Reviewerly/exHarmony huggingface dataset:
Studying the Long-Term Value Answers on Stack Overflow

Context Stack Overflow uses a voting mechanism to reflect the value of its answers. An answer receiving more upvotes indicates more recognition received from the community. Prior studies mainly focus on studying the quality of answers in terms of their received answers and received upvotes at a particular moment, which is static. However, the value of answers may change over time. For example, some answers may receive remarkable recognition (e.g., many upvotes) in the first month, while they never receive recognition later due to obsolescence.Objective The goal of the paper is to understand how the value of answer changes over time and the potential factors that are associated with the likelihood of an answer having long-term value. By knowing this, we can provide insights to Stack Overflow and users into how to create and maintain such long-term value answers.Method In this paper, we study 5,108,171 answers and their associated questions and users on Stack Overflow. We investigate how answers receive recognition from the community over time and what factors are associated with the long-term value of an answer by constructing logistic regression models.Results We find that: 1) 70% of the answers only received upvotes in the first month. Only a small portion (1.4%) of the studied answers received upvotes in more than half of their months. 2) The likelihood of an answer having long-term value relies mainly on the answer itself (e.g., number of received revisions and code percentage) and its tags (e.g., the popularity of the tags). For instance, an answer with more revisions, a proper ratio of code, and popular tags is more likely to have long-term value.Conclusion Our findings provide insights for Stack Overflow and its users to maintain the long-term value of answers.
Multi-scale redistribution feature pyramid for object detection

Many feature pyramid models now use simple contextual feature aggregation, which does not make full use of the semantic information of multi-scale features. Therefore, Multi-scale Redistribution Feature Pyramid Network (MRFPN) is proposed. In order to strengthen feature fusion and solve the two problems of feature redundancy and high abstraction, modified-BiFPN is designed. The features output by the modified-BiFPN module are semantically balanced through the balanced feature map, so as to alleviate the semantic differences between multi-scales. Then a new channel attention module is proposed, which realizes the multi-scale association of the feature information fused to the balanced feature map. Finally, a new feature pyramid is formed through the residual edge for prediction. MRFPN have been evaluated on PASCAL VOC 2012 dataset and MS COCO dataset, which has higher detection accuracy compared with other state-of-the-art detectors.
Anomaly Detection of Cloud Network Resource State Based on Deep Learning

Nowadays, cloud network resources increasingly exist in different practical systems. Anomaly detection is the important technique in these systems, which is able to monitor the system’s status and predict when system maintenance is required. Although many types of data are generated by such systems, time series data is the major type of data which is usually meaningful and worth to analyze. So we mainly consider time series anomaly detection problem in this work. In most cases, the systems are in healthy state, thus the abnormal cases related to cloud network resources are rare, which motivates us to treat this as an unsupervised learning problem. By leveraging the strong learning ability of deep learning, we propose to combine LSTM and autoencoder to detect outliers in cloud network resources. We compare our approach with other basedline models via several benchmark datasets and the effectiveness is verified.
A Blockchain-Powered Energy Monitoring System

The current system of energy monitoring and billing, being centralized, creates many challenges in terms of reliability and efficiency of energy systems, for which decentralized control of power systems has been encouraged in recent years. Due to the involvement of energy intermediaries and third parties in a centralized system, data tampering and erroneous transactions have been a major issue in the current infrastructure of the electricity monitoring and billing system. To curb these issues, blockchain-based system is essential and implemented to provide secure and immutable energy records only to the registered users. Blockchain is an emerging technology that is a decentralized, highly immutable, and cryptographically secured distributed ledger system, using important transaction data, energy records, and contracts, which are digitally maintained in real time across a peer-to-peer network. In this research, a blockchain-based prototype is proposed by integrating advanced metering infrastructure and smart contracts to come up with a peer-to-peer monitoring system that will ensure transparency in energy usage and automation of the billing process and detect energy theft in the power lines. Furthermore, we target the major issues associated with the existing infrastructure of energy monitoring and billing systems such as the manual intervention of the billing process, overdue bill payment, illegal tapping of power lines, and unethical selling of energy units by the electricity board. It is observed that the proposed smart system is capable in executing the smart contract functionality in automated billing and storing transactional data in the blockchain. This prototype can be used in large-scale applications for monitoring energy usage in real time as well as tracking the electricity billing process.
A Secure E-commerce Environment Using Multi-agent System

Providing security for the customers in the e-commerce system is an essential issue.Providing security for each single online customer at the same time is considered a time consuming process.For a huge websites such task may cause several problems including response delay, losing the customer orders and system deadlock or crash, in which reduce system performance.This paper aims to provide a new prototype structure of multi agent system that solve the problem of providing security and avoid the problems that may reduce system performance.This is done by creating a software agent which is settled into the customer device to be responsible of purchase and encryption process without the customer interfering.The proposed agent avoids the problem of deadlock (i.e., break down) and the loss of requests which provides the required protection for information transmitted among all entities.Experimental results showed that employing software agent to manage purchase and encryption tasks improve system performance by 10% and increase system response time by 30.5%.
Design and Implementation of English Teaching System Based on Artificial Intelligence

Today's society has entered the Internet age, and the Internet has been deeply integrated into all aspects of people's life, study, and work. Education is no exception. With the rapid development and wide application of network technology, a new education method is booming, and English teaching is one of the important teaching aspects in the education industry. However, the level of network technology is uneven, resulting in weak development capabilities of the education system. Therefore, based on artificial intelligence technology, the research and design of English teaching system has very important value for English teaching. This article first expounds the basic principles of artificial intelligence, and conducts in-depth research on the technology of artificial intelligence, and then designs the framework of the English teaching system and tests its performance. The final test result shows that the average running time of the system is basically around 12ms, and the running efficiency is good.
Automatic 3D Skeleton-based Dynamic Hand Gesture Recognition Using Multi-Layer Convolutional LSTM

Accurate and real-time recognition of skeleton-based dynamic hand gestures has gained increasing attention in recent years with the development of depth sensors and the improved hand joints estimation algorithms. This task is challenging due to the spatial and temporal features that exacerbate the task complexity. Although previous works have applied different techniques, it remains challenging to efficiently and simultaneously encode the spatiotemporal features. To address this problem, this work presents a Deep Convolutional LSTM (DConvLSTM) model to learn more discriminative spatiotemporal features from skeleton data implicitly. The model employs multi-layer ConvLSTM to accurately capture the multiscale spatial and sequential information of the gesture and preserves a fast inference and lightweight size. Extensive experiments on three publicly available datasets show strong performance and demonstrate the superiority of our method by outperforming other methods. Furthermore, our method can achieve comparable recognition accuracy while maintaining small models and short inference time.
Legal Regime for the Protection of Genetic Information of Indigenous Peoples and Local Communities in International Law

The present study aims at providing an idea that the protection of genetic and genomic information of indigenous peoples and local communities should be legally established at universal and regional levels. There is a trend in many countries towards the disappearance of rare nations and peoples representing genetic diversity. In the case of the collection, processing, storage, transmission of data in the application of artificial intelligence take further action to ensure cybersecurity, develop ethical guidelines and confidentiality requirements for collection and processing genomic and genetic information on the health of indigenous peoples and local communities bearing in mind the provisions of the United Nations Declaration on the Rights of Indigenous Peoples (2007) and the International Convention on the Elimination of All Forms of Racial Discrimination (1965). Although human genes are not covered by the Convention on Biodiversity (1992), it should be applied by analogy in the case of the protection of the “genetic” heritage of mankind. The research uses general scientific and special cognitive techniques wherein legal analysis and synthesis, systemic, formal-legal, comparative-legal, historical-legal and dialectical methods are applied. The author calls on the international community to recognize indigenous genetic information from medical research as the common heritage of mankind and to establish special legal responsibility of present generations for the future of mankind at the universal level. The author of the article notes the importance of prevention the development of racial and ethnic weapons against a certain population group and to prevent the commission of the crime of “genomocide” against indigenous peoples and local communities and to comply with biosafety measures in conducting scientific research and obtaining certain genetic information, to preserve the uniqueness of the biocode of the nations and peoples inhabiting our planet.
An Improved Method to Train the Model Rapidly for the Classification of Ceramic Tiles

In the production line of ceramic tiles, different types of tiles need to be classified with the algorithm of image classification. For most of common image classification tasks, the classification precision and inference time are our focus results which represent how effective the method proposed is. However, the time of training the model is also important in the image classification of ceramic tiles due to the need of no much time of waiting the model to be trained. In this paper, we propose a method to meet the requirements of training the model rapidly and high classification precision. The method is mainly based on the matrix features from traditional image processing algorithms and classifier from Artificial neural networks (ANN). The output feature is the combination of HSV model and Gray-level Co-occurrence Matrix (GLCM). The classifier is the AngleLinear classifier which is usually used to in combination with Convolutional Neural Network (CNN). The experimental result shows that our proposed method performs better than other common methods in accuracy under the constraints of only a few minutes of training time, especially on the image datasets that exhibit high similarity in the feature of color and texture.
On the Reconstruction of 3-Uniform Hypergraphs from Degree Sequences of Span-Two

Abstract A nonnegative integer sequence is k -graphic if it is the degree sequence of a k -uniform simple hypergraph. The problem of deciding whether a given sequence $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> is 3-graphic has recently been proved to be NP-complete, after years of studies. Thus, it acquires primary relevance to detect classes of degree sequences whose graphicality can be tested in polynomial time in order to restrict the NP-hard core of the problem and design algorithms that can also be useful in different research areas. Several necessary and few sufficient conditions for $$\pi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>π</mml:mi> </mml:math> to be k -graphic, with $$k\ge 3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> , appear in the literature. Frosini et al. defined a polynomial time algorithm to reconstruct k -uniform hypergraphs having regular or almost regular degree sequences. Our study fits in this research line providing a combinatorial characterization of span-two sequences, i.e., sequences of the form $$\pi =(d,\ldots ,d,d-1,\ldots ,d-1,d-2,\ldots ,d-2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>π</mml:mi> <mml:mo>=</mml:mo> <mml:mo>(</mml:mo> <mml:mi>d</mml:mi> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mi>d</mml:mi> <mml:mo>,</mml:mo> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , $$d\ge 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> , which are degree sequences of some 3-uniform hypergraphs. Then, we define a polynomial time algorithm to reconstruct one of the related 3-uniform hypergraphs. Our results are likely to be easily generalized to $$k \ge 4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> and to other families of degree sequences having simple characterization, such as gap-free sequences.
Distinguishing Between Real and Posed Smiles from Observers’ Accelerometer Data

This paper demonstrates a system for distinguishing between real and posed smiles from 25 observers’ accelerometer data. The data are gathered using Empatica E4 wristband when observers were watching four kinds of smiles, namely single image (SI), single video (SV), paired images (PI) and paired videos (PV). A two sample K-S test shows that the accelerometer data, which are recorded when observers are watching a single video (SV) of displayers’ smiles, is more reliable compared to other conditions. We computed performance of the accelerometer-based smile detection process using decision tree, logistic regression, K-nearest neighbor, stochastic gradient descent, naïve bayes, and random forest classifiers. Based on the selected sub-datasets, it is found that the decision tree outperforms all the other classifiers, with 80% classification accuracy on our two classes of smiles (real vs. posed). This high accuracy indicates the possibility that the accelerometer data can be used for distinguishing between human real and posed smiles.
Multi-angle Dual-task Consistency for Semi-Supervised Medical Image Segmentation

Semi-supervised learning has shown its effectiveness in the field of medical image segmentation tasks, since it mitigates the heavy burden of obtaining abundant reliable annotations, which is scarce and time-consuming, by leveraging the available unlabeled data. In this paper, we propose a novel Multi-angle Dual-task Consistency Network (MDC-Net) for semi-supervised medical image segmentation. Particularly, our MDC-Net can be formulated as a model similar to the Mean Teacher with both employing exponential moving average (EMA). The difference is that we utilize one of the networks to augment unlabeled data, thus generating its pseudo labels and exploiting MixUp on both labeled and unlabeled data, whose outputs are then utilized as the inputs of another dual-task network based on region-based shape constraints and boundary-based surface mismatch. Finally, by jointly learning pseudo labels, the segmentation probability map and the signed distance map of the target, our framework not only overcomes the problem of incomplete segmentation caused by noise in pseudo labels, but also enforces geometric constraints and makes the model more robust. Experimental results on the public Left Atrium (LA) database show that our method achieves performance gains by incorporating unlabeled data and outperforms six state-of-the-art semi-supervised segmentation methods.
Analysis of M-learning application in Chinese Learning: Take hellotalk as an example

M-learning is mobile learning, generally relying on wireless network and multimedia technology, using mobile phones, laptops and other wireless devices to achieve interactive teaching activities. [1] In this era, with the rapid development of information technology, wireless network technology is becoming more and more mature, and almost every person has a smart phone, this mode of using mobile devices to teach is becoming more and more popular. Taking hellotalk as an example, this paper first expounds the current development of modern educational technology in teaching Chinese as a foreign language, and then summarizes the current situation of the application of mobile software in teaching Chinese as a foreign language. Then it briefly introduces the functions of hellotalk and analyzes its advantages and disadvantages in application. Finally, it puts forward some suggestions on the learning software combined with teaching Chinese as a foreign language, hoping to learn this way. Learning software can be further improved, so as to promote TCFL more diversified.
ADVRET: An Adversarial Robustness Evaluating and Testing Platform for Deep Learning Models

Recent studies have shown that deep learning models such as image classification and object detection models are vulnerable to attacks from adversarial examples. These malicious examples may trigger potential security threats and cause damage in security-related fields. In this paper, in order to find model robustness weakness, we obtain the adversarial robustness metric against several adversarial attacks, including gradient-based attacks, optimization-based attacks, etc. The metric quantizes the adversarial robustness of deep learning models and can be used as a guide for extra model training. Meanwhile, we design a testing platform to achieve model adversarial robustness evaluation. Utilizing the front and back end separation strategy, the testing platform consist of 2 modules: one is multiple adversarial example attacks executor and the other is HCI module. In addition, the tool allows parallel comparison among different adversarial attack algorithms under given conditions and measures the quality of generated adversarial examples utilizing FID(Fréchet Inception Distance) metrics. Besides, we developed ERFGSM(edge-RFGSM), a new gradient-based attack using edge information through canny operator.
Iterative Semi-Supervised Learning Using Softmax Probability

For the classification problem in practice, one of the challenging issues is to obtain enough labeled data for training.Moreover, even if such labeled data has been sufficiently accumulated, most datasets often exhibit long-tailed distribution with heavy class imbalance, which results in a biased model towards a majority class.To alleviate such class imbalance, semisupervised learning methods using additional unlabeled data have been considered.However, as a matter of course, the accuracy is much lower than that from supervised learning.In this study, under the assumption that additional unlabeled data is available, we propose the iterative semi-supervised learning algorithms, which iteratively correct the labeling of the extra unlabeled data based on softmax probabilities.The results show that the proposed algorithms provide the accuracy as high as that from the supervised learning.To validate the proposed algorithms, we tested on the two scenarios: with the balanced unlabeled dataset and with the imbalanced unlabeled dataset.Under both scenarios, our proposed semi-supervised learning algorithms provided higher accuracy than previous state-of-the-arts.Code is available at https://github.com/HeewonChung92/iterative-semi-learning.
Model design and compression

Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address these limitations, in this chapter, we introduce the current lightweight model design and compression methods. First, we introduce efficient lightweight network designs by presenting some classical lightweight models. Then we summarize the model compression methods and detailedly introduce two typical methods, model pruning and knowledge distillation.
Sampling May Not Always Increase Detector Performance

In recent years, the research of computer vision is popular. However, the image data that can be used for computer vision training is very limited, so it is necessary to find an effective method to expand the datasets based on the existing image data. In this paper, we study methods to collect more training data from existing datasets and compare detectors’ performance trained with datasets generated by different methods. One method is to perform sampling-based on statistical properties of feature descriptors. For every feature, the underlying assumption is that a probability density function (PDF) exists, such PDF is approximated with existing training examples and new training examples are sampled from the approximated PDF. The other method is simply to expand the existing datasets by flipping each training example along its symmetric axis. Locally Adaptive Regression Kernel (LARK) feature is used in this paper because it is robust against illumination changes and noise. Our experimental results demonstrate that an expanded training dataset is not always preferable, even if the expanded dataset includes all original training data.