Reviewerly/exHarmony huggingface dataset:
RETRACTED: Analysis of the Fine Management Strategy for the Cultivation of Vocational College Students’ Core Quality based on Big Data

Abstract The key to the development of vocational education is to improve the training quality of college students, and the key to improve the quality of college students is to cultivate the core quality of vocational college students. Based on the above background, the purpose of this paper is the application of big data in the fine management strategy analysis of Vocational College Students’ core literacy cultivation. This study adopts the methods of literature research, questionnaire survey and interview to study and investigate the current situation of vocational college students’ core literacy. This paper expounds the theory, practical significance and feasibility of the integration of data mining technology and fine management analysis in the era of big data from three aspects: the problems in the refined management analysis of traditional college students’ core literacy cultivation, the expansion and deepening of the fine management analysis by data mining technology, and the construction path of the fine management analysis platform. This paper puts forward the reflection and promotion strategies for the fine management of college students from three aspects: Deepening the concept of student management, internalizing the concept into the practice of student management, and constructing the mechanism of campus characteristic culture education. Finally, the study found that the core quality that college students lack most is social participation, accounting for 37.1%. Therefore, in order to change the current situation of college students training, the most important thing is to enhance the social participation of college students, so that college students can adapt to the needs of life-long development and social development.
Performance Analysis of Deep Dense Neural Networks on Traffic Signs Recognition

Traffic signs are a vital tool of the transport system because they serve to keep pedestrians and drivers readily informed that they can be alerted and notified. Thus, different traffic sign recognition systems were found in the last few years. It is implied that their identification and recognition is a confined issue that signs may be special, distinctive functions, or fragile shapes and solid shapes. Some recent and effective approaches of traffic sign detection and classification showed the success of using deep neural networks in this field. In terms of this domain, the development of an accurate real-time traffic signs recognition system is still a challenging task. This paper discusses the recognition system of traffic signs using four dense CNN-based models, DenseNet121, DenseNet161, DenseNet169, and DenseNet201. However, the present study aims mainly at evaluating the performance of the proposed system using deep dense neural networks on recognizing traffic signs. Results show the feasibility of using DenseNet pre-trained models to perform this task. In terms of testing accuracy, DenseNet201 achieved the best performance with 99.7%.
A new perceptual evaluation method of video quality based on neural network

This paper proposes a novel method for video quality evaluation based on machine learning technique. The current research deals with the correct interpretation of objective video quality evaluation (Quality of Service – QoS) in relation to subjective end-user perception (Quality of Experience – QoE), typically expressed by mean opinion score (MOS). Our method allows us to interconnect results obtained from video objective and subjective assessment methods in the form of a neural network (computing model inspired by biological neural networks). So far, no unified interpretation scale has been standardized for both approaches, therefore it is difficult to determine the level of end-user satisfaction obtained from the objective assessment. Thus, contribution of the proposed method lies in description of the way to create a hybrid metric that delivers fast and reliable subjective score of perceived video quality for internet television (IPTV) broadcasting companies.
&lt;i&gt;m&lt;/i&gt;-to-1 Mappings over Finite Fields 𝔽&lt;i&gt;&lt;sub&gt;q&lt;/sub&gt;&lt;/i&gt;

Permutation polynomials over finite fields have been widely studied due to their important applications in mathematics and cryptography. In recent years, 2-to-1 mappings over finite fields were proposed to build almost perfect nonlinear functions, bent functions, and the semi-bent functions. In this paper, we generalize the 2-to-1 mappings to m-to-1 mappings, including their construction methods. Some applications of m-to-1 mappings are also discussed.
D2.2 Uncertainty-Aware Sensor Fusion in Sensor Networks

Uncertainty assignment for measurement values is a common process for single sensors, but this procedure grows in complexity for sensor networks. Often measured values are processed further in such networks and uncertainty must be evaluated for virtual values. A simple example is the fusion of homogeneous values and faulty or drifting sensors can harm the virtual value. We introduce a method from the field of key-comparison into the domain of sensor fusion. The method is evaluated in three different scenarios within an agent-framework.
Construction and Application of Comprehensive Evaluation Model of “Golden Classroom”

Abstract The survey shows that the intensity of learners’ interest in courses is the strong positive energy for the construction of “Golden Lessons”, and the optimization of classroom teaching is the primary factor among many factors in the construction of “Golden Lessons”. The director who constructs the “golden lesson” is the teacher, the main body is the student, and the measurement of the “golden content” is the classroom teaching. Therefore, the continuous “Golden Classroom” is an inevitable move to build a “Golden Lessons”, it is imperative to find the evaluation standard of “Golden Classroom” scientifically and reasonably. Based on this, through the combination of qualitative and quantitative, using the principle of fuzzy mathematics, a comprehensive evaluation model of “Golden Classroom” is constructed, and the standard score of the final evaluation result is used to give the threshold of “Golden Classroom”. For the objective determination of “Golden Classroom”, building “Golden Lessons” has certain theoretical enlightenment and application value.
Direct Loss Minimization for Sparse Gaussian Processes

The Gaussian process (GP) is an attractive Bayesian model for machine learning which combines an elegant formulation with model flexibility and uncertainty quantification. Sparse Gaussian process (sGP) algorithms provide an approximate solution that mitigates the high computational complexity of GP and the variational approximation is the current best practice for such approximations. Recent theoretical work has shown that an alternative approach, direct loss minimization (DLM), which directly minimizes predictive loss, comes with strong guarantees on the expected loss of the algorithm. In this paper we explore this approach experimentally. We develop the DLM algorithm for sGP and show that with appropriate hyperparameter optimization it provides a significant improvement over the variational approach. In particular, optimizing sGP for log loss provides better calibrated predictions for regression, classification and count prediction, and optimizing sGP for square loss improves the mean square error in regression.
vqSGD: Vector Quantized Stochastic Gradient Descent

In this work, we present a family of vector quantization schemes \emph{vqSGD} (Vector-Quantized Stochastic Gradient Descent) that provide an asymptotic reduction in the communication cost with convergence guarantees in first-order distributed optimization. In the process we derive the following fundamental information theoretic fact: $\Theta(\frac{d}{R^2})$ bits are necessary and sufficient to describe an unbiased estimator ${\hat{g}}({g})$ for any ${g}$ in the $d$-dimensional unit sphere, under the constraint that $\|{\hat{g}}({g})\|_2\le R$ almost surely. In particular, we consider a randomized scheme based on the convex hull of a point set, that returns an unbiased estimator of a $d$-dimensional gradient vector with almost surely bounded norm. We provide multiple efficient instances of our scheme, that are near optimal, and require only $o(d)$ bits of communication at the expense of tolerable increase in error. The instances of our quantization scheme are obtained using the properties of binary error-correcting codes and provide a smooth tradeoff between the communication and the estimation error of quantization. Furthermore, we show that \emph{vqSGD} also offers strong privacy guarantees.
Community for Data Integration 2019 annual report

First posted April 29, 2021 For additional information, contact: Director, Science Analytics and SynthesisU.S. Geological SurveyP.O. Box 25046, Mail Stop 302Denver, CO 80225 The Community for Data Integration is a community of practice whose purpose is to advance the U.S. Geological Survey’s data integration capabilities. In fiscal year 2019, the Community for Data Integration held 9 monthly forums, facilitated 11 collaboration areas, held several workshops and training events, and funded 14 projects. The activities supported the U.S. Geological Survey priorities of enabling integrated predictive science, producing FAIR (Findable, Accessible, Interoperable, Reusable) data, building modular and reusable tools, building authoritative national datasets for hazards or assets, and developing tools and methods for biosurveillance of emerging invasive species and health threats. Through these efforts, community members were informed of new and emerging technologies and data topics that helped them in their professional responsibilities.
Reform of the Innovation and Entrepreneurship Education System in Colleges and Universities under the Application of Internet of Things Technology

This article mainly introduces the necessity of innovative colleges and universities' innovation and entrepreneurship education system under the application research of Internet of Things technology, and elaborates the principles that colleges and universities' innovation and entrepreneurship education system should follow. The author analyzes the problems existing in the innovation and entrepreneurship education of colleges and universities under the current application of Internet of Things technology. This paper discusses the effective measures of the innovation and entrepreneurship education system in colleges and universities under the research of the application of Internet of things technology, in order to strengthen the emphasis on the reform of the innovation and entrepreneurship education system in colleges and universities, and adapt to the development requirements of the Internet of things environment. This article aims to encourage college students to bravely start their own businesses and cultivate more outstanding entrepreneurial talents, so as to ensure the quality of college education and promote the sustainable development of college education.
Redefining Search Terms for Cybersecurity: A Bibliometric Perspective

Cyber Security is a global challenge and experts are working hard to address the security cropping up day by day. The delineation of the keywords for the emerging area is important to enlighten the emerging technology’s research path. In this paper, an overview of redefining the keywords for cyber security from a bibliometrics perspective has been provided. For the construction of keywords, a number of sources have been consulted. The collected keywords cleansed according to the definition provided by the National Institute of Standards and Technology (NIST) and the correlated keywords have been clustered manually. As a result, a set of keywords has been identified and justification for the same has been provided. These keywords can be used to download the related bibliographic records for the emerging discipline Cyber Security from the indexing databases.
Asymmetric Mutual Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification

Unsupervised domain adaptive person re-identification aims to solve the problem of poor performance caused by transferring an unlabeled target domain from the labeled source domain in the re-identification task. The clustering pseudo-labels method in unsupervised learning is widely used in unsupervised adaptive person re-identification tasks, and it maintains state-of-the-art performance. However, pseudo-labels obtained through clustering often have much noise, and the use of a single network model structure and a single clustering algorithm can easily cause model learning to stagnate, making the model not generalizable. To solve this problem, this paper proposes an asymmetric mutual mean-teaching method for unsupervised adaptive person re-identification. In terms of feature extraction, two asymmetric network models with different structures are used for mutual mean-teaching on the target domain, making the features extracted by the network more robust. In terms of feature clustering, two clustering methods are used for mutual teaching to dynamically update the centroid of clusters to improve the confidence of clustering pseudo-labels. Finally, the triplet loss is improved based on the updated cluster centroid to improve the clustering effect. The proposed method is used to perform a large number of verification experiments on three public datasets. The experimental results show that the proposed method has better accuracy than other unsupervised person re-identification based on clustering pseudo-labels.
Variational Quantum Algorithm and Its Application on Non-Linear Equations

Abstract Quantum algorithms of factoring problem have been paid more and more attention since Shor’s algorithm was proposed. Combining the current quantum computer hardware level and integer factorization quantum algorithms, this paper proposes a “classical + quantum” hybrid solution. This scheme first adopts classical methods and corresponding rules in the preprocessing step to simplify the nonlinear equations, which is used to reduce the number of qubits needed for the cost Hamiltonian. Then the variational quantum algorithm is used to find the approximate ground state of the cost Hamiltonian, which encodes the solution of the nonlinear equations. The program was verified on the IBM QX4 quantum machine, and the results showed that this hybrid solution can effectively reduce the quantum resources required to solve the nonlinear equations.
Research on Automatic Scoring Algorithm of Chinese Subjective Questions Based on Text Mining

At present, the research on automatic evaluation of computer online examination system has become a hot issue. Natural language processing technology based on text mining has unique advantages in text similarity calculation. This paper designs the TR-BFS-WE-WMD integrated algorithm for automatic review of Chinese subjective questions based on text mining, uses the word database to integrate the BFS algorithm, realizes the calculation of the text full sentence similarity and keyword matching, and solves the problem of text semantic similarity. Experimental results prove that this algorithm has good accuracy and effectiveness. The TR-BFS-WE-WMD algorithm provides a useful attempt for the intelligent research of the computer automatic review system and has good practical value.
Exploiting Correlation Characteristics to Detect Covert digital communication

As a widely used way to exfiltrate information, wireless covert channel (WCC) brings a serious threat to communication security, which enables the wireless communication process to bypass the authorized access control mechanism to disclose information.Unlike the covert channel on the network layer, wireless covert channels on the physical layer (WCC-P) is a new covert communication mode to implement and improve covert wireless communication.Existing WCC-P scheme modulates the secret message bits into the Gaussian noise, which is also called covert digital communication system based on the joint normal distribution (CJND).Finding the existence of this type of covert channel remains a challenging work due to its high undetectability.In this paper, we exploit the square autocorrelation coefficient (SAC) characteristic of the CJND signal to distinguish the covert communication from legitimate communication.We study the sharp increase of the SAC value when the offset is equal to the symbol length, which is caused by embedding secret information.Then, the SAC value of the measured sample is compared with the threshold value to determine whether the measured sample is CJND sample.When the signal-to-noise ratio reaches 20db, the detection accuracy can reach more than 90%.
LITERACY AND NUMERACY DRIVE: AN EVALUATION OF CLASS THREE ENGLISH TEXTBOOK OF PUNJAB

This study aims to evaluate whether the English textbook taught in government schools at class three-level is satisfying students' objectives, needs, and wants according to literacy and numeracy drive (LND). The study carries two main purposes; the first purpose is to investigate the objectives of the book whether it attains to students' needs or not. Secondly, it investigates the weaknesses and strengths of the textbook to recommend a few suggestions to improve the book according to the needs of students and teachers expectations. For this particular work, the major areas of inquiry were practical concerns of the book, language issues, objectives compatibility, subject matter issues, skills and strategies, exercises and activities, layout and content. The findings revealed that there were significant differences between teachers' responses and student's responses and chi-square findings also exposed that students had different views with respect to teachers excluding a few items (item, 1, 10, 16, 17).
Graphical Normalizing Flows

Normalizing flows model complex probability distributions by combining a base distribution with a series of bijective neural networks. State-of-the-art architectures rely on coupling and autoregressive transformations to lift up invertible functions from scalars to vectors. In this work, we revisit these transformations as probabilistic graphical models, showing that a flow reduces to a Bayesian network with a pre-defined topology and a learnable density at each node. From this new perspective, we propose the graphical normalizing flow, a new invertible transformation with either a prescribed or a learnable graphical structure. This model provides a promising way to inject domain knowledge into normalizing flows while preserving both the interpretability of Bayesian networks and the representation capacity of normalizing flows. We demonstrate experimentally that normalizing flows built on top of graphical conditioners are competitive density estimators. Finally, we illustrate how inductive bias can be embedded into normalizing flows by parameterizing graphical conditioners with convolutional networks.
Evaluation of MPI Allreduce for Distributed Training of Convolutional Neural Networks

Training deep neural networks is a costly procedure, often performed via sophisticated deep learning frameworks on clusters of computers. As faster processor technologies are integrated into these cluster facilities (e.g., NVIDIA's graphics accelerators or Google's tensor processing units), the communication component of the training process rapidly becomes a performance bottleneck. In this paper, we offer a complete analysis of the key collective communication primitive for the distributed data-parallel training of convolutional network networks (CNNs) focused on three relevant instances of the Message Passing Interface (MPI): MPICH, OpenMPI, and IntelMPI. In addition, our experimental evaluation is extended to expose the practical impact of this collective primitive when the training is performed using TensorFlow+ Horovod on a 16-node cluster. Finally, the theoretical analysis is further refined to a number of accelerated cluster configurations that are emulated by adjusting the communication-arithmetic ratio of the training process.
CAPTCHA: A Systematic Review

With the massive growth of accessing Internet-based services by users, there is a need to limit services use to human beings only rather than intelligent bots. The biggest challenge in front of researchers is to determine incoming requests are from benign users or bots. A CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) is a basic tool for preventing access to Internet-based services by intelligent bots. It plays a vital role in numerous security applications for denying automatic registration conducted by bots. This paper presents a systematic review of different types of CAPTCHAs and primarily focuses on the benefits of "text-based CAPTCHAs" over other schemes. Further, this paper highlights the major deficiencies of the text-based CAPTCHA schemes.
Linguistic Question-Answering Reasoning Based on Intelligent Perception of Attribute Weight

Abstract Intelligent Question Answering System aims to realize the communication between human and machine. Based on the real data of question-answer, of front end design, and of daily chat in Chinese, this paper proposes a design method of Intelligent Question-Answering System based on deep neural network and optimizes it. Through real data from authentic view and online daily chat, the paper constructs the property weight, laying foundation for the system to play its role in the real scene such as text cleaning and word segmentation, part of speech tagging, word vector representation, word weight adjustment, etc. By using the mixed model of BOW and Skip-gram to represent the word vector, the paper describes the correlation between words and makes for the shortcomings of BOW. Meanwhile, it retains Bow’s excellent ability of discrete-feature processing.
Algorithm-hardware co-design of ultra-high radix based high throughput modular multiplier

This paper presents an algorithm-hardware co-design of ultra-high radix modular multiplier for high throughput modular multiplication. First, to speed up the modular multiplication, we exploit an ultra-high radix interleaved modular multiplication algorithm with a novel segmented reduction method, which reduces the number of iterations and pre-computations. Then, to further improve the throughput of the modular multiplication, we design a highly parallel modular multiplier architecture. Finally, we implement and verify the modular multiplier using the Xilinx Virtex-7 FPGA. Experimental results show it can perform a 256-bit modular multiplication in 0.56 µs with the throughput rate of up to 4999.7 Mbps.
Design and Implementation of Remote Upgrade System for Vehicle Terminal Based on GPRS

The function of vehicle telematics box(T-BOX) changes with the change of user's demand. In order to adapt to the new demands and changes with the same terminal at different stages, the telematics box should be able to adjust themselves dynamically or the supplier can remotely upgrade the telematics box. In this paper, a remote upgrade scheme of T-box terminal based on GPRS network is proposed, and the remote upgrade system is designed and implemented. The remote upgrade system adopts passive mode that is once the T-box terminal receives the firmware upgrade command, it will download the latest version firmware from the server and does the MD5 verification. The MD5 algorithm is used to ensure the authenticity and integrity of firmware updates. If the MD5 value is the same to the value that received from the upgrade command, the firmware will be written to the internal flash area by the application programming technology IAP of MCU. This scheme is suitable for the firmware remote upgrade of all MCU that based on flash. It is proved to work steadily in several field experimentation and to meet satisfactorily the design request. The remote upgrade system is advanced, safe and reliable.