Reviewerly/exHarmony huggingface dataset:
Discussion of the Key Technology and Application of Big Data Platform for New Energy Vehicles and V2X

Abstract With the development of the economy and social progress, China’s science and technology industry has made remarkable breakthroughs and progress. New energy vehicles began to appear in people’s lives and gain popularity, in which context, a big data platform about new energy vehicles and V2X (vehicle-to-everything) emerged. Meanwhile, the state regulation of new energy vehicles has been improved. Whether it is new energy vehicles or V2X, these are both products of the information era. The lack of experience in the past had left this unpredictable technology problem in the development process. With such a background, we need to conduct further analysis of the core technology of the big data platform and related applications, and the corresponding improvement approach needs to be put forward.
Optimized Spatial Matching for Visual Object Tracking

The biggest challenge for visual object tracking is the simultaneous requirements for robustness and discrimination. Although there are many algorithms to study current problems, this problem still cannot be overcome. In this paper, inspired by Siamese network SPM-Tracker, a new target tracking algorithm-OSM-Tracker is proposed. The algorithm is a two-stage Siamese network tracker composed of an optimized space network and a correction network. Through the cooperation of these two aspects with SPM-Tracker, compared OSM-Tracker has produced good results. Experiments have proved that our tracker has achieved a considerable performance improvement and achieved real-time effects on OTB-100and LaSOT.
A Real-Time Algorithm for Multiple Data Matrix Codes Localization

As two-dimensional barcodes can store data efficiently with error correction ability, they are widely used as landmarks in Automatic Ground Vehicle (AGV) guidance systems. AGV with a scanner can obtain the approximate position in its operation field if the position information is decoded into landmarks pasted on the ground, and a more specific position can be calculated if the landmark center is determined in an image. In this paper, we propose an algorithm to detect the position for the two-dimensional barcode landmark composed of four Data Matrix symbols since four symbols are more robust to a single one. Firstly, a voting strategy is adopted to detect the finder pattern (L shape) of a Data Matrix symbol after candidate contours are selected. Then each Data Matrix code is isolated and decoded by libdmtx—an open-source software library. Finally, each block’s quality is evaluated by bipolarity as a weight to calculate the position of the landmark. The experiment shows that our algorithm can achieve real-time detection with high accuracy on embedded systems.
Comparison of Semantic Role Labeling Tools on Previously Unseen Hand Annotated Texts

In this article state of the art semantic role labeling (SRL) tools have been compared on published lexical resources and custom hand annotated previously unseen texts. Semantic role labeling is one of the first steps into understanding the meaning of the sentence. The process of identifying semantic roles includes several steps from which the main are predicate identification, argument identification and argument classification. All the approaches compared in this article suggest supervised machine learning algorithms on lexical resources. The comparison of the tools for semantic role labeling are tested against hand annotated corpora independent of FrameNet or PropBank corpus. The precision, recall tests were made on PropBank data as well. Because the results of classification can be biased, the objective of this research was to comprise all the mentioned tools with hand annotated previously unseen data. The results have shown that mate-tools has achieved best results on independent hand annotated data with the best micro average F-score of 78% on custom hand annotated dataset. Results from this article were used to evaluate which tool can be used for knowledge extraction tasks and question generation in communication module of intelligent tutoring system. The overview of all freely available tools, mostly from classifier implementation point of view is given in this article.
Research on Real-time Sensing Technology of Aerospace Interactive Behavior Based on Infrared Array Force Tactile

Abstract Aerospace robots can replace astronauts to complete dangerous work in the expanded space. How to efficiently and reliably interact with robots in complex and harsh space environment has become an urgent problem and technical challenge. Based on the infrared array force tactile technology, an infrared point force touch detection system is developed to realize the intention sensing of aerospace human-robot interaction. Based on multipoint positioning technology combined with image method and global combined optimal tracking method, the control software is designed, and the effective human-robot interaction in the process of cooperation is realized. Finally, the touch panel test effect of the system is given. This research can provide a theoretical basis for the practical application of aerospace human-robot interaction.
Library Information Construction and Service Trend under Big Data

Abstract With the rapid development of knowledge economy and the rapid progress of science and technology, in order to adapt to the pace of the times, university libraries have also undergone tremendous changes, especially in the context of information technology. The traditional service mode of library cannot meet the demand of teachers and students for cultural knowledge information. Is not a single demand for knowledge, but more pursuit of personalization, specialization and diversification? Therefore, it is urgent for the library to change the old service mode. Under this background, the university library has been greatly challenged in the service concept and service mode. Only through the application of personalized service mode through innovative service mode, the university library sets up individualized information service channel according to the user’s information demand behavior. And through the analysis of user’s habits and personal characteristics to meet the user’s potential information needs. Through the investigation and investigation of a university in our province, the number of people who do not understand the personalized recommendation service is up to 115, accounting for 40.35% of the total.
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer

As the applications of deep learning models on edge devices increase at an accelerating pace, fast adaptation to various scenarios with varying resource constraints has become a crucial aspect of model deployment. As a result, model optimization strategies with adaptive configuration are becoming increasingly popular. While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space. Existing methods tend to either limit the architecture search space to a small set of options or limit the quantization policy search space to fixed precision policies. To this end, we propose BatchQuant, a robust quantizer formulation that allows fast and stable training of a compact, single-shot, mixed-precision, weight-sharing supernet. We employ BatchQuant to train a compact supernet (offering over $10^{76}$ quantized subnets) within substantially fewer GPU hours than previous methods. Our approach, Quantized-for-all (QFA), is the first to seamlessly extend one-shot weight-sharing NAS supernet to support subnets with arbitrary ultra-low bitwidth mixed-precision quantization policies without retraining. QFA opens up new possibilities in joint hardware-aware neural architecture search and quantization. We demonstrate the effectiveness of our method on ImageNet and achieve SOTA Top-1 accuracy under a low complexity constraint ($<20$ MFLOPs). The code and models will be made publicly available at this https URL.
Using a Mobile Augmented Reality APP on Mathematics Word Problems for Children

Augmented reality provides students a presence and an immersive learning environment. The purposes of this study were to develop an augmented-reality mathematics word-problem (AR-MW) learning system and to explore its effects on students’ learning. The study had a pre-test–post-test quasi-experimental design. It was conducted with 37 fourth-grade students in Taiwan. Nineteen students used the AR-MW system and 18 students used the traditional approach to learn mathematics word problems about decimal concepts. Overall mathematics achievement scores did not differ between groups. On the post-test, students in the experimental group, but not those in the control group, showed improvement in the word problem score. The overall number of errors decreased significantly in both groups. The number of misunderstood sentence errors decreased significantly in both groups, and the number of computing errors decreased significantly in the experimental group. These findings demonstrate that the AR-MW system effectively enhances students learning of mathematics word problems.
A New Reversible Data hiding Scheme in Encrypted Images using Predefined Pixel Mapping

One of the universally studied research topics is a reversible data hiding approach because of its wide applications in medical image transmission, cloud computing as well as in military services. In the introduced scheme stated in this paper, a new RDH scheme in encrypted images is proposed which ensures a good embedding rate without compromising the bit error rate. The scheme introduced in this manuscript uses a new measure that is computationally efficient and ensures real-time performance. In the proposed scheme, the original image is divided into blocks and in each block, one secret message bit is hidden by the data hider. All the pixels in the image block are categorized into two classes: black pixels and white pixels based on the predefined checkerboard pattern. The data hiding is performed by mapping the pixel values in the white position to a new value, only if the data hider wants to hide a secret bit 1. At the time of data extraction and image recovery, the closeness between the adjacent pixels is analyzed by considering a pair of adjacent black pixels and white pixels from the quantized image blocks. The overall experimental study of the proposed scheme is carried out using the standard images from the USC-SIPI image dataset managed by the University of Southern California.
Ellipsoidal Approximation of the Set of Solutions of Interval Systems of Linear Algebraic Equations

The article presents the results of the approximation of the set of solutions of interval systems of linear algebraic equations. These systems are used in the problems of modeling linear deterministic processes. It is assumed that the modeled process is described by an output variable and a set of input variables, the measurement errors of which are assumed to be set by known intervals symmetric with respect to the zero value. Traditionally, the sets of solutions of interval systems of linear algebraic equations in applied problems are approximated by a hyper-rectangular whose sides are parallel to the axes of the selected coordinate system. In this paper, we propose to use an ellipsoidal approximation of these sets, which is more efficient. The main results of the work include the substantiation of assumptions about the properties of the modeled process, the choice of a mathematical method for constructing an approximating ellipsoid, the proposed method for forming boundary points, and a numerical method for solving the problem. A computer simulation of the problem of estimating the parameters of a linear process is performed in Excel, which is used for a comparative study of approximations of solutions of interval systems of linear algebraic equations by a hyper-rectangular and an ellipse.
VR Reality of the Relationship between Augmented Reality and Virtual Reality in the Context of Virtual Reality

Abstract With the development of virtual reality technology and its application in various fields, how to realize the natural and efficient interaction between human and virtual environment has always been a hot research issue. This paper mainly studies the realistic analysis of the relationship between augmented reality and virtual reality under the background of VR virtual reality. Starting from the research on virtual reality, this paper combines the features of virtual reality with the elements of visual presentation to conduct research on visual presentation in the sensory experience of virtual reality, analyze and find out the types and quantitative methods of visual presentation. This paper is supported by advance research, which is more in line with people’s perceptual needs for visual presentation. This study expands the research content of virtual reality visual presentation, provides guidance for virtual reality design practitioners, and has certain value and practical application prospects.
An Adaptive Nonlinear Filter for Uncertain Measurement Noise

An improved adaptive variational Bayesian (VB) nonlinear filter is proposed. Through Cubature sampling, inverse Wishart distribution is introduced to jointly estimate system states and inaccurate measurement noise for nonlinear systems. Furthermore, to avoid non-positive definite problem of the state covariance matrix in joint estimation, Singular Value Decomposition (SVD) is introduced to VBCKF, based on which SVD-VBCKF is designed. Besides, computational complexity of proposed filter is analyzed theoretically. Simulation results demonstrate that proposed SVD-VBCKF has higher filtering accuracy.
Examining Security for Different Data Models*

Efficient data storage and retrieval in many sectors led to the development of various modeling techniques such as relational model, Entity Attribute Value model and dynamic tables. The aim of this study is to classify the standard threats of the database according to the violation of security properties followed by the examination of different data models from their security viewpoint. A system is said to be secure if it follows three basic pillars of security i.e. confidentiality, integrity and availability. The current research analyses the security threats in database according to violation of basic pillars of security with detailed analysis of SQL injection attack for three data modeling techniques, namely relational model, Entity Attribute Value (EAV) model and dynamic tables. It presents a comparison of achieving security parameters by performing various experiments on the database stored in MySQL and proposes techniques for the application of mandatory access control in EAV model and dynamic table. After the rigorous survey and experiments performed, it has been found that EAV model is still not a completely secured model. The data leakage in EAV model is more and the application of security properties is relatively more complex than relational model and dynamic tables. Researches have been conducted in the past on these models but very few of them have discussed the security concerns of EAV model and dynamic tables. This paper tries to compare various data models based on security concerns and highlights the security issues in EAV model.
Research on Intelligent Fire Risk Perception System of High-rise Building Based on Big Data of Internet of Things

Abstract Due to the characteristics of high-rise buildings, such as dense personnel, complex structure and many combustibles, fire safety problems are more prominent. Aiming at the fire problem of high-rise buildings, based on the Internet of Things and big data technology, the intelligent fire risk perception system of high-rise buildings is studied. Through the functional modules of fire water monitoring system, intelligent electricity monitoring system and fire automatic alarm system, the hydraulic and water level of fire water, the current and voltage values of key nodes such as distribution cabinets, and the on-duty personnel in fire control room are mastered in real time, so as to grasp the specific location of fire in time and accurately, so as to realize the intelligent control of fire safety of high-rise buildings.
Cocktail - An ARX based Cryptographic Hash Function Designed using Modified ChaCha Core

Cryptographic Hash functions are extensively used to achieve multiple security goals like message integrity and authentication. In this paper, we propose a new simple, flexible and efficient cryptographic hash function - Cocktail, that blends security with speed. Cocktail makes use of Addition, Rotation and XOR operations only and is based on Modified ChaCha core as its core primitive. Cocktail can work on 32-bit as well as 64-bit word size and generate hash output of variable sizes. Cocktail can be used in different other operating modes like HMAC, Randomized hashing, PRF ensemble etc. Cocktail can achieve considerably high level of parallelism. This paper presents the Cocktail's specifications, design philosophy, security aspects, usage and performance comparison with SHA-3 winner and other SHA-3 finalists.
Statistical file-matching of non-Gaussian data: A game theoretic approach

The statistical file-matching problem is a data integration problem with structured missing data. The general form involves the analysis of multiple datasets that only have a strict subset of variables jointly observed across all datasets. Missing-data imputation is complicated by the fact that the joint distribution of the variables is nonidentifiable as there are no completely observed cases. Nonparametric imputation methods typically involve an implicit conditional independence assumption that is forced by the missing-data pattern. Parametric imputation does not require conditional independence assumptions, but can be challenging due to identifiability issues and the difficulty of parameter estimation. The identification problem can be studied using game theory, and it is possible to establish a general characterization of the minimax optimal strategy under negative log likelihood loss. For non-Gaussian models, imputation using the minimax optimal strategy can lead to different results compared to generic methods. Computationally feasible procedures for parameter estimation can be implemented using data augmentation schemes and the EM algorithm. Comparisons of the minimax optimal imputation scheme to standard algorithms on real data from flow cytometry show that minimax strategies can better preserve the joint distribution of the variables.
Face Recognition in Law Enforcement: A Comparative Analysis of China and the United States

The use of facial recognition is becoming increasingly widespread in China, with mobile phones, banks, police stations and so on.Facial recognition has brought a certain benefit to social life in China, but in some areas of the United States facial recognition is prohibited.Facial recognition.This article discusses the use of face recognition in national law enforcement and compares commercial applications with those in China and the United States.Through comparison, we can understand the influence of face recognition in a different environment and the different effects of different application forms.The macro interaction between technology, the state and business shapes the social construction, and at a micro level everyone in society needs to be informed and aware of the costs and risks of using face recognition.Increased individual self-awareness helps to counteract the potential technological cage.
Chinese News Text Abstract Extraction Using Improved MMR

When the traditional maximal marginal relevance (MMR) algorithm extracts text summarization, only the similarity between the sentence and the text is used as the score of the sentence. Although the extracted abstract is rich in content, the sentences in the abstract are not all important sentences, and the context information and global information of the sentence are ignored. In this paper, an improved maximum marginal relevance algorithm (WT-MMR) is proposed to extract text summarization with rich content, important sentences, and no redundancy. The WT-MMR follows two principles: “Sentences relevance sentences in the abstract”. The experimental results show that the quality of abstracts extracted by the WT-MMR algorithm is about 10% higher than that of the traditional MMR algorithm, and proves that using Word2Vec, TextRank and Semantic information for extracting text summarization improves the performance of the MMR algorithm.
DNN-based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel

We study the SIMP method with a density field generated by a fully-connected neural network, taking the coordinates as inputs. In the large width limit, we show that the use of DNNs leads to a filtering effect similar to traditional filtering techniques for SIMP, with a filter described by the Neural Tangent Kernel (NTK). This filter is however not invariant under translation, leading to visual artifacts and non-optimal shapes. We propose two embeddings of the input coordinates, which lead to (approximate) spatial invariance of the NTK and of the filter. We empirically confirm our theoretical observations and study how the filter size is affected by the architecture of the network. Our solution can easily be applied to any other coordinates-based generation method.
Old-looking yet Usable!: An Investigation of Consumer’s Usability Perception of Retro Products

As the market competition increases, brands are trying to come up with strategies that can establish a bond with the users. Using the retro style is rapidly becoming one of these strategies by targeting past emotions however, the perception of the usability of this style hasn’t been investigated. The aim of this study is to investigate the user’s perception of the product’s usability features of retro consumer electronics from the appearance compared to non-retro products. A 5-factor scale was developed to measure the perception of the usability, including aesthetic, durability, reliability, serviceability, and learnability, was applied to 60 people. Paired samples t-tests and ANOVA tests were performed to explore the data. According to the results of the study, the scores of the perception of the usability were not significant between retro and non-retro products. However, retro products have been found to be more durable than non-retro products. In contrast, non-retro products were rated significantly higher for the factor of aesthetic. It was found that the effect of style on the perception of the usability was dependent on the type of the device that retro style didn’t have any effect on the perception of usability for toasters while non-retro style was found to be more usable for refrigerators. All statistical tests involving gender or other demographics were not significant.
Modelling and Implementation of a Knowledge Question-answering System for Product Quality Problem Based on Knowledge Graph

Abstract Aiming at the problem of difficulty in understanding the semantics of the problem in the traditional quality problem management system, the knowledge retrieval technology of product quality problem based on the knowledge graph is carried out. The process model for knowledge retrieval of quality problem based on semantic templates is constructed. A domain corpus is built, which consisting of thousands of quality problem handling records. The TF-IDF (Term Frequency-inverse Document Frequency) algorithm was used to extracted the vocabulary from the quality problem analysis reports. A natural language question semantic classification process model based on Naive Bayes classifier is established to improve the accuracy of semantic template matching. On the basis of theoretical study, a quality problem knowledge question-answering system-QQ-KQAS based on knowledge graph is developed, and the effectiveness of the proposed method is verified through examples.