Reviewerly/exHarmony huggingface dataset:
A Blockchain Approach for Decentralized V2X (D-V2X)

New mobility paradigms have appeared in recent years, and everything suggests that some more are coming. This fact makes apparent the necessity of modernizing the road infrastructure, the signalling elements and the traffic management systems. Many initiatives have emerged around the term Intelligent Transport System (ITS) in order to define new scenarios and requirements for this kind of applications. We even have two main competing technologies for implementing Vehicular communication protocols (V2X), C-V2X and 802.11p, but neither of them is widely deployed yet. One of the main barriers for the massive adoption of those technologies is governance. Current solutions rely on the use of a public key infrastructure that enables secure collaboration between the different entities in the V2X ecosystem, but given its global scope, managing such infrastructure requires reaching agreements between many parties, with conflicts of interest between automakers and telecommunication operators. As a result, there are plenty of use cases available and two mature communication technologies, but the complexity at the business layer is stopping the drivers from taking advantage of ITS applications. Blockchain technologies are defining a new decentralized paradigm for most traditional applications, where smart contracts provide a straightforward mechanism for decentralized governance. In this work, we propose an approach for decentralized V2X (D-V2X) that does not require any trusted authority and can be implemented on top of any communication protocol. We also define a proof-of-concept technical architecture on top of a cheap and highly secure System-on-Chip (SoC) that could allow for massive adoption of D-V2X.
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks

This paper studies an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) based on the datasets uploaded from a multi-technology-supported IoT network. The data uploading performance of IoT network and the computational capacity of edge servers are entangled with each other in influencing the FL model training process. We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network as well as their local data processing capacity and only request the amount of data that is sufficient for training a satisfactory model. We evaluate the energy cost for data uploading when two widely-used IoT solutions: licensed band IoT (e.g., 5G NB-IoT) and unlicensed band IoT (e.g., Wi-Fi, ZigBee, and 5G NR-U) are available to each IoT device. We prove that the cost minimization problem of the entire IoT network is separable and can be divided into a set of subproblems, each of which can be solved by an individual edge server. We also introduce a mapping function to quantify the computational load of edge servers under different combinations of three key parameters: size of the dataset, local batch size, and number of local training passes. Finally, we adopt an Alternative Direction Method of Multipliers (ADMM)-based approach to jointly optimize energy cost of the IoT network and average computing resource utilization of edge servers. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network. Simulation results show that our proposed framework significantly improves the resource efficiency of the IoT network and edge servers with only a limited sacrifice on the model convergence performance.
Analysis of student satisfaction of e-learning using the end-user computing satisfaction method during the Covid-19 pandemic

Abstract The government’s physical distancing policy to prevent transmission of the covid19 virus caused changes in learning patterns from face-to-face learning to distance education. The application of distance education at Universitas Negeri Yogyakarta is one of them through e-learning. Measurement of the level of e-learning user satisfaction is an assessment of the success of e-learning itself. The method used to measure students’ level of satisfaction using e-learning is End-User Computing Satisfaction (EUCS). EUCS has five indicators, namely, Content, Accuracy, Format, Ease of Use, and Timelines. The research sample was taken using a random sampling technique. The data collection technique was carried out using a questionnaire. Data analysis uses descriptive statistical analysis and inferential statistical analysis. Based on the analysis results, the content evaluation value is 4.0300, the accuracy evaluation value is 4.0200, the evaluation value is 4.2550, the evaluation value for easy use is 4.0288, and the evaluation value for timeliness is 4.0900. The overall analysis results showed that the students were satisfied with the e-learning course in vocational learning media.
Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action Recognition

One-shot action recognition allows the recognition of human-performed actions with only a single training example. This can influence human-robot-interaction positively by enabling the robot to react to previously unseen behaviour. We formulate the one-shot action recognition problem as a deep metric learning problem and propose a novel image-based skeleton representation that performs well in a metric learning setting. Therefore, we train a model that projects the image representations into an embedding space. In embedding space similar actions have a low euclidean distance while dissimilar actions have a higher distance. The one-shot action recognition problem becomes a nearest-neighbor search in a set of activity reference samples. We evaluate the performance of our proposed representation against a variety of other skeleton-based image representations. In addition we present an ablation study that shows the influence of different embedding vector sizes, losses and augmentation. Our approach lifts the state-of-the-art by 3.3% for the one-shot action recognition protocol on the NTU RGB+D 120 dataset under a comparable training setup. With additional augmentation our result improved over 7.7%.
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification

Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification.However, most classification datasets assume a large amount labeled data, which is commonly not the case in practical settings.In particular, in this paper we compare the performance of a light-weight linear classifier based on word embeddings, i.e., fastText (Joulin et al., 2017), versus a pre-trained language model, i.e., BERT (Devlin et al., 2019), across a wide range of datasets and classification tasks.In general, results show the importance of domain-specific unlabeled data, both in the form of word embeddings or language models.As for the comparison, BERT outperforms all baselines in standard datasets with large training sets.However, in settings with small training datasets a simple method like fastText coupled with domain-specific word embeddings performs equally well or better than BERT, even when pre-trained on domain-specific data.
FACE-KEG: Fact Checking Explained using KnowledgE Graphs

In recent years, a plethora of fact checking and fact verification techniques have been developed to detect the veracity or factuality of online information text for various applications. However, limited efforts have been undertaken to understand the interpretability of such veracity detection, i.e. explaining why a particular piece of text is factually correct or incorrect. In this work, we seek to bridge this gap by proposing a technique, FACE-KEG, to automatically perform explainable fact checking. Given an input fact or claim, our proposed model constructs a relevant knowledge graph for it from a large-scale structured knowledge base. This graph is encoded via a novel graph transforming encoder. Our model also simultaneously retrieves and encodes relevant textual context about the input text from the knowledge base. FACE-KEG then jointly exploits both the concept-relationship structure of the knowledge graph as well as semantic contextual cues in order to (i) detect the veracity of an input fact, and (ii) generate a human-comprehensible natural language explanation justifying the fact's veracity. We conduct extensive experiments on three large-scale datasets, and demonstrate the effectiveness of FACE-KEG while performing fact checking. Automatic and human evaluations further show that FACE-KEG significantly outperforms competitive baselines in learning concise, coherent and informative explanations for the input facts.
Fermion Sampling: A Robust Quantum Computational Advantage Scheme Using Fermionic Linear Optics and Magic Input States

Demonstrating quantum computational advantage with strong hardness guarantees is shown to be possible using fermionic linear optics with magic input states, and an experimentally feasible setup is proposed.
Robust laser stripe extraction for 3D measurement of complex objects

Abstract Laser stripe center extraction is a critical step for a structured-light system, which plays an important role in industrial fields. However, existing center extraction methods are mainly for simple objects, where the stripe contours are relatively clear and no fake stripe areas caused by surface texture or surface reflection disturb the extraction of the real stripe area. To develop center extraction methods for complex objects, this paper proposed a new laser stripe extraction method based on the features of the laser stripe. After extracting seed points of the laser stripe using an extreme value method and connected component analysis, we can determine the stripe directions from the seed points and iteratively grow the centerline of the laser stripe. Finally, a gray-gravity method is used to extract the accurate center points of the laser stripe based on the obtained centerline. Experiments on 2D image laser stripe extraction and real 3D reconstruction application verified the robustness and accuracy of the proposed method.
How to evaluate data visualizations across different levels of understanding

Understanding a visualization is a multi-level process. A reader must extract and extrapolate from numeric facts, understand how those facts apply to both the context of the data and other potential contexts, and draw or evaluate conclusions from the data. A well-designed visualization should support each of these levels of understanding. We diagnose levels of understanding of visualized data by adapting Bloom’s taxonomy, a common framework from the education literature. We describe each level of the framework and provide examples for how it can be applied to evaluate the efficacy of data visualizations along six levels of knowledge acquisition - knowledge, comprehension, application, analysis, synthesis, and evaluation. We present three case studies showing that this framework expands on existing methods to comprehensively measure how a visualization design facilitates a viewer’s understanding of visualizations. Although Bloom’s original taxonomy suggests a strong hierarchical structure for some domains, we found few examples of dependent relationships between performance at different levels for our three case studies. If this level-independence holds across new tested visualizations, the taxonomy could serve to inspire more targeted evaluations of levels of understanding that are relevant to a communication goal.
A semi-supervised deep learning image caption model based on Pseudo Label and N-gram

Image caption is an important application field of artificial intelligence technique. When a machine can describe a picture reasonably like a human, it represents that the machine has higher intelligence to understand the picture. However, for complex machine learning tasks such as image caption, data annotation is time-consuming and laborious. Usually in a new application scenario, data annotation rarely results in poor model performance. A large number of easily available unlabeled image data make it possible to semi-supervised learning of image caption methods. Based on the existing end-to-end deep learning paradigm, a semi-supervised deep learning method is proposed in this paper, called N-gram + Pseudo Label NIC method. The method combines the current mainstream deep neural network method, e.g. the NIC (Neural Image Caption) model, and the semi-supervised deep learning idea of pseudo labels, and N-gram. This method generates pseudo labels by N-gram Search algorithm, and improves the effect of the model by utilizing the prior knowledge of the N-gram table and people's descriptive habits. This method has achieved better results than the original NIC model on different sub-data sets of Flickr 8K data set and MSCOCO data set of 0.5k, 1k, 2k and 3k under BLEU-1 evaluation criteria.
Least-squares gamma estimation in fringe projection profilometry

This paper introduces a novel, to the best of our knowledge, method to estimate and compensate the nonlinear gamma factor introduced by the optical system in fringe projection profilometry. We propose to determine this factor indirectly by adjusting the least-squares plane to the estimated phase coming from the reference plane. We only require a minimal set of three fringe sinusoidal images to estimate the gamma factor. This value can be used to rectify computational legacy data and also to generate and project the new set of fringe patterns for which we perform the inverse gamma compensation. Experimental results demonstrate the feasibility of the proposed method to estimate and correct the gamma distortion.
An Alternative of LiDAR in Nighttime: Unsupervised Depth Estimation Based on Single Thermal Image

Most existing autonomous driving vehicles and robots rely on active LiDAR sensors to detect the depth of the surrounding environment, which usually has limited resolution, and the emitted laser can be harmful to people and the environment. Current passive image-based depth estimation algorithms focus on color images from RGB sensors, which is not suitable for dark and night environment with limited lighting resource. In this paper, we propose a framework to estimate the scene depth directly from a single thermal image that can still observe the scene in the low lighting condition. We learn the thermal image depth estimation frame-work together with RGB cameras, which also mitigates the training condition due to the easy availability of RGB cameras. With the translated thermal images from color images from our generative adversarial network, our depth estimation method can explore the unique characteristics in thermal images through our novel contour and edge-aware constraints to obtain a stable and anti-artifact disparity. We apply the commonly available color cameras to navigate the learning process of thermal image depth estimation frame-work. With our approach, an accurate depth map can be predicted without any prior knowledge under various illumination conditions.
Deep Learning-based Sentiment Analysis of Facebook Data: The Case of Turkish Users

Abstract Sentiment analysis (SA) is an essential task for many domains where it is crucial to know users’ public opinion about events, products, brands, politicians and so on. Existing works on SA have concentrated on English texts including Twitter feeds and user reviews on hotels, movies and products. On the other hand, Facebook, as an online social network (OSN), has attracted quite limited attention from the research community. Among these, SA work on Turkish text obtained from OSNs are extremely scarce. In this paper, our aim is to perform SA on public Facebook data collected from Turkish user accounts. Our study differs from existing studies in terms of the data set scale, the natural language of the texts in the data set and the extent of experimental analyses that include both machine learning and deep learning techniques. We extensively report not only the results of different learning models involving SA but also statistical distribution of metadata of user activities across various user attributes (e.g. gender and age). Our experimental results indicate that recurrent neural networks achieve the best accuracy (i.e. 0.916) with word embeddings. To the best of our knowledge, this is the best result for SA on Facebook data in the context of the Turkish language.
Symmetrical Enhanced Fusion Network for Skeleton-Based Action Recognition

A novel method for skeleton-based action recognition by fusing multi-level spatial features and multi-level temporal features is proposed in this article. Recently, Graph Convolutional Network (GCN) for skeleton-based action recognition has attracted the eyes of many researchers and has a great performance in the field of action recognition. But most of them focus on changing architecture of single-stream network and only use simple methods like average fusion to fuse different forms of skeleton data. In this article, we shift the focus to the problem that insufficient interactions between the different forms of features for that networks are unable to fully capture efficient information from skeleton data. To tackle this problem, we propose a multi-stream network called Symmetrical Enhanced Fusion Network (SEFN). The network is composed of a spatial stream, a temporal stream and a fusion stream. The spatial stream extracts spatial features from skeleton data by GCN. The temporal stream is able to extract temporal features from skeleton data with the help of the embedded Motion Sequence Calculation Algorithm. The fusion stream provides an early fusion method and extra fusion information for the whole network. It gathers multi-level features from two feature extractions and fuses them with the Multi-perspective Attention Fusion Module (MPAFM) we propose. The MPAFM enables different forms of data to enhance each other and can strengthen feature extractions. In the final, we generalize the skeleton data from joint data to bone data and evaluate our network in three large-scale benchmarks: NTU-RGBD, NTU-RGBD 120 and Kinetics-Skeleton. Experiment results demonstrate that our method achieves competitive performance.
Physician-Friendly Tool Center Point Calibration Method for Robot-Assisted Puncture Surgery

After each robot end tool replacement, tool center point (TCP) calibration must be performed to achieve precise control of the end tool. This process is also essential for robot-assisted puncture surgery. The purpose of this article is to solve the problems of poor accuracy stability and strong operational dependence in traditional TCP calibration methods and to propose a TCP calibration method that is more suitable for a physician. This paper designs a special binocular vision system and proposes a vision-based TCP calibration algorithm that simultaneously identifies tool center point position (TCPP) and tool center point frame (TCPF). An accuracy test experiment proves that the designed special binocular system has a positioning accuracy of ±0.05 mm. Experimental research shows that the magnitude of the robot configuration set is a key factor affecting the accuracy of TCPP. Accuracy of TCPF is not sensitive to the robot configuration set. Comparison experiments show that the proposed TCP calibration method reduces the time consumption by 82%, improves the accuracy of TCPP by 65% and improves the accuracy of TCPF by 52% compared to the traditional method. Therefore, the method proposed in this article has higher accuracy, better stability, less time consumption and less dependence on the operations than traditional methods, which has a positive effect on the clinical application of high-precision robot-assisted puncture surgery.
Multi-image and color image encryption via multi-slice ptychographic encoding

Multi-slice ptychography is a diffractive imaging technique that can reconstruct the information of a 3-D object from its diffraction intensities. In this article, we propose a multi-slice ptychographic encoding method for the encryption of multiple and color images. Full-phase images to be encrypted together with inserted phase keys are transformed into diffraction intensities (i.e., cipher texts) via multi-slice ptychographic encoding and double random phase encoding. The multi-slice ptychographic encoding refers to the process of using ptychographic scanning and recording to encrypt a multi-slice object that is composed of secret images and inserted phase keys. During decryption, an iterative phase retrieval algorithm is modified. The feasibility, robustness and security of the proposed method are analyzed. Numerical simulation results show that the proposed method can achieve high-quality decryption, and has high security and robustness against noise, compression, and occlusion.
Study on robust aerial docking mechanism with deep learning based drogue detection and docking

This paper proposes a simple and a robust bistable docking system with a deep learning based real-time drogue detection and tracking system for Unmanned Aircraft Systems (UAS) for mid-air autonomous aerial docking. Secure aerial docking mechanisms between the leader and follower aerial vehicles with effective drogue detection and tracking strategies are fundamental challenges during the air-to-air docking phase of autonomous aerial docking. To confront those issues, this paper not only presents the design of a bistable-based aerial docking mechanism, but also proposes effective deep learning based real-time drogue detection using a convolutional neural network (CNN) and real-time tracking algorithm using a point cloud algorithm. To ensure novelty and robustness for the aerial docking mechanism, a foldable bistable gripper-type mechanism is designed to increase the grasping performance with simplicity and adaptability. The proposed gripper acts as a drogue by itself to grasp a probe which is attached to the follower aerial vehicle. To employ an effective drogue detection method, the deep learning based real-time object detection algorithm, YOLOv3, is used to implement the drogue detection system. The proposed new probe-and-drogue type bistable docking system has the advantages of being simple and robust. The deep learning based real-time drogue detection method increases the detection rate. Moreover, the real-time tracking algorithm with a depth camera system does not require a GPS/INS system and many other sensors to follow the drogue movement in the air.
Distillation Multiple Choice Learning for Multimodal Action Recognition

In this work, we address the problem of learning an ensemble of specialist networks using multimodal data, while considering the realistic and challenging scenario of possible missing modalities at test time. Our goal is to leverage the complementary information of multiple modalities to the benefit of the ensemble and each individual network. We introduce a novel Distillation Multiple Choice Learning framework for multimodal data, where different modality networks learn in a cooperative setting from scratch, strengthening one another. The modality networks learned using our method achieve significantly higher accuracy than if trained separately, due to the guidance of other modalities. We evaluate this approach on three video action recognition benchmark datasets. We obtain state-of-the-art results in comparison to other approaches that work with missing modalities at test time.