Reviewerly/exHarmony huggingface dataset:
Health Management for Next-gen Blockchain: Smart Construction, Dynamic Evolution and Stochastic Transformation

In this paper, we propose the conceptual framework, formal methods, techniques and issues of Blockchain-oriented Health Management (BHM). Regard blockchain as complex system. Analyse the challenge from uncertain environment and the requirements for smart construction and continuous evolution. BHM is an environment-sensitive intelligent system within blockchain, aimed to keep the blockchain system perpetual health by drive it evolve dynamically, continuously and uncertainly. We formalize BHM as to solve dynamic multiple equilibrium optimization problem in uncertain problem domain and output random solution. We create Nondeterministic Perpetual Automata (NPA) to formalize the next-gen blockchain. Organically combine mimic defense, parallel intelligence, Prognostics and Health Management (PHM), together with agent-based evolution to make the technology framework, and apply BHM to smart city cyber-physical-social system. BHM can be a novel area.
A De-redundant Network with Enhanced Classifier for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is a challenging problem, which requires the model to classify both the categories that have appeared in the training set and those that have not. Existing generative models usually generate a large number of samples for unseen classes to tackle the serious class bias problem in GZSL. However, in the existing fine-grained data sets, the appearance of images in different categories exhibit slightly differences, which challenges the ability of models to generate discriminative examples and will certainly deteriorate the performance of GZSL. In order to tackle this problem, this paper integrates a de-redundant network (DR) and an enhanced classifier (EC) into a new model (DR-EC) to learn a feature space on which redundant information is removed and category information is enhanced. Specifically, the proposed model employs the de-redundant network to remove the redundant information from the original visual features and uses the enhanced classifier to make the non-redundant information in learned redundancy-free feature space more prominent. We evaluate our DR-EC model on three fine-grained benchmark datasets (i.e., CUB, SUN, and FLO) in GZSL. The results show that our DR-EC approach can outperform state-of-the-art methods with significant improvements.
Preventing Financial Illegality and Crime by Using Internet Technology

In this paper, we believe that the development of Internet technology makes the form and scale of financial crimes change rapidly. How to prevent financial crimes from causing losses to enterprises and individuals under the new technology is a problem we need to solve. We note that ensuring the practical application of Internet technology in the financial field is a key measure to prevent financial crimes, especially Internet financial crimes. We need to master the application of Internet technology in the financial field to prevent financial crimes at this stage.
Change and Logic

With the additional background on the operation of Logic in Reality in hand, we will apply it to some specific additional familiar categories of change. Brenner demonstrated in 2017 that the principles of the Lupasco logic recapture something of the unity of knowledge present in classical Western thought and provide a bridge to a new picture of knowledge that includes the Chinese. As necessary complementary background, we now include Sections on major Western precursors of Lupasco. Some major philosophical, scientific and logical issues connected with change–chance, continuity, epistemic change and ontological change—will be discussed in this Chapter. As one example, we place our logic in relation to the Philosophy of Vacuum!
Left Truncated of Mixture Topp-Leone and Exponential Distribution with Estimation by Maximum likelihood

In this research, which contains three parameters, where a truncated state was taken from the left and a probability density function was found, as well as the moment function and the survival function, using the estimation method is the maximum likelihood estimation (MLE). And draw the figure of some of the mentioned functions using the Matlab program as well as estimating the values for the parameters.
Bookending the Enlightenment: Scandinavia’s first novel and the Anthropocene condemnation of its TV adaptation

Niels Klim’s Underground Travels (1741) was the European breakthrough for the Norwegian Enlightenment polymath Ludvig Holberg. The emerging novel format inspired Holberg to trust his readers to use their own rationality to decide on the contentious issues of their era. The intellectual contrarian had always been sceptical of his contemporaries’ ability to reason, but he died content that his writings had made a positive impact. Over two centuries later, a Danish TV adaptation of Niels Klim casts a more misanthropic verdict. The mini-series concludes that humanity lacks reason and is an environmentally disastrous mistake. This article compares narrative and thematic argument in these two works to explore the evolution of Western views on rationality, nationality, gender, and environmentalism. If it is the case that, as the TV adaptation and many modern critics suggest, human reason is unlikely to solve the twenty-first-century’s existential threats, what is the alternative for humanity?
Proxy-based Losses and Pair-based Losses for Face Image Retrieval

Distance metric learning has been considered an effective strategy to represent data in computer vision problems such as image retrieval and face verification. Metric learning attempts to minimize a loss function in order to transform data into a more optimal representation for further applications. In this paper, we compare 4 different types of loss functions, e.g. 2 pair-based losses (Contrastive loss and Triplet Margin Ranking loss), and 2 proxy-based losses (Proxy-NCA loss and Proxy-Anchor loss) in a multi-class classification task. Our experiments show that the Proxy-Anchor loss could achieve 70.8% accuracy on average compared to the Proxy-NCA loss, Triplet Margin Ranking loss and Contrastive loss which could only achieve 65.5%, 62.2%, and 36.6% respectively. Furthermore, we also present the qualitative results using high dimensional plot visualization in order to evaluate data distribution and sample image retrieval results. Overall, the Proxy-Anchor loss performs better than the other losses in terms of accuracy, recall, data separation, and image retrieval.
SICI Fusion Kalman Filter for Multi-sensor Networked Systems with One-step Random Delays and Correlated Noises

The fusion estimation problem for multi-sensor networked systems with random delays is a popular research field, and accompanied with correlated noises, it becomes more complicated, because in ordinary fusion criterion, the cross-covariance matrices are required, which are always difficult to be obtained. In order to solve it, a SICI (Sequential Inverse Covariance Intersection) fusion Kalman filtering method is presented. Firstly, by the augmented state technology and the fictitious noise algorithm, the original systems with one-step random delays and correlated noises are transformed into a new one without delays. Secondly, using the Lyapunov equation method, the variance matrices of the fictitious noise is derived, and applying the classical Kalman filtering method, the local Kalman filters can be obtained. Inspired by the idea of sequential fusion structure, the ICI fusion algorithm is applied to avoid the calculation of the cross-covariance matrices, and then the SICI fusion structure is presented. Lastly, the accuracy relations among several fusion methods are proved, and compared with the SCI (Sequential Covariance Intersection) method, the SICI method has less conservativeness. A simulation example shows the effectiveness and the consistency of the presented SICI fusion Kalman filter.
A Hybrid Nonrigid Medical Image Registration Method on Chest Radiography

Accurate non-rigid registration of chest radiographs facilitates image diagnosis and occupies an important position in medical image analysis. In this paper, we proposed a non-rigid registration framework that combines the advantages of B-spline FFD (free form deformation) and inertial demons. The proposed method applied B-spline FFD to match structures in the lung area and prevent lesion being destroyed; at the same time, the inertial demons model is used to refine the detail of results observed by FFD. Temporal subtraction images created from the chest radiography image pairs are given to demonstrate the registration accuracy. Multiple experiments on clinical data have shown that the proposed algorithm is more accurate in chest radiographs registration than the widely used B-spline FFD and demons algorithm alone.
Assembly and error analysis of back focal plane-typed apertometer

Generating reference on the back focal plane (BFP) of objective lens has been an excellent solution for numerical aperture (NA) measurement. Although the effectiveness of the concept of BFP reference has been verified theoretically and experimentally, there has been no practical apertometer based on this concept. The main challenge is how to realize the assembly of the system. In this work, we present the instrument assembly of the apertometer based on BFP reference, which mainly has three requirements. After that, we specifically investigate the influence of the location of the coverslip on BFP image and NA value by using ZEMAX. This work promotes the instrumentation of BFP-typed apertometer significantly.
Design of a Branch-and-Bound Strategy for Process Scheduling in Multi-core Smartphone

With the rise of mobile phones and the popularity of the Internet, 5G, and e-commerce, there should be more various tasks processed on smartphone, which requires a good scheduling strategy to handle running processes. Compared with a single core, one of the biggest advantages of multi-core is the efficiency of running processes. In the multi-core, each core is responsible for serving a process at each moment, hence the performance of smartphone executing programs can become much better. This paper focuses on designing a branch-and-bound strategy for process scheduling in multi-core smartphone. Since the shifts of cores must be allocated by the operating system (like Android or iOS), the scheduler in the operating system would adopt the proposed scheme to improve efficiency of process scheduling.
Coarse to Fine: Weak Feature Boosting Network for Salient Object Detection

Abstract Salient object detection is to identify objects or regions with maximum visual recognition in an image, which brings significant help and improvement to many computer visual processing tasks. Although lots of methods have occurred for salient object detection, the problem is still not perfectly solved especially when the background scene is complex or the salient object is small. In this paper, we propose a novel Weak Feature Boosting Network (WFBNet) for the salient object detection task. In the WFBNet, we extract the unpredictable regions (low confidence regions) of the image via a polynomial function and enhance the features of these regions through a well‐designed weak feature boosting module (WFBM). Starting from a coarse saliency map, we gradually refine it according to the boosted features to obtain the final saliency map, and our network does not need any post‐processing step. We conduct extensive experiments on five benchmark datasets using comprehensive evaluation metrics. The results show that our algorithm has considerable advantages over the existing state‐of‐the‐art methods.
Rancang Bangun Sistem Informasi Keuangan Dengan Pendekatan Instrumen Borang Akreditasi

STMIK STIKOM Indonesia, commonly called STIKI, is one of the private tertiary institutions in the field of information technology in Bali with a bachelor level (S1) program. All business processes in support of the academic community are carried out with computers and information systems. From student data management, Kartu Rencana Study (KRS), graduation, library to financial data using a computer system. Management of financial data on STIKI campus is under the responsibility of the Assistant Head II (WK2). Until now, financial management does use a computer system, but is limited to the use of the MS Office Excel application in its management. This has become a problem in the allocation of finance to the preparation of financial reports that lead to forms of study programs and institutions. With these problems will be designed and built a financial information system in the division of Assistant Head II STMIK STIKOM Indonesia that can help accounting staff in managing financial data and simplify the preparation of reports.
Dynamical Perspective of Machine Learning: An Introductory Review

In this paper, we will briefly introduce the recent progress on machine learning based on the dynamical perspective. By combining machine learning with the dynamics, we get many continuous time algorithms. By discretizing the continuous time dynamic equations, we can not only review the existing algorithms, but also get new discrete-time algorithms. Firstly, gradient based algorithms is presented, which include the accelerated algorithm, policy gradient algorithm and stochastic gradient algorithm. Then, we come to the non-gradient algorithms, including the Pontryagins maximum principle based successive approximation algorithm and the optimal tensor algorithm. Next, continuous-time perspective of machine learning is reviewed, which to some extent establish a theoretical basis for machine learning. And in the end, we propose the advantage and the disadvantage of the above achievements, and try to give our own opinion about the application prospect and the development direction.
Minimal Rare-Pattern-Based Outlier Detection Method for Data Streams by Considering Anti-monotonic Constraints

In the collected associated data streams, some potential outliers are often fixed with the normal data instances, thus, it is necessary to accurately detect the outliers to improve the reliability of the data streams. In real life, people are more concerned about whether some outliers existed in the small scale data instances that satisfy their constraints, rather than in the huge entire datasets. However, the existing association-based outlier detection methods were proposed to detect the outliers from the entire data streams, thus, the time consumption is very long. To content with the existence of the constraints, this paper proposes an efficient constrained minimal rare pattern-based outlier detection method for data streams, namely AMCMRP-Outlier, to process the succinct and convertible anti-monotonic constraints. In the pattern mining phase, the matrix structure is used to quickly mine the minimal rare patterns that satisfy the constraints, thus providing the pattern basis for the outlier detection. In the outlier detection phase, two deviation indices are defined to measure the deviation degree of each transaction, and then the transactions having large deviation degrees are determined as the outliers. Finally, extensive experiments on one synthetic dataset and two public datasets verify that the AMCMRP-Outlier method can accurately detect the outliers with less time cost.
Methods for Constructing Measurable and Metrizable Models of Knowledge Bases within Algebraic Approach

Conventional methods to provide measurability and metrization of knowledge models cover only a small part of possible models, namely Bayesian networks and probabilistic analysis of models expressed by formulas of propositional calculus. The report proposes a new approach to building a wider class of measurable and metrizable knowledge models based on n-tuple algebra developed by the authors earlier. Besides. the proposed approach makes it possible to use clustering methods in models of knowledge bases.
WITHDRAWN: Transmitting data in a secure way using double protection encryption scheme

This proposed system introduces a new double protection data dividing and equal with system in cloud environment. This proposed system maintains the patient’s encrypted medical reports to cloud data base with specification based distribute encryption (SBDI) technique, and it is to be shared to a consulting group in a sound and protective approach. It takes characteristic -based action on text data decryption mechanism, which empower the consultant to access the patient’s record without discharging any users relevant information. Further, it provides a resume checking system using specification based distribute encryption that helps users to find consultation in a secured way, and attain their easy approved way on the protected medical reports.
Local Neighborhood Reliability Weighted Support Vector Machine

Support vector machine (SVM) is a classification model, which learns the decision surface that maximizes the margin in the feature space. Such a decision surface has a good classification ability for unknown new samples. In real-world applications, the data set usually contains many noises and outliers, which will affect the learning of the decision surface, thus the maximum margin cannot be obtained, and the generalization ability of SVM will be reduced. In this paper, we introduce an adjacency factor to each input point to characterize the local neighbor relationship between each point. Weighting each sample point by the adjacency factor can let different sample points make different contributions to the learning of the decision surface. Thus, we can filter out the influence of noises and outliers on the decision surface by this weighting method. We propose this new method namely local neighborhood reliability weighted support vector machine (LN-SVM).
Analysis of Perceptron Quantum Artificial Neural Networks to Classify the Feasibility of Prospective Debtors

Abstract Bank is a business entity whose activities are collecting funds from the public in the form of deposits and channeling them to the public in the form of loans or other forms to improve the lives of many people. This study aims to classify the feasibility data of prospective borrowers with the perceptron algorithm using quantum computing to facilitate the bank in determining the prospective debtor. The results of this study are an analysis of the feasibility of prospective borrowers using the perceptron algorithm with quantum computing.
Perspective as a way of information visualization in V. Yavorivsky's journalism

The article analyzes the peculiarities of perspective expression in the journalistic works of V. Yavorivsky of the early, middle and mature period of creativity, reveals the features of the dynamics of the idiolect. Consideration of journalistic works of V. Yavorivsky early and mature period of creativity shows that the perspective as a means of visualization of information is a constant category, but the prospects of these periods of creativity of the Master of the word has its own characteristics, which is noticeable when choosing the themes of works, systematization of relevant information, selection of means of nomination of persons and events, represented by the connotation of the text. Worldview values, characteristic of the second half of the twentieth century, are transmitted through the prism of the author's vision or beliefs of the heroes of the essays. The means of expressing a point of view are direct or implicit direct speech, comparison or opposition of several points of view, their overlap, motivation of proper names, etc. Journalistic essays of the books „What kind of people are we?” (works of the mature period of creativity) are united by one macrotheme, although each time one of the problems of this theme is raised. The worldview perspective in these works is realized first of all by the structure of the political discourse itself, the publicist's polemic with the respondents, the authors of the letters. The category of point of view here is represented by the author's evidence, the selection of appropriate arguments, passionate emotional reflections, in which the narrator takes over the function of a commentator. The author expresses his thoughts with sincere confidence in the correctness of his beliefs, which he defends, seeking to „look the truth in the eye”. The status-role perspective is conditioned by the realities of the present and past life of Ukrainians, revealed in the powerful axiological nature of the text, which is often achieved by selecting accurate paraphrases. The situational perspective in these works is revealed primarily due to the change of participants in the discussion, quoting letters in which the contributors express their point of view.
Deep Group Recommender System Model Based on User Trust

Today's group recommender systems rarely consider the trust relationship between users, but the interaction between users often has a significant impact on each other's preferences. A group recommendation model based on deep learning and user interaction is proposed, which has stronger representational capability than previous models. In addition, an improved weight fusion method based on user implicit vector is applied to the preference fusion of group members. Epinions data set shows that this model is superior to other comparison algorithms in RMSE and hit ratio.
Extension of Iron Nutrition Anemia Using Media Booklets to Increase Knowledge and Attitudes of Students of SMAN 7 Malang

One of the causes of anemia in young women is the lack of knowledge of of anemia in young women. Objective: The purpose of this study was to conduct iron nutrition anemia counseling with booklet media to increase the knowledge and attitudes of female students of SMAN 7 Malang. Design: This type of research is quasy experiment with the research of two group pre-post test design. The research was conducted in December 2019. The sample group was selected using purposive sampling and the sample was 24 people divided into control groups and treatment groups. Results: The results showed that the media booklet of  Happy Healthy Anemia Young Women was suitable to be used as an iron nutrition anemia education media for adolescents. Counseling with lecture and counseling methods with the lecture method using booklet media can improve student knowledge and attitudes regarding iron nutrition anemia. This is because the respondents have received information where previously the respondents did not know the information about iron nutrition anemia. The results of the analysis showed a significant difference in the increase in knowledge (p = 0.017) and attitude (p = 0,000) between counseling with the lecture method and counseling with lecture methods and booklet media. Conclusions: Counseling with booklet media was 8% superior compared to counseling with the lecture method for increasing knowledge and 8% in improving attitudes.
Hybrid Features Extraction and Machine Learning Based Arabic Speaker Classification

In this era of technological advancement the machine learning and the artificial intelligence are becoming vital techniques which are extensively employedfor the establishment of smart cognitive systems. In this context, a hybrid model based Arabic speaker recognition tactic is devised. The target is to attain an effectual way out with an elevated accuracy. It is reachable by tactfullyutilizing the hybrid features extraction and the robust classification tactics. The incoming Arabic speech is denoisedand conditioned by using appropriate pre-conditioning. The Perceptive Linear Prediction Coding Coefficients (PLPCC) and the Mel-Frequency Cepstral Coefficients (MFCCs) are mined from the enhanced Arabic speech. Afterward,k-Nearest Neighbor (KNN) classifier is employed to recognize the speaker. The system attains an Arabic speaker classification accuracy of 90.8 %. It confirms the interest of embedding the designed framework in modern systems such as smart spaces like buildings, offices and homes for an effective realization of resources sharing and management.