Illegal insider trading undermines the integrity of financial markets by exploiting non-public information for personal gain, posing risks to market fairness and investor trust. Addressing this issue is crucial to ensure transparency and maintain investor confidence. Estimates indicate that illegal insider trading occurs in about 20% of merger and acquisition events and 5% of quarterly earnings announcements. Our research introduces a deep learning-based approach that combines text classification, time series forecasting, anomaly detection and explainable AI to detect and predict potential illegal insider trading. By analyzing litigation related press releases and incorporating market and social media data, we develop a method to identify suspicious trading patterns. Our findings demonstrate that the proposed approach is promising in detecting potential instances of illegal insider trading, uncovering subtle patterns and anomalies. These insights offer financial regulators and institutions an enhanced surveillance mechanism, contributing to ongoing efforts to safeguard market fairness and reliability.

Our course provides an essential overview of 3D reconstruction and generative AI-based 3D generation approaches that have transformed the field. The course provides an understanding of the fundamentals of photogrammetry, neural radiance fields-based techniques such as Instant NeRF, and generative AI-based 3D generation such as text-to-3D, with a focus on reflecting on these approaches, their applications in 3D world building, and how participants can integrate them into their research.

We present a Multilingual Open Text (MOT), a new multilingual corpus containing text in 44 languages, many of which have limited existing text resources for natural language processing. The first release of the corpus contains over 2.8 million news articles and an additional 1 million short snippets (photo captions, video descriptions, etc.) published between 2001–2022 and collected from Voice of America’s news websites. We describe our process for collecting, filtering, and processing the data. The source material is in the public domain, our collection is licensed using a creative commons license (CC BY 4.0), and all software used to create the corpus is released under the MIT License. The corpus will be regularly updated as additional documents are published.

As Jerald (2018) states, though virtual reality (VR) has existed for over 50 years, its use as a creative medium is relatively new. In the last four years, as part of the 'second wave of VR', new affordability and accessibility of hardware and software for experiencing and creating VR has incited a surge of interest for the technology from creative industries. Meanwhile, interest and attempts to create VR projects has expanded into other forms of Extended Reality (XR) technologies, like Augmented Reality (AR) and Mixed Reality (MR).

course Share on Introduction to processing: technology into arts and arts into technology Author: June Kim UNSW Art & Design UNSW Art & DesignView Profile Authors Info & Claims SA '17: SIGGRAPH Asia 2017 CoursesNovember 2017 Article No.: 10Pages 1–46https://doi.org/10.1145/3134472.3134495Published:27 November 2017Publication History 0citation106DownloadsMetricsTotal Citations0Total Downloads106Last 12 Months11Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Immersive technologies and particularly the Virtual Reality (VR) provide new exciting ways to see the world. Today, we introduce our research that successfully employed VR to the biodiversity conservation sciences. Jaguars are one of the endangered animals. It is certainly critical and compelling to preserve ecosystem for endangered animals. With the awareness of this, we endeavoured to establish a multidisciplinary VR project that implemented data from indigenous villagers (jaguar experts group A), the conventional knowledge of the field of jaguar ecosystem (from jaguar experts group B), and mathematical and statistical models. Our fascination lies in these questions: can we effectively bring together VR and analytical capabilities? Can VR be used to make this world a better place for living beings? Please enjoy our 360-degree images of jaguar habitats taken in the Peruvian Amazon.

This paper proposes the online noise model adaptation technique using the modified quantile based noise estimation method for feature compensation of noisy speech that is based on the Gaussian mixture model for a robust speech recognition interface in real car environments. The proposed method is designed for an active online model adaptation method to cope with varying environmental noise conditions, and enhance speech recognition accuracy. This method is compensated on logarithmic filter-bank energies domain, and modified quantile based noise estimation method using beta-order harmonic mean is employed to the online noise estimation procedure. Experimental evaluation is done by using Aurora 2 speech database, and robust results were obtained than from other comparative algorithms.

This paper proposes the prediction based occluded multitarget tracking method using spatio-temporal attention mechanism. To cope with occlusion between targets, the proposed method provides an efficient method for more complex analysis by combining object association with partial probability model in spatially attentive window and occlusion activity detection in predicted temporal location. While multiple objects are moving or occluding between them in areas of visual field, a simultaneous tracking of multiple objects tends to fail. This is due to the fact that incompletely estimated feature vectors such as location, color, velocity, and acceleration of a target can provide only ambiguous and missing information. Thus, the spatially and temporally considered mechanism is proposed to track each target before, during, and after occlusion. Robustness of the proposed method is demonstrated with representative simulations.

In this paper, we examine the problems of conventional multimedia data models that deal with mainly temporal relationships among the component media data of multimedia data and propose a unified spatio-temporal relationship model for representing and presenting multimedia data dynamically. And, we design and implement IMAT (Internet Multimedia Authoring Tool) based on the proposed model. IMAT is developed by JAVA technologies, therefore it can be ported and operated on any kind of platform in which JVM runs.