Synthetic data from generative models emerges as the privacy-preserving data-sharing solution. Such a synthetic data set shall resemble the original data without revealing identifiable private information. The backbone technology of tabular synthesizers is rooted in image generative models, ranging from Generative Adversarial Networks (GANs) to recent diffusion models. Recent prior work sheds light on the utility-privacy tradeoff on tabular data, revealing and quantifying privacy risks on synthetic data. We first conduct an exhaustive empirical analysis, highlighting the utility-privacy tradeoff of five state-of-the-art tabular synthesizers, against eight privacy attacks, with a special focus on membership inference attacks. Motivated by the observation of high data quality but also high privacy risk in tabular diffusion, we propose DP-TLDM, Differentially Private Tabular Latent Diffusion Model, which is composed of an autoencoder network to encode the tabular data and a latent diffusion model to synthesize the latent tables. Following the emerging f-DP framework, we apply DP-SGD to train the auto-encoder in combination with batch clipping and use the separation value as the privacy metric to better capture the privacy gain from DP algorithms. Our empirical evaluation demonstrates that DP-TLDM is capable of achieving a meaningful theoretical privacy guarantee while also significantly enhancing the utility of synthetic data. Specifically, compared to other DP-protected tabular generative models, DP-TLDM improves the synthetic quality by an average of 35% in data resemblance, 15% in the utility for downstream tasks, and 50% in data discriminability, all while preserving a comparable level of privacy risk.

This paper investigates the impact of corpus creation decisions on large multi-lingual geographic web corpora. Beginning with a 427 billion word corpus derived from the Common Crawl, three methods are used to improve the quality of sub-corpora representing specific language-country pairs like New Zealand English: (i) the agreement of independent language identification systems, (ii) hash-based deduplication, and (iii) location-specific outlier detection. The impact of each of these steps is then evaluated at the language level and the country level by using corpus similarity measures to compare each resulting corpus with baseline data sets. The goal is to understand the impact of upstream data cleaning decisions on downstream corpora with a specific focus on under-represented languages and populations. The evaluation shows that the validity of sub-corpora is improved with each stage of cleaning but that this improvement is unevenly distributed across languages and populations. This result shows how standard corpus creation techniques can accidentally exclude under-represented populations.

Negation is an important perspective of knowledge representation. Existing negation methods are mainly applied in probability theory, evidence theory and complex evidence theory. As a generalization of evidence theory, random permutation sets theory may represent information more precisely. However, how to apply the concept of negation to random permutation sets theory has not been studied. In this paper, the negation of permutation mass function is proposed. Moreover, in the negation process, the convergence of proposed negation method is verified. The trends of uncertainty and dissimilarity after each negation operation are investigated. Numerical examples are used to demonstrate the rationality of the proposed method.

How could we have an outlier detector that works even with nondimensional data, and ranks together both singleton microclusters ('one-off' outliers) and nonsingleton microclusters by their anomaly scores? How to obtain scores that are principled in one scalable and 'hands-off' manner? Microclusters of outliers indicate coalition or repetition in fraud activities, etc.; their identification is thus highly desirable. This paper presents McCatch: a new algorithm that detects microclusters by leveraging our proposed 'Oracle' plot (1NN Distance versus Group 1NN Distance). We study 31 real and synthetic datasets with up to 1M data elements to show that McCatch is the only method that answers both of the questions above; and, it outperforms 11 other methods, especially when the data has nonsingleton microclusters or is nondimensional. We also showcase McCatch's ability to detect meaningful microclusters in graphs, fingerprints, logs of network connections, text data, and satellite imagery. For example, it found a 30-elements microcluster of confirmed 'Denial of Service' attacks in the network logs, taking only ~3 minutes for 222K data elements on a stock desktop.

Open-sourced, user-friendly tools form the bedrock of scientific advancement across disciplines. The widespread adoption of data-driven learning has led to remarkable progress in multi-fingered dexterity, bimanual manipulation, and applications ranging from logistics to home robotics. However, existing data collection platforms are often proprietary, costly, or tailored to specific robotic morphologies. We present OPEN TEACH, a new teleoperation system leveraging VR headsets to immerse users in mixed reality for intuitive robot control. Built on the affordable Meta Quest 3, which costs $500, OPEN TEACH enables real-time control of various robots, including multi-fingered hands and bimanual arms, through an easy-to-use app. Using natural hand gestures and movements, users can manipulate robots at up to 90Hz with smooth visual feedback and interface widgets offering closeup environment views. We demonstrate the versatility of OPEN TEACH across 38 tasks on different robots. A comprehensive user study indicates significant improvement in teleoperation capability over the AnyTeleop framework. Further experiments exhibit that the collected data is compatible with policy learning on 10 dexterous and contact-rich manipulation tasks. Currently supporting Franka, xArm, Jaco, and Allegro platforms, OPEN TEACH is fully open-sourced to promote broader adoption. Videos are available at https://open-teach.github.io/.

The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying a small subset of the inputs sufficient to support them. Since existing methods still suffer from adopting the shortcuts in data to compose rationales and limited large-scale annotated rationales by human, in this paper, we propose a Shortcuts-fused Selective Rationalization (SSR) method, which boosts the rationalization by discovering and exploiting potential shortcuts. Specifically, SSR first designs a shortcuts discovery approach to detect several potential shortcuts. Then, by introducing the identified shortcuts, we propose two strategies to mitigate the problem of utilizing shortcuts to compose rationales. Finally, we develop two data augmentations methods to close the gap in the number of annotated rationales. Extensive experimental results on real-world datasets clearly validate the effectiveness of our proposed method.

Self-admitted technical debt (SATD) refers to a form of technical debt in which developers explicitly acknowledge and document the existence of technical shortcuts, workarounds, or temporary solutions within the codebase. Over recent years, researchers have manually labeled datasets derived from various software development artifacts: source code comments, messages from the issue tracker and pull request sections, and commit messages. These datasets are designed for training, evaluation, performance validation, and improvement of machine learning and deep learning models to accurately identify SATD instances. However, class imbalance poses a serious challenge across all the existing datasets, particularly when researchers are interested in categorizing the specific types of SATD. In order to address the scarcity of labeled data for SATD \textit{identification} (i.e., whether an instance is SATD or not) and \textit{categorization} (i.e., which type of SATD is being classified) in existing datasets, we share the \textit{SATDAUG} dataset, an augmented version of existing SATD datasets, including source code comments, issue tracker, pull requests, and commit messages. These augmented datasets have been balanced in relation to the available artifacts and provide a much richer source of labeled data for training machine learning or deep learning models.

We present HiRA-Pro, a novel procedure to align, at high spatio-temporal resolutions, multimodal signals from real-world processes and systems that exhibit diverse transient, nonlinear stochastic dynamics, such as manufacturing machines. It is based on discerning and synchronizing the process signatures of salient kinematic and dynamic events in these disparate signals. HiRA-Pro addresses the challenge of aligning data with sub-millisecond phenomena, where traditional timestamp, external trigger, or clock-based alignment methods fall short. The effectiveness of HiRA-Pro is demonstrated in a smart manufacturing context, where it aligns data from 13+ channels acquired during 3D-printing and milling operations on an Optomec-LENS MTS 500 hybrid machine. The aligned data is then voxelized to generate 0.25 second aligned data chunks that correspond to physical voxels on the produced part. The superiority of HiRA-Pro is further showcased through case studies in additive manufacturing, demonstrating improved machine learning-based predictive performance due to precise multimodal data alignment. Specifically, testing classification accuracies improved by almost 35% with the application of HiRA-Pro, even with limited data, allowing for precise localization of artifacts. The paper also provides a comprehensive discussion on the proposed method, its applications, and comparative qualitative analysis with a few other alignment methods. HiRA-Pro achieves temporal-spatial resolutions of 10-1000 us and 100 um in order to generate datasets that register with physical voxels on the 3D-printed and milled part. These resolutions are at least an order of magnitude finer than the existing alignment methods that employ individual timestamps, statistical correlations, or common clocks, which achieve precision of hundreds of milliseconds.

Signed graphs are an emergent way of representing data in a variety of contexts were conflicting interactions exist. These include data from biological, ecological, and social systems. Here we propose the concept of communicability geometry for signed graphs, proving that metrics in this space, such as the communicability distance and angles, are Euclidean and spherical. We then apply these metrics to solve several problems in data analysis of signed graphs in a unified way. They include the partitioning of signed graphs, dimensionality reduction, finding hierarchies of alliances in signed networks as well as the quantification of the degree of polarization between the existing factions in systems represented by this type of graphs.

The increasing utilization of emerging technologies in the Food & Agriculture (FA) sector has heightened the need for security to minimize cyber risks. Considering this aspect, this manuscript reviews disclosed and documented cybersecurity incidents in the FA sector. For this purpose, thirty cybersecurity incidents were identified, which took place between July 2011 and April 2023. The details of these incidents are reported from multiple sources such as: the private industry and flash notifications generated by the Federal Bureau of Investigation (FBI), internal reports from the affected organizations, and available media sources. Considering the available information, a brief description of the security threat, ransom amount, and impact on the organization are discussed for each incident. This review reports an increased frequency of cybersecurity threats to the FA sector. To minimize these cyber risks, popular cybersecurity frameworks and recent agriculture-specific cybersecurity solutions are also discussed. Further, the need for AI assurance in the FA sector is explained, and the Farmer-Centered AI (FCAI) framework is proposed. The main aim of the FCAI framework is to support farmers in decision-making for agricultural production, by incorporating AI assurance. Lastly, the effects of the reported cyber incidents on other critical infrastructures, food security, and the economy are noted, along with specifying the open issues for future development.