Reviewerly/exHarmony huggingface dataset:
On Universality and Training in Binary Hypothesis Testing

The classical binary hypothesis testing problem is revisited. We notice that when one of the hypotheses is composite, there is an inherent difficulty in defining an optimality criterion that is both informative and well-justified. For testing in the simple normal location problem (that is, testing for the mean of multivariate Gaussians), we overcome the difficulty as follows. In this problem there exists a natural “hardness” order between parameters as for different parameters the error-probabilities curves (when the parameter is known) are either identical, or one dominates the other. We can thus define minimax performance as the worst-case among parameters which are below some hardness level. Fortunately, there exists a universal minimax test, in the sense that it is minimax for all hardness levels simultaneously. Under this criterion we also find the optimal test for composite hypothesis testing with training data. THIS criterion extends to the wide class of local asymptotic normal models, in an asymptotic sense where the approximation of the error probabilities is additive. Since we have the asymptotically optimal tests for composite hypothesis testing with and without training data, we quantify the loss of universality and gain of training data for these models.
Detection of natural disaster victims using You Only Look Once (YOLO)

Abstract Natural disasters are events that cannot be predicted both by location and time of occurrence. Natural disasters cause property losses and can even take lives. The handling of rapid evacuation must be done by the SAR team to help victims of natural disasters to reduce the amount of loss. But in reality, there are many obstacles in the evacuation process. Starting from facing difficult terrain to necessary equipment limitation. In this research, a system designed to detect victims of natural disasters uses image processing where the picture is carried out using a drone that aims to help find victims in delicate or vulnerable locations when reached directly by humans. Based on this background, this research proposes the development of a method for the detection of victims of natural disasters that aims to assist the SAR team and natural disaster volunteers in searching for victims who are in hard to reach places. The You Only Look Once (YOLO) method is implemented using the python programming language related to image processing. From the research that has been done, the accuracy result of detecting objects of disaster victims is 89%.
Research on an evaluation method for home-based network teaching quality based on analytic hierarchy process

Students can't go to school during COVID-19 pandemic, so schools across China actively responded to the call of Ministry of Education to “stop classes without stopping studying”, that is, through the online platform, teachers can teach and students can study at home. Therefore, it is important to evaluate home-based teaching quality via Network. Based on the analytic hierarchy process, this paper selects five indicators, i.e. learning users, teaching content, teaching methods, teaching platform and teaching effect, to build an evaluation system of online teaching effect and evaluate online teaching quality. It is shown that the impact of the home environment under the student user criterion module, the perfect teaching platform function under the teaching platform criterion module, and the student learning requirements under the student user criterion module have the most significant impact on the effect of online teaching in the evaluation system, while the basic knowledge is less influential. The evaluation system provides technical support for the evaluation of online teaching effects.
A Cascaded Unsupervised Model for PoS Tagging

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.
Análisis de usabilidad web a través de métricas estandarizadas y su aplicación práctica en la plataforma SAEFI

Today, all Universities and Educational Institutions seek to achieve quality in their services. In this project, it analyzes the performance of a school management web application, taking into account “the quality of the product and the quality in use”, according to ISO 25010: 2011, in order to have the data to improve or optimize the system. A methodology for the evaluation of usability is proposed, applying two evaluation methods: the empirical method contemplates the design and application of a data collection instrument that evaluates indicators and metrics by users of the web application; and the heuristic method that discriminates and applies specific use software tools that automatically evaluates the web application. The usability evaluation in its first phase in the empirical method on the product quality model gives us a linear trend between 60% and 80%; in the second phase about the heuristic method on the quality of use with respect to accessibility, it obtained compliance values between 32% and 93%, and in speed from 71% to 99%.
Data-driven feature learning for myocardial registration and segmentation

This chapter discusses novel techniques for the tasks of segmentation and registration separately and jointly. In particular, the feature learning is tested on cardiac phase-resolved blood oxygen-level-dependent (CP-BOLD) MR images, which is a new contrast agent- and stress-free imaging technique for the assessment of myocardial ischemia at rest. CP-BOLD MRI introduces varying contrast in medical image analysis applications. Therefore, establishing voxel to voxel correspondences throughout the cardiac sequence, an inevitable component of statistical analysis of these images remains challenging. Furthermore, medical background and specific segmentation difficulties associated to these images are present. Alongside with the inconsistency in myocardial intensity patterns, the changes in myocardial shape due to the heart's motion lead to low registration performance for state-of-the-art methods. The problem of low accuracy can be explained by the lack of distinguishable features in CP-BOLD and inappropriate metric definitions in current intensity-based registration and segmentation frameworks. In this chapter, sparse representations, which are defined by a discriminative dictionary learning approach, are used to improve myocardial segmentation and registration. Initially appearance information is combined with Gabor and HOG features in a dictionary learning framework to sparsely represent features in a low-dimensional space. For registering, the cardiac sequence a new similarity metric is proposed utilizing the sparse representations. Also, an alternating optimization scheme for dictionary learning-based feature representations is proposed using the sparse coefficients and dictionary residuals. The superior performance of the dictionary-based descriptors are showcased with several experimental results.
Use of Bots to Support Management Software Development and Streamline Client/Producer Communication in the 5.0 Industry

Bots built on management software platforms are a new tool to improve daily communication between users of the same application. Its use shows advantages thanks to the possibility of automating the process of communication, production and distribution of products in general, and even to follow live events or news information that is published through the respective modules of the management systems. Gradually, companies are adapting their strategies to the use of these tools and process automation. This research presents the results of a discussion group with forty-six professionals in the software developer, which aims to analyze the bot phenomenon in Ecuador. The initial results reveal that software development and the industry are in a stage of adoption of this technology, although the first success stories can already be seen. The discussion group was formed taking into account the experience in the area of software development and the practice in the implementation of bots.
“Plants and their functions” e-module based on flip book

Abstract The purpose of this research was to describe the development of E-module based on flip book. This research and development used ADDIE (analysis, design, development, implementation, and evaluation) Model. However, the researcher stopped at the development stage. The result of this developmental research is producing an e-module using the Ncesoft flip book Maker application. According to the evaluation from the material and language experts, the developed e-module obtained an average score of 80% and from the media expert, it obtained 84%. Thus, the developed media was considered “Valid”. The development of e-module based on Ncesoft flip book Maker can help children to learn Plants and their functions.
Density-Based Population Initialization Strategy for Continuous Optimization

The population initialization is the first and crucial step in many swarm intelligence and evolutionary algorithms. In this paper, we propose a new density-based population initialization strategy, which is concerned about both uniformity and randomness of the initial population. In experiments, first, the empty space statistic is adopted to indicate its favorable uniformity. Then, the proposed strategy is used to generate an initial population for CMA-ES, and compared with typical initialization strategies over the CEC-2013 multimodal optimization benchmark. The experimental results demonstrate that the density-based initialization strategy could generate more uniform distribution than the random strategy, and such a strategy is beneficial to evolutionary multimodal optimization.
Evaluate of digital book criteria using fuzzy analytical hierarchy process

Abstract Recently, digital technology is developing rapidly. Digital technology also needs to be developed in the field of education, especially Digital Book. In evaluating digital books, stakeholders need appropriate criteria. Therefore, this study aims to evaluate the criteria of an excellent digital book by using a fuzzy analytical hierarchy process. A digital book evaluated is elementary school mathematics learning books by considering aspects of Azaz Pancasila. The criteria evaluation method uses a fuzzy analytical hierarchy process. Evaluation criteria used are easy to use, multimedia, interaction. The evaluation results of digital book criteria show that easy criteria have the highest importance. Furthermore, multimedia and interaction criteria have the importance of the second and third importance. This research implies that the selection of the right criteria can be a reference in the development of digital books.
Image Steganography using Hybrid Soft Computing Techniques–A Survey

The technique of concealing information within photos, videos, music etc. is called image steganography. Steganography excels in that it does not disclose the presence of any data as opposed to other methods such as cryptology which only try to prevent reading the said data. A major factor in assessing the steganography algorithms is its security. Steganography has subsequently earned remarkable ground in the drawn-out battle with steganalysis. Steganography must be able to resist identification by steganalysis algorithms in order to boost the reliability of image steganography. Conventional steganography methods embed secret data into an image, which eventually leaves a sign of alteration that can be detected by increasingly propelled AI-based steganalysis algorithms. In this survey, various methods for hiding sensitive data within pictures using a combination of different soft computation techniques have been explored. This is aimed at evading detection by modern steganalysis tools and achieving higher quality output.
Building Image Classification Using CNN

A dataset is being prepared with at least 1000 number of images of different buildings. Convolutional neural network(CNN) is used in this study to classify a building image from rest of the others. With around 85 percent data being used in the training set, the model is trained. It has obtained around 80 percent accuracy in all cases. Results are obtained in three different sets of data. It also shows a comparison study to show the change in validation, loss and accuracy with respect to the training step.
Accelerated Stereo Vision Using Nvidia Jetson and Intel AVX

Stereo vision is a low cost and passive mechanism to perceive the environment for robotic applications. The huge compute requirements of stereo vision algorithms have been a major challenge for their usage in real world applications on small robots. Standard stereo depth estimation algorithms Sum of Absolute Differences (SAD), census transform and an advanced algorithm Semi-Global Matching (SGM) are discussed in this work. This paper presents novel real time implementation of these three stereo vision algorithms on two different compute platforms i) Intel AVX (Advanced Vector Extension) and ii) Nvidia Jetson GPU (Graphical Processing Unit). The Intel CPU implementation of stereo algorithms is optimized by using OpenMP (Open Multi-Processing) for multi-threading, AVX registers for vectorization and several other novel ideas for real time processing. Nvidia Jetson implementation is efficiently designed for maximum speed-up on a low end GPU such as Jetson TK1. Post processing steps such as local extrema detection, left-right consistency and median filter are used to improve the final disparity image. We have achieved speedup of the order of 30x when compared with naïve CPU implementation.
Chaotic key generators for data security

Abstract The cryptography approach plays a very major role in securing systems by providing authentication, integrity, and confidentiality for the data. In recent decades, the cryptographic systems have provided novel and efficient ways of encryption. Most of these cryptographic systems need unpredictable entities-Keys. Hence Random number generator is an essential component for cryptography. In cryptography, generating random numbers is an important task, not only for generating cryptographic keys, but also for implementing cryptographic algorithms and protocols. This paper presents chaotic analog circuits to generate random numbers that is used as keys in the conventional cipher to protect the image data.
Fringe Center Extraction for Free Surface Measurement Based on R Channel

The accuracy and stability of laser fringe center extraction are very important for surface measurement in 3D measurement based on linear structured light. At present, most algorithms of fringe center extraction are easy to have the problem of line breaking and are sensitive to strong light; The complex high-order free-form surface is taken as the experimental object in this paper. On the one hand, due to its high-order derivability at any point on the surface, the light scattering problem is serious and this leads to the line breaks at the slope surface. On the other hand, in order to realize complete measurement of free-form surface, strong illumination condition is needed but most fringe center extraction algorithms are sensitive to strong light. To solve the above problems, this paper proposes a gray gravity center method merging R channel. The algorithm firstly carries out median filtering operation on the fringe image, then extracts the R channel and obtains the binary image by using the fixed threshold segmentation technique. In order to improve the accuracy of extraction, morphological operation is carried out for the binary image to remove the interference area. In order to further improve the real-time performance of processing, contour processing is used to find the target stripe area and gray gravity center method is carried out in the area. Experimental results show that the proposed method has good real-time performance and resistance to line breaks and strong light.
Prospects for the Online Education Promoting the Reform of Traditional Higher Education Under the COVID-19 Situation

With the development of educational technology modernization, online education is booming in recent years. E-learning is a method of content dissemination and fast learning through the application of information technology and Internet technology. With the rapid development of information technology, especially from the Internet to the mobile Internet, it has created a way of life, work and learning across time and space, which has changed the way of knowledge acquisition. Teaching and learning can be independent of time, space and location, and access to knowledge has become flexible and diversified. The rapid global spread of COVID-19 at the beginning of 2020 has affected about 190 countries around the world by suspending offline classes and affecting the educational progress of more than 1.5 billion students. Most schools in China try to provide courses through online education, which has a great impact on the traditional education model. Based on the analysis of the origin and development of online education, this paper discusses that online education has promoted the rapid transformation of traditional higher education and looks forward to the trend of the transformation of China's higher education after the COVID-19 disease.
Receptive Field Size Optimization with Continuous Time Pooling

The pooling operation is a cornerstone element of convolutional neural networks. These elements generate receptive fields for neurons, in which local perturbations should have minimal effect on the output activations, increasing robustness and invariance of the network. In this paper we present an altered version of the most commonly applied method, maximum pooling, where pooling in theory is substituted by a continuous time differential equation, which generates a location sensitive pooling operation, more similar to biological receptive fields. We present how this continuous method can be approximated numerically using discrete operations which fit ideally on a GPU. In our approach the kerned size is substituted by diffusion strength which is a continuous valued parameter, this way it can be optimized, by gradient descent algorithms. We evaluate the effect of continuous pooling on accuracy and computational need using commonly applied network architectures and datasets.
Dynamic Relationship of Cryptocurrency Prices and Investor Attention

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DOES PLAYING LOCATION-BASED AUGMENTED REALITY GAME INCREASES THE LEVEL OF PHYSICAL ACTIVITY?

Recently, there are an increasing trend in location-based augmented reality (AR) games that require players to move around physically to acquire the in-game features as well as game bonuses. The introduction of this location-based augmented reality (AR) games, specifically, Pokémon Go, has made the players physically move around to achieve higher levels and indirectly, improves the level of physical activity. Thus, the objective of the current study is to examine the association between the time spent playing location-based AR games specifically Pokémon Go, and the level of physical activity of the players in Malaysia. A self-administered questionnaire was circulated among Pokémon Go players and based on the inclusion and exclusion criteria, 47 players were recruited in the study. Global Physical Activity Questionnaire (GPAQ) was used to identify the level of physical activity. The association between time spent playing Pokémon Go and level of physical activity were examined using the Chi-square test. The results of the current study showed no significant association between days spent playing Pokémon Go and level of physical activity (p = .14), hours spent playing Pokémon Go and physical activity (p = .516), or between daily hours spent playing Pokémon Go and daily sedentary time (p = .283). Nevertheless, the mean of the study reported that the physical activity level of the players increased concurrently as the player’s game frequency increases. Further studies are required to shed light on how location-based AR games can be implemented as potential strategies to engage an active lifestyle.
One Shot Learning Based Human Tracking in Multiple Surveillance Cameras

Video surveillance based human tracking is rapidly developing field in the Computer Vision domain, which is basically aimed at automatically detecting of abnormal activities and securing high alert areas like airports, frontiers, public places, banks, etc. Through this research work, we propose an unsupervised method of human tracking in multiple surveillance cameras using one shot learning. Facial feature extraction and detection is carried out using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM), and further Siamese Network combined with GoogleNet is used for learning, differentiating and tracking. The proposed model is computationally efficient because the training phase requires less GPU time and avoids re-training whenever new individual is added into the system, and also very useful when only few images of a person are available. The experimental results show that the accuracy of tracking is not compromised even in case variable illumination and partial facial occlusion.