Reviewerly/exHarmony huggingface dataset:
Optimization of Parallel Stochastic Gradient Descent on Sunway TaihuLight

It is well known that deep neural networks (DNN) have shown outstanding expressive learning ability in machine learning and other tasks. Stochastic gradient descent (SGD) algorithm is widely used to train a number of machine learning models due to its advantages of simple use, fast convergence, and reliable effect. In the background of big data, asynchronous parallel implementations of SGD have been widely applied for accelerating the training of machine learning in a distributed computing environment. However, there are a vast number of time-consuming calculations in training of DNNs. The emerging Sunway (SW) many-core processor has powerful computing capacity and high bandwidth, which makes it particularly suitable for processing parallel tasks with large computational overhead. In this paper, we present a scheme to update the parameter through scaling the asynchronous stochastic gradient descent (ASGD) method to multiple processors efficiently. Our novel method is based on swCaffe, which is an efficient parallel framework on Sunway TaihuLight supercomputer. We evaluate our implementation by training LENET neural network with the MNIST dataset and CIFAR10-quick with the CIFAR10 dataset. Experimental results show that our implementation achieves considerable speedup on TaihuLight supercomputer, compared to synchronous stochastic gradient descent (SSGD) used in swCaffe.
High-Resolution View Synthesis In Camera-Projector Systems Using Compressive Dual Imaging

In this paper, a method for high-resolution view synthesis in a calibrated camera-projector system is proposed. Camera’s resolution is significantly lower than the projector’s resolution. Compressive sensing and sum-to-one transformation are used for an efficient light transport matrix estimation. The light transport matrix and Helmholtz reciprocity enable dual imaging, i.e. generating virtual images from the projector’s viewpoint and virtual relighting of the observed scene. Virtual fringe profilometry enables 3D reconstruction of the observed scene. Performing 3D reconstruction in the dual configuration of the camera-projector system results in a significantly denser point cloud. Reconstructed 3D points are back-projected onto the camera sensor using the camera’s projective matrix, resulting in a high-resolution image, synthesized from the camera’s viewpoint. The proposed method overcomes the spatial resolution limit of the imaging sensor, resulting in significantly increased spatial resolution of the camera’s images.
Statistics and Numerical Methods

Abstract The chapter gives an outline of some statistical and numerical methods that will be applied in later chapters. The first section deals with the minimization of functions. Several gradient-based methods and a popular non-gradient method are discussed. The following section discusses statistical models and the estimation of model parameters. The basics of linear and nonlinear regression models and state space models are presented, including least-squares estimation and the (extended) Kalman filter. The final section gives a brief overview of clustering and different types of clustering algorithms.
Evaluation of educational media of private body parts for down syndrome children

Abstract Educational media of private body parts for Down syndrome children is an educational media in the form of audio-video animation containing information about private body parts. This application developed as part of the supportive learning for Down syndrome children who have entered puberty so that they can avoid sexual abuse. An evaluation is carried out to determine whether the educational media created are according to the needs. The evaluation process is done by interviewing the respondent after conducting a trial on the educational media. The selected respondents were teachers, parents and students. Testing is held at SLB Tk. National Bagian C Malang. The interview questions based on several criteria for assessing multimedia applications, including 1) Ease of use of navigation, 2) Cognition content, 3) Presentation of information, 4) Media integration, 5) Artistic and aesthetics, 6) Learning function. The results of evaluations show that the educational media developed have met the criteria of multimedia assessment. Application of private body parts can provide understanding to Down syndrome children about personal body changes and social contact.
Semantic Graph Representation and Evaluation for Generated Image Annotations

The rapid advancement in generated image annotations, also known as captions, puts a crucial need for efficient and automated methods for those annotations’ evaluation. State-of-art in the automatic evaluation metrics has proven to be inefficient for evaluating the quality of the generated annotations because they rely on certain aspects of word matching, such as the n-gram overlap. This paper presents a semantic-based graph representation and evaluation for generated image annotations. The semantic graph explicitly encodes objects, attributes, and natural language annotations relationships, while consulting ConceptNet as an external knowledge source to provide a rich semantic generated graph. For annotations evaluation, the ConceptNet is extended for use in a proposed semantic evaluation metric, whose input is the semantic graphs. Experimental results show that, over the Flickr8k dataset, the proposed graph-based evaluation metric achieves a higher system-level correlation and rank correlation coefficient value compared to existing related works.
Teaching Analytics Medical-Data Common Sense

The availability of Electronic Medical Records (EMR) has spawned the development of analytics designed to assist caregivers in monitoring, diagnosis, and treatment of patients. The long-term adoption of these tools hinges upon caregivers’ confidence in them, and subsequently, their robustness to data anomalies. Unfortunately, both complex machine-learning-based tools, which require copious amounts of data to train, and a simple trend graph presented in a patient-centered dashboard, may be sensitive to noisy data. While a caregiver would dismiss a heart rate of 2000, a medical analytic relying on it may fail or mislead its users. Developers should endow their systems with medical-data common sense to shield them from improbable values. To effectively do so, they require the ability to identify them. We motivate the need to teach analytics common sense by evaluating how anomalies impact visual-analytics, score-based sepsis-analytics SOFA and qSOFA, and a machine-learning-based sepsis predictor. We then describe the anomalous patterns designers should look for in medical data using a popular public medical research database - MIMIC-III. For each data type, we highlight methods to find these patterns. For numerical data, statistical methods are limited to high-throughput scenarios and large aggregations. Since deployed analytics monitor a single patient and must rely on a limited amount of data, rule-based methods are needed. In light of the dearth of medical guidelines to support such systems, we outline the dimensions upon which they should be defined upon.
Real-Time Panorama and Image Stitching with Surf-Sift Features

This article proposes an all-encompassing picture sewing that works continuously at the installed climate by applying ROI and PROSAC calculation. The ordinary picture was sewed to apply SURF or SIFT calculation which contains convoluted tasks and bunches of information, and this is performed to distinguish the pictures. Likewise, it applies a RANSAC calculation to eliminate anomalies with the goal that consumes extra time to check the needed pictures because of its arbitrariness. In this article, pointless information is elimated by setting ROI dependent on the attributes of scene pictures, and the PROSAC calculation is applied for eliminating exceptions to decrease the checking time. This examination presents the advancement of a 360° framework adjusted to transports. Here, four cameras were placed around the vehicle, and an elevated top view is made to get the driver”s environmental factors. The advancement has been centered on actualizing a twisting calculation that creates the best arrangement between camera pictures. The investigation is based on the cutting-edge twisting calculations. Here, an adjusted strategy to the particular 360° vision framework is planned and executed. Likewise, this undertaking has tried distinctive alignment designed both in static and genuine conditions, and an adjustable design for the latest application is proposed.
An Approach to Investigate the Effectiveness of Multi-Task Learning

Multi-task learning has been widely used in different applications where it is required to build a single model that optimizes many associated learning tasks at the same time. Thus, in this modeling task, the same set of features is used by a single model that is trained and optimized to produce simultaneously more than one output. In this paper, an approach for investigating the relationship between the features and modeled outputs is presented. First, the set of relevant features to each individual output is generated. Then, a different set of features is used depending on the correlation between the outputs. The used features include the intersection, union, and difference between features relevant to each output individually. The performance of the multitask-learning model is evaluated and compared to the performance of the corresponding single-task model in each case. Obtained results indicate that if the outputs to be detected are correlated, then the best performance of a multi-task model is obtained, in the case where the intersection of relevant features to each output is used. On the other hand, in the case where the outputs are not correlated, the union of the set of relevant features to each individual output produces the best results. Moreover, building multi-task models for uncorrelated outputs gives worse results in all cases when compared to the single-task model. Thus, obtained results shed the light on when it is more viable to build multi-task learning models as compared to single task models. In addition, it presents some guidelines on the features to be used based on the correlations between the outputs to be detected by the multi-task models.
An Hotspot Prediction Approach of Scientific Research Based on Autonomously Evolutionary Learner

Abstract Hotspot prediction of scientific research is a new application in information domain. Big-data analytics technique is a critical way of improving prediction efficiency. Scientific researching data have a high dimension and are from different domains. Just using a single learner is hard to solve the problem. In the paper we adopt several deep neural networks to find the proper data dimension and network structure. The genetic algorithm is introduced as well to fasten the whole prediction. The proposed approach performs well in accuracy ratio and recall ratio compared with the benchmark algorithms. The genetic algorithm and pruning method largely improve its performance as well.
Application Research of Cubature Kalman Filter in Vehicle Positioning

In the era of the Internet of Everything, accurate vehicle location information in the transportation system is particularly important. In order to obtain accurate vehicle position, after acquiring basic data through GPS or other hardware collection, a series of data processing can be performed, which can improve the accuracy of vehicle positioning to a certain extent. In this paper, Cubature Kalman Filter (CKF) algorithm is used to predict the position of the vehicle through the model and then fuse the collected data to improve the accuracy of vehicle position. And the combination of adaptive Interactive Multi-mode algorithm and Cubature Kalman Filter algorithm can adapt to various maneuvering states of vehicles. Simulation experiments show that the positioning accuracy of the Interactive Multi-mode Cubature Kalman Filter (Imm-CKF) algorithm can be improved by about 20% compared to the Cubature Kalman Filter algorithm.
A Novel Reinforcement Learning Algorithm Based on Hierarchical Memory

Reinforcement learning algorithm is considered to be the most robust algorithm used in recent years to teach machines by interacting with the environment and learn from their mistakes. Despite the success, the Reinforcement learning algorithm still requiring many amounts of trials with the environment to obtain an accurate performance. Thus to improve the learning algorithm, we present RLHTM a new learning algorithm of Reinforcement learning based on brain-inspired known as HTM. We are exploring the performance of Hierarchical Temporal Memory (HTM) in a reinforcement learning system by replacing the Q function in RL with HTM and benchmarks it with the Q Learning, a state of the art method improve the learning efficiency and accuracy of the reinforcement learning. The experimental results show that our RLHTM method obtained an accurate reward of 200 means 100% accurate at the first trial over the 10 iterations while the current reinforcement learning algorithm has to execute many trials before getting the highest reward of 96% and 98% in the CartPole environment. Our method also demonstrated successfully improvement on solving traffic light problem.
Handwriting character recognition system in documents containing abbreviations using artificial neural networks

Offline Handwriting Character Recognition (HCR) is more challenging than online HCR because of inadequate temporal information such as number and direction of the stroke, ink pressure, unpredictable and high handwriting variations. These difficulties cause low accuracy achieved. The purpose of this study was to perform handwriting pattern recognition on documents containing abbreviations using ANN. The document was written in Indonesian. Some steps are taken to detect abbreviations: collecting handwritten samples, doing image processing, practicing ANN. From the classification process, there are two indicators of accuracy used. Character accuracy based on classes and unusual abbreviations detected. Character accuracy based on class achieved is 60.47%, and for accuracy of abbreviations detected is 27.89%. Low accuracy results because the accuracy of the introduction of 9 of 26 letters is not more than 50%. This study contributes to providing knowledge about image processing and the application of ANN to pattern recognition problems, namely handwriting. In addition, this research also contributes in providing a model for the development of a system for detecting abbreviations in Indonesian written which can be applied in essay exams in education. In future research, adaptive thresholding can be applied to improve system performance. Besides, to minimize recognition errors caused by imperfect character segmentation, recognition is done in one word.
Image Restoration and Evaluation Based on Saliency Detection

An image restoration and evaluation algorithm based on saliency detection is proposed in this paper. Firstly, the saliency map corresponding to the original image is extracted based on the local feature and regional feature, and then the binary image corresponding to the saliency map is obtained by threshold segmentation method, so as to obtain the salient object image and background image. Then the salient object and background images which are only disturbed by noise are prepared. Finally, through the quality evaluation index to compare the restoration effect of only restoring the salient object and only restoring the background in the whole degraded image, it is concluded that the salient object has more important influence on the image quality.
Bipartite Matrix-Valued Tensor Product Correlations That are Not Finitely Representable

We consider the matrix-valued generalizations of bipartite tensor product quantum correlations and bipartite infinite-dimensional tensor product quantum correlations, respectively. These sets are denoted by $$C_q^{(n)}(m,k)$$ and $$C_{qs}^{(n)}(m,k)$$ , respectively, where m is the number of inputs, k is the number of outputs, and n is the matrix size. We show that, for any $$m,k \ge 2$$ with $$(m,k) \ne (2,2)$$ , there is an $$n \le 4$$ for which we have the separation $$C_q^{(n)}(m,k) \ne C_{qs}^{(n)}(m,k)$$ .
Identifying Subjective Content Descriptions among Texts

An agent pursuing a task may work with a corpus of documents as a reference library. Subjective content descriptions (SCDs) provide additional information that add value in the context of the task. On the pursuit of new documents to add to the corpus, an agent may come across new documents where content text and SCDs from another agent are interleaved and no distinction can be made unless the agent knows the content from somewhere else. Therefore, this paper presents an Hidden Markov model (HMM)-based approach to identifying SCDs based on SCD and word distributions in a previously unknown text where descriptions occur inline among content text. We present a case study evaluating the performance of identifying inline SCDs in a text based on real-world and simulated data.
White Blood Cell Components Separation for Hematological Disease Detection

Hematological diseases are blood-related diseases which include leukemia, leucopenia, hemophilia, etc. In which leukemia is severe hematological disease and death rate is very high and hence need proper and faster diagnosis. Leukemia is the formation of giant nuclei or immature increase of leukocytes or WBCs; hence, the need of extracting the shape features and other symptoms must be investigated. This paper suggested and experimented a technique for separating or segmenting white blood cell stain images of leukemia-affected patients. The successful segmentation of an infected cell divides the images into nucleus and outer fluid part cytoplasm, since these two regions contain features that correlate to different forms of leukemia. The algorithm checks and experiments 250 images, and the performance validation using cross-validation techniques shows the nucleus segmentation accuracy as 94% and cytoplasm segmentation accuracy as 78%, respectively.
DenGue CarB: Mosquito Identification and Classification using Machine Learning

This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.
Analysis of Vocational High School Teachers’ Perception of Teacher Competency and School Organizational Culture: Focusing on Comparison With General High Schools in Busan

Drawing on the 3rd wave data from Busan Education Longitudinal Study, this study was conducted to investigate whether there are differences in the individual competencies and school organizational culture of teachers of vocational and general high schools(GHS). The analysis results are as follows. First, the proportion of regular teachers in vocational high schools(VHS) in Busan was relatively lower than that of GHS, and this difference in distribution was statistically significant. Second, VHS teachers showed higher ICT-related competencies, such as ICT utilization ability, Degree of using ICT, and Degree of participation in ICT training, than GHS teachers, whereas the degree of participation in curricular meeting and the degree of participation in TLC was lower than that of GHS teachers. Third, VHS teachers were more negatively aware of the democratic school climate and learning organization culture(p<.10) of the schools they were in, and were found to have lower teacher job satisfaction. These findings imply that it is necessary to encourage small-scale TLC and curricular meeting, which become more important after the Corona 19 crisis, and to create a democratic school climate and a culture of learning organizations as a vocational high school innovation direction.
Ontological Knowledge Inferring Approach based on Term-Clustering and Intra-Cluster Permutations

Ontological representation of knowledge has the advantage of being easy to reason with, but ontology construction with knowledge facts, automatically acquiring them from open domain text is often challenging. This research introduces a novel approach to infer new ontological knowledge in a fully automated manner. Such ontological knowledge can be utilized in both constructing new ontologies and extending existing ontologies. Basic level triples that can be extracted from open domain text are used as the data source for this study. A simple mechanism has been introduced to convert the triple into an ontological knowledge fact and such ontological knowledge facts are further processed to infer new ontological knowledge. The main focus of this research is to infer new ontological knowledge using an advanced term-clustering mechanism followed by an intra-cluster permutation generation task. Generated permutations are potential to be selected as good ontological knowledge facts. Inferred ontological knowledge was tested with inter-rater agreement method with high reliability and variability. Results demonstrated that, out of 43,103 triples, this method inferred 127,874 ontological knowledge (approximately 3 times) of which 66% were estimated to be effective. Finally, this research contributes a reliable approach which requires a single pass over the corpus of triples to infer a large number of ontological knowledge facts that can be used to construct/extend ontologies.
Visual Analysis of Public Opinion Based on Keywords

With the development of social media, the development of large-scale social media has become the main platform for public opinion today. Visual analysis of public opinion for keywords under specific conditions uses data mining technology, data cleaning technology and data visualization technology. Through keyword reviews and content mining under specific conditions, discrete data is denoised, cleaned, keyword events and geographic information of interest are extracted. Through the visual expression of the geographical distribution of the focus area, the tag cloud and the geographical map are combined to obtain the public opinion dynamics of keyword events, which opens up ideas for the visualization of public opinion analysis of keywords under specific conditions.
Research on the Enhancement Strategy of Teaching Effect in Entrepreneurship and Innovation Course Based on SPOC

As entrepreneurship education is of great significance nowadays, it is particularly necessary to strengthen researches on its models and combine macro requirements with students’ practical condition. SPOC, as a mixed teaching combining online and offline teaching, has injected fresh blood into college education and teaching. Supported by the data extracted from Chaoxing learning platform, this paper analyzes problems existed in the SPOC-based teaching of entrepreneurship and innovation. Moreover, by conducting active explorations on the enhancement of learning effects, this paper also puts forward specific and effective measures in order to improve teaching quality in an all-round way and provide new ideas and methods for education reform and innovation.