recently released LLMs (davinci-002, davinci-003, GPT-3.5-turbo, GPT-4, Flan-T5-xxl and Cohere command-xlarge) on a mixture of six question-answering datasets, including datasets from scientific and medical domains. Our findings demonstrate that while some variations in effectiveness occur, gains from CoT reasoning strategies remain robust across different models and datasets. GPT-4 has the most benefit from current state-of-the-art reasoning strategies and exhibits the best performance by applying a prompt previously discovered through automated discovery.

Breast cancer is one of the leading causes of cancer mortality. Breast cancer patients in developing countries, especially sub-Saharan Africa, South Asia, and South America, suffer from the highest mortality rate in the world. One crucial factor contributing to the global disparity in mortality rate is long delay of diagnosis due to a severe shortage of trained pathologists, which consequently has led to a large proportion of late-stage presentation at diagnosis. The delay between the initial development of symptoms and the receipt of a diagnosis could stretch upwards 15 months. To tackle this critical healthcare disparity, this research has developed a deep learning-based diagnosis system for metastatic breast cancer that can achieve high diagnostic accuracy as well as computational efficiency. Based on our evaluation, the MobileNetV2-based diagnostic model outperformed the more complex VGG16, ResNet50 and ResNet101 models in diagnostic accuracy, model generalization, and model training efficiency. The visual comparisons between the model prediction and ground truth have demonstrated that the MobileNetV2 diagnostic models can identify very small cancerous nodes embedded in a large area of normal cells which is challenging for manual image analysis. Equally Important, the light weighted MobleNetV2 models were computationally efficient and ready for mobile devices or devices of low computational power. These advances empower the development of a resource-efficient and high performing AI-based metastatic breast cancer diagnostic system that can adapt to under-resourced healthcare facilities in developing countries. This research provides an innovative technological solution to address the long delays in metastatic breast cancer diagnosis and the consequent disparity in patient survival outcome in developing countries.

This paper proposes a machine learning-based approach for detecting the exploitation of vulnerabilities in the wild by monitoring underground hacking forums. The increasing volume of posts discussing exploitation in the wild calls for an automatic approach to process threads and posts that will eventually trigger alarms depending on their content. To illustrate the proposed system, we use the CrimeBB dataset, which contains data scraped from multiple underground forums, and develop a supervised machine learning model that can filter threads citing CVEs and label them as Proof-of-Concept, Weaponization, or Exploitation. Leveraging random forests, we indicate that accuracy, precision and recall above 0.99 are attainable for the classification task. Additionally, we provide insights into the difference in nature between weaponization and exploitation, e.g., interpreting the output of a decision tree, and analyze the profits and other aspects related to the hacking communities. Overall, our work sheds insight into the exploitation of vulnerabilities in the wild and can be used to provide additional ground truth to models such as EPSS and Expected Exploitability.

This paper provides an overview of the approach we used as team ISISTANITOS for the ACM RecSys Challenge 2023. The competition was organized by ShareChat, and involved predicting the probability of a user clicking an app ad and/or installing an app, to improve deep funnel optimization and a special focus on user privacy. Our proposed method inferring the probabilities of clicking and installing as two different, but related tasks. Hence, the model engineers a specific set of features for each task and a set of shared features. Our model is called Weighted Multi-Level Feature Factorization because it considers the interaction of different order features, where the order is associated to the depth in a neural network. The prediction for a given task is generated by combining the task specific and shared features on the different levels. Our submission achieved the 11 rank and overall score of 55 in the competition academia-track final results. We release our source code at: https://github.com/knife982000/RecSys2023Challenge

Aspect-Opinion Pair Extraction (AOPE) from Chinese financial texts is a specialized task in fine-grained text sentiment analysis. The main objective is to extract aspect terms and opinion terms simultaneously from a diverse range of financial texts. Previous studies have mainly focused on developing grid annotation schemes within grid-based models to facilitate this extraction process. However, these methods often rely on character-level (token-level) feature encoding, which may overlook the logical relationships between Chinese characters within words. To address this limitation, we propose a novel method called Graph-based Character-level Grid Tagging Scheme (GCGTS). The GCGTS method explicitly incorporates syntactic structure using Graph Convolutional Networks (GCN) and unifies the encoding of characters within the same syntactic semantic unit (Chinese word level). Additionally, we introduce an image convolutional structure into the grid model to better capture the local relationships between characters within evaluation units. This innovative structure reduces the excessive reliance on pre-trained language models and emphasizes the modeling of structure and local relationships, thereby improving the performance of the model on Chinese financial texts. Through comparative experiments with advanced models such as Synchronous Double-channel Recurrent Network (SDRN) and Grid Tagging Scheme (GTS), the proposed GCGTS model demonstrates significant improvements in performance.

Recommender systems that include some reliability measure of their predictions tend to be more conservative in forecasting, due to their constraint to preserve reliability. This leads to a significant drop in the coverage and novelty that these systems can provide. In this paper, we propose the inclusion of a new term in the learning process of matrix factorization-based recommender systems, called recklessness, which enables the control of the risk level desired when making decisions about the reliability of a prediction. Experimental results demonstrate that recklessness not only allows for risk regulation but also improves the quantity and quality of predictions provided by the recommender system.

Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enabling unsupervised representation learning is extremely crucial for time series data, due to its unique annotation bottleneck caused by its complex characteristics and lack of visual cues compared with other data modalities. In recent years, unsupervised representation learning techniques have advanced rapidly in various domains. However, there is a lack of systematic analysis of unsupervised representation learning approaches for time series. To fill the gap, we conduct a comprehensive literature review of existing rapidly evolving unsupervised representation learning approaches for time series. Moreover, we also develop a unified and standardized library, named ULTS (i.e., Unsupervised Learning for Time Series), to facilitate fast implementations and unified evaluations on various models. With ULTS, we empirically evaluate state-of-the-art approaches, especially the rapidly evolving contrastive learning methods, on 9 diverse real-world datasets. We further discuss practical considerations as well as open research challenges on unsupervised representation learning for time series to facilitate future research in this field.