Since their proposal in the 2014 paper by Ian Goodfellow, there has been an explosion of research into the area of Generative Adversarial Networks. While they have been utilised in many fields, the realm of malware research is a problem space in which GANs have taken root. From balancing datasets to creating unseen examples in rare classes, GAN models offer extensive opportunities for application. This paper surveys the current research and literature for the use of Generative Adversarial Networks in the malware problem space. This is done with the hope that the reader may be able to gain an overall understanding as to what the Generative Adversarial model provides for this field, and for what areas within malware research it is best utilised. It covers the current related surveys, the different categories of GAN, and gives the outcomes of recent research into optimising GANs for different topics, as well as future directions for exploration.

Leveraging contextual knowledge has become standard practice in automated claim verification, yet the impact of temporal reasoning has been largely overlooked. Our study demonstrates that time positively influences the claim verification process of evidence-based fact-checking. The temporal aspects and relations between claims and evidence are first established through grounding on shared timelines, which are constructed using publication dates and time expressions extracted from their text. Temporal information is then provided to RNN-based and Transformer-based classifiers before or after claim and evidence encoding. Our time-aware fact-checking models surpass base models by up to 9% Micro F1 (64.17%) and 15% Macro F1 (47.43%) on the MultiFC dataset. They also outperform prior methods that explicitly model temporal relations between evidence. Our findings show that the presence of temporal information and the manner in which timelines are constructed greatly influence how fact-checking models determine the relevance and supporting or refuting character of evidence documents.

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Last Thursday, the US State Department outlined a new vision for developing, testing, and verifying military systems--including weapons--that make use of AI. The Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy represents an attempt by the US to guide the development of military AI at a crucial time for the technology. The document does not legally bind the US military, but the hope is that allied nations will agree to its principles, creating a kind of global standard for building AI systems responsibly. Among other things, the declaration states that military AI needs to be developed according to international laws, that nations should be transparent about the principles underlying their technology, and that high standards are implemented for verifying the performance of AI systems. It also says that humans alone should make decisions around the use of nuclear weapons.

Artificial intelligence (AI) offers a new way to track the insect pollinators essential to farming. In a new study, we installed miniature digital cameras and computers inside a greenhouse at a strawberry farm in Victoria, Australia, to track bees and other insects as they flew from plant to plant pollinating flowers. Using custom AI software, we analysed several days' video footage from our system to build a picture of pollination behaviour over a wide area. In the same way that monitoring roads can help traffic run smoothly, our system promises to make pollination more efficient. This will enable better use of resources and increased food production.

The next time you buy a flashy new outfit after browsing Instagram, or tap the heart button on a particularly compelling TikTok video, you might discover that the person who posted it isn't real--and you might not care at all. That is, if virtual influencers (and their creators) get their way. A virtual influencer is a digital personality that posts on social media to build an audience of passionate fans, just like a human influencer; at least, that's how it seems. In reality, a team of humans uses computer-generated imagery (CGI), motion capture, and marketing magic to give a digital avatar a voice, a life, and a brand. The result makes virtual influencers seem like, well, real people.

We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions. For fusing cues, we explore decision and feature-level fusion, and an additive attention-based fusion strategy which quantifies the relative importance of the three modalities for trait prediction. Examining various long-short term memory (LSTM) architectures for classification and regression on the MIT Interview and First Impressions Candidate Screening (FICS) datasets, we note that: (1) Multimodal approaches outperform unimodal counterparts; (2) Efficient trait predictions and plausible explanations are achieved with both unimodal and multimodal approaches, and (3) Following the thin-slice approach, effective trait prediction is achieved even from two-second behavioral snippets.

We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with little computational overhead. From an empirical observation that the log-marginal likelihood often exhibits a linear trend once a sufficient subset of a dataset has been observed, we conclude that many large datasets contain redundant information that only slightly affects the posterior. Based on this, we provide probabilistic bounds on the full model evidence that can identify such subsets. Remarkably, these bounds are largely composed of terms that appear in intermediate steps of the standard Cholesky decomposition, allowing us to modify the algorithm to adaptively stop the decomposition once enough data have been observed.

Modelling stockpile is a key factor of a project economic and operation in mining, because not all the mined ores are not able to mill for many reasons. Further, the financial value of the ore in the stockpile needs to be reflected on the balance sheet. Therefore, automatically tracking the frontiers of the stockpile facilitates the mine scheduling engineers to calculate the tonnage of the ore remaining in the stockpile. This paper suggests how the dynamic of stockpile shape changes caused by dumping and reclaiming operations can be inferred using polygon models. The presented work also demonstrates how the geometry of stockpiles can be inferred in the absence of reclaimed bucket information, in which case the reclaim polygons are established using the diggers GPS positional data at the time of truck loading. This work further compares two polygon models for creating 2D shapes.

In this paper, we introduce two methods to solve the American-style option pricing problem and its dual form at the same time using neural networks. Without applying nested Monte Carlo, the first method uses a series of neural networks to simultaneously compute both the lower and upper bounds of the option price, and the second one accomplishes the same goal with one global network. The avoidance of extra simulations and the use of neural networks significantly reduce the computational complexity and allow us to price Bermudan options with frequent exercise opportunities in high dimensions, as illustrated by the provided numerical experiments. As a by-product, these methods also derive a hedging strategy for the option, which can also be used as a control variate for variance reduction.