Then, I address the identified research problems Hate speech on social media has severe negative in hate speech detection, by investigating the impacts, not only on its victims (Sticca et al., impact of bias in NLP models on hate speech 2013) but also on the moderators of social detection models from three perspectives: 1) the media platforms (Roberts, 2019). This is why explainability perspective ( 4), where I address the it is crucial to develop tools for automated hate first research problem and investigate the impact speech detection. These tools should provide of bias in NLP models on their performance of a safer environment for individuals, especially hate speech detection and whether the bias in for members of marginalized groups, to express NLP models explains their performance on hate themselves online. However, recent research shows speech detection; 2) the offensive stereotyping that current hate speech detection models falsely bias perspective ( 5), where I address the second flag content written by members of marginalized research problem and investigate the impact of communities, as hateful (Sap et al., 2019; Dixon imbalanced representations and co-occurrences of et al., 2018; Mchangama et al., 2021). Similarly, hateful content with marginalized identity groups recent research indicates that there are social biases on the bias of NLP models; and 3) the fairness in natural language processing (NLP) models (Garg perspective ( 6), where I address the third research et al., 2018; Nangia et al., 2020; Kurita et al., 2019; problem and investigate the impact of bias in Ousidhoum et al., 2021; Nozza et al., 2021, 2022). NLP models on the fairness of the task of hate Yet, the impact of these biases on the task of speech detection. For each research problem, I hate speech detection has been understudied. In summarize the work done to highlight its main my thesis, I identify and study three research findings, contributions, and limitations. Thereafter, problems: 1) the impact of bias in NLP models on I discuss the general takeaways from my thesis and the performance and explainability of hate speech how it can benefit the NLP community at large ( 7).

Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially drive a new era in biomedical research, reducing the barriers for accessing existing medical evidence. This work examines the potential of LLMs for dialoguing with biomedical background knowledge, using the context of antibiotic discovery. The systematic analysis is applied to ten state-of-the-art models, from models specialised on biomedical scientific corpora to general models such as ChatGPT, GPT-4 and Llama 2 in two prompting-based tasks: chemical compound definition generation and chemical compound-fungus relation determination. The work provides a systematic assessment on the ability of LLMs to encode and express these relations, verifying for fluency, prompt-alignment, semantic coherence, factual knowledge and specificity of generated responses. Results show that while recent models have improved in fluency, factual accuracy is still low and models are biased towards over-represented entities. The ability of LLMs to serve as biomedical knowledge bases is questioned, and the need for additional systematic evaluation frameworks is highlighted. The best performing GPT-4 produced a factual definition for 70% of chemical compounds and 43.6% factual relations to fungi, whereas the best open source model BioGPT-large 30% of the compounds and 30% of the relations for the best-performing prompt. The results show that while LLMs are currently not fit for purpose to be used as biomedical factual knowledge bases, there is a promising emerging property in the direction of factuality as the models become domain specialised, scale-up in size and level of human feedback.

Understanding 3D scenes from multi-view inputs has been proven to alleviate the view discrepancy issue in 3D visual grounding. However, existing methods normally neglect the view cues embedded in the text modality and fail to weigh the relative importance of different views. In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding exploring how to grasp the view knowledge from both text and 3D modalities. For the text branch, ViewRefer leverages the diverse linguistic knowledge of large-scale language models, e.g., GPT, to expand a single grounding text to multiple geometry-consistent descriptions. Meanwhile, in the 3D modality, a transformer fusion module with inter-view attention is introduced to boost the interaction of objects across views. On top of that, we further present a set of learnable multi-view prototypes, which memorize scene-agnostic knowledge for different views, and enhance the framework from two perspectives: a view-guided attention module for more robust text features, and a view-guided scoring strategy during the final prediction. With our designed paradigm, ViewRefer achieves superior performance on three benchmarks and surpasses the second-best by +2.8%, +1.5%, and +1.35% on Sr3D, Nr3D, and ScanRefer. Code is released at https://github.com/Ivan-Tang-3D/ViewRefer3D.

Membership inference attacks (MIA) determine whether a specific data point has been used in training of a model [45]. These attacks represent a foundational tool in evaluating the privacy risks of unintentional exposure of information due to training machine learning models on different types of data in a wide range of scenarios. These scenarios encompass diverse settings such as statistical models [19, 2, 44, 36], machine learning as a service [45], federated learning [38, 26, 21], generative models [6], and also privacy-preserving machine-learning [47, 39, 20]. Membership inference attacks originated within the realm of summary statistics on high-dimensional data [19]. In this context, multiple hypothesis testing methods were developed to optimize the trade-off between test power and associated errors for relatively straightforward computations [44, 14, 36].

Rosário said the chatbot processed a 250-character command and took some 15 seconds to employ its algorithmic magic and spit out a policy -- a process that would normally take him about three days. The result, he said, showcased how artificial intelligence can be a useful tool for optimizing and improving public service. Yet Brazil's first ChatGPT-crafted law has launched the South American nation into a debate ringing across the globe: As artificial intelligence takes the world by storm, is society gearing toward a future where automation replaces humans?

Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary. We also present a study that assesses the quality of explanations created with this approach.

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT data/devices, the high dimensionality of training data, and the imbalanced data. Moreover, the deployment of IDSs on IoT systems is challenging, and sometimes impossible, due to the limited resources such as memory/storage and computing capability of typical IoT devices. To tackle these challenges, this article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) that can feed classifiers of IDSs with more separable/distinguishable and lower-dimensional representation data. Additionally, in comparison to the state-of-the-art neural networks used in IDSs, CTVAE requires less memory/storage and computing power, hence making it more suitable for IoT IDS systems. Extensive experiments with the 11 most popular IoT botnet datasets show that CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods, whilst the running time for attack detection is lower than 2E-6 seconds and the model size is lower than 1 MB. We also further investigate various characteristics of CTVAE in the latent space and in the reconstruction representation to demonstrate its efficacy compared with current well-known methods.

Bridge detection in remote sensing images (RSIs) plays a crucial role in various applications, but it poses unique challenges compared to the detection of other objects. In RSIs, bridges exhibit considerable variations in terms of their spatial scales and aspect ratios. Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with large-size VHR RSIs limits the deep learning algorithms' performance on bridge detection. Due to the limitation of GPU memory in tackling large-size images, deep learning-based object detection methods commonly adopt the cropping strategy, which inevitably results in label fragmentation and discontinuous prediction. To ameliorate the scarcity of datasets, this paper proposes a large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from diverse geographic locations across the globe. These images encompass a wide range of sizes, varying from 2,048*2,048 to 16,38*16,384 pixels, and collectively feature 59,737 bridges. Furthermore, we present an efficient network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net presents a separate detector-based feature fusion (SDFF) architecture and is optimized via a shape-sensitive sample re-weighting (SSRW) strategy. Based on the proposed GLH-Bridge dataset, we establish a bridge detection benchmark including the OBB and HBB tasks, and validate the effectiveness of the proposed HBD-Net. Additionally, cross-dataset generalization experiments on two publicly available datasets illustrate the strong generalization capability of the GLH-Bridge dataset.

Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.

The recent advancements in Large Language Models (LLMs) have sparked interest in harnessing their potential within recommender systems. Since LLMs are designed for natural language tasks, existing recommendation approaches have predominantly transformed recommendation tasks into open-domain natural language generation tasks. However, this approach necessitates items to possess rich semantic information, often generates out-of-range results, and suffers from notably low efficiency and limited extensibility. Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency. Nevertheless, the incapacity of LLMs to model IDs presents a formidable challenge when seeking to leverage LLMs for personalized recommendations. In this paper, we introduce an Elegant Effective Efficient Extensible solution for large language models for Sequential Recommendation (E4SRec), which seamlessly integrates LLMs with traditional recommender systems that exclusively utilize IDs to represent items. Specifically, E4SRec takes ID sequences as inputs, ensuring that the generated outputs fall within the candidate lists. Furthermore, E4SRec possesses the capability to generate the entire ranking list in a single forward process, and demands only a minimal set of pluggable parameters, which are trained for each dataset while keeping the entire LLM frozen. We substantiate the effectiveness, efficiency, and extensibility of our proposed E4SRec through comprehensive experiments conducted on four widely-used real-world datasets. The implementation code is accessible at https://github.com/HestiaSky/E4SRec/.