Reviewerly/exHarmony huggingface dataset:
Combined System Identification and State Estimation for a Quadrotor UAV

Precise system identification is an important aspect of adequate control design and parameter definition to allow for accurate and reliable navigation. While this is well known in robotics, the community working with small rotorcraft Unmanned Aerial Vehicles (UAVs) has yet to discover the benefits. In contrast to existing work, which often performs offline or deterministic (i.e. closed-form) system identification, we present a probabilistic approach to the online estimation of system identification parameters and self-calibration states. Instead of decoupling system identification and state estimation for vehicle control, we merge the entire process into a holistic probabilistic framework to allow self-awareness and self-healing. Our observability analysis shows that most of the system identification parameters are observable and converge quickly to the optimal value using a combination of inertial cues, dynamic modeling, and an additional exteroceptive sensor. We support our theoretical findings with extensive tests simulating realistic data in Gazebo.
Zhang Neurodynamics for Cholesky Decomposition of Matrix Stream Using Pseudo-Inverse with Transpose of Unknown

Cholesky decomposition is a well-known decomposition for positive definite matrix. On account of its significantly fundamental roles in linear algebra and matrix theory, there are a lot of researches and applications based on it. In recent years, solving time-varying problems has been a research hotspot, but Cholesky decomposition of matrix stream (i.e., continuous time-varying matrix) in a simple, direct and effective equation-solving manner remains a challenging issue. In this paper, the problem of Cholesky decomposition of matrix stream is attempted and solved. First, with the aid of Zhang neurodynamics (ZN), the objective equation at time-derivative level, including the time derivatives of matrix variable and its transpose, is obtained. In order to handle the objective equation with transpose of unknown, Kronecker product, vectorization technique and vectorized transpose matrix are utilized for better derivation. Thus, a ZN solution model using pseudo-inverse is proposed and numerically experimented. Finally, numerical experiment results substantiate the efficacy of the pseudo-inverse type ZN solution model.
Optimization-based Trajectory Planning for Tethered Aerial Robots

This paper presents a non-linear optimization method for trajectory planning of tethered aerial robots. Particularly, the paper addresses the planning problem of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) by means of a tether. The result is a collision-free trajectory for UAV and tether, assuming the UGV position is static. The optimizer takes into account constraints related to the UAV, UGV and tether positions, obstacles and temporal aspects of the motion such as limited robot velocities and accelerations, and finally the tether state, which is not required to be tense. The problem is formulated in a weighted multi-objective optimization framework. Results from simulated scenarios demonstrate that the approach is able to generate obstacle-free and smooth trajectories for the UAV and tether.
Optimum Outlier Detection in Internet of Things Industries Using Autoencoder

In recent years, due to the increase of researches based on data management, outlier detection has attracted enormous interest. There is always a possibility of having outliers within a dataset. Outliers are data that are usually so far away from data trend. Traditional data management systems are responsible for handling outliers, retrieving main data stream, and updating data precisely. On the other hand, the Internet of Things (IoT) is a new perspective in the information technology that includes all technical, social, and economic concepts. In this view, products, commodities, cars, trucks, industrial equipment, and various sensors are always connected via Internet. In the context of the Internet of Things, data management systems would receive giant amount of information continuously. As a result, it is essential to have a powerful outlier detection algorithm before any data analysis and decision-making. The method proposed in this research is to identify and manage outlier sources using the autoencoder neural network.
Improving Emotional Speech Synthesis by Using SUS-Constrained VAE and Text Encoder Aggregation

Learning emotion embedding from reference audio is a straightforward approach for multi-emotion speech synthesis in encoder-decoder systems. But how to get better emotion embedding and how to inject it into TTS acoustic model more effectively are still under investigation. In this paper, we propose an innovative constraint to help VAE extract emotion embedding with better cluster cohesion. Besides, the obtained emotion embedding is used as query to aggregate latent representations of all encoder layers via attention. Moreover, the queries from encoder layers themselves are also helpful. Experiments prove the proposed methods can enhance the encoding of comprehensive syntactic and semantic information and produce more expressive emotional speech.
An Investigation on the Impact of Digital Revolution and Machine Learning in Supply Chain Management

The machine learning has made it possible to discover various disciplines and patterns of the supply chain management (SCM). The entire industrial world is trying to gain contextual intelligence of machine learning discovering various new disciplines of the supply chain network. Here, the study has highlighted the iterative queries of digital revolution in supply chain. Physical inspection and potential applications have enlisted in the discussion here. The aim of the study is to acquire more knowledge and insight about the digital revolution and machine learning in activating the supply chain. The literary view in this article has illustrated significant role of machine learning addressing digital revolution in reducing the freight costs, enhancing delivery performance and minimizing the challenges in suppliers as the best advantages of supply network. It has processed discussing how machine learning targets the linear collaboration synergies in present day industrial world. Discussion on these factors has given a better understanding about machine learning. The article has been processed with secondary qualitative analysis highlighting the issues with formulation of appropriate themes. The core construction of machine learning is ideally suited offering knowledge into SCM performance which is not available in the prior technologies. Machine learning is offering today to be a new technology with highest efficiency in strengthening the Supply chain. The qualitative approach of this article expresses the power and emphasize its rate of use pointing the measurement in a comprehensive manner. The major purpose is to define the reach of endpoints of machine learning to ensure the highest productivity in SCM. In addition to this, global industrial pace is broadening day by day where the power of machine learning has become more applicable observing the recent trend in global enterprises. This research article is hopeful enough in giving the meaningful view on the topic following all its variables.
Mixture Model Auto-Encoders: Deep Clustering Through Dictionary Learning

State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures. Many of these networks require a large number of parameters and suffer from a lack of interpretability, due to the black-box nature of the auto-encoders. We introduce Mixture Model Auto-Encoders (MixMate), a novel architecture that clusters data by performing inference on a generative model. Built on ideas from sparse dictionary learning and mixture models, MixMate comprises several auto-encoders, each tasked with reconstructing data in a distinct cluster, while enforcing sparsity in the latent space. Through experiments on various image datasets, we show that MixMate achieves competitive performance versus state-of-the-art deep clustering algorithms, while using orders of magnitude fewer parameters.
Fuzzy-Based Distributed Behavioral Control With Wall-Following Strategy for Swarm Navigation in Arbitrary-Shaped Environments

This paper addresses novel fuzzy-based distributed behavioral control with the wall-following strategy for robot swarm navigation in arbitrary-shaped environments.Instead of avoiding large-size obstacles during the swarm navigation, the proposed distributed control enables the robot swarm to follow obstacle boundaries by transforming from aggregation configuration to one-chain configuration.The wallfollowing strategy and one-chain configuration empower the swarm navigation to avoid local minima caused by obstacles and connectivity maintenance without dealing with the alignment control of swarm behaviours.The fuzzy-logic control is applied to calculate the parameters of the distributed control strategies.The proposed method is examined and evaluated in both simulation and real experiments.
Identifying Non-Technical Skill Gaps in Software Engineering Education: What Experts Expect But Students Don’t Learn

As the importance of non-technical skills in the software engineering industry increases, the skill sets of graduates match less and less with industry expectations. A growing body of research exists that attempts to identify this skill gap. However, only few so far explicitly compare opinions of the industry with what is currently being taught in academia. By aggregating data from three previous works, we identify the three biggest non-technical skill gaps between industry and academia for the field of software engineering: devoting oneself to continuous learning , being creative by approaching a problem from different angles , and thinking in a solution-oriented way by favoring outcome over ego . Eight follow-up interviews were conducted to further explore how the industry perceives these skill gaps, yielding 26 sub-themes grouped into six bigger themes: stimulating continuous learning , stimulating creativity , creative techniques , addressing the gap in education , skill requirements in industry , and the industry selection process . With this work, we hope to inspire educators to give the necessary attention to the uncovered skills, further mitigating the gap between the industry and the academic world.
Learning Via Watching: A Weakly Supervised Moving Object Detector for Satellite Videos

Moving Object Detection (MOD) from satellite videos plays one of the most fundamental roles in satellite video surveillance. Training a supervised moving object detector typically requires boundary box annotations for object instances, however, this annotation process is time-consuming for satellite videos. In this paper, we propose a weakly supervised method for sidestepping this process, where the supervised information for detecting moving objects on a frame is instead provided by the unsupervised method based on motion knowledge across a video. We adopt the Extended Low-rank and Structured Sparse Decomposition (E-LSD) approach for generating pixel-wise pseudo labels for moving objects. Then the extracted pseudo labels are used for training a Deep Convolutional Neural Network (DCNN) following the Encoder-Decoder architecture with lateral connections for segmenting moving objects from a new frame. We demonstrate the effectiveness of the proposed method on a satellite video dataset, and, compared with five state-of-the-art MOD methods tested, it achieves both improved detection accuracy and promising frame rate.
Community Generated VR Painting using Eye Gaze

The social experience is an important part of art exhibitions. This demo introduces an eye-gaze based generative art prototype for virtual reality (VR) art exhibitions. Our work extends the visitors' experience from individual art exploration to become content co-creators. The design generates live community artworks based on all visitors' visual interactions with VR paintings. During our VR exhibition at a public gallery, over 100 visitors participated in the new creative process for community-generated artworks.
Multimodal Global Relation Knowledge Distillation for Egocentric Action Anticipation

In this paper, we consider the task of action anticipation on egocentric videos. Previous methods ignore explicit modeling of the global context relation among past and future actions, which is not an easy task due to the vacancy of unobserved videos. To solve this problem, we propose a Multimodal Global Relation Knowledge Distillation (MGRKD) framework to distill the knowledge learned from full videos to improve the action anticipation task on partially observed videos. The proposed MGRKD has a teacher-student learning strategy, where either the teacher or student model has three branches of global relation graph networks (GRGN) to explore the pairwise relations between past and future actions based on three kinds of features (i.e., RGB, motion or object). The teacher model has a similar architecture with the student model, except that the teacher model uses true feature of the future video snippet to build the graph in GRGN while the student model uses a progressive GRU to predict an initialized node feature of future snippet in GRGN. Through the teacher-student learning strategy, the discriminative features and relation knowledge of the past and future actions learned in the teacher model can be distilled to the student model. The experiments on two egocentric video datasets EPIC-Kitchens and EGTEA Gaze+ show that the proposed framework achieves state-of-the-art performances.
Safety Metrics for Semantic Segmentation in Autonomous Driving

Within the context of autonomous driving, safety-related metrics for deep neural networks have been widely studied for image classification and object detection. In this paper, we further consider safety-aware correctness and robustness metrics specialized for semantic segmentation. The novelty of our proposal is to move beyond pixel-level metrics: Given two images with each having <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> pixels being class-flipped, the designed metrics should, depending on the clustering of pixels being class-flipped or the location of occurrence, reflect different levels of safety criticality. The result evaluated on an autonomous driving dataset demonstrates the validity and practicality of our proposed methodology.
Quantum Computing As a Topic in Computer Science Education

Quantum technologies are currently among the most promising technological developments, with quantum computing, in particular, playing a crucial role. This is accompanied by promising opportunities, but also new challenges for our society. However, quantum computing as a subject of computer science education is still at the very beginning. This paper aims to discuss quantum computing as a topic in computer science education and to make a first approach to central terms and ideas as well as their explanatory approaches. With the help of an explorative focus group interview with experts, five core ideas of quantum computer science are identified in this study. A literature review is then used to identify, categorize, and contrast different explanatory approaches for these ideas. The results thus contribute to making quantum computer science accessible for computing education and raise further questions for the computing education research community.
3D Posture Estimation and Automatic Adjustment of Zebrafish Larvae

Due to the irregular surface of zebrafish larvae and the difficulty of fixing the body, there are high requirements for its injection target point and posture control. By establishing a zebrafish data set containing key points and three-dimensional posture annotations, a network model for zebrafish key point recognition is designed using convolutional neural networks. The posture angle information is further calculated through three-dimensional coordinate transformation. Experiments demonstrate that this method can obtain a more accurate zebrafish posture angle under the condition of posture change. In response to this method, an operating platform that can fix zebrafish juveniles and precisely adjust their posture is designed to lay the foundation for subsequent experimental operations.
Investigation of Unmanned Aerial Vehicle Gesture Perceptibility and Impact of Viewpoint Variance

Unmanned Aerial Vehicle (UAV) flight paths have been shown to communicate meaning to human observers, similar to human gestural communication. This paper presents the results of a UAV gesture perception study designed to assess how observer viewpoint perspective may impact how humans perceive the shape of UAV gestural motion. Robot gesture designers have demonstrated that robots can indeed communicate meaning through gesture; however, many of these results are limited to an idealized range of viewer perspectives and do not consider how the perception of a robot gesture may suffer from obfuscation or self-occlusion from some viewpoints. This paper presents the results of three online user-studies that examine participants' ability to accurately perceive the intended shape of two-dimensional UAV gestures from varying viewer perspectives. We used a logistic regression model to characterize participant gesture classification accuracy, demonstrating that viewer perspective does impact how participants perceive the shape of UAV gestures. Our results yielded a viewpoint angle threshold from beyond which participants were able to assess the intended shape of a gesture's motion with 90% accuracy. We also introduce a perceptibility score to capture user confidence, time to decision, and accuracy in labeling and to understand how differences in flight paths impact perception across viewpoints. These findings will enable UAV gesture systems that, with a high degree of confidence, ensure gesture motions can be accurately perceived by human observers.
A New Approach for Video Action Recognition: CSP-Based Filtering for Video to Image Transformation

In this paper we report on the design of a pipeline involving Common Spatial Patterns (CSP), a signal processing approach commonly used in the field of electroencephalography (EEG), matrix representation of features and image classification to categorize videos taken by a humanoid robot. The ultimate goal is to endow the robot with action recognition capabilities for a more natural social interaction. Summarizing, we apply the CSP algorithm to a set of signals obtained for each video by extracting skeleton joints of the person performing the action. From the transformed signals a summary image is obtained for each video, and these images are then classified using two different approaches; global visual descriptors and convolutional neural networks. The presented approach has been tested on two data sets that represent two scenarios with common characteristics. The first one is a data set with 46 individuals performing 6 different actions. In order to create the group of signals of each video, OpenPose has been used to extract the skeleton joints of the person performing the actions. The second data set is an Argentinian Sign Language data set (LSA64) from which the signs performed using just the right hand have been used. In this case the joint signals have been obtained using MediaPipe. The results obtained with the presented method have been compared with a Long Short-Term Memory (LSTM) method, achieving promising results.
Selective Context-Sensitivity for k-CFA with CFL-Reachability

k-CFA provides the most well-known context abstraction for program analysis, especially pointer analysis, for a wide range of programming languages. However, its inherent context explosion problem has hindered its applicability. To mitigate this problem, selective context-sensitivity is promising as context-sensitivity is applied only selectively to some parts of the program. This paper introduces a new approach to selective context-sensitivity for supporting k-CFA-based pointer analysis, based on CFL-reachability. Our approach can make k-CFA-based pointer analysis run significantly faster while losing little precision, based on an evaluation using a set of 11 popular Java benchmarks and applications.
LiDAR few-shot domain adaptation via integrated CycleGAN and 3D object detector with joint learning delay

he success of supervised LiDAR perception methods relies on the availability of large sets of labeled point cloud data, for which the labeling process is costly and time consuming. Given unpaired LiDAR datasets of similar sizes from two domains, with one (source) containing task-specific labels e.g. 3D bounding boxes for all frames, but only a small percentage of frames being labeled in the other (target) domain, it is challenging to train a model that generalizes well on validation data from the target domain. In this paper we propose a novel LiDAR few-shot domain adaptation architecture and training strategy to address this challenge. Our method is based on adapting a task-specific network (3D object detector) to work within the CycleGAN framework modified to operate with LiDAR features, and on the joint end-to-end training of generators, discriminators, and task-specific layers. To overcome nonconvergence issues we propose a training strategy that introduces a mechanism to delay the joint learning between the generators/discriminators and the task-specific network by allowing them to start learning independently, while slowly introducing joint learning as they converge, hence avoiding instability during the early stages of the training. Our proposed integrated architecture enables a direct way to evaluate the performance of the model instead of feeding pre-computed generated data into a separate pretrained model. We include an experimental section where we evaluate our proposed architecture on the publicly available KITTI and Nuscenes datasets, as well as on our own labeled dataset. We present useful mean average precision plots that illustrate the benefits of our domain adaptation architecture as a function of number of labeled target domain frames.
Solving Markov Decision Processes with Partial State Abstractions

Autonomous systems often use approximate planners that exploit state abstractions to solve large MDPs in real-time decision-making problems. However, these planners can eliminate details needed to produce effective behavior in autonomous systems. We therefore propose a novel model, a partially abstract MDP, with a set of abstract states that each compress a set of ground states to condense irrelevant details and a set of ground states that expand from a set of expanded abstract states to retain relevant details. This papers offers (1) a definition of a partially abstract MDP that (2) generalizes its ground MDP and its abstract MDP and exhibits bounded optimality depending on its abstract MDP along with (3) a lazy algorithm for planning and execution in autonomous systems. The result is a scalable approach that computes near-optimal solutions to large problems in minutes rather than hours.