Reviewerly/exHarmony huggingface dataset:
Reducing numerical precision preserves classification accuracy in Mondrian Forests

Mondrian Forests are a powerful data stream classification method, but their large memory footprint makes them ill-suited for low-resource platforms such as connected objects. We explored using reduced-precision floating-point representations to lower memory consumption and evaluated its effect on classification performance. We applied the Mondrian Forest implementation provided by OrpailleCC, a C++ collection of data stream algorithms, to two canonical datasets in human activity recognition: Recofit and Banos et al. Results show that the precision of floating-point values used by tree nodes can be reduced from 64 bits to 8 bits with no significant difference in F1 score. In some cases, reduced precision was shown to improve classification performance, presumably due to its regularization effect. We conclude that numerical precision is a relevant hyperparameter in the Mondrian Forest, and that commonly-used double precision values may not be necessary for optimal performance. Future work will evaluate the generalizability of these findings to other data stream classifiers.
Machine Intelligence as a Foundation of Self-Driving Automotive (SDA) Systems

Machine intelligence is a backbone of self-driving automotive (SDA) systems. Presently, ResNet, DenseNet, and ShuffleNet V2 are excellent convolution choices, whereas object detection focuses on YOLO and F-RCNN design. This study discovers the uniqueness of methods and argues the suitability of using each design in SDA technology. Real-time object detection is imperative in SDA technology, for CNN, as well as to object detection algorithms, an architecture that is a balance between speed and accuracy is important. The most favorable architecture in the scope of this case study would be ShuffleNetV2 and YOLO since both are networks that prioritize speed. But the drawback of speed prioritization is that they suffer from slight inaccuracies. One way to overcome this is to replace the neural network with a more accurate (albeit slower) model. The other solution is to use reinforcement learning to find the best architecture, basically using neural networks to create neural networks. Both approaches are resource-intensive in the sense of capital, talent, and computational budget.
A Video Synopsis Method for Object Interactive Preservation Combined with Face Orientation

Video synopsis is an effective technology for surveillance video browsing and retrieval. Traditional video synopsis methods focus on reducing collisions among objects and maintaining their chronological order, which may ignore the interaction among the objects and make the interacting tubes lose their original interactivity, resulting in an unreality condensed video. This paper proposes a video synopsis method that preserves the interactive relationship among moving objects. First, a new interactive judgment algorithm is proposed, which uses the height-to-distance ratio and face orientation to determine whether there is an interactive relationship among objects. Second, optimize the correlation energy function, take the interaction relationship as a consideration to avoid destroying the interaction relationship during the video synopsis process. The experimental results show that the proposed video synopsis method can effectively compress the video based on preserving the interaction among the objects.
An Enhanced Method for Entity Trigger Named Entity Recognition Based on POS Tag Embedding

In the task of Named Entity Recognition, plenty of human annotations are required in deed. However, a large number of annotations in articles are time-consuming and labor-intensive. In order to solve these problems above, an enhanced method for entity trigger named entity recognition based on POS tag embedding is proposed in this paper. Firstly, by employing lexical annotation tool, it can not only obtain the POS tag of the word, but also connect the word embedding with the POS tag embedding. Secondly, train the attention representation of sentences and triggers, and learn the semantic relationship between entity triggers and sentences based on the attention model. Lastly, the model is instructed with a new sentence attention representation as the input of the CRF (Conditional Random Fields) network. The simulation experiments explicate that the proposed can expand the semantic information of words, so as to improve the recognition ability of entities in a relatively small amount of labeled training data.
Simulation Environment for Neural Network Dataset Generation

We present a simulation setup in the robot simulation software CoppeliaSim which is used for a synthetic dataset generation for training the neural network. In the simulator we can generate either color and depth images which can be tuned according to the real cameras mounted to the robot or robotic workplace. Vision sensors capture the simulated scene which contains different environment features, obstacles and objects of interest which can be labeled automatically with another filtering vision sensor. Except static environment which can be imported in case of known setup or generated based on height-field or simple objects. We can simulate randomly or with a specific pose oriented and positioned objects which may appear in the field of view of the robot. As an output the system produce RGB or depth information which is stored as a RGB or a gray-scale image or a combined RGBA image including the RGB data extended by depth data stored in the alpha channel. Second product of the system is a label describing different detectable classes for the neural network. The simulator is able to generate large datasets in a short period of time and produce a highly customized learning base for the neural network.
An Iterative Correction Phase of Light Field for Novel View Reconstruction

We present an iterative correction phase algorithm (ICPA) for light field reconstruction. We study novel views of light field that satisfy certain conditions can be reconstructed from the phase spectrum. The ICPA includes phase corrections in both time domain and frequency domain of discrete light filed. Furthermore, the phase corrections are “light field truncation” in the time domain and “phase replacement” in the frequency domain. Thus, the estimation of the reconstructed light field improves with each iteration. Our ICPA predicts the characteristics of light field such as phase and amplitude. Predictions on the frequency content can then be used to improve the rendering quality of novel views. Finally, to verify the claimed performance, we also compare the ICPA with the most advanced light field reconstruction algorithms. The experimental results show that the proposed ICPA outperforms other known reconstruction schemes.
Design and Implementation of Mobile Interactive Learning Platform for Art Teaching of Universities

To improve the art teaching level of university teachers under mobile network, this paper studies the domestic and foreign art education market and the use of education app to design an interactive system by Android platform. We first analyze the feasibility of mobile learning platform from three aspects of economy, technology and operation. Then, from the login module, learning base module, examination center module, courseware center module, online chat module and personal center module, the function of the client is designed in detail. The mobile teaching of users is realized, and the data interaction with the existing e-learning platform is also performed. Finally, the system is tested and the function display of each module shows that the software has achieved the expected goal. The safety also meets the requirements and the performance is proved to be good.
Mixed-type data generation method based on generative adversarial networks

Abstract Data-driven based deep learing has become a key research direction in the field of artificial intelligence. Abundant training data is a guarantee for building efficient and accurate models. However, due to the privacy protection policy, research institutions are often limited to obtain a large number of training data, which would lead to a lack of training sets circumstance. In this paper, a mixed-type data generation model based on generative adversarial networks is proposed to synthesize fake data that have the same distribution with the real data, so as to supplement the real data and increase the number of available samples. The model first pre-trains the autoencoder which maps given dataset into a low-dimensional continuous space. Then, the generator constructed in the low-dimension space is obtained by training it adversarially with discriminator constructed in the original space. Since the constructed discriminator not only consider the loss of the continuous attributes but also the labeled attributes, the generator nets formed by the generator and the decoder can effectively learn the intrinsic distribution of the mixed data. We evaluate the proposed method both in the independent distribution of the attribute and in the relationship of the attributes, and the experiment results show that the proposed generate method has a better performance in preserve the intrinsic distribution compared with other generation algorithms based on deep learning.
A Novel Limbs-free Human-Computer Interface: Face-Computer Interface (FCI) with Channels Optimization

In order to develop a human-computer interface(HCI) for people with impaired or weakened limbs, a novel limbs-free face-computer interface(FCI) was proposed. According to the proposed FCI framework based on facial electromyography(fEMG), this article focuses on the optimization of the fEMG collection locations and facial movements. In order to optimize the collection locations and select actions, mechanism-based analysis and data-based analysis were used. In the mechanism-based analysis, the relationship between the main facial muscles and movements was analyzed in detail. In data-based analysis, a novel method activation degree matrix(ADM) based on fEMG was proposed to analyze the relevant between channels and facial actions. As a result, 8 channels from 32 and 6 movements were picked out for online experiments. After optimizing the collection locations, the features calculation time was reduced by 71.2%, and the accuracy of intent recognition was improved by 5-8%. Finally, some online experiments of FCI-based computer typing application, robotic arm and wheelchair control were successfully carried out.
Multi-label Image Classification Model via Label Correlation Matrix

Abstract Multi-label classification is one of the most essential tasks of computer vision. In the multi-label image classification model, using the correlation between labels is a powerful method for improving the classification effectiveness of the model. However, common methods ignore the interrelationship between the label pairs. On the other hand, introducing a spatial attention mechanism into the model could also improve the classification effectiveness of the model. However, most methods that use the attention mechanism module do not use the correlation information between labels. To solve these issues, we propose a novel multi-label image classification model using the label correlation in the paper. Our model generates label word vectors based on the BERT model that can describe the potential relationship between labels. And then we combine these vectors with static statistics information on labels to construct a new label correlation matrix. Moreover, we introduce label semantic information into the spatial attention mechanism. With the semantic information, the generated spatial attention map could focus on the image feature regions with label correlation, and complete the accurate classification of the model. On the Microsoft COCO data set, this model achieves the best score of 84.3% on mAP, which shows the effectiveness of our model.
Generation and Authentication of Digital Certificates using Ethereum based Decentralized Mechanism for Mitigating Data Fraud on RISC-V

Traditional paper certificates have difficulties in preservation and management, causes tampering and forgery. A secure decentralized-based platform could mitigate the risk with-out any third-party intervention using Ethereum in the core. This work proposed a novel architecture to generate digital certificates using a distributed ledger automatically. Further, it authenticates the same using private keys with trusted certificate authorities. The framework is tested on FU540 (RISC-V) architecture. A detailed mechanism for generating and authenticating certificates is validated on FU540 board with blockchain integration. Mechanism flow starts with the admin. It adds the source organization blocks having the user detail records to the distributed ledger. Verifiers put the request to the source authorizer to perform verifications. This protocol could protect private information and identify the inter mediators who tried to diversify
On the Robustness of Metric Learning: An Adversarial Perspective

Metric learning aims at automatically learning a distance metric from data so that the precise similarity between data instances can be faithfully reflected, and its importance has long been recognized in many fields. An implicit assumption in existing metric learning works is that the learned models are performed in a reliable and secure environment. However, the increasingly critical role of metric learning makes it susceptible to a risk of being malicious attacked. To well understand the performance of metric learning models in adversarial environments, in this article, we study the robustness of metric learning to adversarial perturbations, which are also known as the imperceptible changes to the input data that are crafted by an attacker to fool a well-learned model. However, different from traditional classification models, metric learning models take instance pairs rather than individual instances as input, and the perturbation on one instance may not necessarily affect the prediction result for an instance pair, which makes it more difficult to study the robustness of metric learning. To address this challenge, in this article, we first provide a definition of pairwise robustness for metric learning, and then propose a novel projected gradient descent-based attack method (called AckMetric) to evaluate the robustness of metric learning models. To further explore the capability of the attacker to change the prediction results, we also propose a theoretical framework to derive the upper bound of the pairwise adversarial loss. Finally, we incorporate the derived bound into the training process of metric learning and design a novel defense method to make the learned models more robust. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed methods.
Students’ Perceptions and Attitudes Towards Using Blogs as a Supplementary Learning Tool to Enhance Writing Skills at a Private University in Dubai

Writing is one of the English language skills that is crucial for university students’ academic success. However, it can be very challenging for second language learners to master this skill, and English language instructors often face challenges when trying to motivate students to write using the target language. The modernization of education has introduced Web 2.0 technologies that can assist in the teaching and learning process, and they include social networking sites, media sharing tools, and collaborative editing tools such as blogs. Blogs can easily be integrated into writing courses to make this particular skill more interesting for students. The following study aims to explore and investigate students’ perceptions and attitudes towards using blogs as a supplementary learning tool to enhance writing at a private university in Dubai, UAE. The findings of this study have proven to show positive attitudes towards this learning tool that was used to improve students’ writing skills in an English Communication Skills course.
A Penalized Neural Network Model for Predicting Unobserved Scores of Construct Indicators and Reproducing Latent Scores of the Theoretical Constructs by Using Text Information

This paper discusses a novel application of construct measurement with a penalized neural network in text analysis. Conducting a survey with the measures of constructs is a traditional approach to scoring customers’ mental states such as feelings, perceptions, and attitudes. Many kinds of scales are continuously developed for marketing and consumer behavior research. To connect this traditional methodology with machine leaning and big data analysis, our proposed neural network model employs two penalty functions, which specify the confirmatory factor model as a measurement equation. In empirical analysis, the proposed model shows stable parameter estimates compared with a standard unconstrained neural network, and supports the theoretical interpretations. With applying the learned proposed model, predictive item scores of each questionnaire and alternative scores of theoretical constructs can be obtained through text information. The proposed methodology helps marketers and researchers visualize consumers’ representations of perceived theoretical constructs without the survey. To improve the accuracy and validity of the proposed model, limitations and potential works are also discussed for future study.
The Development and Trend of Vehicle Functional Safety

In the development stage of vehicle applications, ensuring the safety of the vehicle is always a key condition. However, the current automotive functional safety design is challenged by a variety of factors, such as the complexity of the new generation of automotive electrical and electronic (E/E) architecture, the continuous update of the automotive functional safety standard ISO26262, and the new AUTOSAR adaptive platform standard. The release of different types of cost increases. This paper summarizes the latest developments of vehicle functional safety design methods through analysis, design, optimization and operation stages: 1) functional safety analysis; 2) functional safety assurance; 3) safety perception cost optimization; 4) safety-critical multi-functional dispatch. It then provides the future trend of functional safety design methods, which will be directly oriented to automatic vehicles.
Design of VR Experimental System Based on Leap Motion Gesture Recognition

Virtual Reality (VR) has gradually developed into a new type of means and resources, especially in the field of experimental teaching. This paper will start from the perspective of virtual simulation, based on virtual reality technology and gesture recognition technology, combined with actual electrical experiments Application scenarios, using Maya software, next-generation game engine Unreal Engine 4 (UE4) and deep learning algorithms to design and implement a set of low investment costs, flexible experiment development, high experiment safety factor and strong simulation interaction VR experimental system based on Leap Motion gesture recognition. Including the creation of the 3D model and detailed processing of the VR electrical experiment system based on Maya software, the scene construction and blueprint logic implementation of the VR electrical experiment system based on the UE4 engine, and using python programming to complete the development of the gesture recognition algorithm based on Deep Neural Networks(DNN).
A Lightweight Convolutional Neural Network for End to End Autonomous Driving

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Persistent Identification for Conferences

Persistent identification of entities plays a major role in the progress of digitization of many fields. In the scholarly publishing realm there are already persistent identifiers (PID) for papers (DOI), people (ORCID), organisation (GRID, ROR), books (ISBN) but there is no generally accepted PID system for scholarly events such as conferences or workshops yet. This article describes the relevant use cases that motivate the introduction of persistent identifiers for conferences. The use cases were mainly derived from interviews, discussions with experts and their previous work. As primary stakeholders who are involved in the typical conference event life cycle researchers, conference organizers, and data consumers were identified. The resulting list of use cases illustrates how PIDs for conference events will improve the current situation for these stakeholders and help with problems they are facing today.
Unsupervised Anomaly Detection Approach for Multivariate Time Series

Multivariate time series anomaly detection algorithms has important research significance in many application fields such as system state estimation, fault prediction and diagnosis, and network behavior anomaly detection. Due to the large noise, high dimensionality, and hidden features of abnormal data in various application data, it is difficult to learn abnormal features in multivariate time series data, which brings many challenges to anomaly detection. In response to these problems, we propose a deep neural network model utilizing convolutional network and Long short-term memory (LSTM) to achieve unsupervised anomaly detection algorithm. This method consists of two steps using feature fusion and timing prediction. The convolutional network automatically learns the correlation among features in multivariate time series data, abstracts the features of multiple timing data. And LSTM deep neural network can effectively identify anomalies in sequences and predict the offset value of time series data. We applied this method to server fault diagnosis and internal network traffic anomaly detection, and obtained significant application effects, realizing anomaly detection in complex scenarios. Extensive experimental evaluations over large multivariate time series data have shown that our method has outperformed other existing time series anomaly detection methods based on traditional data mining methodologies.
The Trajectory Planning Method for UAV in Large Airspace Based on Deep Reinforcement Learning

This paper proposes an improved deep reinforcement learning algorithm, which uses meta-learning pre-training and a new exploration mechanism to accelerate the convergence in large-scale trajectory planning problems. The algorithm can eliminate neural network non-convergence caused by the excessive planning range and has high portability in different flight scenarios.
Information Extraction of Air-Traffic Control Instructions via Pre-trained Models

The air traffic controllers always use air-traffic control instructions (ATC instructions) to command aircrafts. The ATC instruction consists of some situation mentions such as flight number, status, and target location etc. The deep-learning based approach can extract such information for situation awareness. In practice, it is difficult to prepare huge amount of labelled ATC instructions for training the deep-learning model due to expensive costs of handcraft annotations. The large scale pre-trained model (PTMs) can solve this problem by “pre-training” and “fine-tuning”. This paper proposes: 1) pre-trained models to extract information from few scale ATC instructions; 2) the probing task to find which layer of model achieves the best performance of information extraction task.