OpenRaiser/Intern-Atlas huggingface dataset:
W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others. Among several merits of transformers, the ability to capture long-range temporal dependencies and interactions is desirable for time series forecasting, leading to its progress in various time series applications. In this paper, we build a transformer model for non-stationary time series. The problem is challenging yet crucially important. We present a novel framework for univariate time series representation learning based on the wavelet-based transformer encoder architecture and call it W-Transformer. The proposed W-Transformers utilize a maximal overlap discrete wavelet transformation (MODWT) to the time series data and build local transformers on the decomposed datasets to vividly capture the nonstationarity and long-range nonlinear dependencies in the time series. Evaluating our framework on several publicly available benchmark time series datasets from various domains and with diverse characteristics, we demonstrate that it performs, on average, significantly better than the baseline forecasters for short-term and long-term forecasting, even for datasets that consist of only a few hundred training samples.
Endeavouring Intelligent Process Self-Control by Employing Digital Twin Methodology: Proof-of-Concept Study for Cooking Applications
Venue: arXiv cs.CE 2022
Year: 2022

This work demonstrates the use of the Digital Twin methodology to predict the water concentration and temperature of chicken meat. It marks a milestone on the path to autonomous cooking devices that do not only control device temperatures but culinary food quality markers as well. A custom water transport equation is coupled to the energy equation. The transport equations are implemented in ANSYS Fluent 2019R2 via User Defined Function (UDF) code written in C. The model is in good agreement with experiments provided by project partners. Thermal fluid-structure interaction simulations of pan-frying are performed to obtain realistic heat transfer coefficients. They indicate that the coupling of food transport equations to the surrounding heat transfer mechanisms, such as radiation and natural convection, seems promising for future research. Co-simulation of the process is not feasible during operation in the field, so reduced-order models (ROM) are introduced. An evaluation of ROM toolkits on the ANSYS platform reveals that linear time-invariant (LTI) models are unsuitable for cooking applications. In contrast, the recently launched Dynamic ROM Builder predicts the core temperatures with significantly low errors (factor ten below the model and discretization errors of the full-order model). Two examples demonstrate the usage of a Digital Twin controlling the core temperature of chicken fillets. The PI closed-loop control system remains insensitive to errors induced by the Dynamic ROM evaluation.
Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals
Venue: arXiv cs.AI 2022
Year: 2022

Artificial Intelligence (AI) is one of the most promising technologies of the 21. century, with an already noticeable impact on society and the economy. With this work, we provide a short overview of global trends, applications in industry and selected use-cases from our international experience and work in industry and academia. The goal is to present global and regional positive practices and provide an informed opinion on the realistic goals and opportunities for positioning B&H on the global AI scene.
Evaluating the Security of Aircraft Systems
Venue: arXiv cs.CR 2022
Year: 2022

The sophistication and complexity of cyber attacks and the variety of targeted platforms have been growing in recent years. Various adversaries are abusing an increasing range of platforms, e.g., enterprise platforms, mobile phones, PCs, transportation systems, and industrial control systems. In recent years, we have witnessed various cyber attacks on transportation systems, including attacks on ports, airports, and trains. It is only a matter of time before transportation systems become a more common target of cyber attackers. Due to the enormous potential damage inherent in attacking vehicles carrying many passengers and the lack of security measures applied in traditional airborne systems, the vulnerability of aircraft systems is one of the most concerning topics in the vehicle security domain. This paper provides a comprehensive review of aircraft systems and components and their various networks, emphasizing the cyber threats they are exposed to and the impact of a cyber attack on these components and networks and the essential capabilities of the aircraft. In addition, we present a comprehensive and in-depth taxonomy that standardizes the knowledge and understanding of cyber security in the avionics field from an adversary's perspective. The taxonomy divides techniques into relevant categories (tactics) reflecting the various phases of the adversarial attack lifecycle and maps existing attacks according to the MITRE ATT&CK methodology. Furthermore, we analyze the security risks among the various systems according to the potential threat actors and categorize the threats based on STRIDE threat model. Future work directions are presented as guidelines for industry and academia.
TEACH: Temporal Action Composition for 3D Humans
Venue: arXiv cs.CV 2022
Year: 2022

Given a series of natural language descriptions, our task is to generate 3D human motions that correspond semantically to the text, and follow the temporal order of the instructions. In particular, our goal is to enable the synthesis of a series of actions, which we refer to as temporal action composition. The current state of the art in text-conditioned motion synthesis only takes a single action or a single sentence as input. This is partially due to lack of suitable training data containing action sequences, but also due to the computational complexity of their non-autoregressive model formulation, which does not scale well to long sequences. In this work, we address both issues. First, we exploit the recent BABEL motion-text collection, which has a wide range of labeled actions, many of which occur in a sequence with transitions between them. Next, we design a Transformer-based approach that operates non-autoregressively within an action, but autoregressively within the sequence of actions. This hierarchical formulation proves effective in our experiments when compared with multiple baselines. Our approach, called TEACH for "TEmporal Action Compositions for Human motions", produces realistic human motions for a wide variety of actions and temporal compositions from language descriptions. To encourage work on this new task, we make our code available for research purposes at our $\href{teach.is.tue.mpg.de}{\text{website}}$.
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk
Venue: arXiv cs.LG 2022
Year: 2022

Prior work on safe Reinforcement Learning (RL) has studied risk-aversion to randomness in dynamics (aleatory) and to model uncertainty (epistemic) in isolation. We propose and analyze a new framework to jointly model the risk associated with epistemic and aleatory uncertainties in finite-horizon and discounted infinite-horizon MDPs. We call this framework that combines Risk-Averse and Soft-Robust methods RASR. We show that when the risk-aversion is defined using either EVaR or the entropic risk, the optimal policy in RASR can be computed efficiently using a new dynamic program formulation with a time-dependent risk level. As a result, the optimal risk-averse policies are deterministic but time-dependent, even in the infinite-horizon discounted setting. We also show that particular RASR objectives reduce to risk-averse RL with mean posterior transition probabilities. Our empirical results show that our new algorithms consistently mitigate uncertainty as measured by EVaR and other standard risk measures.
A Variational Approach to Parameter Estimation for Characterizing 2-D Cluster Variation Method Topographies
Venue: arXiv cs.NE 2022
Year: 2022

One of the biggest challenges in characterizing 2-D topographies is succinctly communicating the dominant nature of local configurations. In a 2-D grid composed of bistate units, this could be expressed as finding the characteristic configuration variables such as nearest-neighbor pairs and triplet combinations. The 2-D cluster variation method (CVM) provides a theoretical framework for associating a set of configuration variables with only two parameters, for a system that is at free energy equilibrium. This work presents a method for determining which of many possible two-parameter sets provides the ``most suitable'' match for a given 2-D topography, drawing from methods used for variational inference. This particular work focuses exclusively on topographies for which the activation enthalpy parameter (epsilon_0) is zero, so that the distribution between two states is equiprobable. This condition is used since, when the two states are equiprobable, there is an analytic solution giving the configuration variable values as functions of the h-value, where we define h in terms of the interaction enthalpy parameter (epsilon_1) as h = exp(2*epsilon_1). This allows the computationally-achieved configuration variable values to be compared with the analytically-predicted values for a given h-value. The method is illustrated using four patterns derived from three different naturally-occurring black-and-white topographies, where each pattern meets the equiprobability criterion. We achieve expected results, that is, as the patterns progress from having relatively low numbers of like-near-like nodes to increasing like-near-like masses, the h-values for each corresponding free energy-minimized model also increase. Further, the corresponding configuration variable values for the (free energy-minimized) model patterns are in approximate alignment with the analytically-predicted values.
MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification
Venue: arXiv cs.SD 2022
Year: 2022

Imbalanced music genre classification is a crucial task in the Music Information Retrieval (MIR) field for identifying the long-tail, data-poor genre based on the related music audio segments, which is very prevalent in real-world scenarios. Most of the existing models are designed for class-balanced music datasets, resulting in poor performance in accuracy and generalization when identifying the music genres at the tail of the distribution. Inspired by the success of introducing Multi-instance Learning (MIL) in various classification tasks, we propose a novel mechanism named Multi-instance Attention (MATT) to boost the performance for identifying tail classes. Specifically, we first construct the bag-level datasets by generating the album-artist pair bags. Second, we leverage neural networks to encode the music audio segments. Finally, under the guidance of a multi-instance attention mechanism, the neural network-based models could select the most informative genre to match the given music segment. Comprehensive experimental results on a large-scale music genre benchmark dataset with long-tail distribution demonstrate MATT significantly outperforms other state-of-the-art baselines.
An Artificial Chemistry Implementation of a Gene Regulatory Network
Venue: arXiv cs.NE 2022
Year: 2022

Gene Regulatory Networks are networks of interactions in biological organisms responsible for determining the production levels of proteins and peptides. Proteins are workers of a cell factory, and their production defines the goal of a cell and its development. Various attempts have been made to model such networks both to understand these biological systems better and to use inspiration from understanding them to solve computational problems. In this work, a biologically more realistic model for gene regulatory networks is proposed, which incorporates Cellular Automata and Artificial Chemistry to model the interactions between regulatory proteins called the Transcription Factors and the regulatory sites of genes. The result of this work shows complex dynamics close to what can be observed in nature. Here, an analysis of the impact of the initial states of the system on the produced dynamics is performed, showing that such evolvable models can be directed towards producing desired protein dynamics.
ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference
Venue: arXiv cs.AR 2022
Year: 2022

Edge training of Deep Neural Networks (DNNs) is a desirable goal for continuous learning; however, it is hindered by the enormous computational power required by training. Hardware approximate multipliers have shown their effectiveness for gaining resource-efficiency in DNN inference accelerators; however, training with approximate multipliers is largely unexplored. To build resource efficient accelerators with approximate multipliers supporting DNN training, a thorough evaluation of training convergence and accuracy for different DNN architectures and different approximate multipliers is needed. This paper presents ApproxTrain, an open-source framework that allows fast evaluation of DNN training and inference using simulated approximate multipliers. ApproxTrain is as user-friendly as TensorFlow (TF) and requires only a high-level description of a DNN architecture along with C/C++ functional models of the approximate multiplier. We improve the speed of the simulation at the multiplier level by using a novel LUT-based approximate floating-point (FP) multiplier simulator on GPU (AMSim). ApproxTrain leverages CUDA and efficiently integrates AMSim into the TensorFlow library, in order to overcome the absence of native hardware approximate multiplier in commercial GPUs. We use ApproxTrain to evaluate the convergence and accuracy of DNN training with approximate multipliers for small and large datasets (including ImageNet) using LeNets and ResNets architectures. The evaluations demonstrate similar convergence behavior and negligible change in test accuracy compared to FP32 and bfloat16 multipliers. Compared to CPU-based approximate multiplier simulations in training and inference, the GPU-accelerated ApproxTrain is more than 2500x faster. Based on highly optimized closed-source cuDNN/cuBLAS libraries with native hardware multipliers, the original TensorFlow is only 8x faster than ApproxTrain.
Joint Caching and Transmission in the Mobile Edge Network: A Multi-Agent Learning Approach
Venue: arXiv cs.MA 2022
Year: 2022

Joint caching and transmission optimization problem is challenging due to the deep coupling between decisions. This paper proposes an iterative distributed multi-agent learning approach to jointly optimize caching and transmission. The goal of this approach is to minimize the total transmission delay of all users. In this iterative approach, each iteration includes caching optimization and transmission optimization. A multi-agent reinforcement learning (MARL)-based caching network is developed to cache popular tasks, such as answering which files to evict from the cache and which files to storage. Based on the cached files of the caching network, the transmission network transmits cached files for users by single transmission (ST) or joint transmission (JT) with multi-agent Bayesian learning automaton (MABLA) method. And then users access the edge servers with the minimum transmission delay. The experimental results demonstrate the performance of the proposed multi-agent learning approach.
I'm stuck! How to efficiently debug computational solid mechanics models so you can enjoy the beauty of simulations
Venue: arXiv cs.CE 2022
Year: 2022

A substantial fraction of the time that computational modellers dedicate to developing their models is actually spent trouble-shooting and debugging their code. However, how this process unfolds is seldom spoken about, maybe because it is hard to articulate as it relies mostly on the mental catalogues we have built with the experience of past failures. To help newcomers to the field of material modelling, here we attempt to fill this gap and provide a perspective on how to identify and fix mistakes in computational solid mechanics models. To this aim, we describe the components that make up such a model and then identify possible sources of errors. In practice, finding mistakes is often better done by considering the symptoms of what is going wrong. As a consequence, we provide strategies to narrow down where in the model the problem may be, based on observation and a catalogue of frequent causes of observed errors. In a final section, we also discuss how one-time bug-free models can be kept bug-free in view of the fact that computational models are typically under continual development. We hope that this collection of approaches and suggestions serves as a "road map" to find and fix mistakes in computational models, and more importantly, keep the problems solved so that modellers can enjoy the beauty of material modelling and simulation.
Modelling Patient Trajectories Using Multimodal Information
Venue: arXiv cs.LG 2022
Year: 2022

Electronic Health Records (EHRs) aggregate diverse information at the patient level, holding a trajectory representative of the evolution of the patient health status throughout time. Although this information provides context and can be leveraged by physicians to monitor patient health and make more accurate prognoses/diagnoses, patient records can contain information from very long time spans, which combined with the rapid generation rate of medical data makes clinical decision making more complex. Patient trajectory modelling can assist by exploring existing information in a scalable manner, and can contribute in augmenting health care quality by fostering preventive medicine practices. We propose a solution to model patient trajectories that combines different types of information and considers the temporal aspect of clinical data. This solution leverages two different architectures: one supporting flexible sets of input features, to convert patient admissions into dense representations; and a second exploring extracted admission representations in a recurrent-based architecture, where patient trajectories are processed in sub-sequences using a sliding window mechanism. The developed solution was evaluated on two different clinical outcomes, unexpected patient readmission and disease progression, using the publicly available MIMIC-III clinical database. The results obtained demonstrate the potential of the first architecture to model readmission and diagnoses prediction using single patient admissions. While information from clinical text did not show the discriminative power observed in other existing works, this may be explained by the need to fine-tune the clinicalBERT model. Finally, we demonstrate the potential of the sequence-based architecture using a sliding window mechanism to represent the input data, attaining comparable performances to other existing solutions.
Tracking Small and Fast Moving Objects: A Benchmark
Venue: arXiv cs.CV 2022
Year: 2022

With more and more large-scale datasets available for training, visual tracking has made great progress in recent years. However, current research in the field mainly focuses on tracking generic objects. In this paper, we present TSFMO, a benchmark for \textbf{T}racking \textbf{S}mall and \textbf{F}ast \textbf{M}oving \textbf{O}bjects. This benchmark aims to encourage research in developing novel and accurate methods for this challenging task particularly. TSFMO consists of 250 sequences with about 50k frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box. To the best of our knowledge, TSFMO is the first benchmark dedicated to tracking small and fast moving objects, especially connected to sports. To understand how existing methods perform and to provide comparison for future research on TSFMO, we extensively evaluate 20 state-of-the-art trackers on the benchmark. The evaluation results exhibit that more effort are required to improve tracking small and fast moving objects. Moreover, to encourage future research, we proposed a novel tracker S-KeepTrack which surpasses all 20 evaluated approaches. By releasing TSFMO, we expect to facilitate future researches and applications of tracking small and fast moving objects. The TSFMO and evaluation results as well as S-KeepTrack are available at \url{https://github.com/CodeOfGithub/S-KeepTrack}.
On Specification-based Cyber-Attack Detection in Smart Grids
Venue: arXiv cs.CR 2022
Year: 2022

The transformation of power grids into intelligent cyber-physical systems brings numerous benefits, but also significantly increases the surface for cyber-attacks, demanding appropriate countermeasures. However, the development, validation, and testing of data-driven countermeasures against cyber-attacks, such as machine learning-based detection approaches, lack important data from real-world cyber incidents. Unlike attack data from real-world cyber incidents, infrastructure knowledge and standards are accessible through expert and domain knowledge. Our proposed approach uses domain knowledge to define the behavior of a smart grid under non-attack conditions and detect attack patterns and anomalies. Using a graph-based specification formalism, we combine cross-domain knowledge that enables the generation of whitelisting rules not only for statically defined protocol fields but also for communication ows and technical operation boundaries. Finally, we evaluate our specification-based intrusion detection system against various attack scenarios and assess detection quality and performance. In particular, we investigate a data manipulation attack in a future-orientated use case of an IEC 60870-based SCADA system that controls distributed energy resources in the distribution grid. Our approach can detect severe data manipulation attacks with high accuracy in a timely and reliable manner.
MIntRec: A New Dataset for Multimodal Intent Recognition
Venue: arXiv cs.AI 2022
Year: 2022

Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. The full dataset and codes are available for use at https://github.com/thuiar/MIntRec.