OpenRaiser/Intern-Atlas huggingface dataset:
ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction
Venue: arXiv cs.CV 2023
Year: 2023

Predicting trajectories of pedestrians based on goal information in highly interactive scenes is a crucial step toward Intelligent Transportation Systems and Autonomous Driving. The challenges of this task come from two key sources: (1) complex social interactions in high pedestrian density scenarios and (2) limited utilization of goal information to effectively associate with past motion information. To address these difficulties, we integrate social forces into a Transformer-based stochastic generative model backbone and propose a new goal-based trajectory predictor called ForceFormer. Differentiating from most prior works that simply use the destination position as an input feature, we leverage the driving force from the destination to efficiently simulate the guidance of a target on a pedestrian. Additionally, repulsive forces are used as another input feature to describe the avoidance action among neighboring pedestrians. Extensive experiments show that our proposed method achieves on-par performance measured by distance errors with the state-of-the-art models but evidently decreases collisions, especially in dense pedestrian scenarios on widely used pedestrian datasets.
Toward matrix multiplication for deep learning inference on the Xilinx Versal
Venue: arXiv cs.DC 2023
Year: 2023

The remarkable positive impact of Deep Neural Networks on many Artificial Intelligence (AI) tasks has led to the development of various high performance algorithms as well as specialized processors and accelerators. In this paper we address this scenario by demonstrating that the principles underlying the modern realization of the general matrix multiplication (GEMM) in conventional processor architectures, are also valid to achieve high performance for the type of operations that arise in deep learning (DL) on an exotic accelerator such as the AI Engine (AIE) tile embedded in Xilinx Versal platforms. In particular, our experimental results with a prototype implementation of the GEMM kernel, on a Xilinx Versal VCK190, delivers performance close to 86.7% of the theoretical peak that can be expected on an AIE tile, for 16-bit integer operands.
LP-Duality Theory and the Cores of Games
Venue: arXiv cs.GT 2023
Year: 2023

LP-duality theory has played a central role in the study of the core, right from its early days to the present time. However, despite the extensive nature of this work, basic gaps still remain. We address these gaps using the following building blocks from LP-duality theory: 1. Total unimodularity (TUM). 2. Complementary slackness conditions and strict complementarity. Our exploration of TUM leads to defining new games, characterizing their cores and giving novel ways of using core imputations to enforce constraints that arise naturally in applications of these games. The latter include: 1. Efficient algorithms for finding min-max fair, max-min fair and equitable core imputations. 2. Encouraging diversity and avoiding over-representation in a generalization of the assignment game. Complementarity enables us to prove new properties of core imputations of the assignment game and its generalizations.
A Survey on Process Variants Meta-modelling Approaches
Venue: arXiv cs.SE 2023
Year: 2023

This paper introduces the concept of process variants in process-aware information systems (PAIS) during the design-time phase, where multiple variants of a single process must be specified. Today's organizations have to manage multiple variants of a given process, such as multiple order processes or payment processes for a specific product or service they offer. Traditional business process management tools lack in adequately capture and represent explicitly these variants. Hence, for more than a decade an array of approaches have been proposed to tackle this gap. A reference or customizable process model has been introduced to model these variants collections in a way that each variant could be derived by inserting/removing an activity according to a process context. This survey reviews current literature by providing an overview of meta-modelling approaches that have been extended in order to capture the variations of business processes. Moreover, we give a comparative analysis of these approaches based on different criteria we identified from the inventory activity, providing insights into their strengths and limitations. This paper concludes that current approaches to process variants meta-modelling provide a comprehensive view of the conceptual level of process variants and the control-flow process perspective. While some approaches go a step further by capturing variability in resources or specialization among activities/processes.
A Subspace Projection Approach to Autoencoder-based Anomaly Detection
Venue: arXiv cs.LG 2023
Year: 2023

Autoencoder (AE) is a neural network (NN) architecture that is trained to reconstruct an input at its output. By measuring the reconstruction errors of new input samples, AE can detect anomalous samples deviated from the trained data distribution. The key to success is to achieve high-fidelity reconstruction (HFR) while restricting AE's capability of generalization beyond training data, which should be balanced commonly via iterative re-training. Alternatively, we propose a novel framework of AE-based anomaly detection, coined HFR-AE, by projecting new inputs into a subspace wherein the trained AE achieves HFR, thereby increasing the gap between normal and anomalous sample reconstruction errors. Simulation results corroborate that HFR-AE improves the area under receiver operating characteristic curve (AUROC) under different AE architectures and settings by up to 13.4% compared to Vanilla AE-based anomaly detection.
Genetic Micro-Programs for Automated Software Testing with Large Path Coverage
Venue: arXiv cs.NE 2023
Year: 2023

Ongoing progress in computational intelligence (CI) has led to an increased desire to apply CI techniques for the purpose of improving software engineering processes, particularly software testing. Existing state-of-the-art automated software testing techniques focus on utilising search algorithms to discover input values that achieve high execution path coverage. These algorithms are trained on the same code that they intend to test, requiring instrumentation and lengthy search times to test each software component. This paper outlines a novel genetic programming framework, where the evolved solutions are not input values, but micro-programs that can repeatedly generate input values to efficiently explore a software component's input parameter domain. We also argue that our approach can be generalised such as to be applied to many different software systems, and is thus not specific to merely the particular software component on which it was trained.
Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring
Venue: arXiv cs.CV 2023
Year: 2023

Contemporary deep learning multi-scale deblurring models suffer from many issues: 1) They perform poorly on non-uniformly blurred images/videos; 2) Simply increasing the model depth with finer-scale levels cannot improve deblurring; 3) Individual RGB frames contain a limited motion information for deblurring; 4) Previous models have a limited robustness to spatial transformations and noise. Below, we extend the DMPHN model by several mechanisms to address the above issues: I) We present a novel self-supervised event-guided deep hierarchical Multi-patch Network (MPN) to deal with blurry images and videos via fine-to-coarse hierarchical localized representations; II) We propose a novel stacked pipeline, StackMPN, to improve the deblurring performance under the increased network depth; III) We propose an event-guided architecture to exploit motion cues contained in videos to tackle complex blur in videos; IV) We propose a novel self-supervised step to expose the model to random transformations (rotations, scale changes), and make it robust to Gaussian noises. Our MPN achieves the state of the art on the GoPro and VideoDeblur datasets with a 40x faster runtime compared to current multi-scale methods. With 30ms to process an image at 1280x720 resolution, it is the first real-time deep motion deblurring model for 720p images at 30fps. For StackMPN, we obtain significant improvements over 1.2dB on the GoPro dataset by increasing the network depth. Utilizing the event information and self-supervision further boost results to 33.83dB.
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
Venue: arXiv cs.LG 2023
Year: 2023

We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers. We find that a non-contrastive (positive-pair only) auxiliary task aids in supervised training of Euclidean neural networks (E3NNs) and increases manifold smoothness (MS) around point-cloud geometries. We demonstrate this property for multiple drug-activity prediction tasks while maintaining relevant performance metrics, and propose an extension of MS to probabilistic and regression settings. We provide an analysis of representation collapse, finding substantial effects of task-weighting, latent dimension, and regularization. We expect the presented protocol to aid in the development of reliable E3NNs from molecular conformers, even for small-data drug discovery programs.
Fairness in Socio-technical Systems: a Case Study of Wikipedia
Venue: arXiv cs.CY 2023
Year: 2023

Problems broadly known as algorithmic bias frequently occur in the context of complex socio-technical systems (STS), where observed biases may not be directly attributable to a single automated decision algorithm. As a first investigation of fairness in STS, we focus on the case of Wikipedia. We systematically review 75 papers describing different types of bias in Wikipedia, which we classify and relate to established notions of harm from algorithmic fairness research. By analysing causal relationships between the observed phenomena, we demonstrate the complexity of the socio-technical processes causing harm. Finally, we identify the normative expectations of fairness associated with the different problems and discuss the applicability of existing criteria proposed for machine learning-driven decision systems.
Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks
Venue: arXiv cs.RO 2023
Year: 2023

When arranging objects with robotic arms, the quality of the end result strongly depends on the achievable placement accuracy. However, even the most advanced robotic systems are prone to positioning errors that can occur at different steps of the manipulation process. Ignoring such errors can lead to the partial or complete failure of the arrangement. In this paper, we present a novel approach to autonomously detect and correct misplaced objects by pushing them with a robotic arm. We thoroughly tested our approach both in simulation and on real hardware using a Robotiq two-finger gripper mounted on a UR5 robotic arm. In our evaluation, we demonstrate the successful compensation for different errors injected during the manipulation of regular shaped objects. Consequently, we achieve a highly reliable object placement accuracy in the millimeter range.
An Efficient B-tree Implementation for Memory-Constrained Embedded Systems
Venue: arXiv cs.DB 2023
Year: 2023

Embedded devices collect and process significant amounts of data in a variety of applications including environmental monitoring, industrial automation and control, and other Internet of Things (IoT) applications. Storing data efficiently is critically important, especially when the device must perform local processing on the data. The most widely used data structure for high performance query and insert is the B-tree. However, existing implementations consume too much memory for small embedded devices and often rely on operating system support. This work presents an extremely memory efficient implementation of B-trees for embedded devices that functions on the smallest devices and does not require an operating system. Experimental results demonstrate that the B-tree implementation can run on devices with as little as 4 KB of RAM while efficiently processing thousands of records.
Learning Random Access Schemes for Massive Machine-Type Communication with MARL
Venue: arXiv cs.IT 2023
Year: 2023

In this paper, we explore various multi-agent reinforcement learning (MARL) techniques to design grant-free random access (RA) schemes for low-complexity, low-power battery operated devices in massive machine-type communication (mMTC) wireless networks. We use value decomposition networks (VDN) and QMIX algorithms with parameter sharing (PS) with centralized training and decentralized execution (CTDE) while maintaining scalability. We then compare the policies learned by VDN, QMIX, and deep recurrent Q-network (DRQN) and explore the impact of including the agent identifiers in the observation vector. We show that the MARL-based RA schemes can achieve a better throughput-fairness trade-off between agents without having to condition on the agent identifiers. We also present a novel correlated traffic model, which is more descriptive of mMTC scenarios, and show that the proposed algorithm can easily adapt to traffic non-stationarities
Quantifying gender imbalance in East Asian academia: Research career and citation practice
Venue: arXiv cs.DL 2023
Year: 2023

Gender imbalance in academia has been confirmed in terms of a variety of indicators, and its magnitude often varies from country to country. Europe and North America, which cover a large fraction of research workforce in the world, have been the main geographical regions for research on gender imbalance in academia. However, the academia in East Asia, which accounts for a substantial fraction of research, may be exposed to strong gender imbalance because Asia has been facing persistent and stronger gender imbalance in society at large than Europe and North America. Here we use publication data between 1950 and 2020 to analyze gender imbalance in academia in China, Japan, and South Korea in terms of the number of researchers, their career, and citation practice. We found that, compared to the average of the other countries, gender imbalance is larger in these three East Asian countries in terms of the number of researchers and their citation practice and additionally in Japan in terms of research career. Moreover, we found that Japan has been exposed to the larger gender imbalance than China and South Korea in terms of research career and citation practice.
Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression
Venue: arXiv cs.LG 2023
Year: 2023

Multiple sclerosis is a disease that affects the brain and spinal cord, it can lead to severe disability and has no known cure. The majority of prior work in machine learning for multiple sclerosis has been centered around using Magnetic Resonance Imaging scans or laboratory tests; these modalities are both expensive to acquire and can be unreliable. In a recent paper it was shown that disease progression can be predicted effectively using performance outcome measures and demographic data. In our work we build on this to investigate the modeling side, using continuous time models to predict progression. We benchmark four continuous time models using a publicly available multiple sclerosis dataset. We find that the best continuous model is often able to outperform the best benchmarked discrete time model. We also carry out an extensive ablation to discover the sources of performance gains, we find that standardizing existing features leads to a larger performance increase than interpolating missing features.
Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) demonstrate remarkable machine translation (MT) abilities via prompting, even though they were not explicitly trained for this task. However, even given the incredible quantities of data they are trained on, LLMs can struggle to translate inputs with rare words, which are common in low resource or domain transfer scenarios. We show that LLM prompting can provide an effective solution for rare words as well, by using prior knowledge from bilingual dictionaries to provide control hints in the prompts. We propose a novel method, DiPMT, that provides a set of possible translations for a subset of the input words, thereby enabling fine-grained phrase-level prompted control of the LLM. Extensive experiments show that DiPMT outperforms the baseline both in low-resource MT, as well as for out-of-domain MT. We further provide a qualitative analysis of the benefits and limitations of this approach, including the overall level of controllability that is achieved.
Almost Sure Saddle Avoidance of Stochastic Gradient Methods without the Bounded Gradient Assumption
Venue: arXiv cs.LG 2023
Year: 2023

We prove that various stochastic gradient descent methods, including the stochastic gradient descent (SGD), stochastic heavy-ball (SHB), and stochastic Nesterov's accelerated gradient (SNAG) methods, almost surely avoid any strict saddle manifold. To the best of our knowledge, this is the first time such results are obtained for SHB and SNAG methods. Moreover, our analysis expands upon previous studies on SGD by removing the need for bounded gradients of the objective function and uniformly bounded noise. Instead, we introduce a more practical local boundedness assumption for the noisy gradient, which is naturally satisfied in empirical risk minimization problems typically seen in training of neural networks.
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild
Venue: arXiv cs.CV 2023
Year: 2023

Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks. Despite this success, they have not outperformed state-of-the-art GAN models on the more challenging blind super-resolution task, where the input images are out of distribution, with unknown degradations. This paper introduces SR3+, a diffusion-based model for blind super-resolution, establishing a new state-of-the-art. To this end, we advocate self-supervised training with a combination of composite, parameterized degradations for self-supervised training, and noise-conditioing augmentation during training and testing. With these innovations, a large-scale convolutional architecture, and large-scale datasets, SR3+ greatly outperforms SR3. It outperforms Real-ESRGAN when trained on the same data, with a DRealSR FID score of 36.82 vs. 37.22, which further improves to FID of 32.37 with larger models, and further still with larger training sets.
From Reality Keys to Oraclize. A Deep Dive into the History of Bitcoin Oracles
Venue: arXiv cs.CR 2023
Year: 2023

Before the advent of alternative blockchains such as Ethereum, the future of decentralization was all in the hands of Bitcoin. Together with Nakamoto itself, early developers were trying to leverage Bitcoin potential to decentralize traditionally centralized applications. However, being Bitcoin a decentralized machine, available non-trustless oracles were considered unsuitable. Therefore, strategies had to be elaborated to solve the so-called oracle problem in the newborn scenario. By interviewing early developers and crawling early forums and repositories, this paper aims to retrace and reconstruct the chain of events and contributions that gave birth to oracles on Bitcoin. The evolution of early trust models and approaches to solving the oracle problem is also outlined. Analyzing technical and social barriers to building oracles on Bitcoin, the transition to Ethereum will also be discussed.
Commonsense Reasoning for Conversational AI: A Survey of the State of the Art
Venue: arXiv cs.CL 2023
Year: 2023

Large, transformer-based pretrained language models like BERT, GPT, and T5 have demonstrated a deep understanding of contextual semantics and language syntax. Their success has enabled significant advances in conversational AI, including the development of open-dialogue systems capable of coherent, salient conversations which can answer questions, chat casually, and complete tasks. However, state-of-the-art models still struggle with tasks that involve higher levels of reasoning - including commonsense reasoning that humans find trivial. This paper presents a survey of recent conversational AI research focused on commonsense reasoning. The paper lists relevant training datasets and describes the primary approaches to include commonsense in conversational AI. The paper also discusses benchmarks used for evaluating commonsense in conversational AI problems. Finally, the paper presents preliminary observations of the limited commonsense capabilities of two state-of-the-art open dialogue models, BlenderBot3 and LaMDA, and its negative effect on natural interactions. These observations further motivate research on commonsense reasoning in conversational AI.
An Experimentation Infrastructure for Quantitative Measurements of Cyber Resilience
Venue: arXiv cs.CR 2023
Year: 2023

The vulnerability of cyber-physical systems to cyber attack is well known, and the requirement to build cyber resilience into these systems has been firmly established. The key challenge this paper addresses is that maturing this discipline requires the development of techniques, tools, and processes for objectively, rigorously, and quantitatively measuring the attributes of cyber resilience. Researchers and program managers need to be able to determine if the implementation of a resilience solution actually increases the resilience of the system. In previous work, a table top exercise was conducted using a notional heavy vehicle on a fictitious military mission while under a cyber attack. While this exercise provided some useful data, more and higher fidelity data is required to refine the measurement methodology. This paper details the efforts made to construct a cost-effective experimentation infrastructure to provide such data. It also presents a case study using some of the data generated by the infrastructure.
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning
Venue: arXiv cs.DC 2023
Year: 2023

Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.