Reviewerly/exHarmony huggingface dataset:
Development of Fast and Soft Landing System for Quadcopter Drone using Fuzzy Logic Technology

The popularity of Drones, also known as unmanned aerial vehicles (UAVs), are highly increasing in various fields because of its multi-skilled performance and operational flexibility.Landing of the drone is the most difficult part of a flight.Literature review reveals, there are many kinds of landing controllers are available for drones but those controllers take more time for landing a drone.In this paper, we developed a fast as well as a soft landing control system for quadcopter drone using fuzzy logic technology.More specifically, we designed a fuzzy logic based controller in MATLAB/Simulink and implemented it to Raspberry pi.The Raspberry pi is connected with an ultrasonic sensor for altitude calculation.Here we used Pixhawk as the main flight controller, and the Pixhawk receives the real-time landing control commands from the Raspberry pi.Several landing tests were conducted using the designed fuzzy landing control system, and the flight test data shows that the quadcopter landed softly with minimum time than the other existing landing control technologies.
One-class classification for highly imbalanced medical image data

Computer-aided diagnosis plays an important role in clinical image diagnosis. Current clinical image classification tasks usually focus on binary classification, which need to collect samples for both the positive and negative classes in order to train a binary classifier. However, in many clinical scenarios, there may have many more samples in one class than in the other class, which results in the problem of data imbalance. Data imbalance is a severe problem that can substantially influence the performance of binary-class machine learning models. To address this issue, one-class classification, which focuses on learning features from the samples of one given class, has been proposed. In this work, we assess the one-class support vector machine (OCSVM) to solve the classification tasks on two highly imbalanced datasets, namely, space-occupying kidney lesions (including renal cell carcinoma and benign) data and breast cancer distant metastasis/non-metastasis imaging data. Experimental results show that the OCSVM exhibits promising performance compared to binary-class and other one-class classification methods.
Multi-demodulation phase-shifting and intensity pattern projection profilometry

Phase-shifting is a powerful phase demodulation method for three-dimensional imaging by fringe projection. However, numerous fringe patterns, which encode a single-phase function, are required. In this paper, the use of intensity patterns, which encode multiple phase functions, is proposed. A multi-demodulation phase-shifting algorithm and the intensity pattern projection profilometry approach are developed. A computer simulation and an experimental three-dimensional object measurement are performed to validate the proposal and demonstrate its effectiveness. The results exhibit that a similar accuracy than conventional fringe projection is achieved while using a reduced number of patterns. The proposed approach is a feasible alternative to increase the efficiency of three-dimensional imaging systems based on phase-shifting.
Computing Maximum and Minimum with Privacy Preservation and Flexible Access Control

With the fast development of Internet of Things, huge volume of data is being collected from various sensors and devices, aggregated at gateways, and processed in the cloud. Due to privacy concern, data are usually encrypted before being outsourced to the cloud. However, encryption seriously impedes both computation over the data and sharing of the computation results. Computing maximum and minimum among a data set are two of the most basic operations in machine learning and data mining algorithms. In this paper, we study how to compute maximum and minimum over encrypted data and control the access to the computation result in a privacy-preserving manner. We present four schemes to realize privacy-preserving maximum and minimum computations with flexible access control that can adapt to various application scenarios. We further analyze their security and show their efficiency through extensive evaluations and comparisons with existing work.
Tracking with (Un)Certainty

One of the highest ambitions in educational technology is the move towards personalized learning. To this end, computerized adaptive learning (CAL) systems are developed. A popular method to track the development of student ability and item difficulty, in CAL systems, is the Elo Rating System (ERS). The ERS allows for dynamic model parameters by updating key parameters after every response. However, drawbacks of the ERS are that it does not provide standard errors and that it results in rating variance inflation. We identify three statistical issues responsible for both of these drawbacks. To solve these issues we introduce a new tracking system based on urns, where every person and item is represented by an urn filled with a combination of green and red marbles. Urns are updated, by an exchange of marbles after each response, such that the proportions of green marbles represent estimates of person ability or item difficulty. A main advantage of this approach is that the standard errors are known, hence the method allows for statistical inference, such as testing for learning effects. We highlight features of the Urnings algorithm and compare it to the popular ERS in a simulation study and in an empirical data example from a large-scale CAL application.
Single photon beat note in an acousto-optic modulator-based interferometer

We present in the following a quantum optics experiment appropriate for advanced undergraduate students with former experience in quantum optics. It extends classical single photon setups to the time dependent domain. We demonstrate self-heterodyning of heralded single photons using a Mach-Zehnder like interferometer where beamsplitters are replaced by two acousto-optic modulators (AOMs). The single photon beat note is recorded over time at the frequency difference between the RF generators driving the AOMs, which makes it observable directly on a human time scale, i.e., with periods above a fraction of a second. To compare with our observations, we tailor the standard quantum optics formalism for beam splitters to take into account the frequency shifts associated with the AOMs.
The Original Sin – Content ‘Moderation’ (Censorship) in the EU

The Original Sin – Content ‘Moderation’ (Censorship) in the EU Martin Senftleben, Ph.D Martin Senftleben, Ph.D Professor of Intellectual Property Law, Institute for Information Law, University of Amsterdam; Visiting Professor, Intellectual Property Research Institute, University of Xiamen m.r.f.senftleben@vu.nl Search for other works by this author on: Oxford Academic Google Scholar GRUR International, Volume 69, Issue 4, April 2020, Pages 339–340, https://doi.org/10.1093/grurint/ikaa025 Published: 10 March 2020 Article history Accepted: 09 February 2020 Published: 10 March 2020
Post-non-classical science in the age of informatization of society: functional aspect

The article researches the transformation of the science functions in the process of changing the scientific rationality type in the conditions of the information society formation. The relevance of the research topic is stipulated by the need for scientific analysis and philosophical understanding of the large-scale changes occurring in modern science under the influence of informatization processes. The theoretical basis of the study are the general philosophical principles - the principles of systematicity, comprehensiveness, concreteness of the study, as well as such socio-philosophical methods as specifically historical, historical-retrospective, comparative-historical method, the method of unity of historical and logical in social cognition. Comparative analysis, interdisciplinary synthesis and other techniques of general scientific methodology are also used. The influence of socio-cultural conditions on the functional transformations of post-non-classical science is substantiated. The basic tendencies of functional transformation of science are revealed: transition to development of complex scientific programs; the convergence of research programs with high-tech manufacturing practices, the integration of computer technologies with methodological tools; the commensuration of subject lines of scientific knowledge with the conditions of formation of socially-human complexes; the coordination of rational-cognitive ethos of science with cultural universals; the extension of the intra-scientific reflection field and the formation of extra-scientific reflection forms.
Design and Implementation of Rapid Information Acquisition and Analysis System

In the era of big data, extracting valuable information from huge and complicated information has become a difficult issue. The work designed a rapid information acquisition and analysis system for Zhihu Forum. The client of system used the Bootstrap front-end development framework, JavaScript framework Jquery, asynchronous JavaScript and XML Ajax to develop visual interface. The main function of the server was to use the crawler technology to quickly obtain the forum information based on python environment. Moreover, Jieba segmentation was used to segment the text data, and then the generalized word cloud was generated according to the frequency of each word in the text. The proportion of positive affectivity was given by using the third-party tool snowNLP to judge the emotion. Finally, the top ten posts are selected as hot topics for reference by the number of likes. The development and application of the system can improve the information acquisition and analysis of users.
Multi-Adversarial Partial Transfer Learning With Object-Level Attention Mechanism for Unsupervised Remote Sensing Scene Classification

In recent years, deep learning methods have been widely applied in remote sensing image classification tasks, providing valuable information for natural monitoring and spatial planning. In an actual application like this, acquiring massive labeled data for deep convolutional networks is costly and difficult especially in the situation that the data sources are diverse and the requirements are changing. Transfer learning methods have already shown superior performance on exploiting domain invariance features in existing data for deep network-based categorization tasks. However, the data imbalance between source and target domains may bring negative transfer and weaken the classifier's ability. Moreover, it is still a difficult problem to extract object-level visual features among easy-mixed categories. In this context, Multi-adversarial Object-level Attention Network (MOAN) is proposed for partial transfer learning and selecting useful features. On the one hand, we present an improved object-level attention proposal network (OANet) for perceiving structural features of the main object in the picture, and weakening the unrelated regions. On the other hand, the extracted features are further enhanced by multi-adversarial framework in order to promote positive transfer, selecting and mapping valuable cross domain features from shared categories and suppressing others. This adversarial learning module can also generate pseudo tags for the samples in target domain so as to perceive integral visual signals, similar to the process in source domain. In addition, virtual adversarial training method is introduced in MOAN so as to regularize the model and maintain stability. Experimental analyses show that our MOAN can significantly promote positive transfer and restrain negative transfer in unsupervised classification problems. MOAN has good performances such as higher accuracies and lower loss values on several benchmark data sets.
Optimal Feature Extraction based Machine Learning Approach for Sarcasm Type Detection in News Headlines

Sarcasm detection has received increasing research in recent years.Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising.Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising.Generally, sarcasm detection task is treated as standard test classification problem.Sarcasm is the unconventional way of conveying a message which conflicts the context.It can lead to a state of ambiguity.As Sarcasm represents contrary sentiment to the literal meaning that is conveyed in the text, it is hard to identify sarcasm even for a human.Existing models mainly focus on designing effective features for improving the detection performance.In this paper, optimal features are to be selected before data passes to classification task.So data pre-processing makes the data clean so that the performance of the classifier will be enhance.Result shows the improve performance in sarcasm detection using the optimal feature sets.
IMPLEMENTASI METODE NAIVE BAYES UNTUK DIAGNOSA PENYAKIT LAMBUNG

Stomach disease is a disease that is very common in humans. This disease can affect anyone, both children and adults, and if it is too late and has spread, the disease is very difficult to cure. Although this disease is very common, not many people know about the symptoms and causes of the disease. To be able to know the symptoms and follow-up of the disease, a person must consult with a specialist in gastric disease and must pay a large cost. The results of this study are expert systems for diagnosing gastric ailments in 5 types of diseases and using the Naive Bayes method to get the highest value in the form of diagnosis of the disease. Based on the results of application testing, it can be concluded that this application is feasible to use and can help the user, namely the patient.
Would you Like to Talk about Sports Now?

To hold a true conversation, an intelligent agent should be able to occasionally take initiative and recommend the next natural conversation topic. This is a challenging task. A topic suggested by the agent should be relevant to the person, appropriate for the conversation context, and the agent should have something interesting to say about it. Thus, a scripted, or one-size-fits-all, popularity-based topic suggestion is doomed to fail. Instead, we explore different methods for a personalized, contextual topic suggestion for open-domain conversations. We formalize the Conversational Topic Suggestion problem (CTS) to more clearly identify the assumptions and requirements. We also explore three possible approaches to solve this problem: (1) model-based sequential topic suggestion to capture the conversation context (CTS-Seq), (2) Collaborative Filtering-based suggestion to capture previous successful conversations from similar users (CTS-CF), and (3) a hybrid approach combining both conversation context and collaborative filtering. To evaluate the effectiveness of these methods, we use real conversations collected as part of the Amazon Alexa Prize 2018 Conversational AI challenge. The results are promising: the CTS-Seq model suggests topics with 23% higher accuracy than the baseline, and incorporating collaborative filtering signals into a hybrid CTS-Seq-CF model further improves recommendation accuracy by 12%. Together, our proposed models, experiments, and analysis significantly advance the study of open-domain conversational agents, and suggest promising directions for future improvements.
TEJU: Fraud Detection and Improving Classification Performance for Bankruptcy Datasets Using Machine Learning Techniques

Fraud detection is one the major challenge problem. In this paper addressing of problem is fraud detection and improving performance. The fraudulent are changing day by day and it became very difficult to identify which data fraud and which is legitimate. In this paper addressing design a framework TEJU as fraud detection and improving classification performance for bankruptcy datasets using machine learning techniques. So we can reduce the problem by using machine learning techniques of kNN and main objective apply distance between two patterns compute similarity with classified into each class wise. Then experimental results based on framework to improve performance analysis of accuracies, ROC curve values and error rate.
Clustering of Voice Pathologies based on Sustained Voice Parameters

Signal processing techniques can be used to extract information that contribute to the detection of laryngeal disorders.The goal of this paper is to perform a statistical analysis through the boxplot tool from 832 voice signals of individuals with different laryngeal pathologies from the Saarbrücken Voice Database in order to create relevant groups, making feasible an automatic identification of these dysfunctions.Jitter, Shimmer, HNR, NHR and Autocorrelation features were compared between several groups of voice pathologies/conditions, resulting in three identified clusters.
Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation

Background Deidentification of clinical records is a critical step before their publication. This is usually treated as a type of sequence labeling task, and ensemble learning is one of the best performing solutions. Under the framework of multi-learner ensemble, the significance of a candidate rule-based learner remains an open issue. Objective The aim of this study is to investigate whether a rule-based learner is useful in a hybrid deidentification system and offer suggestions on how to build and integrate a rule-based learner. Methods We chose a data-driven rule-learner named transformation-based error-driven learning (TBED) and integrated it into the best performing hybrid system in this task. Results On the popular Informatics for Integrating Biology and the Bedside (i2b2) deidentification data set, experiments showed that TBED can offer high performance with its generated rules, and integrating the rule-based model into an ensemble framework, which reached an F1 score of 96.76%, achieved the best performance reported in the community. Conclusions We proved the rule-based method offers an effective contribution to the current ensemble learning approach for the deidentification of clinical records. Such a rule system could be automatically learned by TBED, avoiding the high cost and low reliability of manual rule composition. In particular, we boosted the ensemble model with rules to create the best performance of the deidentification of clinical records.
Linear Complexity of a New Class of Quaternary Generalized Cyclotomic Sequence with Period <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math>

Sequences with high linear complexity property are of importance in applications. In this paper, based on the theory of generalized cyclotomy, new classes of quaternary generalized cyclotomic sequences with order 4 and period <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> are constructed. In addition, we determine their linear complexities over finite field <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> and over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">ℤ</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>, respectively.
Designing the Model of Professional Development of Teachers Taking into Account Axiological Imperatives of Continuing Education

The need for continuous personal and professional development is explained in essence by the continuous education of teachers.The purpose of the study is to define the content of a model for the professional development of teachers, taking into account the axiological imperatives of continuing education.The article describes the axiological imperatives of "a person is a subject of continuous education", "a person is a subject of cultural space of continuous education", "a person is a self-developing personality", "continuous education is a semantic activity", and "strategy of own development in the changing world".The content of the model is presented on the basis of the contextual approach and foundation positions.As a result, the axiological, pedagogical and organizational components of the model are described and correlated with the axiological imperatives of continuous education.These components have determined the development of actual forms of professional teacher development.These include scenario development, expert panels and a roadmap.Axiological imperatives of continuous education should be taken into account in the design of any model for the professional development of teachers in international practice to ensure the successful implementation of the developed model.
CUR LRA at Sublinear Cost Based on Volume Maximization

A matrix algorithm runs at sublinear cost if it uses much fewer memory cells and arithmetic operations than the input matrix has entries. Such algorithms are indispensable for Big Data Mining and Analysis, where input matrices are so immense that one can only access a small fraction of all their entries. Typically, however, such matrices admit their Low Rank Approximation (LRA), which one can access and process at sublinear cost. Can, however, we compute LRA at sublinear cost? Adversary argument shows that no algorithm running at sublinear cost can output accurate LRA of worst case input matrices or even of the matrices of small families of our Appendix A, but we prove that some sublinear cost algorithms output a reasonably close LRA of a matrix W if (i) this matrix is sufficiently close to a low rank matrix or (ii) it is a Symmetric Positive Semidefinite (SPSD) matrix that admits LRA. In both cases supporting algorithms are deterministic and output LRA in its special form of CUR LRA, particularly memory efficient. The design of our algorithms and the proof of their correctness rely on the results of extensive previous study of CUR LRA in Numerical Linear Algebra using volume maximization. In case (i) we apply Cross-Approximation (C-A) iterations, running at sublinear cost and computing accurate LRA worldwide for more than a decade. We provide the first formal support for this long-known empirical efficiency assuming non-degeneracy of the initial submatrix of at least one C-A iteration. We cannot ensure non-degeneracy at sublinear cost for a worst case input but prove that it holds with a high probability (whp) for any initialization in the case of a random or randomized input. Empirically we can replace randomization with sparse multiplicative preprocessing of an input matrix, performed at sublinear cost. In case (ii) we make no additional assumptions about the input class of SPSD matrices admitting LRA or about initialization of our sublinear cost algorithms for CUR LRA, which promise to be practically valuable. We hope that proper combination of our deterministic techniques with randomized LRA methods, popular among Computer Science researchers, will lead them to further progress in LRA.