Open-world machine learning is an emerging technique in artificial intelligence, where conventional machine learning models often follow closed-world assumptions, which can hinder their ability to retain previously learned knowledge for future tasks. However, automated intelligence systems must learn about novel classes and previously known tasks. The proposed model offers novel learning classes in an open and continuous learning environment. It consists of two different but connected tasks. First, it discovers unknown classes in the data and creates novel classes; next, it learns how to perform class incrementally for each new class. Together, they enable continual learning, allowing the system to expand its understanding of the data and improve over time. The proposed model also outperformed existing approaches in open-world learning. Furthermore, it demonstrated strong performance in continuous learning, achieving a highest average accuracy of 82.54% over four iterations and a minimum accuracy of 65.87%.

Graph partitioning (GP), a.k.a. community detection, is a classic problem that divides nodes of a graph into densely-connected blocks. From a perspective of graph signal processing, we find that graph Laplacian with a negative correction can derive graph frequencies beyond the conventional range $[0, 2]$. To explore whether the low-frequency information beyond this range can encode more informative properties about community structures, we propose InfraredGP. It (\romannumeral1) adopts a spectral GNN as its backbone combined with low-pass filters and a negative correction mechanism, (\romannumeral2) only feeds random inputs to this backbone, (\romannumeral3) derives graph embeddings via one feed-forward propagation (FFP) without any training, and (\romannumeral4) obtains feasible GP results by feeding the derived embeddings to BIRCH. Surprisingly, our experiments demonstrate that based solely on the negative correction mechanism that amplifies low-frequency information beyond $[0, 2]$, InfraredGP can derive distinguishable embeddings for some standard clustering modules (e.g., BIRCH) and obtain high-quality results for GP without any training. Following the IEEE HPEC Graph Challenge benchmark, we evaluate InfraredGP for both static and streaming GP, where InfraredGP can achieve much better efficiency (e.g., 16x-23x faster) and competitive quality over various baselines. We have made our code public at https://github.com/KuroginQin/InfraredGP

If your cat's sulking, your dog's whining or your rabbit's doing that strange thing with its paws again, you will recognise that familiar pang of guilt shared by most other pet owners. But for those who wish they knew just what was going on in the minds of their loyal companions, help may soon be at hand – thanks to the establishment of first scientific institution dedicated to empirically investigating the consciousness of animals. The Jeremy Coller Centre for Animal Sentience, based at the London School of Economics and Political Science (LSE), will begin its work on 30 September, researching non-human animals, including those as evolutionarily distant from us as insects, crabs and cuttlefish. One of its most eye-catching projects will be to explore how AI can help humans "speak" with their pets, the dangers of it going wrong – and what we need to do to prevent that happening. "We like our pets to display human characteristics and with the advent of AI, the ways in which your pet will be able to speak to you is going to be taken to a whole new level," said Prof Jonathan Birch, the inaugural director of the centre.

Significant "social ruptures" between people who think artificial intelligence systems are conscious and those who insist the technology feels nothing are looming, a leading philosopher has said. The comments, from Jonathan Birch, a professor of philosophy at the London School of Economics, come as governments prepare to gather this week in San Francisco to accelerate the creation of guardrails to tackle the most severe risks of AI. Last week, a transatlantic group of academics predicted that the dawn of consciousness in AI systems is likely by 2035 and one has now said this could result in "subcultures that view each other as making huge mistakes" about whether computer programmes are owed similar welfare rights as humans or animals. Birch said he was "worried about major societal splits", as people differ over whether AI systems are actually capable of feelings such as pain and joy. The debate about the consequence of sentience in AI has echoes of science fiction films, such as Steven Spielberg's AI (2001) and Spike Jonze's Her (2013), in which humans grapple with the feeling of AIs. AI safety bodies from the US, UK and other nations will meet tech companies this week to develop stronger safety frameworks as the technology rapidly advances.

Madi Hime is taking a deep drag on a blue vape in the video, her eyes shut, her face flushed with pleasure. The 16-year-old exhales with her head thrown back, collapsing into laughter that causes smoke to billow out of her mouth. The clip is grainy and shaky – as if shot in low light by someone who had zoomed in on Madi's face – but it was damning. Madi was a cheerleader with the Victory Vipers, a highly competitive "all-star" squad based in Doylestown, Pennsylvania. The Vipers had a strict code of conduct; being caught partying and vaping could have got her thrown out of the team. And in July 2020, an anonymous person sent the incriminating video directly to Madi's coaches. Eight months later, that footage was the subject of a police news conference. "The police reviewed the video and other photographic images and found them to be what we now know to be called deepfakes," district attorney Matt Weintraub told the assembled journalists at the Bucks County courthouse on 15 March 2021. Someone was deploying cutting-edge technology to tarnish a teenage cheerleader's reputation. The vaping video was just one of many disturbing communications brought to the attention of Hilltown Township police department, Weintraub said. Madi had been receiving messages telling her she should kill herself. Her mother, Jennifer Hime, had told officers someone had been taking images from Madi's social media and manipulating them "to make her appear to be drinking".

Racism and intolerance on social media contribute to a toxic online environment which may spill offline to foster hatred, and eventually lead to physical violence. That is the case with online antisemitism, the specific category of hatred considered in this study. Tracking antisemitic themes and their associated terminology over time in online discussions could help monitor the sentiments of their participants and their evolution, and possibly offer avenues for intervention that may prevent the escalation of hatred. Due to the large volume and constant evolution of online traffic, monitoring conversations manually is impractical. Instead, we propose an automated method that extracts antisemitic themes and terminology from extremist social media over time and captures their evolution. Since supervised learning would be too limited for such a task, we created an unsupervised online machine learning approach that uses large language models to assess the contextual similarity of posts. The method clusters similar posts together, dividing, and creating additional clusters over time when sub-themes emerge from existing ones or new themes appear. The antisemitic terminology used within each theme is extracted from the posts in each cluster. Our experiments show that our methodology outperforms existing baselines and demonstrates the kind of themes and sub-themes it discovers within antisemitic discourse along with their associated terminology. We believe that our approach will be useful for monitoring the evolution of all kinds of hatred beyond antisemitism on social platforms.

Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also performs implicit outlier detection during the clustering. AWT integrates ideas of several well-known K-Means clustering algorithms. It chooses the number of clusters automatically based on a user-defined threshold parameter, and it can be used for heterogeneous meteorological input data as well as for data sets that exceed the available memory size. We apply AWT to crowd sourced 2-m temperature data with an hourly resolution from the city of Vienna to detect outliers and to investigate if the final clusters show general similarities and similarities with urban land-use characteristics. It is shown that both the outlier detection and the implicit mapping to land-use characteristic is possible with AWT which opens new possible fields of application, specifically in the rapidly evolving field of urban climate and urban weather.

Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the performance of network anomaly multi-classification. In our proposed method, we first apply Birch or Kmeans as an unsupervised clustering algorithm to the CICIDS-2017 dataset to pre-group the data. The generated pseudo-label is then added as an additional feature to the training of the MLP-based classifier. The experimental results show that using Birch and K-Means clustering for data pre-grouping can improve intrusion detection system performance. Our method can achieve 99.73% accuracy in multi-classification using Birch clustering, which is better than similar researches using a stand-alone MLP model.

This algorithm is used to perform hierarchical clustering based on trees. These trees are called CFT i.e. The full form of BIRCH is Balanced Iterative Reducing Clusters using Hierarchies. The metric use in this cluster to measure the distance is Euclidean distance measurement. When we get a massive dataset and BIRCH is not fulfilling the requirement because of memory constraints of using the whole dataset then we should consider mini-batches of fixed size from the dataset to get reduced runtime.

Based on this, one can further divide distance-based methods into three categories: 1) assuming number of clusters as Clustering is a fundamental problem in machine known in advance, 2) a distance threshold as known or 3) learning where distance-based approaches have by assuming a limiting number of data points belonging to dominated the field for many decades. This set each particular cluster. of problems is often tackled by partitioning the data into K clusters where the number of clusters While clustering algorithms primarily focus on accurately is chosen apriori. While significant progress has partitioning the data, they also aimed at inferring information been made on these lines over the years, it is well from a data exploration standpoint. In this work, we established that as the number of clusters or dimensions primarily focus on distance-based clustering given its broad increase, current approaches dwell in adoption and propose a new framework, ThetA, which uses local minima resulting in suboptimal solutions.