OpenRaiser/Intern-Atlas huggingface dataset:
Ensuring Reliable and Predictable Behavior of IEEE 802.1CB Frame Replication and Elimination
Venue: arXiv cs.NI 2023
Year: 2023

Ultra-reliable and low-latency communication has received significant research attention. A key part of this evolution are the Time-Sensitive Networking (TSN) standards, which extend Ethernet with real-time mechanisms. To guarantee high reliability, the standard IEEE 802.1CB-2017 Frame Replication and Elimination for Reliability enables redundant communication over disjoint paths. While this mechanism is essential for time-critical applications, the standard contains some fundamental limitations that can compromise safety. Although some of these limitations have been addressed, none of the previous works provide solutions to these problems. This paper presents solutions to four main limitations of the IEEE 802.1CB-2017 standard. These are 1) choosing match versus vector recovery algorithm, 2) defining the length of the sequence history, 3) setting a timer to reset the sequence history, and 4) dimensioning the burst size in case of link failures. We show how these challenges can be solved by using best- and worst-case path delays of the network. We have performed simulations to illustrate the impact of the limitations and prove the correctness of our solutions. Thereby, we demonstrate how our solutions can improve reliability in TSN networks and propose these methods as guidance for users of the IEEE 802.1CB standard.
Efficient Online Processing with Deep Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

The capabilities and adoption of deep neural networks (DNNs) grow at an exhilarating pace: Vision models accurately classify human actions in videos and identify cancerous tissue in medical scans as precisely than human experts; large language models answer wide-ranging questions, generate code, and write prose, becoming the topic of everyday dinner-table conversations. Even though their uses are exhilarating, the continually increasing model sizes and computational complexities have a dark side. The economic cost and negative environmental externalities of training and serving models is in evident disharmony with financial viability and climate action goals. Instead of pursuing yet another increase in predictive performance, this dissertation is dedicated to the improvement of neural network efficiency. Specifically, a core contribution addresses the efficiency aspects during online inference. Here, the concept of Continual Inference Networks (CINs) is proposed and explored across four publications. CINs extend prior state-of-the-art methods developed for offline processing of spatio-temporal data and reuse their pre-trained weights, improving their online processing efficiency by an order of magnitude. These advances are attained through a bottom-up computational reorganization and judicious architectural modifications. The benefit to online inference is demonstrated by reformulating several widely used network architectures into CINs, including 3D CNNs, ST-GCNs, and Transformer Encoders. An orthogonal contribution tackles the concurrent adaptation and computational acceleration of a large source model into multiple lightweight derived models. Drawing on fusible adapter networks and structured pruning, Structured Pruning Adapters achieve superior predictive accuracy under aggressive pruning using significantly fewer learned weights compared to fine-tuning with pruning.
Circuit simulation using explicit methods: singular matrix issues
Venue: arXiv cs.CE 2023
Year: 2023

Some aspects of the ELectrical EXplicit (ELEX) scheme for using explicit integration schemes in circuit simulation are discussed. It is pointed out that the parallel resistor approach, presented earlier to address singular matrix issues arising in the ELEX scheme, is not adequately robust for incorporation in a general-purpose simulator for power electronic circuits. New topology-aware approaches, which are more robust and efficient compared to the parallel resistor approach, are presented. Several circuit examples are considered to illustrate the new approaches.
Onboarding Citizens to Digital Identity Systems
Venue: arXiv cs.CY 2023
Year: 2023

Digital Identity (DI) technologies have the potential to enhance the quality of life of citizens through the provision of seamless services, improve the effectiveness of public services, and increase overall economic competitiveness. However, lack of access to DIs can limit these benefits, while unequal access can lead to uneven distribution of these benefits across social groups and escalate existing tensions. Accessible, user-friendly and efficient onboarding can play a key role in ensuring equitable access and wide adoption of DI technologies. This paper proposes the development of physical locations (Experience Centres) that can be used for citizen onboarding to national DI systems, positively shaping citizens' first impression with the technology and, in turn, promoting adoption. To this end, we outline a multidisciplinary research approach for identifying and addressing the considerations necessary for designing, developing and operating a model Experience Centre for DI onboarding in an inclusive manner.
Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images
Venue: arXiv cs.CV 2023
Year: 2023

This paper takes an important step in bridging the performance gap between DETR and R-CNN for graphical object detection. Existing graphical object detection approaches have enjoyed recent enhancements in CNN-based object detection methods, achieving remarkable progress. Recently, Transformer-based detectors have considerably boosted the generic object detection performance, eliminating the need for hand-crafted features or post-processing steps such as Non-Maximum Suppression (NMS) using object queries. However, the effectiveness of such enhanced transformer-based detection algorithms has yet to be verified for the problem of graphical object detection. Essentially, inspired by the latest advancements in the DETR, we employ the existing detection transformer with few modifications for graphical object detection. We modify object queries in different ways, using points, anchor boxes and adding positive and negative noise to the anchors to boost performance. These modifications allow for better handling of objects with varying sizes and aspect ratios, more robustness to small variations in object positions and sizes, and improved image discrimination between objects and non-objects. We evaluate our approach on the four graphical datasets: PubTables, TableBank, NTable and PubLaynet. Upon integrating query modifications in the DETR, we outperform prior works and achieve new state-of-the-art results with the mAP of 96.9\%, 95.7\% and 99.3\% on TableBank, PubLaynet, PubTables, respectively. The results from extensive ablations show that transformer-based methods are more effective for document analysis analogous to other applications. We hope this study draws more attention to the research of using detection transformers in document image analysis.
Full Transparency in DBI frameworks
Venue: arXiv cs.CR 2023
Year: 2023

Following the increasing trends of malicious applications or cyber threats in general, program analysis has become a ubiquitous technique in extracting relevant features. The current state-of-the-art solutions seem to fall behind new techniques. For instance, dynamic binary instrumentation (DBI) provides some promising results, but falls short when it comes to ease of use and overcoming analysis evasion. In this regard, we propose a two-fold contribution. First, we introduce COBAI (Complex Orchestrator for Binary Analysis and Instrumentation), a DBI framework designed for malware analysis, prioritizing ease-of-use and analysis transparency, without imposing a significant overhead. Second, we introduce an aggregated test suite intended to stand as a benchmark in determining the quality of an analysis solution regarding the protection against evasion mechanisms. The efficiency of our solution is validated by a careful evaluation taking into consideration other DBI frameworks, analysis environments, and the proposed benchmark.
Manifold Contrastive Learning with Variational Lie Group Operators
Venue: arXiv cs.LG 2023
Year: 2023

Self-supervised learning of deep neural networks has become a prevalent paradigm for learning representations that transfer to a variety of downstream tasks. Similar to proposed models of the ventral stream of biological vision, it is observed that these networks lead to a separation of category manifolds in the representations of the penultimate layer. Although this observation matches the manifold hypothesis of representation learning, current self-supervised approaches are limited in their ability to explicitly model this manifold. Indeed, current approaches often only apply augmentations from a pre-specified set of "positive pairs" during learning. In this work, we propose a contrastive learning approach that directly models the latent manifold using Lie group operators parameterized by coefficients with a sparsity-promoting prior. A variational distribution over these coefficients provides a generative model of the manifold, with samples which provide feature augmentations applicable both during contrastive training and downstream tasks. Additionally, learned coefficient distributions provide a quantification of which transformations are most likely at each point on the manifold while preserving identity. We demonstrate benefits in self-supervised benchmarks for image datasets, as well as a downstream semi-supervised task. In the former case, we demonstrate that the proposed methods can effectively apply manifold feature augmentations and improve learning both with and without a projection head. In the latter case, we demonstrate that feature augmentations sampled from learned Lie group operators can improve classification performance when using few labels.
Thoughts on Architecture
Venue: arXiv cs.AI 2023
Year: 2023

The term architecture has evolved considerably from its original Greek roots and its application to buildings and computers to its more recent manifestation for minds. This article considers lessons from this history, in terms of a set of relevant distinctions introduced at each of these stages and a definition of architecture that spans all three, and a reconsideration of three key issues from cognitive architectures for architectures in general and cognitive architectures more particularly.
Best Practices for Machine Learning Systems: An Industrial Framework for Analysis and Optimization
Venue: arXiv cs.SE 2023
Year: 2023

In the last few years, the Machine Learning (ML) and Artificial Intelligence community has developed an increasing interest in Software Engineering (SE) for ML Systems leading to a proliferation of best practices, rules, and guidelines aiming at improving the quality of the software of ML Systems. However, understanding their impact on the overall quality has received less attention. Practices are usually presented in a prescriptive manner, without an explicit connection to their overall contribution to software quality. Based on the observation that different practices influence different aspects of software-quality and that one single quality aspect might be addressed by several practices we propose a framework to analyse sets of best practices with focus on quality impact and prioritization of their implementation. We first introduce a hierarchical Software Quality Model (SQM) specifically tailored for ML Systems. Relying on expert knowledge, the connection between individual practices and software quality aspects is explicitly elicited for a large set of well-established practices. Applying set-function optimization techniques we can answer questions such as what is the set of practices that maximizes SQM coverage, what are the most important ones, which practices should be implemented in order to improve specific quality aspects, among others. We illustrate the usage of our framework by analyzing well-known sets of practices.
A Game-Based Learning Application to Help Learners to Practice Mathematical Patterns and Structures
Venue: arXiv cs.CY 2023
Year: 2023

Purpose - The purpose of this study is to develop a game-based mobile application to help learners practice mathematical patterns and structures. Method - The study followed a mixed-method research design and prototyping methodology to guide the study in developing the mobile application. An instrument based on the Octalysis framework was developed as an evaluation tool for the study. Results - The study developed a mobile application based on the Octalysis framework. The application has fully achieved all its intended features based on the rating provided by the students and IT experts. Conclusion - The study successfully developed a mobile learning application for mathematical patterns and structures. By incorporating GBL principles and the Octalysis framework, the app achieved its intended features and received positive evaluations from students and IT experts. This highlights the potential of the app in promoting mathematical learning. Recommendations - This study recommends that the application be further enhanced to include other topics. Incorporating other game-based principles and approaches like timed questions and the difficulty level is also worth pursuing. Actual testing for end-users is also needed to verify the application's effectiveness. Practical Implications - Successful development of a game-based mobile app for practicing mathematical patterns and structures can transform education technology by engaging learners and enhancing their experience. This study provides valuable insights for future researchers developing similar applications, highlighting the potential to revolutionize traditional approaches and create an interactive learning environment for improving mathematical abilities.
Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems
Venue: arXiv cs.CY 2023
Year: 2023

The increased use of AI systems is associated with multi-faceted societal, environmental, and economic consequences. These include non-transparent decision-making processes, discrimination, increasing inequalities, rising energy consumption and greenhouse gas emissions in AI model development and application, and an increasing concentration of economic power. By considering the multi-dimensionality of sustainability, this paper takes steps towards substantiating the call for an overarching perspective on "sustainable AI". It presents the SCAIS Framework (Sustainability Criteria and Indicators for Artificial Intelligence Systems) which contains a set 19 sustainability criteria for sustainable AI and 67 indicators that is based on the results of a critical review and expert workshops. This interdisciplinary approach contributes a unique holistic perspective to facilitate and structure the discourse on sustainable AI. Further, it provides a concrete framework that lays the foundation for developing standards and tools to support the conscious development and application of AI systems.
On the Functions Which are CCZ-equivalent but not EA-equivalent to Quadratic Functions over $\mathbb F_{p^n}$
Venue: arXiv cs.IT 2023
Year: 2023

For a given function $F$ from $\mathbb F_{p^n}$ to itself, determining whether there exists a function which is CCZ-equivalent but EA-inequivalent to $F$ is a very important and interesting problem. For example, Kölsch \cite{KOL21} showed that there is no function which is CCZ-equivalent but EA-inequivalent to the inverse function. On the other hand, for the cases of Gold function $F(x)=x^{2^i+1}$ and $F(x)=x^3+{\rm Tr}(x^9)$ over $\mathbb F_{2^n}$, Budaghyan, Carlet and Pott (respectively, Budaghyan, Carlet and Leander) \cite{BCP06, BCL09FFTA} found functions which are CCZ-equivalent but EA-inequivalent to $F$. In this paper, when a given function $F$ has a component function which has a linear structure, we present functions which are CCZ-equivalent to $F$, and if suitable conditions are satisfied, the constructed functions are shown to be EA-inequivalent to $F$. As a consequence, for every quadratic function $F$ on $\mathbb F_{2^n}$ ($n\geq 4$) with nonlinearity $>0$ and differential uniformity $\leq 2^{n-3}$, we explicitly construct functions which are CCZ-equivalent but EA-inequivalent to $F$. Also for every non-planar quadratic function on $\mathbb F_{p^n}$ $(p>2, n\geq 4)$ with $|\mathcal W_F|\leq p^{n-1}$ and differential uniformity $\leq p^{n-3}$, we explicitly construct functions which are CCZ-equivalent but EA-inequivalent to $F$.
Improving Panoptic Segmentation for Nighttime or Low-Illumination Urban Driving Scenes
Venue: arXiv cs.CV 2023
Year: 2023

Autonomous vehicles and driving systems use scene parsing as an essential tool to understand the surrounding environment. Panoptic segmentation is a state-of-the-art technique which proves to be pivotal in this use case. Deep learning-based architectures have been utilized for effective and efficient Panoptic Segmentation in recent times. However, when it comes to adverse conditions like dark scenes with poor illumination or nighttime images, existing methods perform poorly in comparison to daytime images. One of the main factors for poor results is the lack of sufficient and accurately annotated nighttime images for urban driving scenes. In this work, we propose two new methods, first to improve the performance, and second to improve the robustness of panoptic segmentation in nighttime or poor illumination urban driving scenes using a domain translation approach. The proposed approach makes use of CycleGAN (Zhu et al., 2017) to translate daytime images with existing panoptic annotations into nighttime images, which are then utilized to retrain a Panoptic segmentation model to improve performance and robustness under poor illumination and nighttime conditions. In our experiments, Approach-1 demonstrates a significant improvement in the Panoptic segmentation performance on the converted Cityscapes dataset with more than +10% PQ, +12% RQ, +2% SQ, +14% mIoU and +10% AP50 absolute gain. Approach-2 demonstrates improved robustness to varied nighttime driving environments. Both the approaches are supported via comprehensive quantitative and qualitative analysis.
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
Venue: arXiv cs.AI 2023
Year: 2023

We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. In this setting, a common approach is to represent the tasks as deterministic finite automata (DFA) and integrate them into the state-space for RL algorithms. However, while these machines model the reward function, they often overlook the causal knowledge about the environment. To address this limitation, we propose the Temporal-Logic-based Causal Diagram (TL-CD) in RL, which captures the temporal causal relationships between different properties of the environment. We exploit the TL-CD to devise an RL algorithm in which an agent requires significantly less exploration of the environment. To this end, based on a TL-CD and a task DFA, we identify configurations where the agent can determine the expected rewards early during an exploration. Through a series of case studies, we demonstrate the benefits of using TL-CDs, particularly the faster convergence of the algorithm to an optimal policy due to reduced exploration of the environment.
Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints
Venue: arXiv cs.LG 2023
Year: 2023

Prediction models have been widely adopted as the basis for decision-making in domains as diverse as employment, education, lending, and health. Yet, few real world problems readily present themselves as precisely formulated prediction tasks. In particular, there are often many reasonable target variable options. Prior work has argued that this is an important and sometimes underappreciated choice, and has also shown that target choice can have a significant impact on the fairness of the resulting model. However, the existing literature does not offer a formal framework for characterizing the extent to which target choice matters in a particular task. Our work fills this gap by drawing connections between the problem of target choice and recent work on predictive multiplicity. Specifically, we introduce a conceptual and computational framework for assessing how the choice of target affects individuals' outcomes and selection rate disparities across groups. We call this multi-target multiplicity. Along the way, we refine the study of single-target multiplicity by introducing notions of multiplicity that respect resource constraints -- a feature of many real-world tasks that is not captured by existing notions of predictive multiplicity. We apply our methods on a healthcare dataset, and show that the level of multiplicity that stems from target variable choice can be greater than that stemming from nearly-optimal models of a single target.
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE
Venue: arXiv cs.LG 2023
Year: 2023

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Correlated clustering and projection (CCP) was recently introduced as an effective method for preprocessing scRNA-seq data. CCP utilizes gene-gene correlations to partition the genes and, based on the partition, employs cell-cell interactions to obtain super-genes. Because CCP is a data-domain approach that does not require matrix diagonalization, it can be used in many downstream machine learning tasks. In this work, we utilize CCP as an initialization tool for uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE). By using eight publicly available datasets, we have found that CCP significantly improves UMAP and t-SNE visualization and dramatically improve their accuracy.
Parallel-batched Interpolation Search Tree
Venue: arXiv cs.DC 2023
Year: 2023

A sorted set (or map) is one of the most used data types in computer science. In addition to standard set operations, like Insert, Remove, and Contains, it can provide set-set operations such as Union,Intersection, and Difference. Each of these set-set operations is equivalent to some batched operation: the data structure should be able to execute Insert, Remove, and Contains on a batch of keys. It is obvious that we want these "large" operations to be parallelized. These sets are usually implemented with the trees of logarithmic height, such as 2-3 trees, treaps, AVL trees, red-black trees, etc. Until now, little attention was devoted to data structures that work asymptotically better under several restrictions on the stored data. In this work, we parallelize Interpolation Search Tree which is expected to serve requests from a smooth distribution in doubly-logarithmic time. Our data structure of size n performs a batch of m operations in O(m log log n) work and poly-log span.