Reviewerly/exHarmony huggingface dataset:
A Study on the Rén and Zhōngyōng of Chinese Ancient Ethics Thought

This paper aims to examine the Rén and Zhōngyōng of Chinese ancient ethical thought and to improve the data that can be applied to us at present. From Qin Han dynasty to the Ming Qing dynasty, Confucian ethical thought became the family ethics, social ethics and national ethics of the general Chinese, and further influenced the Chinese life and cosmos. The intense debates of Confucianism, Mukga, Taoism, and law in the Age of Spring and Autumn and Waring States became the basis for the development of Chinese ethical ideas. Since Confucianism ethics has been in a leading position for over 2,000 years in China, morality has become the basis for almost all cultures and politics, economy, and society. The ethics of ‘Xiào’ and ‘Zhōng’ in China played an important role in establishing and maintaining the order of human relations. This is characterized by the use of the ruling method of the righteous through the integration of morality and politics in the ruling method of the righteous. Therefore, it will be important to understand the basic characteristics of Chinese ancient ethical thought and to understand the traditional ethical thought.
Data Anonymization for Privacy Aware Machine Learning

The increase of data leaks, attacks, and other ransom-ware in the last few years have pointed out concerns about data security and privacy. All this has negatively affected the sharing and publication of data. To address these many limitations, innovative techniques are needed for protecting data. Especially, when used in machine learning based-data models. In this context, differential privacy is one of the most effective approaches to preserve privacy. However, the scope of differential privacy applications is very limited (e. g. numerical and structured data). Therefore, in this study, we aim to investigate the behavior of differential privacy applied to textual data and time series. The proposed approach was evaluated by comparing two Principal Component Analysis based differential privacy algorithms. The effectiveness was demonstrated through the application of three machine learning models to both anonymized and primary data. Their performances were thoroughly evaluated in terms of confidentiality, utility, scalability, and computational efficiency. The PPCA method provides a high anonymization quality at the expense of a high time-consuming, while the DPCA method preserves more utility and faster time computing. We show the possibility to combine a neural network text representation approach with differential privacy methods. We also highlighted that it is well within reach to anonymize real-world measurements data from satellites sensors for an anomaly detection task. We believe that our study will significantly motivate the use of differential privacy techniques, which can lead to more data sharing and privacy preserving.
An explicit representation and enumeration for negacyclic codes of length 2^kn over \mathbb{Z}_4+u\mathbb{Z}_4

<p style='text-indent:20px;'>In this paper, we give an explicit representation and enumeration for negacyclic codes of length <inline-formula><tex-math id="M3">\begin{document}$ 2^kn $\end{document}</tex-math></inline-formula> over the local non-principal ideal ring <inline-formula><tex-math id="M4">\begin{document}$ R = \mathbb{Z}_4+u\mathbb{Z}_4 $\end{document}</tex-math></inline-formula> <inline-formula><tex-math id="M5">\begin{document}$ (u^2 = 0) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M6">\begin{document}$ k, n $\end{document}</tex-math></inline-formula> are arbitrary positive integers and <inline-formula><tex-math id="M7">\begin{document}$ n $\end{document}</tex-math></inline-formula> is odd. In particular, we present all distinct negacyclic codes of length <inline-formula><tex-math id="M8">\begin{document}$ 2^k $\end{document}</tex-math></inline-formula> over <inline-formula><tex-math id="M9">\begin{document}$ R $\end{document}</tex-math></inline-formula> precisely. Moreover, we provide an exact mass formula for the number of negacyclic codes of length <inline-formula><tex-math id="M10">\begin{document}$ 2^kn $\end{document}</tex-math></inline-formula> over <inline-formula><tex-math id="M11">\begin{document}$ R $\end{document}</tex-math></inline-formula> and correct several mistakes in some literatures.
Neighborhood Classifier for Label Noise

One typical case of label noise indicates that some samples have been incorrectly labeled in data. Label noise of training samples will significantly affect the learning performances such that the classification accuracy will be reduced. Presently, many results of identifying samples of incorrect labels have been proposed. Most of them are based on the consideration of classifier based accuracy. Therefore, the performance of used classifier is directly related to the result of filtering samples with noise label. In this paper, a neighborhood strategy is introduced into analyzing label noise data, it is mainly because such classifier is superior to several popular classifiers. Not only the neighborhood classifier based algorithm is designed to remove samples with noise label, but also such type of filter is compared with the nearest neighborhood based filter. The experimental results demonstrate that our neighborhood classifier based filter performs well because higher classification accuracy can be achieved. This study suggests new trends for considering neighborhood approach to complex data.
Attention-Aware Feature Pyramid Ordinal Hashing for Image Retrieval

Due to the effectiveness of representation learning, deep hashing methods have attracted increasing attention in image retrieval. However, most existing deep hashing methods merely encode the raw information of the last layer for hash learning, which result in the following deficiencies: (1) the useful information from the preceding-layer is not fully exploited; (2) the local salient information of the image is neglected. To this end, we propose a novel deep hashing method, called Attention-Aware Feature Pyramid Ordinal Hashing (AFPH), which explores both the visual structure information and semantic information from different convolutional layers. Specifically, two feature pyramids based on spatial and channel attention are well constructed to capture the local salient structure from multiple scales. Moreover, a multi-scale feature fusion strategy is proposed to aggregate the feature maps from multi-level pyramidal layers to generate the discriminative feature for ranking-based hashing. The experimental results conducted on two widely-used image retrieval datasets demonstrate the superiority of our method.
Adversarial computer vision: a current snapshot

Evolution in machine vision systems brought up lately some matters of security. Adversarial computer vision is the field that deals with these matters, producing either adversarial attack proposals or defensive strategies and techniques against them. This article is a review of computer vision security threats and defensive techniques that have been proposed by researchers until now and intents to become a guide for any researcher who is interested to work in the field of adversarial computer vision. Initially, a short history of the subject and main interests of the researchers in this field are presented. After the most important proposed attacks based on adversarial examples and their integrity are analyzed and an updated taxonomy of adversarial computer vision attacks is proposed. Finally, the defensive strategies and techniques that have been proposed are also discussed.
PTLHAR: PoseNet and transfer learning for human activities recognition based on body articulations

This paper introduces a novel approach for human activities recognition (HAR) based on body articulations (joints) that represent the connection between bones in the human body which join the skeletal system such as the knee, shoulder and hand, and which are made to allow different degrees and types of movement. To implement our system, we used PoseNet to extract articulation points, which will be classified employing transfer learning approach to recognize the activity. The created system will be named in the rest of the paper (PTLHAR). The experimental results show that the proposed approach provides a significant improvement over state-of-the-art methods.
Precise Identification of Cell and Tissue Features Important for Histopathologic Diagnosis by a Whole Slide Imaging System

Previous studies have demonstrated the noninferiority of pathologists' interpretation of whole slide images (WSIs) compared to microscopic slides in diagnostic surgical pathology; however, to our knowledge, no published studies have tested analytical precision of an entire WSI system.In this study, five pathologists at three locations tested intra-system, inter-system/site, and intra- and inter-pathologist precision of the Aperio AT2 DX System (Leica Biosystems, Vista, CA, USA). Sixty-nine microscopic slides containing 23 different morphologic features suggested by the Digital Pathology Association as important to diagnostic pathology were identified and scanned. Each of 202 unique fields of view (FOVs) had 1-3 defined morphologic features, and each feature was represented in three different tissues. For intra-system precision, each site scanned 23 slides at three different times and one pathologist interpreted all FOVs. For inter-system/site precision, all 69 slides were scanned once at each of three sites, and FOVs from each site were read by one pathologist. To test intra- and inter-pathologist precision, all 69 slides were scanned at one site, FOVs were saved in three different orientations, and the FOVs were transferred to a different site. Three different pathologists then interpreted FOVs from all 69 slides. Wildcard (unscored) slides and washout intervals were included in each study. Agreement estimates with 95% confidence intervals were calculated.Combined precision from all three studies, representing 606 FOVs in each of the three studies, showed overall intra-system agreement of 97.9%; inter-system/site agreement was 96%, intra-pathologist agreement was 95%, and inter-pathologist agreement was 94.2%.Pathologists using the Aperio AT2 DX System identified histopathological features with high precision, providing increased confidence in using WSI for primary diagnosis in surgical pathology.
A symmetric-based framework for securing cloud data at rest

Cloud computing is the umbrella term for delivering services via the Internet. It enables enterprises and individuals to access services such as virtual machines, storage, or applications on demand. It allows them to achieve more by paying less, and it removes the barrier of installing physical infrastructure. However, due to its openness and availability over the Internet, the issue of ensuring security and privacy arises. This requires careful consideration from enterprises and individuals before the adoption of cloud computing. In order to overcome security issues, cloud service providers are required to use strong security measures to secure their storage and protect cloud data from unauthorized access. In this paper, a novel framework and symmetric-based encryption scheme for securing cloud data at rest is introduced. The performance evaluation of the new framework shows that it has a high level of efficiency, feasibility, and scalability.
A LSTM-Based Method with Attention Mechanism for Adverse Drug Reaction Sentences Detection

Adverse drug reactions (ADRs) are among the top causes of morbidity, mortality and substantial healthcare costs and thus should be detected early to reduce consequences on health outcomes. Many conventional machine learning based methods have been presented to automatically detect adverse drug effect (ADE) mentions from biomedical texts. However, owing to the complexity of natural language text in the biomedical domain, some ADE mentions might not be detected. In this paper, we propose a Long Short-Term Memory with Attention (LSTMA) which incorporates attention mechanism and LSTM network to address the problem of automatic detection of ADR assertive text segments from biomedical texts. Experimental results on standard ADE dataset show that the proposed method outperforms significantly the state-of-the art methods for ADR class with an F-scores of 0.89.
Soma Design - Intertwining Aesthetics, Ethics and Movement

I will discuss soma design - a process that allows designers to examine and improve on connections between sensation, feeling, emotion, subjective understanding and values [1].
3D Virtual-Reality Interaction System

In this paper, we proposed a system that allows users to interact with the virtual world. Users can interact with 3D virtual objects in first-person perspective through active 3D glasses and 3D screens. The system includes three core algorithms: 1) Fingertip Tracking by OpenPose and Kinect kits. 2) First-person perspective. 3) Interaction between users and virtual objects. We hope that this system can bridge the gap between humans and computers.
4D seismic study of the Volve Field - an open subsurface-dataset

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Integration of Prophet Model and Convolution Neural Network on Wikipedia Trend Data

Forecasting a time series is an ever growing area in which various machine learning techniques have been used to predict and analyze the future based on the data gathered from past. “Prophet” forecasting model is the most recent development in forecasting the time series, developed by Facebook. Prophet is much faster and simpler to implement than the previous forecasting model such as ARIMA model. Classification of forecasting output can be done by applying convolution neural network (CNN) on the outcomes of the Prophet model. To get higher accuracy with lesser loss, the method runs CNN with the best possible deep layers. The yearly, weekly, daily seasonality and trends could be realized by Prophet Model. The paper shows classification of these output based on the varying types of seasonality and trends. The labeled output can then, train and test all the trends’ result and find out the accuracy and loss incurred in a CNN model. Applying different depth and parameters of CNN that is a combined unit at each layer, it can achieve more than 96% accuracy with less than 4% loss. The integration of prophet and CNN shows that the training and testing model of a neural network can validate the prediction done by using prophet forecasting model along with the seasonality and trends parameters are in coherence to one another.
Detection of Spam in YouTube Comments Using Different Classifiers

Nowadays thousands of videos are uploaded to YouTube each minute and people start giving likes and comments immediately. Some popular and viral videos get millions of comments, some are healthy with good complements and some are Spams with unrelated, abusive, sometimes provided with a URL for commercial advertisement or redirection to other sites. In this paper, we have taken YouTube comment datasets of five famous singers and detecting Spam comments using some Artificial Neural Network based Classifiers and some Normal Classifiers. The proposed technique compares the derived results of the classifiers and suggests the best classifiers for detecting Spam comments.
Stochastic Coordinate Descent for 01 Loss and Its Sensitivity to Adversarial Attacks

The 01 loss while hard to optimize is least sensitive to outliers compared to its continuous differentiable counterparts, namely hinge and logistic loss. Recently the 01 loss has been shown to be most robust compared to surrogate losses against corrupted labels which can be interpreted as adversarial attacks. Here we propose a stochastic coordinate descent heuristic for linear 01 loss classification. We implement and study our heuristic on real datasets from the UCI machine learning archive and find our method to be comparable to the support vector machine in accuracy and tractable in training time. We conjecture that the 01 loss may be harder to attack in a black box setting due to its non-continuity and infinite solution space. We train our linear classifier in a one-vs-one multi-class strategy on CIFAR10 and STL10 image benchmark datasets. In both cases we find our classifier to have the same accuracy as the linear support vector machine but more resilient to black box attacks. On CIFAR10 the linear support vector machine has 0% on adversarial examples while the 01 loss classifier hovers about 10%. On STL10 the linear support vector machine has 0% accuracy whereas 01 loss is at 10%. Our work here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.
Using Monolingual Speech Recognition for Spoken Term Detection in Code-switched Hindi-English Speech

Code-switching is the alternation of two or more languages in a single utterance or a conversation and is prevalent in multilingual communities all over the world. Spoken Term Detection (STD) is the task of detecting a given word or phrase in audio. STD has applications in audio indexing and mining. In this work, we explore Spoken Term Detection for code-switched conversational Hindi-English speech. Code-switching provides various challenges to this problem, including, 1. lack of training data to build robust code-switched Automatic Speech Recog- nition (ASR) systems, 2. non-standardized transcription due to borrowing and cross-transcription, 3. presence of translated or code-switched variants of the terms. In this work, we assume that a code-switched ASR System for Hindi-English does not exist, and make use of only a monolingual Hindi ASR to retrieve audio containing Hindi and English keywords. We use various techniques to normalize the output of a monolingual ASR system. We evaluate our techniques using Term Weighted Value (TWV) and find that phonetic matching of the query and ASR hypotheses at the utterance level is the most promising approach.
Decomposable Probability-of-Success Metrics in Algorithmic Search

Previous studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results. However, this specific success metric prevents us from applying these results on other forms of machine learning, e.g. transfer learning. We define decomposable metrics as a category of success metrics for search problems which can be expressed as a linear operation on a probability distribution to solve this issue. Using an arbitrary decomposable metric to measure the success of a search, we demonstrate theorems which bound success in various ways, generalizing several existing results in the literature.
Method for Localization Aerial Target in AC Electric Field Based on Sensor Circular Array

The traditional method of using electric field sensors to realize early warning of electric power safety distance cannot measure the distance of dangerous sources. Therefore, aiming at the electric field with a frequency of 50 to 60Hz (AC electric field), a new method for localization of aerial AC target by the capacitive one-dimensional spherical electric field sensor circular array is studied. This method can directly calculate the distance, elevation, and azimuth of the detector from the dangerous source. By combining the measurement principle of the spherical electric field sensor and the plane circular array theory, a mathematical model for the localization of aerial targets in an AC electric field is established. An error model was established using Gaussian noise and the effects of different layout parameters on the localization error were simulated. Based on mutual interference between sensors, minimum induced charge, and localization error, an optimal model for sensor layout was established, and it was solved by using genetic algorithms. The optimization results show that when the number of sensors is 4, the array radius is 20 cm, and the sensor radius is 1.5 cm, the ranging error is 8.4%. The detector was developed based on the layout parameters obtained from the optimization results, and the localization method was experimentally verified at 10 and 35 kV alarm distances. The experimental results show that when the detector is located at 10 kV alarm distance, the distance error is 0.18 m, the elevation error is 6.8°, and the azimuth error is 4.57°, and when it is located at 35 kV alarm distance, the distance error is 0.2 m, the elevation error is 4.8°, and the azimuth error is 5.14°, which meets the safety distance warning requirements of 10 and 35 kV voltage levels.
A Unified Method for Private Exponent Attacks on RSA Using Lattices

Let [Formula: see text] be an RSA public key with private exponent [Formula: see text] where [Formula: see text] and [Formula: see text] are large primes of the same bit size. At Eurocrypt 96, Coppersmith presented a polynomial-time algorithm for finding small roots of univariate modular equations based on lattice reduction and then succussed to factorize the RSA modulus. Since then, a series of attacks on the key equation [Formula: see text] of RSA have been presented. In this paper, we show that many of such attacks can be unified in a single attack using a new notion called Coppersmith’s interval. We determine a Coppersmith’s interval for a given RSA public key [Formula: see text] The interval is valid for any variant of RSA, such as Multi-Prime RSA, that uses the key equation. Then we show that RSA is insecure if [Formula: see text] provided that we have approximation [Formula: see text] of [Formula: see text] with [Formula: see text] [Formula: see text] The attack is an extension of Coppersmith’s result.
Double and triple compression-based forgery detection in JPEG images using deep convolutional neural network

Recompression-based image forensics has been a largely investigated area in the forensic community over recent years. This mainly involves detecting the number of different compressions in an image by exploiting certain artifacts left behind by the compression stages. The artifacts left in images by Joint Photography Experts Group (JPEG) compression coding operations play an important role in passive forensic analysis of images, which help in assessing the authenticity of the image. In this regard, most of the solutions proposed to date are restricted to double compression detection in JPEG images. However, in practical cases, this restriction may not always work. This is because an image having undergone multiple stages of compression, including triple or higher degrees of compression, will be “missed” by such forgery detection algorithms. To address this issue, we propose a deep convolutional neural network (CNN) model that detects both double and triple compression in JPEG images. The model is trained with the compression features specific to single, double, and triple JPEG compressions. The trained model is capable of determining the authenticity of an image by distinguishing between tampered and untampered image regions. Further, the proposed model localizes the forged regions within an image, distinguishing between double and triple compressed regions. Our experimental results establish the efficiency of the proposed model.