OpenRaiser/Intern-Atlas huggingface dataset:
Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making
Venue: arXiv cs.HC 2022
Year: 2022

Many important decisions in daily life are made with the help of advisors, e.g., decisions about medical treatments or financial investments. Whereas in the past, advice has often been received from human experts, friends, or family, advisors based on artificial intelligence (AI) have become more and more present nowadays. Typically, the advice generated by AI is judged by a human and either deemed reliable or rejected. However, recent work has shown that AI advice is not always beneficial, as humans have shown to be unable to ignore incorrect AI advice, essentially representing an over-reliance on AI. Therefore, the aspired goal should be to enable humans not to rely on AI advice blindly but rather to distinguish its quality and act upon it to make better decisions. Specifically, that means that humans should rely on the AI in the presence of correct advice and self-rely when confronted with incorrect advice, i.e., establish appropriate reliance (AR) on AI advice on a case-by-case basis. Current research lacks a metric for AR. This prevents a rigorous evaluation of factors impacting AR and hinders further development of human-AI decision-making. Therefore, based on the literature, we derive a measurement concept of AR. We propose to view AR as a two-dimensional construct that measures the ability to discriminate advice quality and behave accordingly. In this article, we derive the measurement concept, illustrate its application and outline potential future research.
SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos
Venue: arXiv cs.CV 2022
Year: 2022

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.
Proof of Federated Training: Accountable Cross-Network Model Training and Inference
Venue: arXiv cs.DC 2022
Year: 2022

Blockchain has widely been adopted to design accountable federated learning frameworks; however, the existing frameworks do not scale for distributed model training over multiple independent blockchain networks. For storing the pre-trained models over blockchain, current approaches primarily embed a model using its structural properties that are neither scalable for cross-chain exchange nor suitable for cross-chain verification. This paper proposes an architectural framework for cross-chain verifiable model training using federated learning, called Proof of Federated Training (PoFT), the first of its kind that enables a federated training procedure span across the clients over multiple blockchain networks. Instead of structural embedding, PoFT uses model parameters to embed the model over a blockchain and then applies a verifiable model exchange between two blockchain networks for cross-network model training. We implement and test PoFT over a large-scale setup using Amazon EC2 instances and observe that cross-chain training can significantly boosts up the model efficacy. In contrast, PoFT incurs marginal overhead for inter-chain model exchanges.
Handling sign language transcription system with the computer-friendly numerical multilabels
Venue: arXiv cs.CL 2022
Year: 2022

This paper presents our recent developments in the automatic processing of sign language corpora using the Hamburg Sign Language Annotation System (HamNoSys). We designed an automated tool to convert HamNoSys annotations into numerical labels for defined initial features of body and hand positions. Our proposed numerical multilabels greatly simplify annotations' structure without significant loss of gloss meaning. These numerical multilabels can potentially be used to feed the machine learning models, which would accelerate the development of vision-based sign language recognition. In addition, this tool can assist experts in the annotation process and help identify semantic errors. The code and sample annotations are publicly available at \url{https://github.com/hearai/parse-hamnosys}.
Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations
Venue: arXiv cs.HC 2022
Year: 2022

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs -- a series of ideas, approaches and methods taken from existing visualization research and practice -- deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type; and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/
EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning architecture called EEG-ITNet and a more comprehensible method to visualise the network learned patterns. Using inception modules and causal convolutions with dilation, our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals with less complexity (in terms of the number of trainable parameters) than other existing end-to-end architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation on dataset 2a from BCI competition IV and OpenBMI motor imagery dataset, EEG-ITNet shows up to 5.9\% improvement in the classification accuracy in different scenarios with statistical significance compared to its competitors. We also comprehensively explain and support the validity of network illustration from a neuroscientific perspective. We have also made our code open at https://github.com/AbbasSalami/EEG-ITNet
The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark
Venue: arXiv cs.CV 2022
Year: 2022

We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE and MAE by up to 5.48% and 7% respectively compared to competitive baseline methods. The dataset is available at https://humaticslab.github.io/forecasting/visuelle
Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings
Venue: arXiv cs.NI 2022
Year: 2022

A trust management system (TMS) is an integral component of any IoT network. A reliable trust management system must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices' reputations, which can help predict the future behaviours of network entities and subsequently judge the reliability of different network entities in IoT networks. Many TMSs have been proposed in the literature, these systems are designed for small-scale trust attacks, and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this paper, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate large-scale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, suck as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94\% mitigation rate in various network scenarios.
Interpretability of Machine Learning Methods Applied to Neuroimaging
Venue: arXiv cs.CV 2022
Year: 2022

Deep learning methods have become very popular for the processing of natural images, and were then successfully adapted to the neuroimaging field. As these methods are non-transparent, interpretability methods are needed to validate them and ensure their reliability. Indeed, it has been shown that deep learning models may obtain high performance even when using irrelevant features, by exploiting biases in the training set. Such undesirable situations can potentially be detected by using interpretability methods. Recently, many methods have been proposed to interpret neural networks. However, this domain is not mature yet. Machine learning users face two major issues when aiming to interpret their models: which method to choose, and how to assess its reliability? Here, we aim at providing answers to these questions by presenting the most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context. Note that this is not an exhaustive survey: we aimed to focus on the studies which we found to be the most representative and relevant.
A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data
Venue: arXiv cs.CL 2022
Year: 2022

Objective: To comparatively evaluate several transformer model architectures at identifying protected health information (PHI) in the i2b2/UTHealth 2014 clinical text de-identification challenge corpus. Methods: The i2b2/UTHealth 2014 corpus contains N=1304 clinical notes obtained from N=296 patients. Using a transfer learning framework, we fine-tune several transformer model architectures on the corpus, including: BERT-base, BERT-large, ROBERTA-base, ROBERTA-large, ALBERT-base and ALBERT-xxlarge. During fine-tuning we vary the following model hyper-parameters: batch size, number training epochs, learning rate and weight decay. We fine tune models on a training data set, we evaluate and select optimally performing models on an independent validation dataset, and lastly assess generalization performance on a held-out test dataset. We assess model performance in terms of accuracy, precision (positive predictive value), recall (sensitivity) and F1 score (harmonic mean of precision and recall). We are interested in overall model performance (PHI identified vs. PHI not identified), as well as PHI-specific model performance. Results: We observe that the ROBERTA-large models perform best at identifying PHI in the i2b2/UTHealth 2014 corpus, achieving >99% overall accuracy and 96.7% recall/precision on the heldout test corpus. Performance was good across many PHI classes; however, accuracy/precision/recall decreased for identification of the following entity classes: professions, organizations, ages, and certain locations. Conclusions: Transformers are a promising model class/architecture for clinical text de-identification. With minimal hyper-parameter tuning transformers afford researchers/clinicians the opportunity to obtain (near) state-of-the-art performance.
Network state Estimation using Raw Video Analysis: vQoS-GAN based non-intrusive Deep Learning Approach
Venue: arXiv cs.MM 2022
Year: 2022

Content based providers transmits real time complex signal such as video data from one region to another. During this transmission process, the signals usually end up distorted or degraded where the actual information present in the video is lost. This normally happens in the streaming video services applications. Hence there is a need to know the level of degradation that happened in the receiver side. This video degradation can be estimated by network state parameters like data rate and packet loss values. Our proposed solution vQoS GAN (video Quality of Service Generative Adversarial Network) can estimate the network state parameters from the degraded received video data using a deep learning approach of semi supervised generative adversarial network algorithm. A robust and unique design of deep learning network model has been trained with the video data along with data rate and packet loss class labels and achieves over 95 percent of training accuracy. The proposed semi supervised generative adversarial network can additionally reconstruct the degraded video data to its original form for a better end user experience.
cu_FastTucker: A Faster and Stabler Stochastic Optimization for Parallel Sparse Tucker Decomposition on Multi-GPUs
Venue: arXiv cs.DC 2022
Year: 2022

High-Order, High-Dimension, and Sparse Tensor (HOHDST) data originates from real industrial applications, i.e., social networks, recommender systems, bio-information, and traffic information. Sparse Tensor Decomposition (STD) can project the HOHDST data into low-rank space. In this work, a novel method for STD of Kruskal approximating the core tensor and stochastic strategy for approximating the whole gradient is proposed which comprises of the following two parts: (1) the matrization unfolding order of the Kruskal product for the core tensor follows the multiplication order of the factor matrix and then the proposed theorem can reduce the exponential computational overhead into linear one; (2) stochastic strategy adopts one-step random sampling set, the volume of which is much smaller than original one, to approximate the whole gradient. Meanwhile, this method can guarantee the convergence and save the memory overhead. Due to the compactness of the same order matrix multiplication and parallel access from stochastic strategy, the speed of cuFastTucker can be further reinforced by GPU. Furthermore, %because large-scale HOHDST data cannot be accommodated in a single GPU, a data division and communication strategy of cuFastTucker is proposed for data accommodation on Multi-GPU. cuFastTucker can achieve the fastest speed and keep the same accuracy and much lower memory overhead than the SOTA algorithms, e.g., P$-$Tucker, Vest, and SGD$\_$Tucker. The code and partial datasets are publically available on "https://github.com/ZixuanLi-China/FastTucker".
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Venue: arXiv cs.AI 2022
Year: 2022

Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph benchmark datasets that are widely used by the community primarily focus on homogeneous node and edge attributes and are static. In this work, we present a variety of large scale, dynamic heterogeneous academic graphs to test the effectiveness of models developed for multi-step graph forecasting tasks. Our novel datasets cover both context and content information extracted from scientific publications across two communities: Artificial Intelligence (AI) and Nuclear Nonproliferation (NN). In addition, we propose a systematic approach to improve the existing evaluation procedures used in the graph forecasting models.
Alternating Mahalanobis Distance Minimization for Stable and Accurate CP Decomposition
Venue: arXiv cs.LG 2022
Year: 2022

CP decomposition (CPD) is prevalent in chemometrics, signal processing, data mining and many more fields. While many algorithms have been proposed to compute the CPD, alternating least squares (ALS) remains one of the most widely used algorithm for computing the decomposition. Recent works have introduced the notion of eigenvalues and singular values of a tensor and explored applications of eigenvectors and singular vectors in areas like signal processing, data analytics and in various other fields. We introduce a new formulation for deriving singular values and vectors of a tensor by considering the critical points of a function different from what is used in the previous work. Computing these critical points in an alternating manner motivates an alternating optimization algorithm which corresponds to alternating least squares algorithm in the matrix case. However, for tensors with order greater than equal to $3$, it minimizes an objective function which is different from the commonly used least squares loss. Alternating optimization of this new objective leads to simple updates to the factor matrices with the same asymptotic computational cost as ALS. We show that a subsweep of this algorithm can achieve a superlinear convergence rate for exact CPD with known rank and verify it experimentally. We then view the algorithm as optimizing a Mahalanobis distance with respect to each factor with ground metric dependent on the other factors. This perspective allows us to generalize our approach to interpolate between updates corresponding to the ALS and the new algorithm to manage the tradeoff between stability and fitness of the decomposition. Our experimental results show that for approximating synthetic and real-world tensors, this algorithm and its variants converge to a better conditioned decomposition with comparable and sometimes better fitness as compared to the ALS algorithm.
Analysis of Workflow Schedulers in Simulated Distributed Environments
Venue: arXiv cs.DC 2022
Year: 2022

Task graphs provide a simple way to describe scientific workflows (sets of tasks with dependencies) that can be executed on both HPC clusters and in the cloud. An important aspect of executing such graphs is the used scheduling algorithm. Many scheduling heuristics have been proposed in existing works; nevertheless, they are often tested in oversimplified environments. We provide an extensible simulation environment designed for prototyping and benchmarking task schedulers, which contains implementations of various scheduling algorithms and is open-sourced, in order to be fully reproducible. We use this environment to perform a comprehensive analysis of workflow scheduling algorithms with a focus on quantifying the effect of scheduling challenges that have so far been mostly neglected, such as delays between scheduler invocations or partially unknown task durations. Our results indicate that network models used by many previous works might produce results that are off by an order of magnitude in comparison to a more realistic model. Additionally, we show that certain implementation details of scheduling algorithms which are often neglected can have a large effect on the scheduler's performance, and they should thus be described in great detail to enable proper evaluation.
Reputation and Audit Bit Based Distributed Detection in the Presence of Byzantine
Venue: arXiv cs.CR 2022
Year: 2022

In this paper, two reputation based algorithms called Reputation and audit based clustering (RAC) algorithm and Reputation and audit based clustering with auxiliary anchor node (RACA) algorithm are proposed to defend against Byzantine attacks in distributed detection networks when the fusion center (FC) has no prior knowledge of the attacking strategy of Byzantine nodes. By updating the reputation index of the sensors in cluster-based networks, the system can accurately identify Byzantine nodes. The simulation results show that both proposed algorithms have superior detection performance compared with other algorithms. The proposed RACA algorithm works well even when the number of Byzantine nodes exceeds half of the total number of sensors in the network. Furthermore, the robustness of our proposed algorithms is evaluated in a dynamically changing scenario, where the attacking parameters change over time. We show that our algorithms can still achieve superior detection performance.