Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning Based Efficient and Robust Navigation Method For Autonomous Applications

Robust and efficient navigation methods are needed to allow a robot to work independent of human control. Many methods leverage models of the world and robot, maps of the world, and graphs to generate navigation plans. Each of these tasks takes additional time away form the limited computational budget of real time systems present on robots. The methodology presented is called declarative re-planning (DRP) and is graph free, model free, and map free. The proposed method is able to achieve orders of magnitude increase in learning efficiency and to navigate and reach its assigned destination location 100% of the time in an experiment set. DRP leverages an hierarchical interface between a traditional path planner, a deep reinforcement learning based policy, and a classical model controller to achieve its performance. The top level create waypoints that serve as sub goals. The middle level generates a parametric path to reach the subgoal. The lowest level uses a model controller to follow the path. Each operates on a different time scale and is concerned with different aspects of the navigation task. All three levels of the hierarchy work together to obtain robust navigation capabilities.
Poster: Verifiable Blockchain-Based Decentralized Learning

Decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation to avoid the single point of failure problem in federated learning (FL). However, this process is vulnerable to attackers who share false models and data. In this work, we propose Blockchain-based Verifiable Decentralized Federated Learning (BVDFL), which leverages a blockchain for decentralized model verification and auditing. BVDFL includes an auditor committee for model verification, a reputation model to evaluate the trustworthiness of clients, and a protocol suite for dynamic network updates. Simulation results show that, with the reputation mechanism, BVDFL achieves fast model convergence and high accuracy on real datasets with malicious clients in the system.
Verifying Neural Networks with Non-Linear SMT Solvers: a Short Status Report

In the last couple of decades, the popularity of neural networks has soared and they have been successfully utilized in many different domains across computer science. However, their application in safety and security-critical domains has been limited due to concerns regarding their reliability. Traditional methods for verifying neural networks (NNs) often uses linear Satisfiability Modulo Theory (SMT) solvers. These solvers work well for simple and shallow NN architectures but face limitations regarding their inability to handle non-linear activations, pooling layers, and complex activation functions, commonly used in modern deep neural networks.In this paper, we explore the potential of non-linear SMT solvers to verify intricate neural network architectures. By leveraging non-linear SMT solvers, a wider range of activation functions can be considered, leading to more accurate reasoning about the behavior of complex deep neural networks. The focus is on using recent advancements in SMT solver development to verify NNs with non-linear activation functions, particularly in the context of Computer Vision tasks. To test this idea, we conducted an experimental analysis to assess whether current nonlinear SMT solvers can efficiently handle NNs with transcendent activation functions.
Uncovering the Hidden Risks: The Importance of Predicting Bugginess in Untouched Methods

Bugs in untouched code can be a ticking time bomb, but are they worth predicting? Our study dives deep into the importance of predicting bugginess in untouched methods and its impact on bug prediction accuracy. Our analysis of six open-source projects reveals the hidden risks of dormant bugs and the benefits of predicting in isolation untouched methods. Our findings significantly increase prediction accuracy, decrease train dataset sizes, and prove that untouched methods are an overlooked yet vital aspect of bug prediction.
Identifying relevant descriptors for tweet sets

Twitter is a media where information flows in vast volumes. Even considering a particular topic, the collected tweets come in a wide variety of forms. This makes the data description problem complex. In this paper, given a set of tweets, we consider the problem of producing a set of relevant descriptors to characterize the tweets. Messages and words are considered in an embedding space learned with Doc2Vec, a model well suited for short documents. We propose to leverage this model to identify text units that may span over several words. We then propose to measure the impact of the units on their document representation through ablation. The most important units are selected as descriptors. Experiments have been conducted in the context of a tweet cluster description problem on two datasets. One is about the storm Alex, which struck France in October 2020, and the other about the beginning of the Russia-Ukraine war in February 2022. The results show the interest of our method compared to existing work.
Offline Reinforcement Learning Via Optimal Transport And Improved Performance Difference Theorem

Offline reinforcement learning (Offline RL) has garnered significant attention as a method for acquiring effective policy without the need for real-time interaction with the environment. However, existing approaches exhibit subpar performance due to the overestimation of out-of distribution state-action pairs. Constraining the learned policy to closely resemble the behavior policy is crucial in order to avoid generating out-of-distribution actions and misestimating their corresponding values. In this study, we introduce the optimal transmission theory to label offline datasets that lack reward labels. We investigate monotonic improvements to behavior policy by leveraging the performance difference theorem, aiming to restrict the ratio of learned policy to behavior policy, thus ensuring their proximity. Additionally, we propose a mixing loss function to address challenges inherent in the compound action setting. Building upon the aforementioned studies, we propose Optimal Transport Support Behavior Policy Optimization-Mixing Loss (OTSBPO-ML), a solution to the offline RL problem. Extensive experiments conducted on the D4RL benchmark demonstrate that OTSBPO-ML outperforms state-of-the-art offline reinforcement learning algorithms by a significant margin.
One-Class Classifier Performance: Comparing Majority versus Minority Class Training

Our study evaluates the impact of training OneClass Classification (OCC) algorithms on the majority class compared to training them on the minority class, using large and big datasets that are highly imbalanced. It is important to note that class availability can present a significant challenge in model training. In reality, there may be situations where one class is readily obtainable within a reasonable time frame while another class is not. Our task involves detecting instances of fraud in the Credit Fraud Detection Dataset and our Medicare dataset derived from Medicare Part D data and List of Excluded Individuals and Entities (LEIE) data. The Credit Card Fraud Detection Dataset has real-world transaction content as well as a significant class imbalance, making it suitable for use as a benchmark for credit card fraud detection. In addition, it is the only publicly available large data for credit card fraud analysis. Part D is big data, allowing researchers to analyze national trends and patterns in prescription drug usage and expenditures. The algorithms used in the study are One-Class Gaussian Mixture Model (GMM), OneClass Adversarial Nets (OCAN), and One-Class Support Vector Machine (SVM). Their performance is measured with the Area Under the Precision-Recall Curve (AUPRC) and Area Under the Receiver Operating Characteristic Curve (AUC). Our results indicate that OCC produces better results when models are trained on the majority class.
Node Classification in Graph Neural Networks under Dual Consistency

In recent years, graph neural networks (GNNs) have become a popular semi-supervised learning method for processing graph-structured data. However, traditional graph neural network models rely heavily on labeled data during learning while neglecting the potential of unlabeled data. To further exploit unlabeled data, pseudo-labeling-based methods select high-confidence pseudo-labeling during training and assign unlabeled nodes to join the training accordingly. Unfortunately, there is a confidence bias in the training process with this approach. On the other hand, self-supervised learning trains GNNs models to learn embeddings by mining the supervised information in the data. Since the training process does not introduce labeling information, the discriminative information of classes is not learned. To this end, for node classification in GNNs we propose a novel method that combines self-supervised and semi-supervised approaches, namely SLCSL, which mainly consists of two modules, random neighbors node consistency and class-centered consistency. Random neighbors node consistency explores rich node representations in unlabeled data by aligning random neighbor nodes. To make full use of labeled information, class-centered consistency establishes relationships between labeled and unlabeled nodes, thus enabling unlabeled nodes to be better aware of class information. Finally, experiments are conducted on various real-world datasets, and the results show that SLCSL outperforms the current state-of-the-art methods.
TraDance: A dataset for assessing the performance of Greek folk dances recognition models

In computer vision, datasets and benchmarks are widely used to compare algorithms and boost scientific progress. Especially in the human action recognition research field, extracting dance poses from video sequences for fragmentation and recognition of dancer movements is a challenging task, and new datasets are always important. This paper presents a new benchmark dataset and evaluation methodology for dancing analysis and step movements classification. We focus on traditional Greek dances, which are characterized by the repetition of simple steps throughout the duration of the dance track. The dataset, named TraDance, contains 1.4 hours of 3D dance motion videos from 5 different experienced dancers and 3494 annotated dance steps, covering 5 traditional dance genres with known RGB-D camera settings. In addition, to evaluate the quality of TraDance, we provide extensive experiments using existing machine learning algorithms that are commonly used in computer vision. The results show that TraDance is feasible for research-purpose studies and reliable. The dataset can be found online at: https://robotics.pme.duth.gr/tradance.
Recent Progress on Named Entity Recognition Based on Pre-trained Language Models

This paper presents an overview of the latest developments in Named Entity Recognition (NER) using Pretrained Language Models (PLMs). First, it discusses how PLMs have evolved and their impact on NER, including various pretraining techniques. The paper then explores strategies to adapt PLMs to NER, such as fine-tuning and transfer learning, while also considering the challenges in selecting the suitable model. Recent advancements in NER techniques using PLMs, including integrating external knowledge sources, are also covered. Finally, the paper comprehensively evaluates the state-of-the-art NER systems based on PLMs, comparing their performance and discussing their strengths and limitations. Its goal is to provide a thorough understanding of NER progress, insights into ongoing advancements, and potential avenues for further exploration. This paper is a valuable resource for researchers and practitioners seeking to use PLMs in their NER applications.
Graph Active Learning at Subgraph Granularity

Graph active learning algorithms can reduce the amount of labeling and improve the applicability of graph neural networks. However, existing graph active learning algorithms are mainly performed at the node granularity. Those setting does not hold to datasets that are sensitive to edge attributes. To solve this problem, we propose a graph active learning algorithm at subgraph granularity. The algorithm tackles two critical challenges: how to estimate the expected labeling value of subgraphs and how to search for high-value subgraphs in the whole graph efficiently. For the first challenge, we evaluate the expected labeling values of subgraphs based on heuristic metrics, including uncertainty, representativeness, centrality, and diversity. Among them, uncertainty cannot be measured directly. Therefore, we measure subgraph inner cohesion by GNN attention weights and estimate uncertainty based on it. For subgraph search, we propose an efficient subgraph search algorithm. The proposed algorithm includes a simulated annealing search algorithm for a single subgraph and beam search with subgraph-effective reception field algorithms for multiple subgraphs. Experiments demonstrate that the subgraph granularity active learning algorithm proposed in this paper can achieve great results on edge-sensitive datasets.
Symbolic Execution to Detect Semantic Merge Conflicts

Collaborative software development depends on managing multiple versions of a program which requires mechanisms to merge program versions to eventually deploy a single executable. Merging program versions can be challenging as conflicts can arise. The most challenging form is a semantic conflict, which introduces unintended behaviour in the resulting executable while merging. In this paper, we develop an approach that detects such semantic merge conflicts by symbolic execution. We define the program semantics as path conditions, produced by a symbolic executor, and check whether the conditions satisfy established rules that reflect a merge conflict. Our usage of symbolic execution to check these rules is novel. We evaluate the correctness of our approach through mutation testing, and evaluate it empirically by applying the approach to real-world merges sampled from GitHub. We also discuss what challenges arise in the empirical evaluation, including the problems i) that semantic merge conflicts are rare in the wild, ii) and, even in retrospection, hard to find using standard search mechanisms. Our evaluation shows that in specific cases, our approach using symbolic execution is a promising extension to existing mechanisms to merge conflict detection.
Research on Primary School English Teaching Model Against Language Attrition in Blended Learning Environment

English plays an increasingly important role in international communication and technological development. Therefore, the learning of English has gradually become the focus at primary school education in China. However, due to the immaturity of primary school students’ language development system, English language attrition has become a huge dilemma faced by primary school English teaching in China. Blended learning environments provide students with richer learning resources, more independent learning and more personalized learning methods, which has great advantages in promoting the acquisition, understanding and application of language knowledge. Therefore, this study proposed a T-RADIO model based on information technology to resist language attrition, and then designed experiments according to relevant influencing factors for practical verification. Finally, it was found that the digital schema intervention can help solve the problem of language attrition in primary school English learning. And the T-RADIO theoretical framework can minimize the occurrence of language attrition and improve the efficiency of primary school students’ English learning.
Poster: Traffic Scheduler for Cloud VR Applications in Edge Networks

The pulse pattern generated by real-time cloud VR applications poses a new challenge to bandwidth-limited wireless access networks. Concurrent pulse flows can lead to significant congestion in access points, making network providers unable to guarantee the quality of experience (QoE) for all users. To maximize the number of satisfied users, we propose a cross-layer assisted traffic scheduling mechanism, CTS, to manage flows into the edge network. We evaluated CTS using a trace-driven simulator, and the experimental results prove that CTS achieves the theoretical maximum of satisfied users and maintains a more balanced load for the network.
GEDI: A Graph-based End-to-end Data Imputation Framework

Data imputation is an effective way to handle missing data, which is common in practical applications. In this study, we propose and test a novel data imputation process that achieves two important goals: 1) preserving the row-wise similarities among observations and column-wise contextual relationships among features in the feature matrix. 2) tailoring the imputation process to some specific downstream label prediction task. The proposed imputation process uses Transformer and graph structure learning to iteratively refine the contextual relationships among features and similarities among observations. Moreover, it implements a meta-learning framework to select features that are influential to the downstream prediction task of interest. We conduct experiments on real-world datasets, and show that the proposed method consistently improves imputation and label prediction performance over a variety of benchmark methods.
Vision Transformer with Interactive Windows based on Patch Sequence Reconstruction

To further enhance the performance of Transformer-based models in vision task, this paper proposes an algorithm named interactive windows based on patch sequence. Firstly, we introduce a patch internal modeling method that relies solely on convolutional operations to model the internal features of each patch. This not only endows the model with the capability of multi-scale feature representation but also adds translational invariance and inductive bias. Secondly, we propose a sequence reconstruction transformer block, enabling the interaction of attention features between different windows. Finally, we conduct experiments on the ImageNet-1K and COCO 2017 datasets. The experimental results demonstrate that our improved model achieves the best classification accuracy of 87.3% on ImageNet-1K, the best detection accuracy of 59.6% on the COCO dataset, and the best segmentation accuracy of 51.9%.
Low-Cost Remote Monitoring Solutions for Sustainable Water Management in Madagascar

This paper describes a low-cost automated approach for logging data from remote sites in Madagascar directly to a cloud-based repository where it can be accessed by end-users through an online dashboard. SMS alerts can also be triggered to warn operators of conditions that require immediate attention. The system integrates commercial sensors and telemetry with alternative cellular providers and custom back-end processing to automatically format and upload data to the database. The database itself conforms to the Observations Data Model standard. Early results and interpretation of recorded data are provided as an example.
3D Pose Estimation Using a Global and Local Cross-Attention Mechanism

The task of 3D human pose and shape estimation involves the accurate prediction of 3D joint coordinates using a single image or a video sequence and it is crucial in several computer vision fields, such as sign language recognition, human-computer interaction and autonomous vehicles. Existing methodologies typically rely on modeling global and local temporal relationships among image frames without paying much attention to the interaction between these relationships and the modeling of the input space in other manifolds that possess important statistical and geometrical properties. This work proposes a novel multi-stage 3D pose estimation method that seamlessly combines global and local temporal modeling through self-attention mechanisms operating on multiple manifolds, thus leveraging the ability of different manifolds to model complementary features of the input space. Through the extraction of global and local attention maps and the fusion of these maps using a novel cross-attention mechanism, the proposed method aims to enhance the contextual understanding and improve the capacity of the model to capture the intricate human motion dynamics present in a video sequence. The effectiveness of the proposed method in achieving precise 3D pose and shape across successive frames is confirmed by the experimental results on two challenging datasets, namely 3DPW and MPI-INF-3DHP.
MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB

Visualization plays an important role in the analysis and exploration of time series data. To facilitate efficient visualization of large datasets, downsampling has emerged as a well-established approach. This work concentrates on LTTB (Largest-Triangle-Three-Buckets), a widely adopted downsampling algorithm for time series data point selection. Specifically, we introduce MinMaxLTTB, a two-step algorithm that significantly improves the scalability of LTTB. MinMaxLTTB consists of the following two steps: (i) the MinMax algorithm preselects a certain ratio of minimum and maximum data points, followed by (ii) applying the LTTB algorithm on only these preselected data points, effectively reducing LTTB’s time complexity. The MinMax algorithm is computationally efficient and can be parallelized, enabling efficient data point preselection. Additionally, MinMax demonstrates competitive performance in terms of visual representation, making it also an effective data reduction method. Experimental results demonstrate that MinMaxLTTB outperforms LTTB by more than an order of magnitude in terms of computation time. Furthermore, preselecting a small multiple of the desired output size already yields similar visual representativeness compared to LTTB. In summary, MinMaxLTTB leverages the computational efficiency of MinMax to scale LTTB, without compromising on LTTB its favorable visualization properties. The code and experiments associated with this paper can be found at https://github.com/predict-idlab/MinMaxLTTB.
What Exactly is an Insight? A Literature Review

Insights are often considered the ideal outcome of visual analysis sessions. However, there is no single definition of what an insight is. Some scholars define insights as correlations, while others define them as hypotheses or aha moments. This lack of a clear definition can make it difficult to build visualization tools that effectively support insight discovery. In this paper, we contribute a comprehensive literature review that maps the landscape of existing insight definitions. We summarize key themes regarding how insight is defined, with the goal of helping readers identify which definitions of insight align closely with their research and tool development goals. Based on our review, we also suggest interesting research directions, such as synthesizing a unified formalism for insight and connecting theories of insight to other critical concepts in visualization research.