Reviewerly/exHarmony huggingface dataset:
Adversarial machine learning and adversarial risk analysis in multi-source command and control

Over the last decade, various defense and security agencies have focused on methods of data and information fusion across multiple domains (e.g., space, air, land, sea, undersea, cyber, and information). Researchers and practitioners in these communities are currently emphasizing the importance of information warfare, algorithmic warfare, joint all domain operations, and multi-source availability. A related development extending from the data analytics community is adversarial machine learning (AML), i.e., the study of attacking and defending machine learning algorithms. It is generally the case in AML for a single algorithm to be considered. However, AML research regarding multi-source data manipulation is less developed because it compounds the challenges typically addressed. That is, attacks must be perceived over numerous information streams and their effects mitigated accordingly, often across multiple algorithms. This challenge is further complicated in multi-domain applications characterized by distributed control wherein agents have distinct capabilities (e.g., people or technological tools); the AML approaches required for operator infusion, information fusion, and control diffusion likely vary across each actor. Noting these challenges, this manuscript reviews command and control constructs, surveys related literature, and explores opportunities for adversarial risk analysis, a decision-theoretic alternative to game theory, to address AML in multi-source command and control settings.
The Application of Intelligent Speech Recognition Technology in the Tone Correction of College Piano Teaching

Abstract In recent years, with the continuous deepening of college teaching reform and the rapid development of artificial intelligence, the teaching methods used in college classrooms have been greatly different from traditional ones. The original teaching programs and teaching methods need to be changed with the development of the times. The main purpose of this article is to improve the correction rate of intelligent voice recognition technology for college piano timbre without violating the safety of artificial intelligence. This article mainly conducts experiments by consulting relevant domestic and foreign literature, using observation and comparison methods, and using some neural network algorithms (BP neural network algorithm and convolutional neural network algorithm) to obtain experimental results, and finally complete our experimental goal-research intelligence The application of speech recognition technology in the tone correction of piano teaching in colleges. The test results show that the use of multiple algorithms can improve the application of intelligent speech recognition technology in the tone correction of piano teaching in colleges and universities, mainly to improve the correction rate and enhance the ability of guiding teaching.
cs@DravidianLangTech-EACL2021: Offensive Language Identification Based On Multilingual BERT Model

This paper introduces the related content of the task “Offensive Language Identification in Dravidian LANGUAGES-EACL 2021”. The task requires us to classify Dravidian languages collected from social media into Not-Offensive, Off-Untargeted, Off-Target-Individual, etc. This data set contains actual annotations in code-mixed text posted by users on Youtube, not from the monolingual text in textbooks. Based on the features of the data set code mixture, we use multilingual BERT and TextCNN for semantic extraction and text classification. In this article, we will show the experiment and result analysis of this task.
Enhancing text classification performance by preprocessing misspelled words in Indonesian language

Supervised learning using shallow machine learning methods is still a popular method in processing text, despite the rapidly advancing sector of unsupervised methodologies using deep learning. Supervised text classification for application user feedback sentiments in Indonesian Language is one of the applications which is quite popular in both the research community and industry. However, due to the nature of shallow machine learning approaches, various text preprocessing techniques are required to clean the input data. This research aims to implement and evaluate the role of Levenshtein distance algorithm in detecting and preprocessing misspelled words in Indonesian language, before the text data is then used to train a user feedback sentiment classification model using multinomial Naïve Bayes. This research experimented with various evaluation scenarios, and found that preprocessing misspelled words in Indonesian language using the Levenshtein distance algorithm could be useful and showed a promising 8.2% increase on the accuracy of the model’s ability to classify user feedback text according to their sentiments.
Towards Offensive Language Identification for Dravidian Languages

Offensive speech identification in countries like India poses several challenges due to the usage of code-mixed and romanized variants of multiple languages by the users in their posts on social media. The challenge of offensive language identification on social media for Dravidian languages is harder, considering the low resources available for the same. In this paper, we explored the zero-shot learning and few-shot learning paradigms based on multilingual language models for offensive speech detection in code-mixed and romanized variants of three Dravidian languages - Malayalam, Tamil, and Kannada. We propose a novel and flexible approach of selective translation and transliteration to reap better results from fine-tuning and ensembling multilingual transformer networks like XLMRoBERTa and mBERT. We implemented pretrained, fine-tuned, and ensembled versions of XLM-RoBERTa for offensive speech classification. Further, we experimented with interlanguage, inter-task, and multi-task transfer learning techniques to leverage the rich resources available for offensive speech identification in the English language and to enrich the models with knowledge transfer from related tasks. The proposed models yielded good results and are promising for effective offensive speech identification in low resource settings.
RETRACTED: Study of chorus art on students’ aesthetic cognition

With the passage of time, people’s lifestyle and life philosophy have changed a lot; people began to pursue a higher quality of life. Chorus art is a spiritual civilization that shows a harmonious beauty. Through the melody of music works and chorus art, students are resonated, and music is used to guide students to be active and to enjoy beauty from beautiful melodies and strong rhythms. This paper studies the edification of students’ aesthetics from the art of chorus. Through the development history of chorus art in Western countries and China, this paper expounds the diversified development process of chorus, further analyze the aesthetic characteristics of chorus art, and guide people to feel the connotation of chorus art from the perspective of music aesthetics, and provide countermeasures for the development of chorus art.
Discussion on Legal Model of Intellectual Property of Computer Software

Abstract With the development of computer software and information technology, software industry has become one of the important engines of modern economic construction and structural transformation in China. However, because the computer software is very complex and the industry value is high, it is easy to lead to the flood of intellectual property infringement, which is undoubtedly a great blow to the researchers in the field of computer technology and related industries. Therefore, through analyzing the current situation of intellectual property protection of computer software, this paper fully discusses the comprehensive construction mode of copyright law, patent law and trade secret protection law, in order to ensure the innovation and application environment of this intellectual property right.
A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation

Purpose This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation. Design/methodology/approach The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets. Findings The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields. Research limitations/implications The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future. Originality/value The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.
Performance testing on Transparent Data Encryption for SQL Server's reliability and efficiency

Abstract Data security is being one of the most crucial aspects to be focused on system development. However, using such a feature to enhance the security of data might affect the system's performance. This study aims to observe how substantial Transparent Data Encryption as a solution for data security on Microsoft SQL Server will affect the database management system's performance. Each of the system performance is conducted with stress and load test. This paper concentrates on the upsides of using Transparent Data Encryption over standard database by finding how significant performance degradation has occurred in terms of Reliability and Efficiency.
The Application Analysis of Medical Chatbots and Virtual Assistant

With the gradual improvement of China's medical insurance policies, the scale of the market of medical devices has become more large, and people pay more attention to their own health.Based on the issue, this paper mainly carries on the industry investigation on the medical institution chatbots.It analyzes the application of four kinds of medical chatbots in the actual medical institutions, which makes a detailed elaboration on their future development trend.This paper makes a conclusion that it is as a synergistic effect when clinicians and medical chatbot work together, producing better results than either alone.
Exploring the Relationships between Data Complexity and Classification Diversity in Ensembles

Several classification techniques have been proposed in the last years.Each approach is best suited for a particular classification problem, i.e., a classification algorithm may not effectively or efficiently recognize some patterns in complex data.Selecting the best-tuned solution may be prohibitive.Methods for combining classifiers have also been proposed aiming at improving the generalization ability and classification results.In this paper, we analyze geometrical features of the data class distribution and the diversity of the base classifiers to understand better the performance of an ensemble approach based on stacking.The experimental evaluation was conducted using 32 real datasets, twelve data complexity measures, five diversity measures, and five heterogeneous classification algorithms.The results show that stacked generalization outperforms the best individual base classifier when there is a combination of complex and imbalanced data with diverse predictions among weak learners.
PC-SAN: Pretraining-Based Contextual Self-Attention Model for Topic Essay Generation

Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics.To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency.Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics.However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics.To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework.For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays.In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs).Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines.Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.
Distribution Regression for Sequential Data

Distribution regression refers to the supervised learning problem where labels are only available for groups of inputs instead of individual inputs. In this paper, we develop a rigorous mathematical framework for distribution regression where inputs are complex data streams. Leveraging properties of the expected signature and a recent signature kernel trick for sequential data from stochastic analysis, we introduce two new learning techniques, one feature-based and the other kernel-based. Each is suited to a different data regime in terms of the number of data streams and the dimensionality of the individual streams. We provide theoretical results on the universality of both approaches and demonstrate empirically their robustness to irregularly sampled multivariate time-series, achieving state-of-the-art performance on both synthetic and real-world examples from thermodynamics, mathematical finance and agricultural science.
Lightweight End-to-End Blockchain for IoT Applications

Internet of Things (IoT) networks composed of a large number of sensors and actuators generate a huge volume of data and control commands, which should be enforced by strong data reliability.The end-to-end data reliability of IoT networks is an essential industrial enabler.Blockchain technology can provide strong data reliability and integrity within IoT networks.We designed a lightweight end-to-end blockchain network that applies to common IoT applications.Its enhanced modular architecture and lightweight consensus mechanism guarantee its practical applicability for general IoT applications.In addition, the proposed blockchain network is highly software compatible because it adopts the Hyperledger development environment.Directly embedding the proposed blockchain middleware platform in small computing devices proves its practicability.
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs. While each of them addresses specific needs, we re-designed and re-implemented PyKEEN, one of the first KGE libraries, in a community effort. PyKEEN 1.0 enables users to compose knowledge graph embedding models (KGEMs) based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. Besides, an automatic memory optimization has been realized in order to exploit the provided hardware optimally, and through the integration of Optuna extensive hyper-parameter optimization (HPO) functionalities are provided.
Using Self-Supervised Co-Training to Improve Facial Representation.

In this paper, at first, the impact of ImageNet pre-training on Facial Expression Recognition (FER) was tested under different augmentation levels. It could be seen from the results that training from scratch could reach better performance compared to ImageNet fine-tuning at stronger augmentation levels. After that, a framework was proposed for standard Supervised Learning (SL), called Hybrid Learning (HL) which used Self-Supervised co-training with SL in Multi-Task Learning (MTL) manner. Leveraging Self-Supervised Learning (SSL) could gain additional information from input data like spatial information from faces which helped the main SL task. It is been investigated how this method could be used for FER problems with self-supervised pre-tasks such as Jigsaw puzzling and in-painting. The supervised head (SH) was helped by these two methods to lower the error rate under different augmentations and low data regime in the same training settings. The state-of-the-art was reached on AffectNet via two completely different HL methods, without utilizing additional datasets. Moreover, HL's effect was shown on two different facial-related problem, head poses estimation and gender recognition, which concluded to reduce in error rate by up to 9% and 1% respectively. Also, we saw that the HL methods prevented the model from reaching overfitting.
Multiphase Image Segmentation Model Based on Clustering Algorithm

In the traditional Multiphase Image segmentation algorithm, there are some problems, such as long operation time, high calculation cost and high error rate. In order to improve the above problem, originally proposed a multiphase image segmentation enhancement algorithm and model based on the combination of clustering algorithm. In this paper, for the problem of large amount of initial calculation for the segmentation model of multiple targets, a clustering algorithm is proposed to segment the image, and finally the model is established, the function data set is initialized, and the method of multiphase image segmentation is easier to segment idealized goal. This method can reduce the sensitivity of the clustering algorithm in the initialization, making the multiphase image segmentation model under the clustering algorithm easier to segment the ideal image. At the same time, the image segmentation model can quickly get the minimum value, reduce the amount of calculation, fully and effectively improve the efficiency. The M-level set implicit surface of multi-level set function is used to divide the image into m regions. The maximum value of level setting function is calculated to realize fast segmentation and reconstruction of constant value of multiphase segmentation. Experimental results show that the algorithm can greatly improve the contrast and clarity of images, and bring the best visual experience to people. Compared with the traditional clustering model, it has less iteration steps and faster segmentation speed.
Recognition of the standardization of wearing masks during the epidemic of COVID-19

In face of COVID-19, it is very important to wear masks in public. In this article, we use a kind of improved convolutional neural network algorithm DFS (detection with feature fusion and segmentation supervision) for face detection, and YCrCb elliptic skin color model is used for mask recognition. We expect to better supervise the standardization of people wearing masks, through the application of this project in public places such as public transport. With the applying of this project, the risk of human infection can be reduced, so as to better prevent and control the epidemic.
Differential privacy EV charging data release based on variable window

In the V2G network, the release and sharing of real-time data are of great value for data mining. However, publishing these data directly to service providers may reveal the privacy of users. Therefore, it is necessary that the data release model with a privacy protection mechanism protects user privacy in the case of data utility. In this paper, we propose a privacy protection mechanism based on differential privacy to protect the release of data in V2G networks. To improve the utility of the data, we define a variable sliding window, which can dynamically and adaptively adjust the size according to the data. Besides, to allocate the privacy budget reasonably in the variable window, we consider the sampling interval and the proportion of the window. Through experimental analysis on real data sets, and comparison with two representative w event privacy protection methods, we prove that the method in this paper is superior to the existing schemes and improves the utility of the data.
DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial Networks

Single Image Super-Resolution (SISR) has proven itself as a highly challenging and ill-posed problem. Multiple methods have been applied to this problem in the past, with varying degrees of success. Recently, methods using deep learning such as Generative Adversarial Networks (GAN) and Variational Auto-Encoders (VAE) in particular have proven to be extremely effective. However, most of the present methods either create a blurry output, lacking fine details, or use extremely heavy models to achieve better results. We introduce a novel, lightweight GAN architecture for 4× super-resolution of images, which builds on previous methods, showing high quality of features both qualitatively and quantitatively. To achieve this, we use dilated convolutions in our generator architecture, a Markovian discriminator, a modified loss function and a training process more typical of a conditional GAN (cGAN). For testing our results qualitatively, we use Mean Opinion Score (MOS). The obtained MOS show the effectiveness of our model at generating visually superior images. Our code is available at https://www.github.com/kushalchordiya216/Super-Resolution.
Generating Javanese Stopwords List using K-means Clustering Algorithm

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable.