Reviewerly/exHarmony huggingface dataset:
Improved modality-invariant feature learning for degraded face recognition

In this paper, we investigate the degraded face recognition problem. At checkpoints, it is common that a passenger’s photo is digitally taken on the spot and compared with archived images scanned from printed photos. Therefore, the gallery set and the probe set come through two different media. The distortions introduced in the printing and the scanning processes often lead to unsatisfactory identification performance, necessitation further investigations in tackling degraded face recognition. Therefore, we propose an improved modality-invariant feature (IMIF) approach which combines the modality invariant features with a discriminative learning procedure to handle the variations in expression, occlusion and degradation. Experiments on the degraded face database show that the proposed IMIF enhances the degraded face recognition performance compared with other methods and validates the effectiveness of the proposed method.
Turnover and shape filter based feature matching for image stitching

This work intends to deal with the problem of misalignment in image stitching caused by small overlap area. To reduce mismatches between matched features pairs in two connected images, random sample consensus (RANSAC) [1] is usually adopted, which works under the assumption that the sampling of matched feature points with the largest number of inliers should be utilized to compute geometric matrix. However, this assumption does not hold in the case of small overlap area between the connected images, as compressing or turning over the image may result in better spatial consistency of matched feature points. Therefore, we propose a turnover and shape filter based feature matching method for image stitching. In the method, a turnover and shape filter is firstly used to filter out the samplings resulted from turnover and compression, which is then connected to RANSAC to yield final inliers. Experimental results from real-world datasets validate the effectiveness of our method.
Synchronization Criteria for Delayed Fractional-Order Neural Networks via Linear Feedback Control

In this paper, the synchronization problem is addressed for delayed fractional-order neural networks (FNNs). First, a simple linear feedback controller is constructed, which is easy to be implemented in practical application. Then, by using appropriate fractional-order Lyapunov functions, fractional Halanay inequality and inequality techniques, two novel synchronization criteria are established, respectively. Numerical examples are finally offered to display the effectiveness of the developed methods.
Finding Minima Algorithms

The learning process in supervised learningsupervised learning consists of tuning the network parameters (weights and biases) until a certain cost function is minimized. Since the number of parameters is quite large (they can easily be into thousands), a robust minimization algorithm is needed. This chapter presents a number of minimization algorithms of different flavors, and emphasizes their advantages and disadvantages.
Improvement on National Crisis and Emergency Management through a Core System: Lessons of the African Swine Fever Disaster

The purpose of this study is to seek the problem and improvement on the national crisis and emergency management system in Korea through the core system. Based on the lesson of the ASF disaster, this study analyzed the five major elements of the core system : value, institution, leadership, devotion, and expertise. This article suggests recommendations for improving the national crisis and emergency management in Korea as follows; (①value) promoting animal welfare certification policies to spread respect for life, following the method of killing with pre-trainingt, (②prevention) establishing a defense system with North Korea through international organizations, (③preparedness) unification of the crisis warning level in the manual and guidelines, and to raise the crisis warning level when ASF occurs in neighboring countries, (④response) promptly holding Animal Disease Control Council involving livestock farmers to seek expert advice and establish regulations on the extent of preventative killings. (⑤recovery) establishing compensation criteria, including the upper and lower limits for killing compensation, and supporting livelihood security funds before re-entry, (⑥leadership) change of national crisis management system to operate Central disaster and safety Countermeasure Headquarters in social disaster management agencies, (⑦devotion) strengthening disaster capacity of citizens and establishing a cooperative system among governments, corporations and civil society, (⑧expertise) modernization and specialization of livestock farms, expansion of inspection agencies and inspection personnel, expansion of vaccine research institutions and research personnel.
Large-scale book page retrieval by deep hashing networks

Nowadays, more and more printed books are accompanied by electronic resources including videos, audios, games, augmented reality and other mobile apps. However, it is not very convenient to access most of these electronic resources, as the association between printed books and electronic resources is not automatically available [2]. To build a bridge between a book page and the corresponding electronic resources, a large-scale book page retrieval method using deep hashing network is presented in this paper. There are mainly three contributions: First, a pipeline is proposed to make a Convolutional Neural Network (CNN) trained for another unrelated task available for book page retrieval. Second, the high-dimensional features extracted from the CNN is mapped to the low-dimensional binary hash code sequence in Hemming space by the deep hashing network, which not only increases the speed of retrieval but also saves the space of feature storage. Third, a large-scale dataset which is consist of 1.55M book page images is collected. Experimental results on the 1.55M book page dataset show that the proposed deep hashing network achieves a Top-1 hit rate of 92.1% and the response time is less than 0.6 second on a desktop computer with a GeForce 1080Ti GPU.
Research on the Construction of University Curriculum Map Supported by Big Data

Curriculum map is an important basis for student planning, teacher review courses, and school planning courses.It has been widely adopted in Europe, America and Taiwan, China.With the development of big data technology, the connotation of curriculum maps is constantly enriched.With the support of big data, fully explore the connection between data, it select recommendations through accurate courses, deepen the diagnosis of course conflicts, strengthen the feedback of course results, draw a map of college courses, improve students' interest in learning, save the teaching staff, optimize teaching work and has opened up another direction for the application of big data in education.With the support of big data, the curriculum map will develop in the direction of individualization, relevance, interaction and intelligence.
Continuous Improvement Project SALIKSIK as Feedback Mechanism Towards Research Completion, Dissemination and Utilization

Project SALIKSIK (Saliksik Ating Linangin upang Itong Paaralan makamit Kahusayan sa Sining pang Instruksyon tungo sa Karunungan ng Mag-aaral) is a Continous Improvement Project of Mayamot National High School which aims to provide teacher-researchers technical assistance and feedback regarding the conduct of action research. This study aimed to determine the effectiveness of Project SALIKSIK as feedback mechanism towards research completion, dissemination and utilization in Mayamot National High School Year 2019-2020. The researchers utilized qualitative analysis to determine the implication of Project SALIKSIK as feedback mechanism towards research completion, dissemination and utilization in Mayamot National High School. An interview was conducted to determine the feedbacks of the teacher-researchers with regards to research completion, dissemination and utilization before and after the implementation of Project SALIKSIK. and to determine the status of research completion, dissemination and utilization in Mayamot National High School before and after the implementation of Project SALIKSIK, document analysis was conducted to compare the trends from previous academic year to current school year as the CI team implements the project.            Based on the findings, the following conclusions were arrived at (1)  Before the conduct of Project SALIKSIK the following were the identified needs of the teacher-researchers that they thought would be addressed: Feedback Giving and Monitoring, Support from the Admin, Technical Assistance and Utilization and Dissemination of Research. These were all resolved after the intervention of Project SALIKSIK, and (2) The research completion, dissemination and utilization rate increase from 63% to 83% before and after the conduct of Project SALIKSIK respectively. Thus, Project SALIKSIK is effective in increasing the research completion, dissemination and utilization. Based on conclusions, the following recommendations were drawn: (1) The principal should include project SALIKSIK as one of the projects in SIP to sustain the project, (2) The school should sustain the project SALIKSIK to maintain the increasing research completion, dissemination and utilization rate, (3) The schools division office should encourage other schools to do the same project to increase their own research completion, dissemination and utilization rate, and (4) A similar factor that affects the SBM level of practice in their respective schools.
Enhancing Reading Comprehension by Using Teams Games Tournament (TGT) and Index Card Match for EFL Learners

The cause of second semester students of English Study Program of  Baturaja University problem to read text in English are the lack of vocabulary, the monotonous technique, the psychological burden, the lack of group activities in the classroom, and the lack of the students’ motivation in practicing reading. Teams Games and Index Card Match be used to enhance group dynamic and make students more active in a group. The objective of this study was to find out whether there was a significant difference in reading comprehension in narrative text to second semester students of English Study Program of  Baturaja University between the students who are taught by using TGT and those who are taught by using ICM. The study belongs to experiment research which was true experimental design. The total sample was 69 students taken by using purposive sampling . The data was analyzed by using t-test formula and pre- requisite test (normality and homogeneity test. Based on the result of the research, the students’ average score in experiment class 1 was more than the students’ average score in experiment class 2. The students’mean score of pre-test in experiment class 1 was 52,6 and the students’ mean score of post test in experiment class 1 was 71. The students’ mean score of pre-test in experiment class 2 was 53,4 and the studens’ mean score of post test in experiment class 2 was 68,85. Then, it was found that the result of the Independent Sample t-test of the post test score in experimental class 1 and experimental class 2 gave the value of t obtained = 1,970 more than t table = 1,668 and the value of Sig. (2-tailed) = 0,001 less than the value of Significance level (? = 0,05) with df = (N 1 +N 2 ) – 2 = 67. Using Teams Games Tournament, the students’ average score in Experiment class 1 was more than the students’ average score in experiment class 2. It meant that it was a significant difference between the students who are taught by using Teams Games Tournament and those who are taught Index Card. The results also showed that the Teams Games Tournament group made more improvement than Index Card Match  group in their reading ability. Keywords : teaching, reading comprehension, teams games tournament, index card match
Analysis of parent-education instructors' experiences on the education of their own children

본 연구의 목적은 학부모에게 자녀교육에 관하여 가르치고 있는 전문가로서 학부모 자녀교육 역량강사가 자기 자녀를 교육하면서 어떠한 경험을 하는지 깊이 있게 분석하는 것이다. 본 연구에서는 청소년기 자녀를 두고 있는 D광역시 교육청 학부모 자녀교육 역량강사 아홉 명을 연구 참여자로 선정하였다. 학부모 자녀교육 역량강사의 자기 자녀교육에 관한 경험을 탐색하기 위해서 합의적 질적 연구방법(Consensual Qualitative Research: CQR)을 사용하였다. 합의적 질적 분석을 통해서 ‘지식과 태도의 변화’, ‘자녀교육 실천에서 긍정 경험’, ‘자녀교육 실천에서 부정 경험’, ‘반성과 성찰’ 의 4개 영역과 18개 범주가 도출되었다. 연구 결과를 바탕으로 학부모 자녀교육 역량강사 양성과 학부모 교육을 위한 시사점을 논의하였다.The purpose of this study was to deeply analyze parent-education instructors’ experiences on the education of their own children. In this study, nineanalysis Parent-education instructors, who had adolescent children, were selected as participants. In order to explore their experiences of the education of their children, semi-structured interviews were conducted. Consensual Qualitative Research(CQR) was used to analyze the interview data. As a result of CQR, four domains were identified including knowledge and attitude change , positive experience in child education practice , negative experience in child education practice , regret and reflection , and 18 sud-categories were determined. Based on findings, implications and suggestions to achieve improvements in training for parent-education instructors and parent education programs were described, and limitations of this study and suggestions for future study were mentioned at the end.
Upaya Meningkatkan Minat Belajar IPS pada Siswa dengan Media Pembelajaran Power Point

This study aims to determine the effect of powerpoint learning media on student interest in social studies subjects VIII in Sumber Harta State Junior High School. The research method used is pure experimentation. Determination of the sample is done by using simple random sampling technique from the population of all students of class VIII, and 50 students selected as samples are class VIII A (experimental class) given learning treatment with powerpoint learning media and class VIII B (control class) is not treated. Data collection techniques in the study using a questionnaire that has been tested for validity and reliability testing. The results of the study using the percentage formula showed that 23 students were categorized as very interested (SB) with a percentage of 92% and 2 students were categorized as interested (B) with a percentage of 8%, while students who were categorized as less interested (KB) and students who were not interested (TB) ) as much as 0% or none. Conclusion, there is a significant influence of learning media using powerpoint on students' learning interest in social studies.&#x0D; Keywords: Influence, Powerpoint, Interest in Learning.
Cost Effective Model for Using Different Cloud Services

The trend of cloud computing has started from the beginning of Amazon’s cloud services in 2006 despite its extravagant beginning in its starting years now cloud computing is easily one of the trending concepts in computer science engineering. Cloud computing has shown tremendous amount of results in various sectors. This case study helps to find out the similarities and contrasts between different cloud service providers in terms of their technical aspects and range of services provided by them. This case study was used as a subjective methodology for sampling. Since we all know the boom of cloud services that play a vital role in our lives, we do not know the background process in the cloud world. Cloud services are mostly offering renting access to enormous pool of computational power and accounting capacities to the resources. Generally, these are cloud-based‬ infrastructure which describes on characterizing the accounting arrangements for obtaining resources. Most vitally cost-effective strategy is most useful so that the cloud can be purchased at low price and can be used as proficiently as conceivable as possible. ‬With this case study, the readers can get insight of what cloud computing is, what are the various cloud services considered for this paper and various cloud providing systems so that users can purchase which is best and cheap related to the platform they are using.
Fully Automated HTML and JavaScript Rewriting for Constructing a Self‐healing Web Proxy

Summary Over the last few years, the complexity of web applications has increased to provide more dynamic web applications to users. The drawback of this complexity is the growing number of errors in the front‐end applications. In this paper, we present an approach to provide self‐healing for the web. We implemented this approach in two different tools: (i) BikiniProxy, an HTTP repair proxy, and (ii) BugBlock, a browser extension. They use five self‐healing strategies to rewrite the buggy HTML and JavaScript code to handle errors in web pages. We evaluate BikiniProxy and BugBlock with a new benchmark of 555 reproducible JavaScript errors of which 31.76% can be automatically self‐healed by BikiniProxy and 15.67% by BugBlock.
Sentence-Level Semantic Features Guided Adversarial Network for Zhuang Language Part-of-Speech Tagging

The intelligent information processing of the standard Zhuang language spoken mainly in Southern China is presently in its infancy, and lacks a well-defined language corpus and automatic part-of-speech tagging methods. Therefore, this study proposes an adversarial part-of-speech tagging method based on reinforcement learning, which solves the problems associated with a lack of a language corpus, time-consuming laborious manual marking, and the low performance of machine marking. Firstly, we construct a markup dictionary based on the grammatical characteristics of standard Zhuang and the Penn Chinese Treebank. Secondly, a dependency syntax analysis is applied for constructing the semantic information feature vectors of sentences, and long short-term memory is adopted as the policy network architecture to enhance available information using recurrent memory, and a conditional random field is employed as the discriminant network to perform label inference with global normalization. Finally, we use reinforcement learning as the model framework, target parts of speech as the feedback of the environment, and then obtain the optimal policy through adversarial learning. The results show that the combination of reinforcement learning and adversarial network alleviates the dependence of the model on the training corpus to some extent, and can quickly and effectively expand the scale of the annotation dictionary for the Zhuang language, thereby obtaining better labeling results.
Correction to: The Minimum Sampling Rate and Sampling Duration When Applying Geolocation Data Technology to Human Activity Monitoring

Unfortunately the first author’s name was spelled incorrectly. In the contribution it read “Yan Zheng” but it should have read “Yan Zeng”.
Pro-Safe: An IoT based Smart Application for Emergency Help

There can be any sudden situation of panic. It could be because of an intruder entering our house, an accident situation, bad health status or an assault at which we are unable to intimate to the people around us. In this paper, we proposes an implementation of emergency handling system, which is based on fall detection. The proposed system consist of an android based smart device embedded with real time fall detection.The main objective of this project is to build an automated emergency handling device with an auto triggered emergency button. Once the application is triggered it will initiate live recording of images and audio of the situation.The encrypted copy of this will be uploaded in a cloud storage which is accessible by an authentic police official. This application will also give an alert to the nearest police station with the users current location.In this paper, we compare three machine learning algorithms, Decision Tree(DT) algorithm, K-Nearest Neighbour(KNN) algorithm and Support Vector Machine(SVM) algorithm using Sis Fall dataset for training and validation so as to provide high efficiency in fall detection.In this scenario, SVM algorithm gives better accuracy and less false positive rate.Here we used triaxial accelerometer and gyroscope to collect the data. The proposed device achieves high performance by using Machine learning techniques to detect harmful fall of the person and to auto initiate by itself.
Learning with One-dimensional Inputs

This chapter deals with the case of a neural network whose input is bounded and one-dimensional, $$x\in [0,1]$$ . Besides its simplicity, this case is important from a few points of view: it can be treated elementary, without the arsenal of functional analysis (as shall we do in Chapter 9) and, due to its constructive nature, it provides an explicit algorithm for finding the network weights.
General Formula for Finding Optimize Hologram Reflector Dimension to Project Images on Screen

As the technology emerges, the platform of media that used to showcase a product has risen in variety. Technology allows us to introduce a product by displaying a holographic version by projecting 2D images into reflector rather than using the physical prototype as a sample. However, producing a holographic image is not easily performed, as it is affected by the size of the screen as the media of projection and reflectors. This paper proposes formulas to calculate the parallel sides and height of trapezoid reflectors based on the screen’s dimension. The proposed formula has been tested using various types of images with acrylic based reflectors. The validation results show that the proposed formula achieves a better result by examining the projected image and dimension of the acrylic reflectors.
Peer Group and Hybrid Vector Filter for Removal of Impulse Noise in Color Images

In this paper, we propose a peer group and hybrid vector filter for removal of impulse noise in color images. The proposed filter employs two rounds of noise detections and the image restoration. Through the processing of the proposed filter, the noises in images can be efficiently removed, but preserving the edge. When the density of the random-value impulse noise is equal to 10%, the peak signal-to-noise ratio (PSNR) is 31 dB. Although the PSNR of the restoration result is not as expected when the noise density is more than 70%, it can be still recognizable by humans compared with other existing filters. Obviously, the proposed filter can effectively remove impulse noises and preserve the edge of the image.
A Comparative Study of Big Data Processing: Hadoop vs. Spark

Apache Spark and Hadoop’s MapReduce are two very important tools used for Big Data processing. The processing started with Hadoop’s MapReduce Framework but suffers from many disadvantages due to multiple disc processing operations. The drawbacks of the traditional big data processing have been overcome by in memory handling framework like Spark. In some aspects they go hand in hand as due to lack of file system in Spark, it needs to depend upon MapReduce. This paper has shown the extensive study on various tools related to Big Data processing and has done extensive comparison on MapReduce Vs Spark. The frameworks have been studied on real time datasets and finally compared in terms of processing time. Spark showing the remarkable improvement over MapReduce.
Improvement of Employment Employees Through Prescriptioned Children

This article reveals the role of theatrical performances in the education of such qualities as diligence, love for the homeland, care and sympathy for others, respect for elders, ability to see and observe the beauty around in preschool children. This article reveals the role of theatrical performances in the education of such qualities as diligence, love for the homeland, care and sympathy for others, respect for elders, ability to see and observe the beauty around in preschool children.