Reviewerly/exHarmony huggingface dataset:
Analysis on the Human Development Path of Vocational Education in the Era of Artificial Intelligence

At present, under the background of multiple national policies, the demand for intelligent development talents in the manufacturing industry is particularly tight based on the company's own cost-driven and the demand-driven technology in related fields; intelligent innovative talents are cultivated through the linkage of enterprises and schools, and application-oriented talents are exported. Has become a new trend. Through the analysis of the structural reform of vocational education, teachers’ concepts, methods, models and teaching environment and content are adjusted accordingly to enable teachers to find problems in teaching, update teachers’ concepts on the development of teaching ability, improve curriculum development, and build a stable profession , To build a content system that adapts to the development of vocational teachers' teaching ability in the era of artificial intelligence, and better promote talent training.
Mask Removal Algorithm Using GAN Based Models

Recent deep learning network models have been enhanced dramatically to generating images, removing objects, and improving resolution of images. From a pragmatic purpose, we must utilize it to solve some occurrent real-world problems, for example, facial mask removal. People's frequency of wearing masks has increased rapidly due to the pandemic. The objective of this research is to remove the facial mask on face images using generative adversarial network, or GAN, model. We set up three different training process to explore how to remove a facial mask effectively. By comparing among these training processes, we find out that adding an additional binary mask segmentation is conducive to both the generation quality of face region other than mask and inpainting quality of missing mask region. Moreover, our customized GAN model outperforms the Pix2Pix GAN model in both region inpainting.
A Data Mining Approach to Guide the Physical Design of Distributed Big Data Warehouses

Improving OLAP query performance in a distributed system such as Hadoop and Spark is a challenging task. An OLAP query is composed of several operations, such as projection, filtering, join, and grouping. The star join operation is the most expensive one and usually involve considerable communication cost. The common method used to decrease the network traffic for the star join operation is to co-partition some tables of a data warehouse on their join key. However, this operation still requires many MapReduce cycles in existing data warehouses partitioning schemes. In this paper, we propose a new physical design of distributed big data warehouses over Hadoop cluster. We propose two methods called “FKey” and “NewKey” based on a data mining technique to guide our physical design. Our partitioning and distribution scheme helps the query’s optimizer to make a good query processing plan, such it can performing the star join operation in only one Spark stage without the shuffle phase. To evaluate our approach we have done some experiments on a cluster of data nodes using the TPC-DS benchmark. The results show that our proposal outperforms the existing approaches in terms of query runtime.
On the Feasible Region of Efficient Algorithms for Attributed Graph Alignment

Graph alignment aims at finding the vertex correspondence between two correlated graphs, a task that frequently occurs in graph mining applications such as social network analysis. Attributed graph alignment is a variant of graph alignment, in which publicly available side information or attributes are exploited to assist graph alignment. Existing studies on attributed graph alignment focus on either theoretical performance without computational constraints or empirical performance of efficient algorithms. This motivates us to investigate efficient algorithms with theoretical performance guarantee. In this paper, we propose two polynomial-time algorithms that exactly recover the vertex correspondence with high probability. The feasible region of the proposed algorithms is near optimal compared to the information-theoretic limits. When specialized to the seeded graph alignment problem, the proposed algorithms strictly improve the best known feasible region for exact alignment by polynomial-time algorithms.
CA-Market: A Partially Categorical AnnotatingApproach Based on Market1501 Dataset for Attribute Detection

In this paper, a new partial categorical attributes dataset (CA-Market) based on images of the Market1501 dataset has been introduced, for the sake of improving the attribute detection task. Most attributes detection datasets (human appearance features detection) are not partially categorical and do not properly take into account the inner classes diversity. Increasing the diversity of inner parts (gender, head, upper-body clothes, lower-body clothes, bags, shoes, and colors) before annotating can ease the decision-making process by dividing labels into individual categories. CA-Market contains 46 binary attributes in 10 parts from head to foot and their colors which are annotated in image-level. For example, the attributes of the leg part are skirts, shorts, and pants which are carefully chosen to be categorized for a classification task. In this research, the effect of the labeling approach is studied. Hence, a common classification method is used and only datasets or baselines are changed for comparisons. Baselines are based on Omni-Scale, Resnet50, and Hydra-Plus architectures to compare the CA-Market1501 dataset with the Market1501 attribute dataset in the same setting. CA-Market demonstrates a new representation of data as a part-based format which can gain better results. This approach, without adding any extra modules, achieved a significant enhancement. For instance, accuracy in the vectorized format is over 92&#x0025;, in the categorized is over 90&#x0025; which shows the effectiveness of part-based attribute annotating. Also, hair, backpack, upper color, and lower color as the common attributes between Market1501-attribute and CA-Market datasets are achieved 90.26, 88.04, 94.55, and 94.18 classification accuracy which can outperform existing state-of- the-art approaches.
Path Planning Based on Segmented Bezier Curves and A* Algorithm for Mobile Robot

Path planning is an important problem for mobile robot in complex environments. In this paper, an optimized path planning method based on segmented Bezier curves and A* algorithm is proposed. The result of A* algorithm is used to guide the direction of segmented Bezier curves, which fit the A* path. The segmented Bezier curves are generated to meet the requirements of fitting index with the consideration of constraints from the environment and robot itself. The set of the optimized Bezier curves for each segmented A* path is connected end to end and regarded as the result of path planning for the mobile robot. The effectiveness of the proposed method is verified through the experiments.
High Fidelity 3D Reconstructions with Limited Physical Views

Multi-view triangulation is the gold standard for 3D reconstruction from 2D correspondences given known calibration and sufficient views. However in practice, expensive multi-view setups -- involving tens sometimes hundreds of cameras -- are required in order to obtain the high fidelity 3D reconstructions necessary for many modern applications. In this paper we present a novel approach that leverages recent advances in 2D-3D lifting using neural shape priors while also enforcing multi-view equivariance. We show how our method can achieve comparable fidelity to expensive calibrated multi-view rigs using a limited (2-3) number of uncalibrated camera views.
Recent Advancements in Semantic Web Service Selection

Conventional web services have more minor role of semantics for finding appropriate web services. In semantic web service selection, the maximum matching process plays an important role in finding accurate results using semantics. Most of the pre-existing web service selection approaches work based on keyword-based searching to find the relevant results for the corresponding user query. These approaches neglect the semantic understanding, which leads to irrelevant results for a user query. This paper reviews existing state-of-the-art for semantic web service selection. The techniques discussed here focused on the semantic capabilities and are also applicable for both web service composition and web service selection. Furthermore, two network flow-based approaches for maximum matching are applied, and an improvement in web service selection is observed.
Performance comparison of image enhancers with and without deep learning

Image enhancement is a computational procedure to improve visibility of details and content of an input image. Several image enhancement algorithms have been developed thus far, from traditional methods that process a single image based on physical models of image acquisition and formation to recent deep learning techniques, where enhancement models are learned from data. Here, we empirically compare a set of traditional and deep learning enhancers, which we selected as representing different methodologies for the improvement of poorly illuminated images. Our experiments are conducted on public data and show that, although all the considered enhancers improve the visibility of the image content and details, the deep-learning-based methods generally produce less noisy images than the traditional ones. This last outcome has to be carefully considered when enhancers are used as preprocessing for algorithms that are sensitive to noise. As a case study, and with the purpose to promote more aware usage of these two groups of enhancers in computer vision applications, we discuss the impact of image enhancement in the framework of image retrieval performed through two popular algorithms, i.e., SIFT and ORB, implementing different image descriptions and having different sensitivities to noise.
Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds

We propose novel randomized optimization methods for high-dimensional convex problems based on restrictions of variables to random subspaces. We consider oblivious and data-adaptive subspaces and study their approximation properties via convex duality and Fenchel conjugates. A suitable adaptive subspace can be generated by sampling a correlated random matrix whose second order statistics mirror the input data. We illustrate that the adaptive strategy can significantly outperform the standard oblivious sampling method, which is widely used in the recent literature. We show that the relative error of the randomized approximations can be tightly characterized in terms of the spectrum of the data matrix and Gaussian width of the dual tangent cone at optimum. We develop lower bounds for both optimization and statistical error measures based on concentration of measure and Fano’s inequality. We then present the consequences of our theory with data matrices of varying spectral decay profiles. Experimental results show that the proposed approach enables significant speed ups in a wide variety of machine learning and optimization problems including logistic regression, kernel classification with random convolution layers and shallow neural networks with rectified linear units.
Distribution Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains

Projecting the input text pair into a common semantic space where the matching function can be readily learned is an essential step for asymmetrical text matching. In the practice, it is often observed that the feature vectors from asymmetrical texts show a tendency to be gradually undistinguishable in the semantic space as the model is trained. However, the phenomenon is overlooked in existing studies. As a result, the feature vectors are constructed without any regularization, which inevitably hinders the learning of the downstream matching functions. In this paper, we first exploit the phenomenon and propose DDR-Match, a novel matching framework tailored for asymmetrical text matching. Specifically, in DDR-Match, a distribution distance-based regularizer is devised to accelerate the fusion of sequence representations corresponding to different domains in the semantic space. Then, we provide three instances of DDR-Match and make a comparison among them. DDR-Match is compatible with existing text matching methods by incorporating them as the underlying matching model. Four popular text matching methods are exploited in the paper. Extensive experimental results based on five publicly available benchmarks showed that DDR-Match consistently outperformed its underlying methods.
Locality Sensitive Hash Aggregated Nonlinear Neighborhood Matrix Factorization for Online Sparse Big Data Analysis

Matrix factorization (MF) can extract the low-rank features and integrate the information of the data manifold distribution from high-dimensional data, which can consider the nonlinear neighborhood information. Thus, MF has drawn wide attention for low-rank analysis of sparse big data, e.g., Collaborative Filtering (CF) Recommender Systems, Social Networks, and Quality of Service. However, the following two problems exist: (1) huge computational overhead for the construction of the Graph Similarity Matrix (GSM) and (2) huge memory overhead for the intermediate GSM. Therefore, GSM-based MF, e.g., kernel MF, graph regularized MF, and so on, cannot be directly applied to the low-rank analysis of sparse big data on cloud and edge platforms. To solve this intractable problem for sparse big data analysis, we propose Locality Sensitive Hashing (LSH) aggregated MF (LSH-MF), which can solve the following problems: (1) The proposed probabilistic projection strategy of LSH-MF can avoid the construction of the GSM. Furthermore, LSH-MF can satisfy the requirement for the accurate projection of sparse big data. (2) To run LSH-MF for fine-grained parallelization and online learning on GPUs, we also propose CULSH-MF, which works on CUDA parallelization. Experimental results show that CULSH-MF can not only reduce the computational time and memory overhead but also obtain higher accuracy. Compared with deep learning models, CULSH-MF can not only save training time but also achieve the same accuracy performance.
vRecruit: An Automated Smart Recruitment Webapp using Machine Learning

The need for global online recruitment has risen tremendously in recent years. However, this procedure presents difficulties for recruiters in managing the flood of applications and maintaining contact with the applicants. Historically, little attention has been paid to a practical solution for virtual recruitment. As a result, the paper proposes "vRecruit - A machine learning-based web application" for virtual recruitment in the current paper. vRecruit&#x2019;s primary features include a client-specific interview process that leverages Machine Learning-based references to context provided by the client, as well as a text-based sentiment analysis engine. All components work in unison to ensure the webapp&#x2019;s end-to-end functionality, which was finally launched on flask. The face recognition method using the face api model achieved a 96% accuracy. The speech to text conversion using the Mozilla DeepSpeech model had a 7.55% word error rate, whereas the rasa Natural Language Understanding (NLU) model trained for chatbots had a 95% accuracy. The webapp provides a hassle-free virtual recruiting experience for candidates and interviewers.
Young Children’s Playful Engagement and Learning with a Fairy-Tale Themed Augmented Reality Coding App

To date, there is no known research examining children’s early coding skills in relation to an AR app and little is known about the types of creativity and creative thinking made possible by children’s engagement with coding apps. This chapter reflects on a study concerning four to six year old children’s engagement and learning with the Augmented Reality (AR) coding application Little Red Coding Club. Four case studies, involving thirty children, were conducted in South Yorkshire (UK) early years settings. Video data were multimodally analysed in relation to The Makerspace Learning Assessment Framework (MLAF) and The Early Coding Skills and Knowledge Framework (ECSKF) (Scott F, Marsh J. Young children’s engagement and learning with the Augmented Reality (AR) coding app ‘Little Red Coding Club’. University of Sheffield. https://content.twinkl.co.uk/document/general/twinkl-childrens-learning-with-ar-fnl-28-10-19_1578567254.pdf , 2019). Creative thinking and behaviours were assessed by mapping the MLAF against the A–E Framework of creativity (Murcia K, Pepper C, Joubert M, Cross E, Wilson S. Issues Edu Res 30(4):1395–1417. http://www.iier.org.au/iier30/murcia2.pdf , 2020). Both the affordances of the application (‘place’) and pedagogy of adults (‘person’) were considered. Children were enabled to be creative when equipped with both technical understanding of the conventions of coding and provided with contexts conducive to ‘freeplay’ and the production of their own digital texts and artefacts. Children exhibited many creative skills in their solo or peer engagement with the app. However, other creative skills required suitable pedagogical intervention from an adult.
Quora Based Insincere Content Classification &amp; Detection for Social Media using Machine Learning

Internet, being a source of infinite amount of information that people use and exchange daily, is not protected from the abusive, inappropriate and toxic content. Sometimes people deteriorate the content on social media which negatively impacts the society and it may have awful results. Regarding this toxic content over social media we examined the data set of Quora, which is a question answer website, to filter the inappropriate questions which not only affects the society but also degrade the quality and the standard of such websites. This paper analysed tokenization and vectorization which are considered to be some of the best technique in natural language processing and machine learning algorithms such as Naive Bayes, Logistic Regression, Support Vector Machine and Random Forest. On the basis of accuracy, F1-score and confusion matrix we evaluated that SVM performs best result followed by Logistic Regression. The count vectorizer techniques resulted better than other text vectorizer techniques. The experimental results showed that SVM worked better by achieving an accuracy of 0.899 for the best case.
Performance measurement of various AI techniques for energy estimation and its optimisation using sensitivity analysis

The objective of this research is to predict energy performance of a building (EPB) in terms of heating and cooling load by using various artificial intelligence (AI) techniques then measuring the corresponding strength of each input and its effect on the output in order to identify the most significant input from the lot by using sensitivity analysis. EPB can help in efficient construction of buildings as well as put a leash on dwindling natural resources and global warming. The various intelligent techniques used in this project are artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), ANFIS-GA (genetic algorithm) and ANFIS-PSO (particle swarm optimisation). In order to identify the most significant input, we are using a technique based on sensitivity analysis, which is called the connection weight algorithm. In the end, performance of the AI techniques is compared to select the best performing model.
Real-Time Cargo Loading Identification Based on Self-Powered Camera

In practice, the statistics of cargo loading capacity needs to be realized by counting the loading actions. Object detection algorithm may not meet the requirements of such tasks under the influence of environmental factors. And the current temporal action localization methods may ignore the spatial information of objects and identify the inaccurate action time boundary of multiple objects in the cargo loading task. To fill this technical gap, this paper designs a cargo loading identification framework. The core of this framework is the Target Area Rise-descend (TAR) algorithm, which is based on the object detection model and uses the regularity of object location and object size to realize real-time recognition of the cargo loading process. We ensure the real-time and robustness of the algorithm by some means like nap mechanism and time regularity. The garbage truck recycling task is taken as an example to complete the elaboration of the proposed method. Experiments show that the accuracy of the proposed method is higher than 99%, and the real-time performance is higher than 20fps on Neural Network Computing Unit (NPU). Considering the limitations of intelligent application scenarios and resources, self-powered camera is applied. For mechanical motion similar to the scene in this paper, the proposed algorithm can use extremely low computing power to capture the motion process and achieve data statistics.
Automatic optimization of temporal monitoring schemes dealing with daily water contaminant concentration patterns

Online algorithms have been developed to automatically adjust monitoring schemes to sample instants characterized by maximum and/or minimum daily concentrations while reducing sampling costs with respect to traditional monitoring schemes.
Concepts, Experts, and Deep Learning

Deep learning has recently emerged as a new powerful source of AI algorithms, applied with amazing success for games (chess, Go), classification, face recognition, natural language translation, and administrative decision making. In this paper I analyze the somewhat surprising philosophical significance of deep learning by three themes. First, concept formation by means of massive amounts of training examples in unsupervised learning seems to reinforce a crude form of Lockean concept empiricism, in contrast to its Kantian alternatives. Secondly, deep learning algorithms seem to achieve the highest status of intuitive experts in Hubert L. Dreyfus' account of skill acquisition, so that his criticism of AI has to be reconsidered. Thirdly, deep learning with neural networks is a bottom-up approach, in contrast to the top-down approach of traditional AI, yet as sub-conceptual it is not associated with conceptual or propositional content.
PMUVis : A Large-Scale Platform to Assist Power System Operators in a Smart Grid

Electric transmission power grids are being revamped with the widespread deployment of GPS-enabled phasor measurement units (PMUs) for real-time wide-area monitoring and control via precise, time-synchronized measurements of voltage and current. Large, concurrently produced volumes of noisy data hinder PMU usability, particularly for the analysis of power oscillation and load fluctuation events in the grid. We examine visualization challenges for events in the electric power grid and develop PMUVis, a visualization platform that supports scalable analysis of grid network topology and anomalous events in near time. PMUVis incorporates a novel FFT-based approach over raw and temporally aggregated data to examine oscillation event propagation through the grid network. We validate PMUVis with expert reviews and a case study and discuss how visualization can be leveraged to enhance real-time spatiotemporal grid analysis by advancing operator capabilities.