OpenRaiser/Intern-Atlas huggingface dataset:
Same or Different? Diff-Vectors for Authorship Analysis
Venue: arXiv cs.LG 2023
Year: 2023

We investigate the effects on authorship identification tasks of a fundamental shift in how to conceive the vectorial representations of documents that are given as input to a supervised learner. In ``classic'' authorship analysis a feature vector represents a document, the value of a feature represents (an increasing function of) the relative frequency of the feature in the document, and the class label represents the author of the document. We instead investigate the situation in which a feature vector represents an unordered pair of documents, the value of a feature represents the absolute difference in the relative frequencies (or increasing functions thereof) of the feature in the two documents, and the class label indicates whether the two documents are from the same author or not. This latter (learner-independent) type of representation has been occasionally used before, but has never been studied systematically. We argue that it is advantageous, and that in some cases (e.g., authorship verification) it provides a much larger quantity of information to the training process than the standard representation. The experiments that we carry out on several publicly available datasets (among which one that we here make available for the first time) show that feature vectors representing pairs of documents (that we here call Diff-Vectors) bring about systematic improvements in the effectiveness of authorship identification tasks, and especially so when training data are scarce (as it is often the case in real-life authorship identification scenarios). Our experiments tackle same-author verification, authorship verification, and closed-set authorship attribution; while DVs are naturally geared for solving the 1st, we also provide two novel methods for solving the 2nd and 3rd that use a solver for the 1st as a building block.
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
Venue: arXiv cs.NE 2023
Year: 2023

Empirical data plays an important role in evolutionary computation research. To make better use of the available data, ontologies have been proposed in the literature to organize their storage in a structured way. However, the full potential of these formal methods to capture our domain knowledge has yet to be demonstrated. In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology. More specifically, we first extend the OPTION ontology with the vocabulary needed to represent modular black-box optimization algorithms. Then, we use the extended OPTION ontology, to create knowledge graphs with fixed-budget performance data for two modular algorithm frameworks, modCMA, and modDE, for the 24 noiseless BBOB benchmark functions. We build the performance prediction model using a knowledge graph embedding-based methodology. Using a number of different evaluation scenarios, we show that a triple classification approach, a fairly standard predictive modeling task in the context of knowledge graphs, can correctly predict whether a given algorithm instance will be able to achieve a certain target precision for a given problem instance. This approach requires feature representation of algorithms and problems. While the latter is already well developed, we hope that our work will motivate the community to collaborate on appropriate algorithm representations.
ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition
Venue: arXiv cs.CV 2023
Year: 2023

The Odeuropa Challenge on Olfactory Object Recognition aims to foster the development of object detection in the visual arts and to promote an olfactory perspective on digital heritage. Object detection in historical artworks is particularly challenging due to varying styles and artistic periods. Moreover, the task is complicated due to the particularity and historical variance of predefined target objects, which exhibit a large intra-class variance, and the long tail distribution of the dataset labels, with some objects having only very few training examples. These challenges should encourage participants to create innovative approaches using domain adaptation or few-shot learning. We provide a dataset of 2647 artworks annotated with 20 120 tightly fit bounding boxes that are split into a training and validation set (public). A test set containing 1140 artworks and 15 480 annotations is kept private for the challenge evaluation.
Fever: Optimal Responsive View Synchronisation
Venue: arXiv cs.DC 2023
Year: 2023

View synchronisation is an important component of many modern Byzantine Fault Tolerant State Machine Replication (SMR) systems in the partial synchrony model. Roughly, the efficiency of view synchronisation is measured as the word complexity and latency required for moving from being synchronised in a view of one correct leader to being synchronised in the view of the next correct leader. The efficiency of view synchronisation has emerged as a major bottleneck in the efficiency of SMR systems as a whole. A key question remained open: Do there exist view synchronisation protocols with asymptotically optimal quadratic worst-case word complexity that also obtain linear message complexity and responsiveness when moving between consecutive correct leaders? We answer this question affirmatively with a new view synchronisation protocol for partial synchrony assuming minimal clock synchronisation, called \emph{Fever}. If $n$ is the number of processors and $t$ is the largest integer $<n/3$, then Fever has resilience $t$, and in all executions with at most $0\leq f\leq t$ Byzantine parties and network delays of at most $δ\leq Δ$ after $GST$ (where $f$ and $δ$ are unknown), Fever has worst-case word complexity $O(fn+n)$ and worst-case latency $O(Δf + δ)$.
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop
Venue: arXiv cs.HC 2023
Year: 2023

This paper presents our project proposal for extracting biomedical information from German clinical narratives with limited amounts of annotations. We first describe the applied strategies in transfer learning and active learning for solving our problem. After that, we discuss the design of the user interface for both supplying model inspection and obtaining user annotations in the interactive environment.
Quorum Tree Abstractions of Consensus Protocols
Venue: arXiv cs.DC 2023
Year: 2023

Distributed algorithms solving agreement problems like consensus or state machine replication are essential components of modern fault-tolerant distributed services. They are also notoriously hard to understand and reason about. Their complexity stems from the different assumptions on the environment they operate with, i.e., process or network link failures, Byzantine failures etc. In this paper, we propose a novel abstract representation of the dynamics of such protocols which focuses on quorums of responses (votes) to a request (proposal) that form during a run of the protocol. We show that focusing on such quorums, a run of a protocol can be viewed as working over a tree structure where different branches represent different possible outcomes of the protocol, the goal being to stabilize on the choice of a fixed branch. This abstraction resembles the description of recent protocols used in Blockchain infrastructures, e.g., the protocol supporting Bitcoin or HotStuff. We show that this abstraction supports reasoning about the safety of various algorithms, e.g., Paxos, PBFT, Raft, and HotStuff, in a uniform way. In general, it provides a novel induction based argument for proving that such protocols are safe.
Multi-color Holograms Improve Brightness in Holographic Displays
Venue: arXiv cs.GR 2023
Year: 2023

Holographic displays generate Three-Dimensional (3D) images by displaying single-color holograms time-sequentially, each lit by a single-color light source. However, representing each color one by one limits brightness in holographic displays. This paper introduces a new driving scheme for realizing brighter images in holographic displays. Unlike the conventional driving scheme, our method utilizes three light sources to illuminate each displayed hologram simultaneously at various intensity levels. In this way, our method reconstructs a multiplanar three-dimensional target scene using consecutive multi-color holograms and persistence of vision. We co-optimize multi-color holograms and required intensity levels from each light source using a gradient descent-based optimizer with a combination of application-specific loss terms. We experimentally demonstrate that our method can increase the intensity levels in holographic displays up to three times, reaching a broader range and unlocking new potentials for perceptual realism in holographic displays.
Solving the Discretised Boltzmann Transport Equations using Neural Networks: Applications in Neutron Transport
Venue: arXiv cs.CE 2023
Year: 2023

In this paper we solve the Boltzmann transport equation using AI libraries. The reason why this is attractive is because it enables one to use the highly optimised software within AI libraries, enabling one to run on different computer architectures and enables one to tap into the vast quantity of community based software that has been developed for AI and ML applications e.g. mixed arithmetic precision or model parallelism. Here we take the first steps towards developing this approach for the Boltzmann transport equation and develop the necessary methods in order to do that effectively. This includes: 1) A space-angle multigrid solution method that can extract the level of parallelism necessary to run efficiently on GPUs or new AI computers. 2) A new Convolutional Finite Element Method (ConvFEM) that greatly simplifies the implementation of high order finite elements (quadratic to quintic, say). 3) A new non-linear Petrov-Galerkin method that introduces dissipation anisotropically.
Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics
Venue: arXiv cs.SD 2023
Year: 2023

We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans. Most importantly, a deep neural lyrics-to-melody net is trained in an encoder-decoder way to predict possible pairs of lyrics-melody when given incomplete lyrics (few keywords). The attention mechanism is exploited to align the predicted lyrics with the melody during the lyrics-to-melody generation. The qualitative and quantitative evaluation metrics reveal that the proposed method is indeed capable of generating proper lyrics and corresponding melody for composing new songs given a piece of incomplete seed lyrics.
Inference of Continuous Linear Systems from Data with Guaranteed Stability
Venue: arXiv cs.LG 2023
Year: 2023

Machine-learning technologies for learning dynamical systems from data play an important role in engineering design. This research focuses on learning continuous linear models from data. Stability, a key feature of dynamic systems, is especially important in design tasks such as prediction and control. Thus, there is a need to develop methodologies that provide stability guarantees. To that end, we leverage the parameterization of stable matrices proposed in [Gillis/Sharma, Automatica, 2017] to realize the desired models. Furthermore, to avoid the estimation of derivative information to learn continuous systems, we formulate the inference problem in an integral form. We also discuss a few extensions, including those related to control systems. Numerical experiments show that the combination of a stable matrix parameterization and an integral form of differential equations allows us to learn stable systems without requiring derivative information, which can be challenging to obtain in situations with noisy or limited data.
Model soups to increase inference without increasing compute time
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we compare Model Soups performances on three different models (ResNet, ViT and EfficientNet) using three Soup Recipes (Greedy Soup Sorted, Greedy Soup Random and Uniform soup) from arXiv:2203.05482, and reproduce the results of the authors. We then introduce a new Soup Recipe called Pruned Soup. Results from the soups were better than the best individual model for the pre-trained vision transformer, but were much worst for the ResNet and the EfficientNet. Our pruned soup performed better than the uniform and greedy soups presented in the original paper. We also discuss the limitations of weight-averaging that were found during the experiments. The code for our model soup library and the experiments with different models can be found here: https://github.com/milo-sobral/ModelSoup
Lexi: Self-Supervised Learning of the UI Language
Venue: arXiv cs.CL 2023
Year: 2023

Humans can learn to operate the user interface (UI) of an application by reading an instruction manual or how-to guide. Along with text, these resources include visual content such as UI screenshots and images of application icons referenced in the text. We explore how to leverage this data to learn generic visio-linguistic representations of UI screens and their components. These representations are useful in many real applications, such as accessibility, voice navigation, and task automation. Prior UI representation models rely on UI metadata (UI trees and accessibility labels), which is often missing, incompletely defined, or not accessible. We avoid such a dependency, and propose Lexi, a pre-trained vision and language model designed to handle the unique features of UI screens, including their text richness and context sensitivity. To train Lexi we curate the UICaption dataset consisting of 114k UI images paired with descriptions of their functionality. We evaluate Lexi on four tasks: UI action entailment, instruction-based UI image retrieval, grounding referring expressions, and UI entity recognition.
ViHOS: Hate Speech Spans Detection for Vietnamese
Venue: arXiv cs.CL 2023
Year: 2023

The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k comments. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Besides, we conduct experiments with various state-of-the-art models. Specifically, XLM-R$_{Large}$ achieved the best F1-scores in Single span detection and All spans detection, while PhoBERT$_{Large}$ obtained the highest in Multiple spans detection. Finally, our error analysis demonstrates the difficulties in detecting specific types of spans in our data for future research. Disclaimer: This paper contains real comments that could be considered profane, offensive, or abusive.
C-SAR: SAT Attack Resistant Logic Locking for RSFQ Circuits
Venue: arXiv cs.LO 2023
Year: 2023

Since the development of semiconductor technologies, exascale computing and its associated applications have required increasing degrees of efficiency. Semiconductor-transistor-based circuits (STbCs) have struggled in increasing the GHz frequency. Emerging as an alternative to STbC, the superconducting electrons (SCE) technology promises higher-speed clock frequencies at ultra-low power consumption. The rapid single flux quantum (RSFQ) circuits have a theoretical potential for three orders of magnitude reduction in power while operating at clock frequencies higher than 100 GHz. Although the security in semiconductor technology has been extensively researched and developed, the security design in the superconducting field requires field demands attention. In this paper, C-SAR is presented that aims to protect the superconducting circuit electronics from Boolean satisfiability (SAT) based attacks. The SAT attack is an attack that can break all the existing combinational logic locking techniques. C-SAR can immunize against SAT attacks by increasing the key search space and prolonging the clock cycles of attack inputs. Even in the worst case of C-SAR, in face of S-SAT a specially designed SAT attack, C-SAR can also soar the attack cost exponentially with key bits first, then linearly with the length of camouflaged DFF array. We have shown in this work that the cost of C-SAR is manageable as it only linearly increases as a function of key bits.
Case study Vanderbilt University Medical Center Data Chaos
Venue: arXiv cs.CY 2023
Year: 2023

The healthcare industry is growing rapidly in the United States because of the increased number of the aging population, shared consciousness of personal health problems, and medical technology improvements. As a result of the growing industry of healthcare, new emerging issues occur in the collection and storage of patient data, and new ways to process, analyze and distribute these data. This has exposed various security threats to personal health data (Lee, 2022). In this case study, we will discuss the issues Vanderbilt University Medical Center (VUMC) challenges while implementing EHR systems which are used to analyze and monitor health records by the users such as doctors, organizations staff, and pharmaceutical agencies(Kaul et al., 2020), and we will analyze these issues and provide solutions and recommendations to solve them.
The Evolution of Real-time Remote Intraoperative Neurophysiological Monitoring (IONM)
Venue: arXiv cs.OH 2023
Year: 2023

Real-time monitoring of nervous system function with immediate communication of relevant information to the surgeon enables prevention and/or mitigation of iatrogenic injury in many surgical procedures. The hardware and software infrastructure and demonstrated usefulness of telemedicine in support of IONM originated in a busy university health center environment and then spread widely as comparable functional capabilities were added by commercial equipment manufacturers. The earliest implementations included primitive data archival and case documentation capabilities and relied primarily on deidentification for security. They emphasized full-featured control of the real-time data display by remote observers. Today, remote IONM is routinely utilized in more than 200,000 high-risk surgical procedures/year in the United States. For many cases, remote observers rely on screen capture to view the data as it is displayed in the remote operating room while providing sophisticated security capabilities and data archival and standardized metadata and case documentation.
Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0
Venue: arXiv cs.CY 2023
Year: 2023

A set of six algorithmic solutions is presented for resolving vaccine production and supply chain bottlenecks. A different set of algorithmic solutions is presented for forecasting risks during a Disease X event.
On solving decision and risk management problems subject to uncertainty
Venue: arXiv cs.AI 2023
Year: 2023

Uncertainty is a pervasive challenge in decision and risk management and it is usually studied by quantification and modeling. Interestingly, engineers and other decision makers usually manage uncertainty with strategies such as incorporating robustness, or by employing decision heuristics. The focus of this paper is then to develop a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them. Based on a review of a dataset of 100 decision problems, this paper found that many decision problems have pivotal properties, i.e. properties that enable solution strategies, and finds 14 such properties. Therefore, an analyst can first find these properties in a given problem, and then utilize the strategies they enable. Multi-objective optimization methods could be used to make investment decisions quantitatively. The analytical complexity of decision problems can also be scored by evaluating how many of the pivotal properties are available. Overall, we find that in the light of pivotal properties, complex problems under uncertainty frequently appear surprisingly tractable.
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge
Venue: arXiv cs.LG 2023
Year: 2023

Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Object Detection. However, many other relevant applications of DL, especially at the edge, are based on time-series processing and require models with unique features, for which NAS is less explored. This work focuses in particular on Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged as a promising alternative to more complex recurrent architectures. We propose the first NAS tool that explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive-field and number of features in each layer. The proposed approach searches for networks that offer good trade-offs between accuracy and number of parameters/operations, enabling an efficient deployment on embedded platforms. We test the proposed NAS on four real-world, edge-relevant tasks, involving audio and bio-signals. Results show that, starting from a single seed network, our method is capable of obtaining a rich collection of Pareto optimal architectures, among which we obtain models with the same accuracy as the seed, and 15.9-152x fewer parameters. Compared to three state-of-the-art NAS tools, ProxylessNAS, MorphNet and FBNetV2, our method explores a larger search space for TCNs (up to 10^12x) and obtains superior solutions, while requiring low GPU memory and search time. We deploy our NAS outputs on two distinct edge devices, the multicore GreenWaves Technology GAP8 IoT processor and the single-core STMicroelectronics STM32H7 microcontroller. With respect to the state-of-the-art hand-tuned models, we reduce latency and energy of up to 5.5x and 3.8x on the two targets respectively, without any accuracy loss.
Designing Data: Proactive Data Collection and Iteration for Machine Learning
Venue: arXiv cs.HC 2023
Year: 2023

Lack of diversity in data collection has caused significant failures in machine learning (ML) applications. While ML developers perform post-collection interventions, these are time intensive and rarely comprehensive. Thus, new methods to track & manage data collection, iteration, and model training are necessary for evaluating whether datasets reflect real world variability. We present designing data, an iterative approach to data collection connecting HCI concepts with ML techniques. Our process includes (1) Pre-Collection Planning, to reflexively prompt and document expected data distributions; (2) Collection Monitoring, to systematically encourage sampling diversity; and (3) Data Familiarity, to identify samples that are unfamiliar to a model using density estimation. We apply designing data to a data collection and modeling task. We find models trained on ''designed'' datasets generalize better across intersectional groups than those trained on similarly sized but less targeted datasets, and that data familiarity is effective for debugging datasets.