Reviewerly/exHarmony huggingface dataset:
Continual learning on 3D point clouds with random compressed rehearsal

Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are an important data type for the precise modeling of three-dimensional environments, but effective processing of this type of data proves to be challenging. In the world of large, heavily-parameterized network architectures and continuously-streamed data, there is an increasing need for machine learning models that can be trained on additional data. Unfortunately, currently available models cannot fully leverage training on additional data without losing their past knowledge. Combating this phenomenon, called catastrophic forgetting, is one of the main objectives of continual learning. Continual learning for deep neural networks has been an active field of research, primarily in 2D computer vision, natural language processing, reinforcement learning, and robotics. However, in 3D computer vision, there are hardly any continual learning solutions specifically designed to take advantage of point cloud structure. This work proposes a novel neural network architecture capable of continual learning on 3D point cloud data. We utilize point cloud structure properties for preserving a heavily compressed set of past data. By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.
TDE2-MBRL: Energy-exchange Dynamics Learning with Task Decomposition for Spring-loaded Bipedal Robot Locomotion

Spring-loaded Inverted Pendulum (SLIP) inspired bipedal robots (SLIP-biped) have high agility owing to their fault tolerance under impacts. Controlling a SLIP-biped requires capturing its dynamics; however, its high complexity makes analytic method implementation challenging. Thus, a Model-based Reinforcement Learning (MBRL) that learns a dynamics model and utilizes it for control design appears to be a reasonable alternative. Nevertheless, modeling high complexity dynamics with conventional MBRL approaches requires enormous samples or a high computation load. Therefore, exploring a simplified and compact dynamics model for SLIP-biped would be a key to increasing the feasibility of MBRL implementation and real-time control. We propose a Task-Decomposed Energy-exchange dynamics learning with MBRL (TDE2-MBRL) to capture simplified SLIP-biped dynamics and utilize them for control. Specifically, under the law of energy conservation, we model the energy exchange to reduce dynamics' dimensionality. Next, we decompose the SLIP-biped dynamics into locomotion task phases to cope with dynamics dissimilarity. The effectiveness is demonstrated by hopping skill acquisition with a precise simulated SLIP-biped replica of a real SLIP-biped. The experiment results show that TDE2-MBRL improves learning efficiency and control frequency while having comparable model accuracy to the standard MBRL.
Adaptive Client Model Update with Reinforcement Learning in Synchronous Federated Learning

Federated learning is widely applied in green wireless communication, mobile technologies and daily life. It allows multiple parties to jointly train a model on their combined data without revealing any of their local data to a centralized server. However, in practical applications, federated learning requires frequent communication between clients and servers, which brings a considerable burden. In this work, we propose a Federated Learning Deep Q-Learning (FL-DQL) method to reduce the communication frequency between clients and servers in federated learning. FL-DQL selects the local-self-update times of a client adaptively and finds the best trade-off between local update and global parameter aggregation. The performance of FL-DQL is evaluated via extensive experiments with real datasets on a networked prototype system. Results show that FL-DQL effectively reduces the communication overhead among the nodes in our experiments which conforms to the green initiative.
Efficient Transformers for on-robot Natural Language Understanding

The main task of a social robot is to interact with humans through spoken natural language. It implies that it must be able to understand the intent of the user and the involved entities. Recently, different solutions have been proposed to deal with the Natural Language Understanding (NLU) task. Extremely accurate results have been obtained by architectures based on transformers, but they require high computational resources to work in real-time. Unfortunately, these resources are not available on embedded systems equipped on board the robot. For these reasons, in this paper we experimentally evaluate the most promising transformers for NLU over the popular ATIS and SNIPS datasets and measured their inference time on the NVIDIA Jetson Xavier NX embedded system. The experimental analysis demonstrates that the Albert model can obtain comparable performance w.r.t. the popular BERT architecture (just a 2% drop on entity recognition), while gaining a speed-up of more than 3x. Thanks to the insights coming out from our analysis, we finally developed a real system for restaurant search running the model over a NVIDIA Jetson Xavier NX equipped on board of a social robot, obtaining a positive user feedback about its effectiveness and responsiveness.
An MPC-Based Framework for Dynamic Trajectory Re-Planning in Uncertain Environments

Online motion re-planning is an important feature for introducing robots into unstructured environments where the close presence of humans at any time can challenge the operation of the robot from the human safety perspective. This work introduces a novel re-planning framework for robotic manipulators operating in dynamic environments where the interactions with humans may occur either in an anticipated or unexpected manner. The contribution of the proposed framework lies in the fact that it allows to account for the uncertainty of human pose and challenges associated with human motion estimation during occlusion phases of the human with respect to the perception system on the robot. To this aim the proposed framework is comprised of an uncertainty estimation component and a model predictive control (MPC) component, the combination of which enables to efficiently and dynamically track a task-space trajectory by the robot while limiting the probability of potential collisions with a moving human obstacle entering the workspace of the robot. Simulations and experimental trials on a robotic platform show the effectiveness of the proposed framework in re-planning the trajectory of the robotic arm under the presence of a human detected by the perception system installed on the robot.
Not All Dependencies are Equal: An Empirical Study on Production Dependencies in NPM

Modern software systems are often built by leveraging code written by others in the form of libraries and packages to accelerate their development. While there are many benefits to using third-party packages, software projects often become dependent on a large number of software packages. Consequently, developers are faced with the difficult challenge of maintaining their project dependencies by keeping them up-to-date and free of security vulnerabilities. However, how often are project dependencies used in production where they could pose a threat to their project's security?
QATest: A Uniform Fuzzing Framework for Question Answering Systems

The tremendous advancements in deep learning techniques have empowered question answering(QA) systems with the capability of dealing with various tasks. Many commercial QA systems, such as Siri, Google Home, and Alexa, have been deployed to assist people in different daily activities. However, modern QA systems are often designed to deal with different topics and task formats, which makes both the test collection and labeling tasks difficult and thus threats their quality.
Sorry, I don’t Understand: Improving Voice User Interface Testing

Voice-based virtual assistants are becoming increasingly popular. Such systems provide frameworks to developers on which they can build their own apps. End-users can interact with such apps through a Voice User Interface (VUI), which allows to use natural language commands to perform actions. Testing such apps is far from trivial: The same command can be expressed in different ways. To support developers in testing VUIs, Deep Learning (DL)-based tools have been integrated in the development environments (e.g., the Alexa Developer Console, or ADC) to generate paraphrases for the commands (seed utterances) specified by the developers. Such tools, however, generate few paraphrases that do not always cover corner cases. In this paper, we introduce VUI-UPSET, a novel approach that aims at adapting chatbot-testing approaches to VUI-testing. Both systems, indeed, provide a similar natural-language-based interface to users. We conducted an empirical study to understand how VUI-UPSET compares to existing approaches in terms of (i) correctness of the generated paraphrases, and (ii) capability of revealing bugs. Multiple authors analyzed 5,872 generated paraphrases, with a total of 13,310 manual evaluations required for such a process. Our results show that, while the DL-based tool integrated in the ADC generates a higher percentage of meaningful paraphrases compared to VUI-UPSET, VUI-UPSET generates more bug-revealing paraphrases. This allows developers to test more thoroughly their apps at the cost of discarding a higher number of irrelevant paraphrases.
A Light Bug Triage Framework for Applying Large Pre-trained Language Model

Assigning appropriate developers to the bugs is one of the main challenges in bug triage. Demands for automatic bug triage are increasing in the industry, as manual bug triage is labor-intensive and time-consuming in large projects. The key to the bug triage task is extracting semantic information from a bug report. In recent years, large Pre-trained Language Models (PLMs) including BERT [4] have achieved dramatic progress in the natural language processing (NLP) domain. However, applying large PLMs to the bug triage task for extracting semantic information has several challenges. In this paper, we address the challenges and propose a novel framework for bug triage named LBT-P, standing for Light Bug Triage framework with a Pre-trained language model. It compresses a large PLM into small and fast models using knowledge distillation techniques and also prevents catastrophic forgetting of PLM by introducing knowledge preservation fine-tuning. We also develop a new loss function exploiting representations of earlier layers as well as deeper layers in order to handle the overthinking problem. We demonstrate our proposed framework on the real-world private dataset and three public real-world datasets [11]: Google Chromium, Mozilla Core, and Mozilla Firefox. The result of the experiments shows the superiority of LBT-P.
Data Augmentation for Improving Emotion Recognition in Software Engineering Communication

Emotions (e.g., Joy, Anger) are prevalent in daily software engineering (SE) activities, and are known to be significant indicators of work productivity (e.g., bug fixing efficiency). Recent studies have shown that directly applying general purpose emotion classification tools to SE corpora is not effective. Even within the SE domain, tool performance degrades significantly when trained on one communication channel and evaluated on another (e.g, StackOverflow vs. GitHub comments). Retraining a tool with channel-specific data takes significant effort since manually annotating a large dataset of ground truth data is expensive.
Answer Summarization for Technical Queries: Benchmark and New Approach

Prior studies have demonstrated that approaches to generate an answer summary for a given technical query in Software Question and Answer (SQA) sites are desired. We find that existing approaches are assessed solely through user studies. Hence, a new user study needs to be performed every time a new approach is introduced; this is time-consuming, slows down the development of the new approach, and results from different user studies may not be comparable to each other. There is a need for a benchmark with ground truth summaries as a complement assessment through user studies. Unfortunately, such a benchmark is non-existent for answer summarization for technical queries from SQA sites.
Understanding and Predicting Docker Build Duration: An Empirical Study of Containerized Workflow of OSS Projects

Docker building is a critical component of containerized workflow, which automates the process by which sources are packaged and transformed into container images. If not run properly, Docker builds can bring long durations (i.e., slow builds), which increases the cost in human and computing resources, and thus inevitably affect the software development. However, the current status and remedy for the duration cost in Docker builds remain unclear and need an in-depth study. To fill this gap, this paper provides the first empirical investigation on 171,439 Docker builds from 5,833 open source software (OSS) projects. Starting with an exploratory study, the Docker build durations can be characterized in real-world projects, and the developers’ perceptions of slow builds are obtained via a comprehensive survey. Driven by the results of our exploratory study, we propose a prediction modeling of Docker build duration, leveraging 27 handcrafted features from build-related context and configuration and 8 regression algorithms for the prediction task. Our results demonstrate that Random Forest model provides the superior performance with a Spearman’s correlation of 0.781, outperforming the baseline random model by 82.9% in RMSE, 90.6% in MAE, and 94.4% in MAPE, respectively. The implications of this study will facilitate research and assist practitioners in improving the Docker build process.
Comparative Study of Deep Learning Parameter Selection for Multi-Output Regression on Head Pose Estimation

This paper presents a systemic analysis on the implications of the loss function, batch size and optimiser function applied within the multi-output regression problem domain of Head Pose estimation. To the best of our knowledge, prior comparative analysis of this calibre has primarily focused on the image classification problem domain, with limited analysis on the optimisation of hyperparameters influencing the performance of a Convolutional Neural Network (CNN) for regression. This motivates the current study which proposes a vanilla regression model based on the EfficientNet models of varying depths between EfficientNet BO and B5, and experiments on multiple protocols incorporating the benchmark datasets of BIWI, 300W-LP and AFLW2000. Therefore, the main contributions of the paper include a study on the impact of: loss functions for models of varying depths; batch sizes for significantly less and more complex models; and different optimiser functions on the model performance. The investigation of the effect of Mean Squared Error (MSE), Mean Absolute Error (MAE) and Huber loss functions on the performance of the models suggested that MAE and Huber loss (d=1.0) yield optimal performance regardless of the model complexity. The comparative analysis based on batch sizes ranging between 8 and 128 evidenced that moderate batch sizes of 16 and 32 yield an optimal performance on less complex models, while large batch training complements more complex models across all tested loss functions, an observation not evident in previous work. Finally, it was proven that the Adam optimiser function generates a minimal loss and is void of overfitting, outperforming other optimiser functions of Stochastic Gradient Descent, RMSProp, AdaGrad and Adam.
Bayesian Model and Feature Selection in Asymmetric Generalized Gaussian Mixtures

This paper addresses an advanced unsupervised Bayesian framework for learning a finite mixture of asymmetric generalized Gaussian distributions (AGGD) using Markov Chain Monte Carlo (MCMC), incorporating Metropolis-Hastings within Gibbs sampling for estimating mixture parameters and reversible jump MCMC (RJMCMC) to dynamically select the number of model components. To enhance learning, we developed feature selection methods that assign weights to features based on their significance. Numerous experiments are carried out to evaluate the model’s performance in a variety of real-world applications. The performance of our model is compared to other benchmark models in order to determine the significance of proposed learning approach.
Matching heterogeneous ontologies with adaptive evolutionary algorithm

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A Fractional-Order Telegraph Diffusion Model for Restoring Texture Images with Multiplicative Noise

Multiplicative noise removal from texture images poses a significant challenge. Different from the diffusion equation-based filter, we consider the telegraph diffusion equation-based model, which can effectively preserve fine structures and edges for texture images. The fractional-order derivative is imposed due to its textural detail enhancing capability. We also introduce the gray level indicator, which fully considers the gray level information of multiplicative noise images, so that the model can effectively remove high level noise and protect the details of the structure. The well-posedness of the proposed fractional-order telegraph diffusion model is presented by applying the Schauder’s fixed-point theorem. To solve the model, we develop an iterative algorithm based on the discrete Fourier transform in the frequency domain. We give various numerical results on despeckling natural and real SAR images. The experiments demonstrate that the proposed method can remove multiplicative noise and preserve texture well.
Analysis of the Leap Motion Controller Workspace for HRI Gesture Applications

The Leap Motion Controller is a sensor for precise hand tracking; it is a device used for human interaction with computer systems via gestures. The study presented in this paper evaluates its workspace in real-world conditions. An exact replica of a human operator’s hand was used to measure the sensor’s precision, and therefore determine its hand tracking abilities in varying positions above the sensor. The replica was moved randomly across the workspace defined by the manufacturer, and precision was measured in each position. The hand model was placed in the furthest distances from the sensor to find every position where the sensor was still capable of tracking. We found the dimensions of the workspace in some cases exceeded the datasheet values; in other cases, the real workspace was smaller than the proclaimed one. We also computed precision in all positions, which shows tracking reliability. This study serves researchers developing HMI and HRI algorithms as a reference for the real dimensions of the Leap Motion Controller workspace as it provides extra and more precise information compared to the datasheet.
Teaching and Learning Mathematics in Primary Education: The Role of ICT-A Systematic Review of the Literature

Nowadays, ICT play a fundamental role in education, as they are essential in all areas of knowledge at any educational stage. Specifically, in the area of mathematics, within the Primary Education stage, they are also very valid resources. This is demonstrated by the latest scientific studies. In recent times, the scientific literature has provided evidence of all this through different research studies. The aim of this paper is to analyze all those publications that deal with the teaching–learning processes of mathematics through ICT in primary education. The aim is to show the current state of the scientific literature and what elements and aspects to highlight are common to the documents analyzed. By means of the systematic literature review method, we analyzed 11 articles indexed in Scopus and the Web of Science, with the result that the use of ICT in this area is still scarce, as the volume of publications in this respect is very low. This could also indicate a prevalence of traditional methodologies associated with the teaching of mathematics that could hinder students’ acquisition of mathematical competence. However, those studies that show the use of ICT demonstrate that its use leads to an improvement in performance, motivation, and problem solving.
DuFNet: Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology.Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis.Semantic segmentation is also a challenging technology for image understanding and scene parsing.We focused on the challenging task of real-time semantic segmentation in this paper.In this paper, we proposed a novel fast architecture for real-time semantic segmentation named DuFNet.Starting from the existing work of Bilateral Segmentation Network (BiSeNet), DuFNet proposes a novel Semantic Information Flow (SIF) structure for context information and a novel Fringe Information Flow (FIF) structure for spatial information.We also proposed two kinds of SIF with cascaded and paralleled structures, respectively.The SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature fusion module.Features from previous stages usually contain rich low-level details but high-level semantics for later stages.The multiple convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational cost.The FIF consists of a pooling layer and an upsampling operator followed by projection convolution layer.The concise component provides more spatial details for the network.Compared with BiSeNet, our work achieved faster speed and comparable performance with 72.34% mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.
Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

In the field of digital data management, content-based image retrieval (CBIR) has become one of the most important research areas, and it is used in many fields. This system searches a database of images to retrieve most visually comparable photos to a query image. It is based on features derived directly from the image data, rather than on keywords or annotations. Currently, deep learning approaches have demonstrated a strong interest in picture recognition, particularly in extracting information about the features of the image. Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant images. The investigations are conducted using a publicly available dataset named Corel, and the f-measure, recall, and precision metrics are used for performance assessment. Investigation outcomes show that the proposed technique outperforms the existing image retrieval techniques.