OpenRaiser/Intern-Atlas huggingface dataset:
Predicting Participants' Performance in Programming Contests using Deep Learning Techniques
Venue: arXiv cs.LG 2023
Year: 2023

In recent days, the number of technology enthusiasts is increasing day by day with the prevalence of technological products and easy access to the internet. Similarly, the amount of people working behind this rapid development is rising tremendously. Computer programmers consist of a large portion of those tech-savvy people. Codeforces, an online programming and contest hosting platform used by many competitive programmers worldwide. It is regarded as one of the most standardized platforms for practicing programming problems and participate in programming contests. In this research, we propose a framework that predicts the performance of any particular contestant in the upcoming competitions as well as predicts the rating after that contest based on their practice and the performance of their previous contests.
Persona-based Assessment of Software Engineering Student Research Projects: An Experience Report
Venue: arXiv cs.SE 2023
Year: 2023

Students enrolled in software engineering degrees are generally required to undertake a research project in their final year through which they demonstrate the ability to conduct research, communicate outcomes, and build in-depth expertise in an area. Assessment in these projects typically involves evaluating the product of their research via a thesis or a similar artifact. However, this misses a range of other factors that go into producing successful software engineers and researchers. Incorporating aspects such as process, attitudes, project complexity, and supervision support into the assessment can provide a more holistic evaluation of the performance likely to better align with the intended learning outcomes. In this paper, we present on our experience of adopting an innovative assessment approach to enhance learning outcomes and research performance in our software engineering research projects. Our approach adopted a task-oriented approach to portfolio assessment that incorporates student personas, frequent formative feedback, delayed summative grading, and standards-aligned outcomes-based assessment. We report upon our continuous improvement journey in adapting tasks and criteria to address the challenges of assessing student research projects. Our lessons learnt demonstrate the value of personas to guide the development of holistic rubrics, giving meaning to grades and focusing staff and student attention on attitudes and skills rather than a product only.
Multi-Modal Interaction Control of Ultrasound Scanning Robots with Safe Human Guidance and Contact Recovery
Venue: arXiv cs.RO 2023
Year: 2023

Ultrasound scanning robots enable the automatic imaging of a patient's internal organs by maintaining close contact between the ultrasound probe and the patient's body during a scanning procedure. Comprehensive, high-quality ultrasound scans are essential for providing the patient with an accurate diagnosis and effective treatment plan. An ultrasound scanning robot usually works in a doctor-robot co-existing environment, hence both efficiency and safety during the collaboration should be considered. In this paper, we propose a novel multi-modal control scheme for ultrasound scanning robots, in which three interaction modes are integrated into a single control input. Specifically, the scanning mode drives the robot to track a time-varying trajectory on the patient's body under the desired impedance model; the recovery mode allows the robot to actively recontact the body whenever physical contact between the ultrasound probe and the patient's body is lost; the human-guided mode renders the robot passive such that the doctor can safely intervene to manually reposition the probe. The integration of multiple modes allows the doctor to intervene safely at any time during the task and also maximizes the robot's autonomous scanning ability. The performance of the robot is validated on a collaborative scanning task of a carotid artery examination.
Verifying Generalization in Deep Learning
Venue: arXiv cs.LG 2023
Year: 2023

Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the state of the art in numerous application domains. However, DNN-based decision rules are notoriously prone to poor generalization, i.e., may prove inadequate on inputs not encountered during training. This limitation poses a significant obstacle to employing deep learning for mission-critical tasks, and also in real-world environments that exhibit high variability. We propose a novel, verification-driven methodology for identifying DNN-based decision rules that generalize well to new input domains. Our approach quantifies generalization to an input domain by the extent to which decisions reached by independently trained DNNs are in agreement for inputs in this domain. We show how, by harnessing the power of DNN verification, our approach can be efficiently and effectively realized. We evaluate our verification-based approach on three deep reinforcement learning (DRL) benchmarks, including a system for Internet congestion control. Our results establish the usefulness of our approach. More broadly, our work puts forth a novel objective for formal verification, with the potential for mitigating the risks associated with deploying DNN-based systems in the wild.
Removing Image Artifacts From Scratched Lens Protectors
Venue: arXiv cs.CV 2023
Year: 2023

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones. The artifacts appear in a wide variety of patterns, making it difficult to see through them clearly. Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts. Though different methods have been proposed for some specific distortions, they seldom consider such inherent challenges. In our work, we consider the inherent challenges in a unified framework with two cooperative modules, which facilitate the performance boost of each other. We also collect a new dataset from the real world to facilitate training and evaluation purposes. The experimental results demonstrate that our method outperforms the baselines qualitatively and quantitatively. The code and datasets will be released after acceptance.
Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards
Venue: arXiv cs.AI 2023
Year: 2023

Reinforcement learning is an effective way to solve the decision-making problems. It is a meaningful and valuable direction to investigate autonomous air combat maneuver decision-making method based on reinforcement learning. However, when using reinforcement learning to solve the decision-making problems with sparse rewards, such as air combat maneuver decision-making, it costs too much time for training and the performance of the trained agent may not be satisfactory. In order to solve these problems, the method based on curriculum learning is proposed. First, three curricula of air combat maneuver decision-making are designed: angle curriculum, distance curriculum and hybrid curriculum. These courses are used to train air combat agents respectively, and compared with the original method without any curriculum. The training results show that angle curriculum can increase the speed and stability of training, and improve the performance of the agent; distance curriculum can increase the speed and stability of agent training; hybrid curriculum has a negative impact on training, because it makes the agent get stuck at local optimum. The simulation results show that after training, the agent can handle the situations where targets come from different directions, and the maneuver decision results are consistent with the characteristics of missile.
OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching
Venue: arXiv cs.CV 2023
Year: 2023

Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behavior to generate dense and accurate matches. Firstly, OAMatcher predicts overlapping areas to promote effective and clean global context aggregation, with the key insight that humans focus on the overlapping areas instead of the entire images after multiple observations when matching keypoints in image pairs. Technically, we first perform global information integration across all keypoints to imitate the humans behavior of observing the entire images at the beginning of feature matching. Then, we propose Overlapping Areas Prediction Module (OAPM) to capture the keypoints in co-visible regions and conduct feature enhancement among them to simulate that humans transit the focus regions from the entire images to overlapping regions, hence realizeing effective information exchange without the interference coming from the keypoints in non overlapping areas. Besides, since humans tend to leverage probability to determine whether the match labels are correct or not, we propose a Match Labels Weight Strategy (MLWS) to generate the coefficients used to appraise the reliability of the ground-truth match labels, while alleviating the influence of measurement noise coming from the data. Moreover, we integrate depth-wise convolution into Tranformer encoder layers to ensure OAMatcher extracts local and global feature representation concurrently. Comprehensive experiments demonstrate that OAMatcher outperforms the state-of-the-art methods on several benchmarks, while exhibiting excellent robustness to extreme appearance variants. The source code is available at https://github.com/DK-HU/OAMatcher.
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
Venue: arXiv cs.LG 2023
Year: 2023

Modern ML applications increasingly rely on complex deep learning models and large datasets. There has been an exponential growth in the amount of computation needed to train the largest models. Therefore, to scale computation and data, these models are inevitably trained in a distributed manner in clusters of nodes, and their updates are aggregated before being applied to the model. However, a distributed setup is prone to Byzantine failures of individual nodes, components, and software. With data augmentation added to these settings, there is a critical need for robust and efficient aggregation systems. We define the quality of workers as reconstruction ratios $\in (0,1]$, and formulate aggregation as a Maximum Likelihood Estimation procedure using Beta densities. We show that the Regularized form of log-likelihood wrt subspace can be approximately solved using iterative least squares solver, and provide convergence guarantees using recent Convex Optimization landscape results. Our empirical findings demonstrate that our approach significantly enhances the robustness of state-of-the-art Byzantine resilient aggregators. We evaluate our method in a distributed setup with a parameter server, and show simultaneous improvements in communication efficiency and accuracy across various tasks. The code is publicly available at https://github.com/hamidralmasi/FlagAggregator
I$^2$SB: Image-to-Image Schrödinger Bridge
Venue: arXiv cs.CV 2023
Year: 2023

We propose Image-to-Image Schrödinger Bridge (I$^2$SB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distributions. These diffusion bridges are particularly useful for image restoration, as the degraded images are structurally informative priors for reconstructing the clean images. I$^2$SB belongs to a tractable class of Schrödinger bridge, the nonlinear extension to score-based models, whose marginal distributions can be computed analytically given boundary pairs. This results in a simulation-free framework for nonlinear diffusions, where the I$^2$SB training becomes scalable by adopting practical techniques used in standard diffusion models. We validate I$^2$SB in solving various image restoration tasks, including inpainting, super-resolution, deblurring, and JPEG restoration on ImageNet 256x256 and show that I$^2$SB surpasses standard conditional diffusion models with more interpretable generative processes. Moreover, I$^2$SB matches the performance of inverse methods that additionally require the knowledge of the corruption operators. Our work opens up new algorithmic opportunities for developing efficient nonlinear diffusion models on a large scale. scale. Project page and codes: https://i2sb.github.io/
Digging Deeper: Operator Analysis for Optimizing Nonlinearity of Boolean Functions
Venue: arXiv cs.NE 2023
Year: 2023

Boolean functions are mathematical objects with numerous applications in domains like coding theory, cryptography, and telecommunications. Finding Boolean functions with specific properties is a complex combinatorial optimization problem where the search space grows super-exponentially with the number of input variables. One common property of interest is the nonlinearity of Boolean functions. Constructing highly nonlinear Boolean functions is difficult as it is not always known what nonlinearity values can be reached in practice. In this paper, we investigate the effects of the genetic operators for bit-string encoding in optimizing nonlinearity. While several mutation and crossover operators have commonly been used, the link between the genotype they operate on and the resulting phenotype changes is mostly obscure. By observing the range of possible changes an operator can provide, as well as relative probabilities of specific transitions in the objective space, one can use this information to design a more effective combination of genetic operators. The analysis reveals interesting insights into operator effectiveness and indicates how algorithm design may improve convergence compared to an operator-agnostic genetic algorithm.
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues
Venue: arXiv cs.CL 2023
Year: 2023

Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs). We investigate multiple tasks for fine-tuning and show that the dialogue-tailored Sentence Ordering task performs best. To locate and exploit discourse information in PLMs, we propose an unsupervised and a semi-supervised method. Our proposals achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for unsupervised and semi-supervised methods, respectively. When restricted to projective trees, our scores improved to 63.3 and 68.1.
Neural Node Matching for Multi-Target Cross Domain Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) simultaneously. Most existing multi-target CDR frameworks primarily rely on the existence of the majority of overlapped users across domains. However, general practical CDR scenarios cannot meet the strictly overlapping requirements and only share a small margin of common users across domains}. Additionally, the majority of users have quite a few historical behaviors in such small-overlapping CDR scenarios}. To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i.e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions. The present framework} mainly contains two modules: (i) intra-to-inter node matching module, and (ii) intra node complementing module. Concretely, the first module conducts intra-knowledge fusion within each domain and subsequent inter-knowledge fusion across domains by fully connected user-user homogeneous graph information aggregating.
Efficient Fog Node Placement using Nature-Inspired Metaheuristic for IoT Applications
Venue: arXiv cs.NI 2023
Year: 2023

Managing the explosion of data from the edge to the cloud requires intelligent supervision such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively in terms of two main factors: connectivity and coverage. The network connectivity is based on fog node deployment which determines the physical topology of the network while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network QoS. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage as well as preserving network connectivity is a non-trivial problem. In this paper, we proposed a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. The experimental results demonstrate that our proposed algorithm is capable of providing very promising results when compared to state-of-the-art methods for determining an optimal deployment of fog nodes.
Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone Programming
Venue: arXiv cs.LG 2023
Year: 2023

Ensemble techniques are frequently encountered in machine learning and engineering problems since the method combines different models and produces an optimal predictive solution. The ensemble concept can be adapted to deep learning models to provide robustness and reliability. Due to the growth of the models in deep learning, using ensemble pruning is highly important to deal with computational complexity. Hence, this study proposes a mathematical model which prunes the ensemble of Convolutional Neural Networks (CNN) consisting of different depths and layers that maximizes accuracy and diversity simultaneously with a sparse second order conic optimization model. The proposed model is tested on CIFAR-10, CIFAR-100 and MNIST data sets which gives promising results while reducing the complexity of models, significantly.
Variational Integrators and Graph-Based Solvers for Multibody Dynamics in Maximal Coordinates
Venue: arXiv cs.RO 2023
Year: 2023

Multibody dynamics simulators are an important tool in many fields, including learning and control for robotics. However, many existing dynamics simulators suffer from inaccuracies when dealing with constrained mechanical systems due to unsuitable integrators with bad energy behavior and problematic constraint violations, for example for contact interactions. Variational integrators are numerical discretization methods that can reduce physical inaccuracies when simulating mechanical systems, and formulating the dynamics in maximal coordinates allows for easy and numerically robust incorporation of constraints such as kinematic loops or contacts. Therefore, this article derives a variational integrator for mechanical systems with equality and inequality constraints in maximal coordinates. Additionally, efficient graph-based sparsity-exploiting algorithms for solving the integrator are provided and implemented as an open-source simulator. The evaluation of the simulator shows improved physical accuracy due to the variational integrator and the advantages of the sparse solvers. Comparisons to minimal-coordinate algorithms show improved numerical robustness and application examples of a walking robot and an exoskeleton with explicit constraints demonstrate the necessity and capabilities of maximal coordinates.
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users' interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-domAin Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method.
Near-Optimal Non-Convex Stochastic Optimization under Generalized Smoothness
Venue: arXiv cs.LG 2023
Year: 2023

The generalized smooth condition, $(L_{0},L_{1})$-smoothness, has triggered people's interest since it is more realistic in many optimization problems shown by both empirical and theoretical evidence. Two recent works established the $O(ε^{-3})$ sample complexity to obtain an $O(ε)$-stationary point. However, both require a large batch size on the order of $\mathrm{ploy}(ε^{-1})$, which is not only computationally burdensome but also unsuitable for streaming applications. Additionally, these existing convergence bounds are established only for the expected rate, which is inadequate as they do not supply a useful performance guarantee on a single run. In this work, we solve the prior two problems simultaneously by revisiting a simple variant of the STORM algorithm. Specifically, under the $(L_{0},L_{1})$-smoothness and affine-type noises, we establish the first near-optimal $O(\log(1/(δε))ε^{-3})$ high-probability sample complexity where $δ\in(0,1)$ is the failure probability. Besides, for the same algorithm, we also recover the optimal $O(ε^{-3})$ sample complexity for the expected convergence with improved dependence on the problem-dependent parameter. More importantly, our convergence results only require a constant batch size in contrast to the previous works.