Reviewerly/exHarmony huggingface dataset:
Implementation of Online Guiding System Based on VR and Face Recognition Algorithms

Implementation of online guiding system based on VR and face recognition algorithms is studied in this paper. The smart online guiding system based on mobile learning concept conforms to the general trend of education informatization and can help schools improve the accuracy of resource planning, resource co-construction, and teaching supervision. To de this, this paper integrates the VR and face recognition. For the VR, it integrates the computer-generated virtual environment with the real environment around the user by means of the photoelectric display technolog which can improve the user experience. For the face recognition, this technology can help to estimate the overall systematic efficiency. The experiment compared with the latest systems proves that the proposed one can improve the efficiency.
Incrementally Finding the Vertices Absent from the Maximum Independent Sets

A vertex v in a graph G is called an absent vertex if it is not in any maximum independent set of G. Absent vertex discovery is useful in various scenarios. For example, if G depicts a wireless communication interference graph, the existence of absent vertices in G may indicate network throughput bottlenecks. However, finding all the absent vertices is hard since it is at least as difficult as finding all the maximum independent sets, which is NP-hard. This paper focuses on a method that finds the absent vertices incrementally, in the hope of finding many such vertices quickly in the early incremental stages. The method iteratively invokes two polynomial-time algorithms to find the 'easy' absent vertices, and then the expensive exact maximum independent set solver to find the 'difficult' ones. At each iteration, the Mirror theorem is used to find extra absent vertices, and then all the absent vertices found so far are removed from the graph before going into the next iteration, until all absent vertices are found. Experimental results show that the above method can find most absent vertices much earlier than the baseline brute-force method on several widely-used datasets, showing its effectiveness.
Reconfigurable Database Processor for Query Acceleration on FPGA

FPGA (Field Programmable Gate Array) has been adopted as an efficient acceleration platform to support high throughput queries to big database. This paper proposes a Reconfigurable Database Processor (RDP) to enable end-to-end query processing. The engines can be reconfigured to support different design parameters. The data processing modules are allocated into stages, and composed to generate the accelerator structure for queries. We further proposed a systematic flow to adapt the design of RDP to the requirements of different queries. The experiments on TPC-DS have shown that RDP attains 56.5% higher throughput over the conventional solution on CPU. When compared with the previous FPGA designs, RDP shows 23.8% advantage on the overall performance while saving 13.6% FPGA resources.
Oscillogram processing algorithm when monitoring cardiac patients’ conditions

Abstract The development of hospital information system requires the improvement of diagnostic and monitoring tools. The tools should provide extensive diagnostics and automatic data processing. In that respect volumetric compression oscillometry is considered a promising technique. However, at present diagnostic findings need to be adjusted by qualified healthcare professionals. The article deals with a process model when measuring arterial blood pressure, compares experimental findings and presents a blood pressure measurement algorithm. The devices designed on the algorithm will allow to conduct an advanced monitoring of patients’ conditions without healthcare professionals directly involved. Implementing algorithm-based devices into hospital system will allow to respond to change in patients’ conditions urgently.
Sensor-independent Pedestrian Detection for Personal Mobility Vehicles in Walking Space Using Dataset Generated by Simulation

Autonomous driving of a personal mobility vehicle such as a wheelchair in a walking space is crucial as a means of transportation for the elderly and the physically handicapped. To realize this, accurate pedestrian detection is indispensable. As existing 3D object detection methods are trained with a roadway dataset, they are widely used for object detection in roadways. These methods have two major drawbacks as regards the detection of objects in walking spaces. The first is that they largely depend on the different LIDAR models. To eliminate this issue, we propose a 3D object detection method, CosPointPillars, that does not take the reflection intensities of the LIDAR point cloud, which causes a sensor model dependency, as input. The second drawback is that networks trained with a roadway dataset cannot sufficiently detect pedestrians (who are major traffic participants in walking spaces) located within a short distance; this is because the roadway dataset hardly includes nearby pedestrians. To solve this issue, we generated a new walking space dataset called SimDataset, which includes nearby pedestrians as a training dataset in the simulations. An experiment on a real walking space showed that SimDataset is suitable for use in pedestrian detection.
Recommender System Techniques and Approaches to Improve the Modern Learning Challenges

Recommendation systems have been developed from the web. These recommendation systems are useful in collecting information from an available set of sources for a user's preferences. The information can be acquired from user's collection of details to share, to review, to do positive ratings by monitoring the user's behavior to improve the quality of top ‘N' recommendations. Now if we come to modern learning system, it has good framework to influence the training factors from the data, triggers, and learner's preferences. Modern learning can be compared to online learning which carries to the future needs. Modern learning can be instituted in schools, engineering colleges, and working campus. The modern learning system combines interrelated data, processes, and resources to create a system of interdependencies that work together, adapting to changing business needs. These interdependencies include multi-level dynamics driven by the organization, training professionals, technological advances, and the learners themselves.
Research on Digital Resource Construction of University Library under Big Data

The construction of a remote access system for digital resources in academic libraries has strong feasibility and is of practical significance for library digital services under the network environment. The wide application of big data has brought unprecedented opportunities and challenges to the construction of digital resources in academic libraries. This paper proposes a linked data-oriented semantic digital library resource description and organization framework. The framework has four levels: metadata layer, ontology layer, linked data layer and application layer. Its core is the construction and association of RDF semantic metadata. Taking the data in the field of "Books, Information and Archives" as an example, the framework is implemented. The focus of the implementation is on the ontology layer and the associated data layer, including the design of related ontology, the automatic conversion of common metadata to semantic metadata, The establishment of RDF semantic links between different data sets, the publication of related data, etc.
MagicCube: A One-Handed Interaction Approach in 3D Environment on Smartphones

When users have only one free hand to operate smartphones, interaction with 3D virtual environment(VE) is restricted. We propose MagicCube, a cube with 5 DOF that perfectly integrates navigation and selection in 3D VE, to address this problem. Three sets of opposing faces are assigned to feature faces with translation, rotation, and selection. Any operation can be performed by dragging the corresponding face. Further, by rotating the cube, users can choose the displayed faces and adjust their relative position according to personal preference. A comparative analysis showed that it could effectively reduce the finger's visual occlusion as well as improve the accuracy and stability of interaction operations.
Perceptual-based super-resolution reconstruction using image-specific degradation estimation

Supervised single-image super-resolution (SISR) reconstruction models are trained with both low-resolution images (ILR) and their corresponding high-resolution images (IHR). During the training process, ILR are obtained by performing a bicubic downscaling on their IHR counterparts. This means that the model learns an inverted version of the bicubic downscaling, resulting in less realistic images that are limited to specific conditions. Generating realistic textures is non-trivial. The obtained details are either blurred or not reminiscent of the usually observed textures. SISR reconstruction with faithful ground-truth texture and no external information remains an issue, especially when the degradation model is not defined. We propose an unsupervised internal learning method of a small convolutional neural network (CNN) using the internal image statistics. We use the power of deep generative models to capture latent representation of patches within the test image across two scales and train a downscaling CNN Dw to learn how to downscale the image by matching these latent distributions. Dw constitutes the downscaling operation with the correct image-specific degradation and is subsequently used in the generation of the training dataset. Obtained results show the effectiveness of our image-degradation estimation method in extracting inner-image statistics for a better super-resolution perceptual reconstruction.
Design and Realization of Monitoring and Management System of Large-Scale Watercraft

Monitoring and management system of large-sized watercraft is a non-linear complex system with many parameter. Monitoring and management system of large-sized watercraft based on networks is different from other conventional system, its basic idea is: at first large-sized watercraft is divdied into many multi-layer subsystem, computation of every rock-bottom system is finished by networks, and then give effect level of every factor, and then the effect level is taked as input value of the previous subsystem, after neural computation, and we can know evaluation of the previous subsystem of the subsystem, analogy according to this until we give evaluation of the large-sized watercraft. Experiment data and result is the same as the reslult the system give. Knowledge library of the system is easy to renew and maintain, and enhance self-learning ability of the system, and avoid the problem of “knowlegye explosion” in conventional expert system.
Capturing SQL Query Overlapping via Subtree Copy for Cross-Domain Context-Dependent SQL Generation

The key challenge of cross-domain context-dependent text-to-SQL generation tasks lies in capturing the relation of natural language utterance and SQL queries in different turns. A line of works attempt to combat this challenge by capturing the overlaps among consecutively generated SQL queries. Existing models sequentially generate the SQL query for a single turn and model the SQL overlaps via copying tokens or segments generated in previous turns. However, they are not flexible enough to capture various overlapping granularities, e.g., columns, filters, or even the whole query, as they neglect the intrinsic structures inhabited in SQL queries. In this paper, we employ tree-structured intermediate representations of SQL queries, i.e., SemQL, for SQL generation and propose a novel subtree-copy mechanism to characterize the SQL overlaps. At each turn, we encode the interaction questions and previously generated trees as context and decode the SemQL tree in a top-down fashion. Each node is either generated according to SemQL grammar or copied from previously generated SemQL subtrees. Our model can capture various overlapping granularities by copying nodes at different levels of SemQL trees. We evaluate our approach on the SParC dataset and the experimental results show the superior performance of our model compared with state-of-the-art baselines.
Toward Programmable Moiré Computation

Abstract Recent advances in optical quantum computation set up a broad discussion on quantum supremacy and its practicability. Lack of programmability and extreme working conditions remain the challenges, calling for a programmable computation scheme. The quasi‐2D layered materials introduce new architectures for the optical neural networks (ONNs), which support various programmable computations following the on‐demand layer design. Compared with the traditional ONNs, Moiré ONNs architectures are more flexible to manufacture via layer number or twist angle control. A general Penn's model to demonstrate the mechanism inside is developed: the dielectric constant control through the layer and twisted bilayer angle dependence, respectively. Theoretically, this device can conduct demo computations ranging from boson sampling to image classification, where quantum computing shows its significant advantages. Instead of redundant 3D‐printing and lithography in traditional ONNs, the Moiré computation framework can train different tasks through programmable twists on single layers without replacing materials.
Adversarially Constrained Interpolation for Unsupervised Domain Adaptation

We address the problem of unsupervised domain adaptation (UDA) which aims at adapting models trained on a labeled domain to a completely unlabeled domain. One way to achieve this goal is to learn a domain-invariant representation. However, this approach is subject to two challenges: samples from two domains are insufficient to guarantee domain-invariance at most part of the latent space, and neighboring samples from the target domain may not belong to the same class on the low-dimensional manifold. To mitigate these shortcomings, we propose two strategies. First, we incorporate a domain mixup strategy in domain adversarial learning model by linearly interpolating between the source and target domain samples. This allows the latent space to be continuous and yields an improvement of the domain matching. Second, the domain discriminator is regularized via judging the relative difference between both domains for the input mixup features, which speeds up the domain matching. Experiment results show that our proposed model achieves a superior performance on different tasks under various domain shifts and data complexity.
Development of artistic abilities of preschool and primary school age children in the digital educational environment of additional education

With the help of the technology of operational diagnostics of artistic abilities, the potential inclinations of artistic and creative activity in children of preschool and primary school age were identified and the range of identified interests in additional education programs was realized. Two groups of respondents of different ages were examined: 6-7 years old in the amount of 45 persons, 9-11 years old in the amount of 85 persons. In the course of the study, it was concluded that the technology of operative diagnostics of artistic abilities is effective at any of these ages. There is a correlation between the motivational and operational readiness of the individual and the success of artistic and creative activity. We have selected the most effective forms of virtual education for the successful development of children’s artistic abilities.
A Bayesian Network Incremental Algorithm for Public Safety Data Analysis

Bayesian network learning is another research field of machine learning and data mining. In the research of Bayesian network learning algorithm, on the one hand we have to consider the issue of how to avoid the disclosure of data privacy. On the other hand, in the real world applications, data may be gradually available for Bayesian network, thus traditional Bayesian network learning algorithms can not be effectively applied. Therefore, this paper proposes a privacy-preserving Bayesian network incremental learning algorithm. We use sufficient statistical information in our incremental learning method, so we firstly proposed a formula for calculating the amount of sufficient statistics information. Then we improve the traditional K 2 algorithm, add the concept of sufficient statistical information in it, and then propose an incremental K 2 algorithm. At last, we proposed a privacy-preserving Bayesian network incremental learning algorithm. The algorithm only need to save the sufficient statistical information of each node and its possible parents in order to calculate score function value of each node and its parents, and then construct the Bayesian network structure. We can use the proposed approach to complete the public safety data analysis with a higher efficiency.
A Survey-Based Empirical Evaluation of Bad Smells in LabVIEW Systems Models

Bad smells are indications of poor designs that decrease the quality and maintainability of software. Compared with extensive research on bad smells in the context of Object-Oriented Programming (code smells), bad smells in systems models (model smells) need much more investigation. Although some works have proposed several model smells in a few modeling domains, the understanding and perception of different types of model smells may vary due to depth of knowledge and area of expertise. To fill this gap, we conducted an empirical study to evaluate the model smells summarized in the existing literature within the context of LabVIEW systems models through an anonymous online survey. Based on the 45 complete responses received from a diverse group of systems modelers, we observed that there exist differences regarding the perception of various model smells. Furthermore, depth of knowledge (experienced and inexperienced users) was observed as a factor that affects a user's understanding of different model smells. However, area of expertise (academia/industry, as well as domain of focus) did not show significant difference in model smells perception. Moreover, we identified additional model smells from this empirical study. In this paper, we provide several recommendations to avoid common model smells and we summarize the lessons learned from this investigation. Our exploratory research provides empirical evidence that drives deeper insights into model smells and lays out recommendations to practitioners on how to avoid some of the prominent smells, thus improving the quality of software artifacts in systems models.
Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization

Matching information across image and text modalities is a fundamental challenge for many applications that involve both vision and natural language processing. The objective is to find efficient similarity metrics to compare the similarity between visual and textual information. Existing approaches mainly match the local visual objects and the sentence words in a shared space with attention mechanisms. The matching performance is still limited because the similarity computation is based on simple comparisons of the matching features, ignoring the characteristics of their distribution in the data. In this paper, we address this limitation with an efficient learning objective that considers the discriminative feature distributions between the visual objects and sentence words. Specifically, we propose a novel Adversarial Discriminative Domain Regularization (ADDR) learning framework, beyond the paradigm metric learning objective, to construct a set of discriminative data domains within each image-text pairs. Our approach can generally improve the learning efficiency and the performance of existing metrics learning frameworks by regulating the distribution of the hidden space between the matching pairs. The experimental results show that this new approach significantly improves the overall performance of several popular cross-modal matching techniques (SCAN [13], VSRN [14], BFAN [15]) on the MS-COCO and Flickr30K benchmarks.
Statistical and Transfer Learning Model to Analyze Endurance Performance with Aging

The decline of human physical ability with aging is an important factor to consider as to athletic performance evaluation. Physiological factors or real-world performance data have been used to build mathematical models. Marathon is a sports event attracting more and more participants. However, due to the limitation of field size, the race organizers usually must qualify each athlete for certain events based on age and gender. We use statistical and machine learning models for analyzing the Boston Marathon results to delineate the decrease in elder athletes' endurance performance. We also apply the transfer learning model on the mobile activity tracking social media platform Strava to classify beginners or professional athletes using performance data and applying sentiment classification to athletes' comments. Our results may provide valuable insights for athletes, coaches, and governing bodies for athletics.
Discriminative Representation Learning for Cross-Domain Sentiment Classification

Cross-domain sentiment classification aims to solve the lack of labeled data in the target domain by using the knowledge of the source domain. Most existing approaches mainly focus on learning transferable feature representations for knowledge transfer across domains. Few of them pay attention to the feature discriminability, which contributes to distinguish different sentiment polarity and improves the classification accuracy. In this work, we propose discriminative representation learning, which extracts transferable and discriminative features. Specifically, we use spectral clustering to reduce the negative effect of low prediction accuracy on the target domain. Centroid alignment enforces samples of the same polarity with smaller distance in the feature space and enlarges the difference between samples of different polarities. Then intra-class compactness benefits true centroid by reducing samples distributed at the edges of the clusters. Experiments on the multiple public datasets demonstrate that discriminative representation learning outperforms state-of-the-art methods.
Categorizing Bibliographic Data for Detection of Transition in Academic Subjects

Decision making for education at universities calls for objective analysis using reliable data. In addition to commonly used methodology, it is inspirational to introduce innovative approaches to large educational data analysis. In this study, we apply text mining techniques to analysis of a bibliography database for detecting temporal differences in academic subjects at higher education. Our approach is designed on the assumption that if automatic text categorization is effective in detecting topical distinction in book titles in different subject areas, it should also be effective to detect time-oriented distinction in the same subject area, wherein state-of-the-art books are sufficiently different from classical books. The experimental results in this study suggest that our proposed approach is feasible to detect and visualize transition in book titles of university libraries in Japan.
Application of Artificial Intelligence Technology in English Learning Platform

With the deep integration of modern artificial intelligence information technology and English learning platforms, the widespread application of artificial intelligence technology has brought new opportunities for optimizing the process and experience of English learning. At the same time, it has provided a new platform with new learning experience for establishing an intelligent and personalized environment. This article starts from the perspective of artificial intelligence technology, and analyzes its integration and application strategies with learning platforms, and explores new learning experiences and improves learning effects.