Reviewerly/exHarmony huggingface dataset:
A Novel Robust Outlier Classification Founded On Localised Rank-Ordered Logarithmic Differences For Fix-Value Impulsive Noise

Outlier classification founded on Localised Rank Ordered Difference (LROD) technique, which is developed from Rank-Ordered Absolute Differences (ROAD), has been presented since 2015 therefore this LROD technique becomes one of the ultimate efficient outlier classification from its performance. This paper proposes the novel and robust outlier classification founded On LROLD (Localised Rank-Ordered Logarithmic Differences) technique, which is developed from LROD and Rank-Ordered Logarithmic Differences (ROLD), which is more efficient than LROD, for Fix-Value Impulsive Noise (FVIN). From the experimental simulation on three testing portraits: Girl, Pepper and Lena, the robust outlier classification founded on LROLD technique has the better efficiency than the previous techniques: LROD technique and ROAD technique at plentiful distribution of FVIN.
Theoretical Quantum Immortality and its Mathematical Authority

Abstract Quantum Immortality usually refers to, in a classical sense, a person who is “lucky” enough to survive in any incident in the world. Such a quantity of luckiness is even big enough to keep that person away from the aging of the human body. That is, in fact, when we say that one becomes “immortal”. Throughout the history of the world, there have been many times when people tried to find out the secret of anti-aging, but this ambitious dream, obviously, has failed every time. This paper will discuss quantum immortality in theoretical and mathematical aspects and evaluate its authority, or possibility, of its existence in real life. In order to accomplish this evaluation, the concepts of the main two interpretations of quantum physics: 1)Copenhagen Interpretation(or non-MWI interpretation), and 2)Many Worlds Interpretation(or MWI)will be necessarily involved. Throughout this paper, the MWI is proven to support the theoretical quantum immortal state, which, in this paper, means to achieve an expected theoretical outcome in the Everett box experiment. In order to show how MWI will give its credit to quantum immortality, the idea of the Infinite Monkey experiment is demonstrated in detail. After that, discussions of the computable probability of the outcome of the Infinite Monkey theorem and its pathological distribution are involved. Regardless of the situation in the Everett box, whether it is pathologically distributed or not, the mathematical outcomes show that MWI will be the only possibility of achieving the quantum immortal state during the Everett box experiment, or quantum gun experiment. Finally, the paper brings out the evaluation of quantum immortality in a real life stance.
Secured Health Data Access in Cloud Computing Using Multiple Attribute-Based Encryptions

In the fast growing world using new Cloud computing technology. In the terms of Sensitive Data Access from the remote cloud computing storage with different users using security measures to avoid the unauthorized users. Even though so many uses in the Cloud, it leads to lot of issues such as in the Data Access of the sensitive data and encryption still remain challenging. To overcome with these issues, In this novel paper focus on multiple attribute-based encryption which features the data access in secured way with different users in the Cloud Data. The proposed system enables on secure Data Access by using the MABE scheme.
Convolutional Autoencoder for Anomaly Detection in Crowded Scenes

Video analysis is an active research area in the computer vision domain. From a security point of view, Anomaly detection is an important topic in video analysis. Anomaly detection means identifying unusual activities. It is hard for humans because these unusual activities rarely happen and are hard to detect in crowded scenes, especially if there were many cameras. In this work, an automated approach is proposed to detect anomalies. The proposed work is divided into two main steps: (a) background removal, and (b) anomaly detection. The background removal step separates the background from each frame. To detect anomalies, a convolutional neural network model is used to detect which frames have anomalies. The proposed approach is verified on UCSD benchmark datasets that are devised to detect anomalies in crowded scenes. Experimental results of contemporary methods are validated.
Research on the Construction of Functional Physical Training Method System for Civil Aviation Safety Officers Based on Computer Empirical Analysis

Abstract The current functional physical training method system for civil aviation safety officers still has many disadvantages, which is difficult to effectively guarantee the healthy development of civil aviation. Based on this, this paper first analyses the current situation of functional physical training of civil aviation safety officers with the help of computer, then studies the problems existing in the functional physical training method system of civil aviation safety officers, and finally gives the construction strategy of the functional physical training method system of civil aviation safety officers.
Teaching Method of “Java Programming” Course Based on Textual Research

Java technology is widely used, and its market share has been high in recent decades. In Colleges and universities, “Java programming” course is the preferred course of computer software specialty, and is the core course of some vocational skill level certificates. Combining with the content of the course, teachers' teaching objectives and students' learning direction can be clear, and various teaching methods and modern teaching methods can be used to improve students' autonomy, so as to achieve the best teaching effect.
Multi-Scale Amplitude Spectrum Substitution for Visual Saliency Detection

Saliency detection is among the fundamental issues in simulating human visual mechanism. In this paper, we propose a new framework called Amplitude Spectrum Substitution (ASS) to summarize typical frequency-based saliency models. We then present a saliency detection model by applying the ASS method to a multi-scale structure, where substituted amplitude spectrums of all scales are trained from plenty of natural images. We further provide a novel measurement of global saliency called Salient Feature Number (SFN) to find out the optimal saliency map in the proper scale. Experimental results clearly demonstrate that the proposed model outperforms fifteen state-of-the-art competing models in predicting human fixations.
Research and Application of Big Data Analysis in Different Levels of Tennis Players' Serve

Objective: To explore the differences between different levels of tennis players' strong serve technique. Methods: By means of Qualisys, Kistler and High-speed photography, the relevant data of serving technical movements of two male professional tennis players and ten amateur tennis players in China were analyzed by using big data. Results: In the throwing stage, the maximum flexion angles of both knee joints of professional athletes were significantly smaller than that of amateur athletes (p<; 0.05, p<; 0.05). In the stage of pushing and turning, the maximum angular velocity of knee joint of the tested professional athletes was significantly higher than that of amateur athletes (p<; 0.05, p<; 0.05), and the maximum ground reaction force recorded in Z axis was significantly higher than that of amateur athletes (p<; 0.05). During the service process, the maximum external rotation angle of the upper arm of the professional athletes was significantly greater than that of the amateur athletes (p<; 0.05); the maximum shoulder and trunk torsion angles were significantly greater than those of the amateur athletes (p<; 0.05, p<; 0.05); the knee joint angle was significantly smaller than that of the amateur athletes (p<; 0.05). In the stage of swing, the maximum angular velocity of hip rotation, shoulder rotation, elbow extension, forearm internal rotation and racquet-head speed were significantly higher than those of amateur athletes (p<; 0.05, p<; 0.05, p<; 0.05, p<; 0.01, p<; 0.01); the impacting point height recorded was significantly higher than that of amateur players (p<; 0.05) The ball speed was significantly higher than that of amateur players (p<; 0.01). There was no significant difference in the other serve technical movement mechanics indexes. Conclusion: The main differences between different levels of tennis players are the range and speed of lower limb push, the amplitude and speed of trunk rotation, the rotation speed of hitting arm, the speed of racquet-head and the height of impacting point.
Complex Action Segmentation in Compressed Videos

Complex action segmentation aims to detect what actions and when they happen in fine-grained level from long videos. Despite the fact that videos are often stored in a compressed format (e.g., MPEG-4), most existing approaches are proposed to directly model raw RGB videos: when only compressed videos are accessible, they have to first decode these videos, which is very time-consuming. In this paper, by explicitly leveraging the ‘compressed’ characteristic of compressed videos, we are the first to address the challenging task of complex action segmentation in compressed videos. To extract meaningful representations for complex action segmentation, we introduce the GOP-Level Compressed features (Golec), which can be obtained directly from compressed videos without video decompression. Importantly, by taking GOPs as the atomic units of actions, our Golec representation is intrinsically suitable for fine-grained action segmentation. Moreover, to remedy the coarser motion vectors (compared with optical flows which are computed from raw frames) used in our Golec representation for capturing the temporal context, we propose a new Bi-path knowledge distillation strategy. Extensive experiments show the effectiveness of our Golec representation and the Bi-path strategy. Importantly, our proposed model for complex action detection not only runs 5.2 times faster but also achieves significantly better results than the state-of-the-art alternatives using raw videos.
Focused Web Crawlers on Domain-Specific Retrieval Systems

Abstract The need for a large and growing internet has formed a new culture that symbolizes widespread dissemination of knowledge, information and data. Everyday hundreds of pages as well as new information were added. deleted and modified. Search engines is a finding information tool. First step of search engines is web crawler, a process crawling webpage to obtain information about its content. Focused web crawler is one of web crawler that scrapping certain relevant webpages to given topic and ignores others that have nothing to do with. Indonesia is an archipelago that number of island reached 17.491. where it makes Indonesia rich of different kinds of travel, culture and food that is characteristic of each region. The amount of information available does not rule out possibility that there are some food recipes do not include staples used so that web crawlers are needed to find out the ingredients. With this application, it is expected to facilitate the search for information on various processed recipes with meat-based ingredients from all regions in Indonesia.
Human Anomalous Activity Detection: Shape and Motion Approach in Crowded Scenes

Abstract Detecting anomalous activities in crowded scenes is a very challenging task in computer vision. An enhanced video anomaly detection framework is proposed for frame-wise anomalous activity detection in crowded scenes that is based on both shape and motion based features. The Histogram of Oriented Gradients (HOG) is used to represent the shape based features of the video frames and for representing the motion, Histogram of Oriented Optical Flow (HOOF) is used. These features are modeled using two-class Support Vector Machines (SVM) to detect abnormal events in every frame. The proposed method is modeled with both normal and abnormal behaviors which are learnt from the training data and it is capable of detecting abnormal activities in a live surveillance video. To evaluate the performance of the proposed work, experiments are conducted on the standard benchmark UCSD data set and the results are compared with the HOOF feature bin values.
Sakai-Kasahara IBE

Public key cryptography (PKC) provides a very robust encryption in networking and electronic communication. The strength of PKC comes from the idea of paired keys that are independent (but mathematically dependent). The encryption-decryption process of PKC requires both parties of communication, i.e., sender and receiver, to provide each other with its public key and the digital certificate of authority, and each party has to keep a directory to store all parties’ public keys so these requirements are considered as drawbacks of PKC. To overcome these drawbacks, the identity-based encryption (IBE) came to existence. IBE is a form of PKC which uses a third-party server to distribute the public parameters to all the parties and extract the private key from the arbitrary public key. To encrypt the message, the sender will use the receiver public key, and to decrypt the message, the receiver will use the extracted private key. In this chapter, we discuss the Sakai-Kasahara IBE and how it differs from other IBE schemes. The additive, multiplicative, and full schemes of IBE are explained along with the encryption and decryption process. The security of this scheme is also discussed and proved.
A Refined NMS Scheme Based on Center Distance and Corner Distribution

Non-maximum suppression (NMS) is a significant post-process step in most object detection frameworks, the main task of which is to find the most optimal inferences. However, the low correlation between the classification confidence score and the location accuracy of bounding box severely affects the performance of object detector. Recently, several novel loss functions about the overlap rate of candidate bounding boxes have been proposed to reduce the negative impact of the low correlation problem, but there is no solution to pay attention to the influence of the background area in bounding box on NMS. In this paper, we present a refined NMS scheme based on the distribution of corner feature and the center distance of bounding box. The correlation between confidence score and location accuracy is optimized through reducing the impact of background area in bounding box. Specially, we first construct the corner feature and reorder the confidence score by its distribution. And then, a dynamically NMS threshold strategy is built by a center distance function. Extensive experiments are conducted on the Pascal VOC and the mean Average Precision (mAP) of FSSD is improved from 78.8% to 80.1%. What's more, the proposed method can be easily embedded into most of object detection frameworks without any extra training.
Improvement of Data Fusion with Threading Technology in Home UbiHealth

According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents' health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. In some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. However, the fusion manipulations of large data volumes within strict time limits impose significant computational overhead. In the present work, threading technology is employed to take advantage of the processing capabilities of modern computers for the data fusion of the contextual parameter sensor readings, along with the selection of appropriate computing architectures and matching algorithms. The advantages offered by the proposed approach are presented and analyzed.
A Study on the Reconstruction of ‘Family’ in Elementary Social Studies Curriculum Based on the Principles of Curriculum Construction

The purpose of this study is to identify problems with the arrangement of ‘family’ in the elementary social studies curriculum, and then seek ways to solve them. To this end, the results of analyzing the curriculum by the principles of curriculum composition, such as sequence, integration, and learner adequacy are as follows. The‘family’ has secured sequence and continuity, however, the content elements did not have a meaningful sequence. It was not integrated not only between subjects but also within social studies. As a result of the questionnaire survey analysis, there is no difference in the degree of understanding of learners who learned family in social studies and learners who learned family in integrated curriculum. It did not conform to the principle of learner adequacy. In this study, we propose as follows. First, differentiate the ‘family’ content in social studies from other subjects. Second, arrange the timing and contents appropriately to enable integration between subjects. Third, the contents of ‘family’ are distributed through integration within the curriculum, do not organize it as a unit.
Application of printing defects detection based on visual saliency

Abstract Visual saliency means that when humans observe a certain area, humans automatically process the area of interest and selectively ignore the uninterested area. This local area is called the saliency area. In recent years, people have introduced image processing and pattern recognition detection methods based on visual saliency to simulate the biological visual attention mechanism, and achieved good results. This paper focuses on the process of printed product defect detection, analyzes the application of visual saliency in printed product detection in recent years, summarizes and compares the advantages and disadvantages of various printed product detection methods, and discusses future development trends.
Formation of Organizational Abilities in High Schol Students as a Requirement of New Pedagogical Thinking

In order to gain an active position in society in the new pedagogical thinking, an individual must have a number of qualities, skills and abilities. It is important to be enterprising, independent and active. Of course, at the same time he must have organizational skills. Students who acquire basic organizational skills in childhood, and then develop these skills in secondary schools and universities and turn them into organizational abilities, will play an important role in society in the future, will successfully lead departments and enterprises, teams. The article considers the formation of organizational skills in high school students as a requirement of new pedagogical thinking.
Performance Comparison of Algorithms Involving Automatic Learned Features and Hand-Crafted Features in Computer Vision

Three case studies in this report provide an analysis between two different machine learning algorithms in computer vision (CV) systems, with the difference in automatic feature learning as found in Deep Learning (DL), and manual hand-crafting feature extraction/selection, as found in traditional CV methods. Furthermore, this report focuses on state-of-the-art between Convolution Neural Networks (CNNs) and Scale-Invariant Feature Transform (SIFT) related-models.In the first case study, a CNN algorithm is trained by a set of 8000 images to binary classify 2000 test images. Adding more epochs and layers to the CNN indicates an increase of performance for the training set and testing set with a trade-off in increased system complexity. In the second case study, an image retrieval system is implemented using SIFT, SIFT-filtered, and CNN algorithms to evaluate an Oxford-5 k dataset. Results using a baseline SIFT and SIFT-filtered algorithm show the mean average precision (mAP) performance to be better than that performed by the CNN algorithm. In the third case study, different datasets are evaluated for image instance retrieval using the CNN and the Fisher Vector (FV) involving SIFT descriptors. Depending on the dataset being evaluated, a baseline FV shows the mAP to be comparable in performance to that of the CNN.
Fuzzy Propositional Configuration Logics

We introduce and investigate a weighted propositional configuration logic over De Morgan algebras. This logic is able to describe software architectures with quantitative features especially the uncertainty of the interactions that occur in the architecture. We deal with the equivalence problem of formulas in our logic by showing that every formula can be written in a specific form. Surprisingly, there are formulas which are equivalent only over specific De Morgan algebras. We provide examples of formulas in our logic which describe well-known software architectures equipped with quantitative features such as the uncertainty and reliability of their interactions.
Selection Biased Positive and Unlabeled Learning Method for Anomaly Detection in Surveillance Videos

Anomaly detection in surveillance videos aims at identifying abnormal event under specific scenarios and it is widely applied in public security, smart city, and pedestrian surveillance. In the weakly-supervised setting, most existing anomaly detection approaches are formulated as the classic multiple-instance learning problem. In this paper, we provide a unique perspective that selection biased positive and unlabeled learning. In such a viewpoint, as long as estimating the label frequency from training set, we can effectively apply supervised classifier tow eakly supervised anomaly detection, and take greater advantage of these well-developed classifiers. For this purpose, we present a novel method to estimate label frequency from the attribute subdomains with large label probability. In the test phase, we only use the label frequency to modify the supervised classifier. Comprehensive experiments are performed on different scales datasets. Our method provides superior on all dataset which demonstrate the effectiveness.
Water Treatment Installation Network Design In Kali Veda Housing - Merauke

Abstract The rapid population growth that makes Merauke Regency need to be anticipated, especially in the amount of waste that can damage infrastructure such as roads, bridges, drainage, good health. To find out the dimensions of a wastewater installation by considering the capacity of the house needed from a house in the planning location. The research method used is the calculation, calculation and design According to the population to Obtain the population, the dimensions of construction of the WWTP and the design results According to the population. The results of the study of wastewater in the residential area of Kali Veda in Merauke vary greatly, ranging from floating in light gray, brown to black, starting from the smell of waste water (replace). The amount of wastewater is determined to be 60-80%. The value of community sanitation in the score is not good Because The ideal average value (Mi) is &lt;37.25 with a value of 37.13%. Communal wastewater with a plan for 20 years. The population is 1,875 people, the criteria is 80 L / person / day. The average discharge is 42 liters / day / person, peak discharge is 75.6 liters / day / person. Processing capacity of 142.8 m3 in 2037. The design of the fat separator volume is 29.54 m3, the reservoir Leveling / wastewater design has a volume of 47.27 m3, the initial settling tank has a volume of 29.54 m3, settling volume 29, 54 m. Filter media is used as a medium for the plastic material, durable, has a large specific area, light and Also has a large volume of the cavity so that the deadlock media is very small. The pipe diameter of (black water) is 100 mm, material from PVC, Pipe diameter (gray water) is 100 mm, material from PVC. The diameter of the main pipe is 150 mm.