Reviewerly/exHarmony huggingface dataset:
A field programmable gate array-based biomedical noise reduction framework using advanced trilateral filter

Filtering is one of the essential tools utilized to remove undesirable features in biomedical images. Most biomedical image denoising systems are used for clinical diagnosis. So, in this paper, we use the advanced trilateral filter in the field programmable gate array (FPGA) for removing noise in the biomedical image. Generally, the trilateral filter is used as an edge preserving smoothing filter. This advanced approach of trilateral filter gives the best noise diminution and enhances the image quality. This paper also proposes the hardware implementation of an efficient FPGA-based advanced trilateral filter on real time execution. In this manuscript, we intend to design and implement the FPGA architecture using an advanced trilateral filter. Biomedical images with different noises are used during implementation and compared with the existing bilateral and trilateral architecture to assess the proposed architecture performance. For evaluating the performance metrics of the proposed advanced trilateral filter on MATLAB platform, peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structured similarity index (SSIM) are calculated for different biomedical images – such as brain (MRI), chest (x-ray) and lungs (CT) – with different noises – such as salt and pepper, Gaussian, Poisson and Speckle noises – compared with existing bilateral filter and trilateral filter, respectively. The proposed advanced trilateral filter implementations are checked on Virtex-6, Virtex-7 and Zynq FPGA development board using Verilog programming language in Xilinx ISE 14.5 design tools. The simulation outcomes display that the FPGA execution of the advanced trilateral filter contains better noise removal efficiency in biomedical images compared with the existing bilateral and trilateral filter.
GrouPen: A Tangible User Interface to Support Remote Collaborative Learning

Tangible User Interfaces (TUIs) have been used in many learning contexts. However, its application in the remote collaborative learning area remains relatively unexplored. In this study, we contribute to this use case with GrouPen. It is a TUI prototype embedded in a regular pen, thus allowing easy-to-use tangible interaction. We showed how it could be used to enable and enhance collaborative learning and engage students. GrouPen uses natural gestures to show statuses in several learning phases. We evaluated the prototype using a survey that yielded positive feedback and supported the hypothesis that TUIs like GrouPen could facilitate learning connectedness and engagement.
Study on the Steps and Conditions of Computerization of Teaching Management in Colleges and Universities

Abstract With the rapid development of information technology, the educational administration of colleges and universities must set up a set of standardized computer network management and quality service system on the platform of network technology. This is the key to improve the efficiency of school management and ensure the quality of teaching. The operation center of colleges and universities is teaching management, which is directly related to the survival, development and status of a university in the competition of contemporary society. Teaching management, in the final analysis, is the management of comprehensive information resources such as students, teachers’ teaching plans, teaching materials and so on. Nowadays, the wide application of computer information processing technology provides a powerful means to improve the teaching management in colleges and universities. At the same time, traditional manual management methods are facing the fate of being eliminated. Therefore, teaching management must be modernized and scientific, and the key is to establish a computer management system to achieve computerization of management. How to conduct teaching management under network conditions, provide students with various relevant information in a timely and effective manner, and establish students and students. At present, in the teaching management of major colleges and universities, it is an urgent task to make good connections between teachers and students.
A Standard Proposed Voice-Based Application for Virtual Assistance on Cloud Service Platforms

Virtual assistants associate the clients utilizing natural language processing with the human language. Implementation of custom skill applications can analyze client’s choices and return intended responses. Subsequently, virtual assistance is becoming an integral of customer services. The main motivation for analyzing the customer’s order data is that the consumer can pass commands on the fly and in response get the details for the ordered products. In this research, the order-related data is taken from the CimCommerce Platform and analyzed. This paper is an implantation of an intelligent virtual assistant in finding the order data on mobile application. This virtual-based application would gather the user preferences and model collective user knowledge base, which will improve the user-to-machine communication.
CPS transformation with affine types for call-by-value implicit polymorphism

Transformation of programs into continuation-passing style (CPS) reveals the notion of continuations, enabling many applications such as control operators and intermediate representations in compilers. Although type preservation makes CPS transformation more beneficial, achieving type-preserving CPS transformation for implicit polymorphism with call-by-value (CBV) semantics is known to be challenging. We identify the difficulty in the problem that we call scope intrusion. To address this problem, we propose a new CPS target language Λ open that supports two additional constructs for polymorphism: one only binds and the other only generalizes type variables. Unfortunately, their unrestricted use makes Λ open unsafe due to undesired generalization of type variables. We thus equip Λ open with affine types to allow only the type-safe generalization. We then define a CPS transformation from Curry-style CBV System F to type-safe Λ open and prove that the transformation is meaning and type preserving. We also study parametricity of Λ open as it is a fundamental property of polymorphic languages and plays a key role in applications of CPS transformation. To establish parametricity, we construct a parametric, step-indexed Kripke logical relation for Λ open and prove that it satisfies the Fundamental Property as well as soundness with respect to contextual equivalence.
Image Matching Using Enhancement Offsets With Adaptive Parameter Selection Via Histogram Analysis

In this paper, an algorithm for improved image matching is presented using enhancement offsets that are created dynamically with adaptive parameter selection. The algorithm operates by analyzing an input image and building image offsets to improve colour contrast, non-uniform illumination and lack of detail which allows for additional keypoints to be detected by numerous different detectors. Keypoint detection testing on the enhanced images is conducted, as well as image matching using SIFT and SURF. It is quantitatively shown that the proposed algorithm results in visual improvements, as well as in additional, stronger keypoints being detected in all images irrespective of the detector used. Matching experiments are conducted using the Webcam and Oxford/EF datasets wherein the proposed algorithm consistently outputs more correct matches and achieves higher or comparable matching accuracy over other related enhancement algorithms using SIFT and SURF.
Routing Security Using Blockchain Technology

Route hijacking attacks exploit the mutual trust that BGP architecture is based on, rather than any vulnerabilities or protocol flaws. For this reason, these attacks are as old as the protocol itself, and today these failures continue to occur and research continues on what is the best strategy to provide security for routing on the Internet. New solutions, such as RPKI, are generating risks due to the centralization of the routing authority, they are not based on mutual trust, but depend on trust in the authority. This work seeks to implement a less centralized model, based on blockchain technology, where security is provided to the BGP protocol through the deployment of a specific application for the allocation of Internet resources in the Ethereum Ropsten network, and the subsequent use of this stored information.
A Temporal Statistics Model For UGC Video Quality Prediction

Blind video quality assessment of user-generated content (UGC) has become a trending and challenging problem. Previous studies have shown the efficacy of natural scene statistics for capturing spatial distortions. The exploration of temporal video statistics on UGC, however, is relatively limited. Here we propose the first general, effective and efficient temporal statistics model accounting for temporal- or motion-related distortions for UGC video quality assessment, by analyzing regularities in the temporal bandpass domain. The proposed temporal model can serve as a plug-in module to boost existing no-reference video quality predictors that lack motion-relevant features. Our experimental results on recent large-scale UGC video databases show that the proposed model can significantly improve the performances of existing methods, at a very reasonable computational expense.
Filtering and Smoothing Theory

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Technology research of SPOC platform in Internet plus Era

In the era of Internet plus, higher education is also constantly looking for it. The new education mode, the current Internet application in education has become a trendWith the application of SPOC platform, education can step onto a larger platform,There are better prospects for development, and after the integration of many higher education and SPOC platforms,With more new education models, students can get better education.
Locop: Local Collaborative Object Presence For Semantic Labeling Via Score Map Re-Inference

Recent research has focused on end-to-end networks for indoor scene semantic labeling. However, in addition to learning bottom-up features, high-level knowledge could be implemented to guide the local classification. In this paper, we take advantage of trained semantic labeling networks by using the intermediate layer output as a per-category local detector and implement the context information in a network structure to boost the semantic segmentation performance. A deep learning-based re-inferencing frame work is proposed to boost any pixel-level labeling outputs using our local collaborative object presence (LoCOP) feature as the global-to-local guidance. Experimental results show that the detection accuracy is improved with our re-inference approach.
Intricate Face Recognition Based On Virtual Sample Generation

Abstract This paper mainly focuses on the problem of low recognition rate and making deep hidden features unable to learn caused by the lack of training samples in intricate facial recognition, a method of virtual image sample generation is proposed. The starGAN network based on style migration is used to generate the image that does not exist in the original database, and multiple images with different facial attributes are added to a single sample, combine the obtained virtual samples with the original database to build a larger face database for face recognition. Experiments on the CelebA face database show that the proposed method can effectively improve the face recognition rate in complex situations.
Design and Realization of Test System for Digital Input and Output Module

Abstract Aiming at the problems of the various types of digital input and output modules in the anti-harsh hardened computer, the lack of a dedicated test system and the incomplete testing, a dedicated digital input and output test system with a wide test range was designed. The system is based on single-chip microcomputer and FPGA and can provide 64 channels of digital output (0∼30V adjustable), 64-channel digital or voltage value input test, 64 channels of switch-type digital signal, and the status of the tested module can be read in real time through the host computer software. The digital input and output modules can solve the current situation of incomplete functional testing, and the test results are accurate, stable, and reliable, with a compact structure and multiple test channels.
Research on 360° 3D point cloud reconstruction technology based on turntable and line structured light stereo vision

In order to obtain the 360° contour feature and size of a processed workpiece, a 360 ° 3D point cloud reconstruction technology based on turntable and line structured light stereo vision is proposed. The technical research is to use the turntable to rotate the object to realize the 360° scan of the object by the line laser, and the camera captures the 360° three- dimensional point cloud data of the object surface, which solves the problem of the angle of view that the object surface and the surface block each other. The axis coordinate system of the rotating shaft is calibrated by the three-dimensional target, and the 360° three-dimensional point cloud data obtained is spliced under the axis coordinate system of the rotating table to realize the three-dimensional reconstruction of the object. Experimental results show that this measurement method is simple to operate, easy to implement, and has a certain measurement accuracy, with a measurement accuracy of 0.5 mm.
Two-Way Guided Super-Resolution Reconstruction Network Based on Gradient Prior

Deep convolutional neural networks (CNNs) have demonstrated remarkable progress on single image super-resolution (SISR). However, most networks do not make full use of the rich prior information of the image itself, and get smoother results. To solve this problem, we propose a two-way guided super-resolution reconstruction network based on gradient prior (TWGSR), which uses gradient branches to achieve two-way guidance and employs multi-level gradient loss constraints to reconstruct high-resolution images with more textures. In addition, we propose a multi-scale module (MSM), which adopts dilated convolution to aggregate feature information of different scales, and we add an error feedback module (EFM) to compensate for sampling errors to refine the extracted feature maps. Furthermore, we propose an improved cross residual-in-residual dense block (CRRDB) to enhance the feature extraction capability of the module. Experimental results show that our TWGSR achieves favorable performance against state-of-the-art methods.
FLS-Based Nonuniform Trajectory Tracking AILC for Uncertain Nonlinear Systems with Nonsymmetric Dead-Zone Input and Initial State Error

This paper proposes an AILC method for uncertain nonlinear system to solve different target tracking problems. The method uses fuzzy logic systems (FLS) to approximate every uncertain term in systems. All closed-loop signals are bounded on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="[" close="]" separators="|"> <mrow> <mn>0</mn> <mo>,</mo> <mi>T</mi> </mrow> </mfenced> </math> according to the Lyapunov theory. A time-varying boundary layer and a typical convergent series are introduced to handle initial state error, unknown bounds of errors, and nonuniform target tracking, respectively. The result is that the tracking error’s norm can converge to a small neighborhood along iteration increasing asymptotically. Finally, the simulation results of mass-spring mechanical system show the correctness of the theory and validity of the method.
Iterative Subnetwork With Linear Hierarchical Ordering for Human Pose Estimation

Human pose estimation is a long-standing and challenging problem in computer vision. Many recent advancements in the field have relied on complex structure refinement and specific human joint graphical relations. However, progress has been saturated in terms of accuracy. Each time, new state-of-the-art approaches only improve accuracy by less than 0.3% in the MPII test set despite using complicated model structures. Most recent developments can be summarized into two main ideas: 1) refinement subnetwork to improve predictions iteratively and 2) exploitation of human joint graphical relations. In this work, we present how efficient and simple iterative subnetworks with linear hierarchical ordering based on the aforementioned ideas can help to improve accuracy on strong backbone models. Different versions of iterative subnetwork are examined. Significant improvements on difficult body part predictions such as wrists and ankles using simple convolution subnetwork are observed. Further improvements can be made by using a large receptive field subnetwork such as axial-transformer [1].
Research on My Country's Population Structure in the New Situation

The evaluation of the short-term effects of the new "comprehensive two-child" policy will be analyzed in terms of the number of new born populations and the demographic structure. Through the regression analysis of the change trend before the implementation of the new policy, and comparing it with the current population, it is concluded that the implementation of the new policy will promote the growth of the new population. In the same way, compare the changing trends of the population proportions of each age group before and after the reform. The implementation of the new policy has played a certain role in increasing the number of new populations and adjusting the population structure. The analysis of population size and population structure requires prediction of various indicators. Population characteristics such as birth rate and death rate are constantly changing and developing. After the population reaches a certain size, its characteristics will stabilize over time. Therefore, the prediction of population structure (such as gender ratio, urban-rural ratio, age structure, etc.) Queue element method. Analyze the relationship between population size and population structure over time. And based on the predicted data and information, a qualitative analysis of economic development was made.
Nonparametric Bayesian estimation of a concave distribution function with mixed interval censored data

Assume we observe a finite number of inspection times together with information on whether a specific event has occurred before each of these times. Suppose replicated measurements are available on multiple event times. The set of inspection times, including the number of inspections, may be different for each event. This is known as mixed case interval censored data. We consider Bayesian estimation of the distribution function of the event time while assuming it is concave. We provide sufficient conditions on the prior such that the resulting procedure is consistent from the Bayesian point of view. We also provide computational methods for drawing from the posterior and illustrate the performance of the Bayesian method in both a simulation study and two real datasets.
Comparative Analysis of Research Status of Mobile Learning at China and Abroad

Mobile learning, as a new form of distance education, is an important part of informal learning. With less time and space constraints and strong support for lifelong autonomous ubiquitous learning, it has gained remarkable development in the last decade. The related research not only proves that it has the same learning effectiveness as the traditional learning method, but also obtains many research results in the aspects of its theory and technology innovation. In order to understand the research status of mobile learning in the world, so as to enable the follow-up research to be targeted, this paper uses the bibliographic method to analyze 1497 papers collected from Web of Science and published in the past 10 years with the title of "mobile learning". On this basis, the research status between China and abroad is compared; the existing problems are pointed; the future development trend is prospected and reasonable suggestions are put forward.
Weakly-Supervised Moment Retrieval Network for Video Corpus Moment Retrieval

This paper proposes Weakly-supervised Moment Retrieval Network (WMRN) for Video Corpus Moment Retrieval (VCMR), which retrieves pertinent temporal moments related to natural language query in a large video corpus. Previous methods for VCMR require full supervision of temporal boundary information for training, which involves a labor-intensive process of annotating the boundaries in a large number of videos. To leverage this, the proposed WMRN performs VCMR in a weakly-supervised manner, where WMRN is learned without ground-truth labels but only with video and text queries. For weakly-supervised VCMR, WMRN addresses the following two limitations of prior methods: (1) Blurry attention over video features due to redundant video candidate proposals generation, (2) Insufficient learning due to weak supervision only with video-query pairs. To this end, WMRN is based on (1) Text Guided Proposal Generation (TGPG) that effectively generates text guided multi-scale video proposals in the prospective region related to query, and (2) Hard Negative Proposal Sampling (HNPS) that enhances video-language alignment via extracting negative video proposals in positive video sample for contrastive learning. Experimental results show that WMRN achieves state-of-the-art performance on TVR and DiDeMo benchmarks in the weakly-supervised setting. To validate the attainments of proposed components of WMRN, comprehensive ablation studies and qualitative analysis are conducted.
Secure Private Comparison of Equality using Quantum Resources

Abstract Private comparison of equality is an essential task in secure multiparty computation. In this paper, we investigate such a task in quantum settings. Particularly, we design a comparison protocol using quantum resources. The protocol allows three users to verify whether the private inputs of three users are identical and preserve their inputs’ privacy without an extra third party who can help the verifying process. The proposed protocol does not require unitary operations for the information encoding, which, to some extent, facilitates the protocol’s implementation. In addition, the Trojan horse attacks are invalid to the proposed protocol as it only employs one-step quantum transmissions. We also show that the presented protocol is secure against some well-known attacks.