Miracle-dz/UniFAR-FTUs huggingface dataset:
Delay-aware Robust Control for Safe Autonomous Driving and Racing

Delays endanger safety of autonomous systems operating in a rapidly changing environment, such as nondeterministic surrounding traffic participants in autonomous driving and high-speed racing. Unfortunately, delays are typically not considered during the conventional controller design or learning-enabled controller training phases prior to deployment in the physical world. In this paper, the computation delay from nonlinear optimization for motion planning and control, as well as other unavoidable delays caused by actuators, are addressed systematically and unifiedly. To deal with all these delays, in our framework: 1) we propose a new filtering approach with no prior knowledge of dynamics and disturbance distribution to adaptively and safely estimate the time-variant computation delay; 2) we model actuation dynamics for steering delay; 3) all the constrained optimization is realized in a robust tube model predictive controller. For the application merits, we demonstrate that our approach is suitable for both autonomous driving and autonomous racing. Our approach is a novel design for a standalone delay compensation controller. In addition, in the case that a learning-enabled controller assuming no delay works as a primary controller, our approach serves as the primary controller's safety guard.
Real-Time Hand Tracking Under Occlusion from an Egocentric RGB-D Sensor

We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments. Existing methods typically fail for hand-object interactions in cluttered scenes imaged from egocentric viewpoints-common for virtual or augmented reality applications. Our approach uses two subsequently applied Convolutional Neural Networks (CNNs) to localize the hand and regress 3D joint locations. Hand localization is achieved by using a CNN to estimate the 2D position of the hand center in the input, even in the presence of clutter and occlusions. The localized hand position, together with the corresponding input depth value, is used to generate a normalized cropped image that is fed into a second CNN to regress relative 3D hand joint locations in real time. For added accuracy, robustness and temporal stability, we refine the pose estimates using a kinematic pose tracking energy. To train the CNNs, we introduce a new photorealistic dataset that uses a merged reality approach to capture and synthesize large amounts of annotated data of natural hand interaction in cluttered scenes. Through quantitative and qualitative evaluation, we show that our method is robust to self-occlusion and occlusions by objects, particularly in moving egocentric perspectives.
Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might be useful for wind power generation; for example, they could acquire important information on how the local wind potential can be usable for a possible wind power plant through understanding future wind speed values. To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation). Thus, it was possible to consider the meteorological characteristics of the study regions. It is possible to verify the hybrid models’ efficiency in providing perfect adjustments to the observed data. This statement is based on the low values found in the error statistical analysis, i.e., an error of approximately 5.0% and a Nash–Sutcliffe coefficient near to 0.96. These results were certainly important in predicting the wind speed time-series, which was similar to the observed wind speed time-series profile. Great similarities of maximums and minimums between the series were evident and showed the capacity of the models to represent the seasonality characteristics.
Root Cause Analysis for Microservice Systems via Hierarchical Reinforcement Learning from Human Feedback

In microservice systems, the identification of root causes of anomalies is imperative for service reliability and business impact. This process is typically divided into two phases: (i)constructing a service dependency graph that outlines the sequence and structure of system components that are invoked, and (ii) localizing the root cause components using the graph, traces, logs, and Key Performance Indicators (KPIs) such as latency. However, both phases are not straightforward due to the highly dynamic and complex nature of the system, particularly in large-scale commercial architectures like Microsoft Exchange. In this paper, we propose a new framework that employs Hierarchical Reinforcement Learning from Human Feedback (HRLHF) to address these challenges. Our framework leverages the static topology of the microservice system and efficiently employs the feedback of engineers to reduce uncertainty in the discovery of the service dependency graph. The framework utilizes reinforcement learning to reduce the number of queries required from O(N2) to O(1), enabling the construction of the dependency graph with high accuracy and minimal human effort. Additionally, we extend the discovered dependency graphs to window causal graphs that capture the characteristics of time series over a specified time period, resulting in improved root cause analysis accuracy and robustness. Evaluations on both real datasets from Microsoft Exchange and synthetic datasets with injected anomalies demonstrate superior performance on various metrics compared to state-of-the-art methods. It is worth mentioning that, our framework has been integrated as a crucial component in Microsoft M365 Exchange service.
Delving into Global Dialogue Structures: Structure Planning Augmented Response Selection for Multi-turn Conversations

Retrieval-based dialogue systems are a crucial component of natural language processing, employing information retrieval techniques to select responses from a predefined pool of candidates. The advent of pre-trained language models (PLMs) has significantly advanced the field, with a prevailing paradigm that involves post-training PLMs on specific dialogue corpora, followed by fine-tuning for the response selection (RS) task. This post-training process aims to capture dialogue-specific features, as most PLMs are originally trained on plain text. However, prior approaches predominantly rely on self-supervised tasks or session-level graph neural networks during post-training, focusing on capturing underlying patterns of coherent dialogues without explicitly refining the global pattern across the entire dialogue corpus. Consequently, the learned knowledge for organizing coherent dialogues remains isolated, heavily reliant on specific contexts. Additionally, interpreting or visualizing the implicit knowledge acquired through self-supervised tasks proves challenging. In this study, we address these limitations by explicitly refining the knowledge required for response selection and structuring it into a coherent global flow, known as "dialogue structure. " This structure captures the inter-dependency of utterances and topic shifts, thereby enhancing the response selection task. To achieve this, we propose a novel structure model comprising a state recognizer and a structure planner. This model effectively captures the flow within the utterance history and plans the trajectory of future utterances. Importantly, the structure model operates orthogonally to the retrieval model, enabling seamless integration with existing retrieval models and facilitating collaborative training. Extensive experiments conducted on three benchmark datasets demonstrate the superior performance of our method over a wide range of competitive baselines, establishing a new state-of-the-art in the field.
Repositório ISCTE-IUL

—Stakeholders’ security decisions play a fundamental role in determining security requirements, yet, little is currently understood about how different stakeholder groups within an organisation approach security and the drivers and tacit biases underpinning their decisions. We studied and contrasted the security decisions of three demographics – security experts, computer scientists and managers – when playing a tabletop game that we designed and developed. The game tasks players with managing the security of a cyber-physical environment while facing various threats. Analysis of 12 groups of players (4 groups in each of our demographics) reveals strategies that repeat in particular demographics, e.g., managers and security experts generally favoring technological solutions over personnel training, which computer scientists preferred. Surprisingly, security experts were not ipso facto better players – in some cases, they made very questionable decisions – yet they showed a higher level of conﬁdence in themselves. We classiﬁed players’ decision-making processes, i.e., procedure, experience, scenario-or intuition-driven. We identiﬁed decision patterns, both good practices and typical errors and pitfalls. Our game provides a requirements sandbox in which players can experiment with security risks, learn about decision-making and its consequences, and reﬂect on their own perception of security.
Binary Classifier Evaluation on Unlabeled Segments using Inverse Distance Weighting with Distance Learning

Binary classification models are ubiquitous, and reliably measuring their performance is critical for their proper usage. Ideally, the performance of supervised models is measured using high-quality labeled datasets that are sufficiently large and representative of the population. However, obtaining labels for all segments of the population can be difficult, and model performance typically varies across different segments of the population (e.g., in different countries). In this work, we present a novel methodology to estimate the performance of a binary classifier in segments of the population where labels are unavailable. The main idea is that if two segments are "similar,'' then the performance of the classifier in these two segments would also be "similar. '' Specifically, we define a way to measure similarity between segments, and propose a statistical model that describes the performance of the model in unlabeled segments as a function of the performance in labeled segments. With extensive numerical experiments on synthetic and real-world datasets, we demonstrate that the proposed method substantially improves over existing methods in both estimation accuracy and computational efficiency. We also showcase the application of our method on the Instagram Adult Classifier to improve the geographic coverage and usability of the model.
l 0 -norm penalized shrinkage LMS algorithm based DFE for underwater acoustic communication

In this paper, we propose an l 0 -norm penalized shrinkage linear least mean squares (l 0 -SH-LMS) algorithm for an adaptive decision feedback equalizer (DFE). The proposed algorithm utilizes the priori and the posteriori errors to calculate the varying step-size. Thus a larger coefficient produces a larger increment to accelerate the convergence, and a small coefficient gives a smaller increment to improve the estimation accuracy, so the algorithm can adapt to the time-varying channel efficiently. Meanwhile, a l 0 -norm penalty term is introduced in the cost function to improve the applicability to a sparse system. Simulation results show that, compared with the conventional LMS-type algorithms, the proposed algorithm achieves better performance in both the convergence rate and steady-state misalignment for the sparse channels. When the proposed algorithm is applied to the DFE, the equalization performance is clearly improved.
Performance Analysis and Enhancement of IEEE 802.11 DCF Contention Window Backoff Scheme in the Presence of Noise *

: The paper presents an in-depth analysis on IEEE 802.11 DCF in the presence of noise. Based on the analysis, the Back to back Retransmission (BR) scheme is proposed. The main idea of the new scheme is to adopt different MAC protocol behaviors to the failures caused by collisions and those by channel errors. Based on the mathematical models, system saturation throughputs and maximum achievable throughputs of DCF and BR are analyzed using numerical methods. Then, ns-2 simulations are performed to study the impact of such factors as bit-error-rate, number of contending nodes on the performance of BR and DCF. Both the numerical calculation results and simulation results have shown that the proposed BR scheme outperforms DCF in the presence of noise.
On-device Integrated Re-ranking with Heterogeneous Behavior Modeling

As an emerging field driven by industrial applications, integrated re-ranking combines lists from upstream sources into a single list, and presents it to the user. The quality of integrated re-ranking is especially sensitive to real-time user behaviors and preferences. However, existing methods are all built on the cloud-to-edge framework, where mixed lists are generated by the cloud model and then sent to the devices. Despite its effectiveness, such a framework fails to capture users' real-time preferences due to the network bandwidth and latency. Hence, we propose to place the integrated re-ranking model on devices, allowing for the full exploitation of real-time behaviors. To achieve this, we need to address two key issues: first, how to extract users' preferences for different sources from heterogeneous and imbalanced user behaviors; second, how to explore the correlation between the extracted personalized preferences and the candidate items. In this work, we present the first on-Device Integrated Re-ranking framework, DIR, to avoid delays in processing real-time user behaviors. DIR includes a multi-sequence behavior modeling module to extract the user's source-level preferences, and a preference-adaptive re-ranking module to incorporate personalized source-level preferences into the re-ranking of candidate items. Besides, we design exposure loss and utility loss to jointly optimize exposure fairness and overall utility. Extensive experiments on three datasets show that DIR significantly outperforms the state-of-the-art baselines in utility-based and fairness-based metrics.
ILRoute: A Graph-based Imitation Learning Method to Unveil Riders' Routing Strategies in Food Delivery Service

Pick-up and delivery (PD) services such as online food ordering are playing an increasingly important role in serving people's daily demands. Accurate PD route prediction (PDRP) is important for service providers to efficiently schedule riders to improve service quality. It is crucial to model the decision-making process behind the route choice of riders for PDRP. Recent years have witnessed the success of utilizing imitation learning (IL) to model user decision-making process. Therefore, we propose to deploy an IL framework to solve the PDRP problem. However, there still exist three main challenges: (1) the rider's route decision is affected by multi-source and heterogeneous features and the complex relationships among these features make it hard to explore how they influence the rider's route decision-making; (2) the large route decision-making space make it easy to explore and predict unreasonable routes; (3) the rider's personalized preference is important in modeling the route decision-making process but cannot be fully explored. To tackle the above challenges, we propose ILRoute, a Graph-based imitation learning method for PDRP. ILRoute utilizes a multi-graph neural network (multi-GNN) to extract the multi-source and heterogeneous features and model their complex relationships. To address the large route decision-making space, ILRoute introduces a mobility regularity-aware constraint as prior route choice knowledge to reduce the exploration route decision-making space. To model the personalized preferences of the rider, ILRoute utilizes a personalized constraint mechanism to enhance the personalization of the rider's route decision-making process. Offline experiments conducted on three real-world datasets and online comparisons demonstrate the superiority of our proposed model.
Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem

Email platforms need to generate personalized rankings of emails that satisfy user preferences, which may vary over time. We approach this as a recommendation problem based on three criteria: closeness (how relevant the sender and topic are to the user), timeliness (how recent the email is), and conciseness (how brief the email is). We propose MOSR (Multi-Objective Stationary Recommender), a novel online algorithm that uses an adaptive control model to dynamically balance these criteria and adapt to preference changes. We evaluate MOSR on the Enron Email Dataset, a large collection of real emails, and compare it with other baselines. The results show that MOSR achieves better performance, especially under non-stationary preferences, where users value different criteria more or less over time. We also test MOSR's robustness on a smaller down-sampled dataset that exhibits high variance in email characteristics, and show that it maintains stable rankings across different samples. Our work offers novel insights into how to design email re-ranking systems that account for multiple objectives impacting user satisfaction.
Mastering Stock Markets with Efficient Mixture of Diversified Trading Experts

Quantitative stock investment is a fundamental financial task that highly relies on accurate prediction of market status and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, the performance of existing DL methods is unstable with sensitivity to network initialization and hyperparameter selection. One major limitation of existing works is that investment decisions are made based on one individual neural network predictor with high uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle this limitation, we propose AlphaMix, a novel three-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up hierarchical trading strategy design workflow of successful trading companies. In Stage one, we introduce an efficient ensemble learning method, whose computational and memory costs are significantly lower comparing to traditional ensemble methods, to train multiple groups of trading experts with personalised market understanding and trading styles. In Stage two, we collect diversified investment suggestions through building a pool of trading experts utilizing hyperparameter level and initialization level diversity of neural networks for post hoc ensemble construction. In Stage three, we design three different mechanisms, namely as-needed router, with-replacement selection and integrated expert soup, to dynamically pick experts from the expert pool, which takes the responsibility of a portfolio manager. Through extensive experiments on US and Chinese stock markets, we demonstrate that AlphaMix significantly outperforms many state-of-the-art baselines in terms of 7 popular financial criteria.
A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation

In this paper, we study the opinion maximization problem for the leader-follower DeGroot model of opinion dynamics in a social network modelled by a directed graph with n nodes, where a small number of nodes are competing leader nodes with binary opposing opinions 0 or 1, and the rest are follower nodes. We address the problem of maximizing the overall opinion by adding k ⇐ n new edges, where each edge is incident to a 1-leader and a follower. We prove that the objective function is monotone and submodular, and then propose a deterministic greedy algorithm with an approximation ratio (1-1 over e) and O(n3) running time. We then develop a fast sampling algorithm based on l-truncated absorbing random walks and sample-materialization techniques, which has sublinear time complexity O(kn1/2 log3/2 n/ε3) for any error parameter ε > 0. We provide extensive experiments on real networks to evaluate the performance of our algorithms. The results show that for undirected graphs our fast sampling algorithm outperforms the state-of-the-art method in terms of efficiency and effectiveness. While for directed graphs our fast sampling algorithm is as effective as our deterministic greedy algorithm, both of which are much better than the baseline strategies. Moreover, our fast algorithm is scalable to large directed graphs with over 41 million nodes.
ON THE DETERMINATION OF A DEVELOPABLE TIMELIKE RULED SURFACE

Abstract: This paper gives a method for determining a developable timelike ruled surface by using dual vector calculus. A developable timelike ruled surface can be parameterized in the form m(t, u) =p(t) +u x(t) ( p(t) is called the base curve of m(t, u)). The dual vectorial expression of a developable timelike ruled surface is obtained from the coordinates and the first derivatives of the base curve. Key words: Minkowski space, Developable surface, Timelike ruled surface. Mathematics Subject Classifications (2000): 53C50, 53A04, 53A05. BIR ACILABILIR TIMELIKE REGLE YUZEYIN BELIRLENMESI UZERINE Ozet: Bu makalede dual vektor analizi kullanilarak bir acilabilir timelike regle yuzeyin belirlenmesi icin bir metot verilmistir. Bir acilabilir timelike regle yuzey m(t, u)=p(t)+u x(t) formunda parametrelestirilebilir ( p(t) ye m(t, u) nin dayanak egrisi denir). Bir acilabilir timelike regle yuzeyin dual vektorel ifadesi koordinatlar ve dayanak egrisinin ilk turevlerinden elde edildi. Anahtar kelimeler: Minkowski Uzayi, Acilabilir Yuzey, Timelike Regle Yuzey.
QTIAH-GNN: Quantity and Topology Imbalance-aware Heterogeneous Graph Neural Network for Bankruptcy Prediction

The timely prediction of bankruptcy is highly desirable to guarantee an upward spiral for overall societal well-being. By extracting multifaceted information from the business interaction networks, Graph Neural Networks (GNNs) may be able to automatically make more informed predictions for bankruptcy, as compared to methods that rely heavily on abundant manpower to a large extent. Yet in real applications, bankruptcy prediction faces the key issue of quantity-imbalance: data usually comes with a long-tailed distribution wherein bankrupt corporates occupy the least of the data proportion but are our target to be identified. Apart from that, the topology-imbalance issue behind graph-structural data exacerbates prediction deterioration: feature propagation is dominated by non-bankrupt nodes through messages passing between nodes; thus, bankrupt nodes receive highly confusing information and could be easily assimilated by nearby non-bankrupt nodes. Unfortunately, the existing GNN methods are not immune to these two imbalance issues. To tackle the challenging but practically useful scenario, we propose a novel bankruptcy prediction model called the Quantity and Topology Imbalance-Aware Heterogeneous Graph Neural Network (QTIAH-GNN) to boost the final performance. Specifically, QTIAH-GNN employs the multi-hierarchy label-aware neighbor selection to conquer the topology-imbalance issue by using the class-semantic representation and the learnable parameterized similarity metric, and employs the imbalance-oriented loss to obtain the optimal tradeoff between the accuracies of the majority and minority classes. In experiments, we evaluate the proposed QTIAH-GNN on two large-scale, real-world datasets. The results show that QTIAH-GNN outperforms other state-of-the-art baselines in terms of prediction accuracy with superior efficiency and generalization ability, has stronger robustness to data imbalance, and provides meaningful model interpretation.