Reviewerly/exHarmony huggingface dataset:
The future of autonomous vehicles

The future of the modern world faces the appearance of different ways of mobility. Huge strive in today's world have gained autonomous vehicles. The paper explains how autonomous vehicles function as well as their advantages and disadvantages. New developments in autonomous vehicles are being accomplished and introduced to the user's demands. Many car companies have developed their own driverless vehicles and detected the problems within them. The major flaw of autonomous vehicles is cyber security because hackers are still able to break into the car's software system and disrupt it. This is a major issue which is still being dealt with. Autonomous vehicles have modernized the mobility of people, which means that people no longer have to come to the vehicle but the vehicle comes to them and are able to share transportation and thus lowering the traffic congestion and cost. Smartphone applications have been developed facilitating the carsharing system. Users consider these cars comfortable and stylish but expect high level of security. Autonomous vehicles enable the elderly, the disabled and physically limited people move much easier. Furthermore, autonomous vehicles reduce pollution and are environmentally friendly. It is anticipated that autonomous vehicles will take over the roads and are the future of transportation. They offer comfort, safety and good driving conditions. Hereafter, this paper represents important characteristics and features of autonomous cars.
Solving the Network Shortest Path Problem on a Quantum Annealer

This article addresses the formulation for implementing a single source, single-destination shortest path algorithm on a quantum annealing computer.Three distinct approaches are presented.In all the three cases, the shortest path problem is formulated as a quadratic unconstrained binary optimization problem amenable to quantum annealing.The first implementation builds on existing quantum annealing solutions to the traveling salesman problem, and requires the anticipated maximum number of vertices on the solution path |P| to be provided as an input.For a graph with |V | vertices, |E| edges, and no self-loops, it encodes the problem instance using |V ||P| qubits.The second implementation adapts the linear programming formulation of the shortest path problem, and encodes the problem instance using |E| qubits for directed graphs or 2|E| qubits for undirected graphs.The third implementation, designed exclusively for undirected graphs, encodes the problem in |E| + |V | qubits.Scaling factors for penalty terms, complexity of coupling matrix construction, and numerical estimates of the annealing time required to find the shortest path are made explicit in the article.
A Benchmark and Evaluation of Non-Rigid Structure from Motion

Abstract Non-rigid structure from motion ( nrs f m ), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrs f m , including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrs f m . This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.
On the computational complexity of Dempster’s Rule of combination, a parallel computing approach

Multisensor data fusion with Dempster–Shafer (D–S) theory is beneficial for context inference without any advanced information. D–S theory includes reasoning based on Dempster’s rule of combination of degrees of belief based on different pieces of evidence. Although, the computational complexity of Dempster’s rule of combination is enormous. Any change in the number of pieces of evidence or hypotheses causes a lot of additional computations. Dempster’s rule of combination is shown to be #P-complete. The combination rule of k frames of evidence with nk elements, such that (k∈N|k≥2) has a time complexity of O(2N), where N=∑knk. In this paper, we propose a parallel computing approach for Dempster’s rule of combination based on the concept of conquer and divide algorithms. The proportion of task benefiting from improvement is p=1−2k, hence k2 of theoretical maximum speed-up according to Amdahl’s law. We have tested our algorithm in different experimental settings and observed that the new parallel computing approach has not only achieved the best results in CPU version, but has also outperformed GPU version using Thrust CUDA in almost scenarios of the experiment.
Vision-based modal analysis of cutting tools

This paper presents the use of vision-based methods for cutting tool motion registration and modal analysis. Motion of three illustrative tools were recorded using low- and high-speed cameras with sufficiently high resolutions. The tool’s own features are used to register motion. Pixels within images from recordings of the vibrating tools are treated as non-contact motion sensors. Comparative analysis of three different methods of motion registration are presented to evaluate their suitability for the application of interest. These include variants of expanded edge detection and tracking schemes, expanded optical flow-based schemes, and established digital image correlation methods. Performance of different methods was observed to be governed by the tool’s own features, illumination conditions, noise, and the image acquisition parameters. Extracted motion was benchmarked against twice integrated measured tool point accelerations, and motion was generally observed to compare well. Modal parameters extracted from vision-based measurements were also observed to agree with those extracted using more traditional experimental modal analysis procedures using a contact type accelerometer as the transducer. Since methods presented are generalized, they can suitably be adapted for other applications of interest.
A pre-expectation calculus for probabilistic sensitivity

Sensitivity properties describe how changes to the input of a program affect the output, typically by upper bounding the distance between the outputs of two runs by a monotone function of the distance between the corresponding inputs. When programs are probabilistic, the distance between outputs is a distance between distributions. The Kantorovich lifting provides a general way of defining a distance between distributions by lifting the distance of the underlying sample space; by choosing an appropriate distance on the base space, one can recover other usual probabilistic distances, such as the Total Variation distance. We develop a relational pre-expectation calculus to upper bound the Kantorovich distance between two executions of a probabilistic program. We illustrate our methods by proving algorithmic stability of a machine learning algorithm, convergence of a reinforcement learning algorithm, and fast mixing for card shuffling algorithms. We also consider some extensions: using our calculus to show convergence of Markov chains to the uniform distribution over states and an asynchronous extension to reason about pairs of program executions with different control flow.
Low Bitrate Light Field Compression With Geometry and Content Consistency

Light field imaging can simultaneously record the position and direction information of light rays; thus, digital refocusing and full depth-of-field extension &#x2014; functions that are inaccessible for conventional images &#x2014; can be achieved using the structural consistency of light field data. To meet the challenges of limited bandwidth and storage, such vast numbers of light field data must be compressed to a low bitrate. However, current compression solutions ignore the intrinsic consistency of light fields in pursuit of a low bitrate, thereby leading to the loss of light field capabilities. To solve this issue, this work focuses on structural consistency to achieve efficient light field compression with a low bitrate. The proposed light field compression method encodes the sparsely selected sub-aperture images (SAIs) and the disparity maps corresponding to the unselected SAIs. From the perspective of geometry consistency, the consistency of the initially estimated disparity maps is improved by using a color-guided refinement algorithm, thereby reducing the bitrate of the disparity maps. From the perspective of content consistency, the consistency of the SAI-transformed pseudo sequence is improved by the proposed content-similarity-based arrangement algorithm along with a specific prediction structure; thereby, the bitrate of the sparsely selected SAIs is reduced. The experimental results show that the proposed compression method can reduce the total bitrate while preserving good structural consistency.
Detection of Fake and Fraudulent Faces via Neural Memory Networks

Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content. Such approaches are seen as a source of disinformation and mistrust, and pose serious concerns to governments around the world. Convolutional Neural Networks (CNNs) demonstrate encouraging results when detecting fake images that arise from the specific type of manipulation they are trained on. However, this success has not transitioned to unseen manipulation types, resulting in a significant gap in the line-of-defense. We propose a Hierarchical Attention Memory Network (HAMN), motivated by the social cognition processes of the human brain, for the detection of fake faces. Through visual cues and by utilising knowledge stored in neural memories, we allow the network to reason about the perceived face and anticipate it's future semantic embeddings. This renders a generalisable face tampering detection framework. Experimental results demonstrate the proposed approach achieves superior performance for fake and fraudulent face detection.
MAPD: An improved multi-attribute pedestrian detection in a crowd

Recently, CNN (convolutional neural networks) based pedestrian detection has made significant progress, but pedestrian detection in a crowd is still a challenge. Pedestrians are close to each other in a crowd, which is difficult for discriminating individuals. To address this issue, we propose an improved Multi-attribute pedestrian detection (MAPD) method, which coptimize person intra-class compactness and inter-class discrepancy. The contributions are fourfold: (1) We analyze the effect of positive setting on the detector and adopt a better positive settings strategy to mitigate extreme class imbalance problems. (2) Inspired by Person Reid, we employ the triplet loss function to learn the advanced id feature of pedestrians. (3) We propose a novel Piecewise NMS algorithm to reduce false positive of small objects. (4) We propose a novel multi-attribute NMS algorithm based on Piecewise NMS algorithm and id information, which can adaptively distinguish predicted boxes of different pedestrians and improve the detector performance. Finally, we evaluate the MAPD detector on two benchmark datasets, including CityPersons and CrowdHuman. Results show that our approach outperforms state-of-the-art methods with a big margin.
To simplify or not? Facilitating English <scp>L2</scp> users' comprehension and processing of open educational resources in English using text simplification

Abstract Open Educational Resources (OER) aim to provide equal access to education. Yet, as the language level used in OER presents a barrier to many learners, there is a need to make these resources more comprehensible. This study combined eye‐tracking methodology and comprehension assessment to explore the effect of text simplification on English second language (L2) users, while also accounting for text organizational structure and individual predispositions. A total of 37 adult English L2 users took part in the study. They had to read either an authentic narrative, authentic expository OER or their linguistically simplified versions. The analysis showed that simplification led to better text comprehension, and text narrativity facilitated text recall, particularly at lower English proficiency levels. Eye‐tracking measures revealed that text simplification led to an increase in processing time during the initial reading of the text and a decrease in processing time during text re‐inspection. These findings have strong practical applications for online teaching with OER.
A Residual BiLSTM Model for Named Entity Recognition

As one of the most powerful neural networks, Long Short-Term Memory (LSTM) is widely used in natural language processing (NLP) tasks.Meanwhile, the BiLSTM-CRF model is one of the most popular models for named entity recognition (NER), and many state-of-the-art models for NER are based on it.In this paper, we propose a new residual BiLSTM model and perform it with a conditional random field (CRF) layer together on NER tasks.Based on the most popular BiLSTM-CRF model, we replace the BiLSTM with our residual BiLSTM blocks to encode words or characters.We evaluate our model on Chinese and English datasets.We utilize both word2vec and BERT to generate word or character vectors.Furthermore, we conduct experiments to compare the performance of NER by using different structures of residual blocks.The experimental results show that our model can improve the performance of both Chinese and English NER effectively without introducing any external knowledge.
A Diversity Framework for Dealing With Multiple Types of Concept Drift Based on Clustering in the Model Space

Data stream applications usually suffer from multiple types of concept drift. However, most existing approaches are only able to handle a subset of types of drift well, hindering predictive performance. We propose to use diversity as a framework to handle multiple types of drift. The motivation is that a diverse ensemble can not only contain models representing different concepts, which may be useful to handle recurring concepts, but also accelerate the adaptation to different types of concept drift. Our framework innovatively uses clustering in the model space to build a diverse ensemble and identify recurring concepts. The resulting diversity also accelerates adaptation to different types of drift where the new concept shares similarities with past concepts. Experiments with 20 synthetic and three real-world data streams containing different types of drift show that our diversity framework usually achieves similar or better prequential accuracy than existing approaches, especially when there are recurring concepts or when new concepts share similarities with past concepts.
A Hybrid Recursive Implementation of Broad Learning With Incremental Features

The broad learning system (BLS) paradigm has recently emerged as a computationally efficient approach to supervised learning. Its efficiency arises from a learning mechanism based on the method of least-squares. However, the need for storing and inverting large matrices can put the efficiency of such mechanism at risk in big-data scenarios. In this work, we propose a new implementation of BLS in which the need for storing and inverting large matrices is avoided. The distinguishing features of the designed learning mechanism are as follows: 1) the training process can balance between efficient usage of memory and required iterations (hybrid recursive learning) and 2) retraining is avoided when the network is expanded (incremental learning). It is shown that, while the proposed framework is equivalent to the standard BLS in terms of trained network weights,much larger networks than the standard BLS can be smoothly trained by the proposed solution, projecting BLS toward the big-data frontier.
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems

How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combining multiple "fair" classifiers can still result in an "unfair" classification system. This presents a significant challenge: how do we understand and improve fairness in recommender systems composed of multiple components?
Preventing COVID-19 Spread Using Information and Communication Technology

Without an effective vaccine, treatment, or therapy, the Coronavirus Disease 2019 (COVID-19) is spreading like fire and claiming lives.Countries began to adopt various strategies such as lockdown, mass testing, tracing, quarantine, sanitization, isolation, and treatment to contain COVID-19.However, it was soon realized that we need to take the help of powerful technologies to combat the spread of deadly COVID-19 until a vaccine or a drug is discovered.In this article, we discuss how the use of cutting edge technologies such as the Internet of Things (IoT), Big data, artificial intelligence (AI), unmanned aerial vehicles (UAVs)/drones, blockchain, robotics, autonomous ground vehicles, communication technologies in screening, testing, contact tracing, spread analysis, sanitization, and protocol enforcements can help prevent the COVID-19 spread.& THE CORONAVIRUS DISEASE 2019 (COVID-19), which is now a global pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).After being initially reported in Wuhan Province, China, in late 2019,
Bitcoin Sentiment Index, Bitcoin Performance and US Dollar Exchange Rate

This study introduces a comprehensive Google search volume based Bitcoin Sentiment Index (BSI) and investigates its symmetric and asymmetric association with Bitcoin's returns, volume, and volatility, and with United States Dollar (USD) exchange rates. Our results indicate a positive association of BSI with Bitcoin's returns and volume, but a negative relationship with its return volatility. Besides, Bitcoin's optimistic sentiments have an asymmetric relationship with the USD exchange rate in the short-run, and the Bitcoin price has an asymmetric and negative association with USD in the short-run and long-run. We also found a 35.47% speed of adjustment to long-run equilibrium.
Measure Utility, Gain Trust: Practical Advice for XAI Researchers

Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.
Adaptive Graph Filters in Reproducing Kernel Hilbert Spaces: Design and Performance Analysis

This paper develops adaptive graph filters that operate in reproducing kernel Hilbert spaces. We consider both centralized and fully distributed implementations. We first define nonlinear graph filters that operate on graph-shifted versions of the input signal. We then propose a centralized graph kernel least mean squares (GKLMS) algorithm to identify nonlinear graph filters' model parameters. To reduce the dictionary size of the centralized GKLMS, we apply the principles of coherence check and random Fourier features (RFF). The resulting algorithms have performance close to that of the GKLMS algorithm. Additionally, we leverage the graph structure to derive the distributed graph diffusion KLMS (GDKLMS) algorithms. We show that, unlike the coherence check-based approach, the GDKLMS based on RFF avoids the use of a pre-trained dictionary through its data-independent fixed structure. We conduct a detailed performance study of the proposed RFF-based GDKLMS, and the conditions for its convergence both in mean and mean-squared senses are derived. Extensive numerical simulations show that GKLMS and GDKLMS can successfully identify nonlinear graph filters and adapt to model changes. Furthermore, RFF-based strategies show faster convergence for model identification and exhibit better tracking performance in model-changing scenarios.
Mission-Oriented 3D Path Planning for High-Altitude Long-Endurance Solar-Powered UAVs With Optimal Energy Management

The conversion efficiency of solar energy and the capacity of energy storage batteries are the key technologies limiting the development of solar-powered aircraft.In this paper, a mission-oriented design for the 3-dimensional (3D) path planning of solar-powered unmanned aerial vehicles (SP-UAVs) using limited solar energy to maximize the mission effectiveness is presented.Based on the solar radiation received model, the energy model, and the kinetic and kinematic model of the SP-UAV, a task planning problem oriented to multiobjective optimization is proposed.Both the pseudospectral and colony algorithms are proposed to search for the optimal mission path, and their joint optimization is employed to realize continuous flight and improve the flight mission capabilities.Explicitly, a multiobjective joint strategy is developed, including maximum power ascending, maximum range flight, maximum glide endurance, and minimum power level flight at night.Numerical and simulation results indicate that our proposed design outperforms the existing approaches not only in solar energy utilization but also in universality.
Research on Medical Equipment Supply Chain Management Method Based on Blockchain Technology

The research of blockchain technology in the field of supply chain management has received extensive attention, but the research results and decision-making model of the perfect blockchain technology to solve the supervision of medical equipment supply chain have not yet been formed. This research analyzes and designs a full life cycle supply chain management method for medical equipment based on blockchain technology. A medical equipment supply chain supervision model based on blockchain technology was constructed, and a medical equipment supply chain supervision system based on blockchain technology was formed based on the full life cycle supply chain management model. Combining full life cycle theory with blockchain technology, a medical equipment management information system covering the entire process of production, supply, tendering, procurement, storage, application, export, use, destruction, and traceability of medical equipment was designed.