Cloud computing services provide scalable and cost-effective solutions for data storage, processing, and collaboration. Alongside their growing popularity, concerns related to their security vulnerabilities leading to data breaches and sophisticated attacks such as ransomware are growing. To address these, first, we propose a generic framework to express relations between different cloud objects such as users, datastores, security roles, to model access control policies in cloud systems. Access control misconfigurations are often the primary driver for cloud attacks. Second, we develop a PDDL model for detecting security vulnerabilities which can for example lead to widespread attacks such as ransomware, sensitive data exfiltration among others. A planner can then generate attacks to identify such vulnerabilities in the cloud. Finally, we test our approach on 14 real Amazon AWS cloud configurations of different commercial organizations. Our system can identify a broad range of security vulnerabilities, which state-of-the-art industry tools cannot detect.

Over the last year, governments, academia, and industry have invested considerable resources into investigating the harms of advanced AI. But one massive factor seems to be continuously overlooked: right now, AI's primary tests and models are confined to English. Advanced AI could be used in many languages to cause harm, but focusing primarily on English may leave us with only part of the answer. It also ignores those most vulnerable to its harms. After the release of ChatGPT in November, 2022, AI developers expressed surprise at a capability displayed by the model: It could "speak" at least 80 languages, not just English.

Northern Ireland's rising prison population means an extra 75 Prison Service staff will have to be recruited at a cost of 3.5m, Justice Minister Naomi Long has announced. A disused cell block at Maghaberry is also being prepared for re-opening as part of contingency planning. The jail currently has 1,245 inmates – almost half of them are on remand, meaning they have not been convicted or sentenced. Mrs Long said the situation is challenging.PA MediaJustice minister Naomi Long says there has been a steep rise in prisoner numbers in recent years Northern Ireland has three prison sites: Maghaberry, Magilligan and Hydebank Wood, which houses women prisoners and young offenders. Over the last three years, inmate numbers across the sites have increased by 500 to 1,900.

The idea of smart cities (SCs) has gained substantial attention in recent years. The SC paradigm aims to improve citizens' quality of life and protect the city's environment. As we enter the age of next-generation SCs, it is important to explore all relevant aspects of the SC paradigm. In recent years, the advancement of Information and Communication Technologies (ICT) has produced a trend of supporting daily objects with smartness, targeting to make human life easier and more comfortable. The paradigm of SCs appears as a response to the purpose of building the city of the future with advanced features. SCs still face many challenges in their implementation, but increasingly more studies regarding SCs are implemented. Nowadays, different cities are employing SC features to enhance services or the residents quality of life. This work provides readers with useful and important information about Amman Smart City.

Human-produced emissions are growing at an alarming rate, causing already observable changes in the climate and environment in general. Each year global carbon dioxide emissions hit a new record, and it is reported that 0.5% of total US greenhouse gas emissions are attributed to data centres as of 2021. The release of ChatGPT in late 2022 sparked social interest in Large Language Models (LLMs), the new generation of Language Models with a large number of parameters and trained on massive amounts of data. Currently, numerous companies are releasing products featuring various LLMs, with many more models in development and awaiting release. Deep Learning research is a competitive field, with only models that reach top performance attracting attention and being utilized. Hence, achieving better accuracy and results is often the first priority, while the model's efficiency and the environmental impact of the study are neglected. However, LLMs demand substantial computational resources and are very costly to train, both financially and environmentally. It becomes essential to raise awareness and promote conscious decisions about algorithmic and hardware choices. Providing information on training time, the approximate carbon dioxide emissions and power consumption would assist future studies in making necessary adjustments and determining the compatibility of available computational resources with model requirements. In this study, we infused T5 LLM with external knowledge and fine-tuned the model for Question-Answering task. Furthermore, we calculated and reported the approximate environmental impact for both steps. The findings demonstrate that the smaller models may not always be sustainable options, and increased training does not always imply better performance. The most optimal outcome is achieved by carefully considering both performance and efficiency factors.

Although not all bots are malicious, the vast majority of them are responsible for spreading misinformation and manipulating the public opinion about several issues, i.e., elections and many more. Therefore, the early detection of bots is crucial. Although there have been proposed methods for detecting bots in social media, there are still substantial limitations. For instance, existing research initiatives still extract a large number of features and train traditional machine learning algorithms or use GloVe embeddings and train LSTMs. However, feature extraction is a tedious procedure demanding domain expertise. Also, language models based on transformers have been proved to be better than LSTMs. Other approaches create large graphs and train graph neural networks requiring in this way many hours for training and access to computational resources. To tackle these limitations, this is the first study employing only the user description field and images of three channels denoting the type and content of tweets posted by the users. Firstly, we create digital DNA sequences, transform them to 3d images, and apply pretrained models of the vision domain, including EfficientNet, AlexNet, VGG16, etc. Next, we propose a multimodal approach, where we use TwHIN-BERT for getting the textual representation of the user description field and employ VGG16 for acquiring the visual representation for the image modality. We propose three different fusion methods, namely concatenation, gated multimodal unit, and crossmodal attention, for fusing the different modalities and compare their performances. Finally, we present a qualitative analysis of the behavior of our best performing model. Extensive experiments conducted on the Cresci'17 and TwiBot-20 datasets demonstrate valuable advantages of our introduced approaches over state-of-the-art ones.

Writing, as an omnipresent form of human communication, permeates nearly every aspect of contemporary life. Consequently, inaccuracies or errors in written communication can lead to profound consequences, ranging from financial losses to potentially life-threatening situations. Spelling mistakes, among the most prevalent writing errors, are frequently encountered due to various factors. This research aims to identify and rectify diverse spelling errors in text using neural networks, specifically leveraging the Bidirectional Encoder Representations from Transformers (BERT) masked language model. To achieve this goal, we compiled a comprehensive dataset encompassing both non-real-word and real-word errors after categorizing different types of spelling mistakes. Subsequently, multiple pre-trained BERT models were employed. To ensure optimal performance in correcting misspelling errors, we propose a combined approach utilizing the BERT masked language model and Levenshtein distance. The results from our evaluation data demonstrate that the system presented herein exhibits remarkable capabilities in identifying and rectifying spelling mistakes, often surpassing existing systems tailored for the Persian language.

While hallucinations of large language models (LLMs) prevail as a major challenge, existing evaluation benchmarks on factuality do not cover the diverse domains of knowledge that the real-world users of LLMs seek information about. To bridge this gap, we introduce WildHallucinations, a benchmark that evaluates factuality. It does so by prompting LLMs to generate information about entities mined from user-chatbot conversations in the wild. These generations are then automatically fact-checked against a systematically curated knowledge source collected from web search. Notably, half of these real-world entities do not have associated Wikipedia pages. We evaluate 118,785 generations from 15 LLMs on 7,919 entities. We find that LLMs consistently hallucinate more on entities without Wikipedia pages and exhibit varying hallucination rates across different domains. Finally, given the same base models, adding a retrieval component only slightly reduces hallucinations but does not eliminate hallucinations.

While large language models are rapidly moving towards consumer-facing applications, they are often still prone to factual errors and hallucinations. In order to reduce the potential harms that may come from these errors, it is important for users to know to what extent they can trust an LLM when it makes a factual claim. To this end, we study the problem of uncertainty quantification of factual correctness in long-form natural language generation. Given some output from a large language model, we study both uncertainty at the level of individual claims contained within the output (via calibration) and uncertainty across the entire output itself (via conformal prediction). Moreover, we invoke multicalibration and multivalid conformal prediction to ensure that such uncertainty guarantees are valid both marginally and across distinct groups of prompts. Using the task of biography generation, we demonstrate empirically that having access to and making use of additional group attributes for each prompt improves both overall and group-wise performance. As the problems of calibration, conformal prediction, and their multi-group counterparts have not been extensively explored previously in the context of long-form text generation, we consider these empirical results to form a benchmark for this setting.

In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer's perception of data, leading to misinterpretations and decisions based on false information. The development of effective automatic detection methods for misleading charts is an urgent field of research. The recent advancement of multimodal Large Language Models (LLMs) has introduced a promising direction for addressing this challenge. We explored the capabilities of these models in analyzing complex charts and assessing the impact of different prompting strategies on the models' analyses. We utilized a dataset of misleading charts collected from the internet by prior research and crafted nine distinct prompts, ranging from simple to complex, to test the ability of four different multimodal LLMs in detecting over 21 different chart issues. Through three experiments--from initial exploration to detailed analysis--we progressively gained insights into how to effectively prompt LLMs to identify misleading charts and developed strategies to address the scalability challenges encountered as we expanded our detection range from the initial five issues to 21 issues in the final experiment. Our findings reveal that multimodal LLMs possess a strong capability for chart comprehension and critical thinking in data interpretation. There is significant potential in employing multimodal LLMs to counter misleading information by supporting critical thinking and enhancing visualization literacy. This study demonstrates the applicability of LLMs in addressing the pressing concern of misleading charts.