Reviewerly/exHarmony huggingface dataset:
Topic-level knowledge sub-graphs for multi-turn dialogue generation

Previous multi-turn dialogue approaches based on global Knowledge Graphs (KGs) still suffer from generic, uncontrollable, and incoherent responses generation. Most of them neither consider the local topic-level semantic information of KGs nor effectively merge the information of long dialogue contexts and KGs into the dialogue generation. To tackle these issues, we propose a Topic-level Knowledge-aware Dialogue Generation model to capture context-aware topic-level knowledge information. Our method thus accounts for topic-coherence, fluency, and diversity of generated responses. Specifically, we first decompose the given KG into a set of topic-level sub-graphs, with each sub-graph capturing a semantic component of the input KG. Furthermore, we design a Topic-level Sub-graphs Attention Network to calculate the comprehensive representation of both sub-graphs and previous turns of dialogue utterances, which then decoded with the current turn into a response. By using sub-graphs, our model is able to attend to different topical components of the KG and enhance the topic-coherence. We perform extensive experiments on two datasets of DuRecDial and KdConv to demonstrate the effectiveness of our model. The experimental results demonstrate that our model outperforms existing strong baselines.
Handling Test Smells in Python: Results from a Mixed-Method Study

Software testing is an activity in the software development process that looks for defects. Automated testing is composed of code that allows run software testing scenarios more quickly, avoiding manual rework. However, testers are likely to employ practices that might negatively impact test quality regarding maintainability, understandability, and effectiveness when writing test code. Such bad practices are also known as test smells. Although test smells are a language-independent concept, different programming languages could present different occurrence standards. Therefore, studies in one programming language may not be generalizable. This study aims to investigate how test smells occurrence in Python test files. Python became the most widely used programming language globally in 2020. However, most research on test code quality only considers the Java language. To accomplish our goals, we built a dataset with 5,303 test files from 90 Python projects collected from GitHub repositories to understand and analyze strategies for handling test smells in Python. This analysis allowed us to propose four new test smells, discussing their potential problems. We carried out a preliminary evaluation with 40 Python developers to validate their thoughts on the proposed test smells. These results are part of an ongoing research project aiming to propose a foundation to better support automation tests in Python.
An Analysis of 3D Steganography Techniques

Steganography has become a preferred technique these days to successfully hide secret messages. Various research has been done in the past to justify and analyze suitable types of cover file, such as images, audio, videos, etc. Advancement in the image-processing domain has opened various possibilities of using three-dimensional (3D) images as cover files. In this paper, a systematic study of the research work done on 3D steganography in the last fifteen years has been carried out. The study is divided into different sections based on the types of algorithms used, additional security features, evaluation parameters, etc. Moreover, certain steganalysis techniques that are applicable for 3D steganography are also discussed.
Robust Fuzzy Association Rule Mining Based on Neighbors-Considered Clustering with Amended Membership Function

Clustering based association rule mining algorithms usually deal with data sets by clustering numerical transactions into boolean ones then using the boolean method. However, the numerical data will often change slightly, which may be caused by errors in the data acquisition process or disturbance in the environment, as a result, the obtained association rules will change greatly. Therefore, the uncertainty in the association rule mining should be considered. In this paper, an improved fuzzy clustering based robust association rule mining algorithm (RFARM) is proposed, where a regularization term is added into the objective function in which each point considers its own k-nearest neighbors to offset small disturbance and we also derive the necessary conditions for the convergence to the local minimum. Meanwhile, fuzzy clustering methods with constraints produce ripple parts in membership functions, which cannot be explained in association rule mining. In order to solve this, we design a variant algorithm (RFARM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> ) that can perform and be understood better than the frequently-used methods. Experimental results have shown that the proposed methods are superior in the accuracy of association rules and the anti-noise capability.
Construction of Visual Question and Answering System Based on Knowledge Graph for Specific Objects

As China’s industry enters the 4.0 digital era, the segmentation of industrial applications is diversified. The knowledge of specific objects in the industrial scene is scattered that brings the pain points for management and operation. Knowledge graph (KG) has a powerful ability for relationship expression and real-time feedback that can integrate multi-source heterogeneous knowledge and provide data support for the Question Answering (Q&A) system. In this paper, a semi-automatic construction scheme for cupping machine knowledge graph is proposed and a visual Q&A system based on KG by the rule-based method is constructed. In the implementation of the KG, we design an ontology schema of the cupping machine and obtain entities and relationships from the network. This work solves the problem of the data island of cupping machines and provides a new technical method for the knowledge management of specific objects in the industrial equipment field.
Radial Distortion Invariant Factorization for Structure from Motion

Factorization methods are frequently used for structure from motion problems (SfM). In the presence of noise they are able to jointly estimate camera matrices and scene points in overdetermined settings, without the need for accurate initial solutions. While the early formulations were restricted to affine models, recent approaches have been show to work with pinhole cameras by minimizing object space errors.In this paper we propose a factorization approach using the so called radial camera, which is invariant to radial distortion and changes in focal length. Assuming a known principal point our approach can reconstruct the 3D scene in settings with unknown and varying radial distortion and focal length. We show on both real and synthetic data that our approach outperforms state-of-the-art factorization methods under these conditions. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Watermarking-based Defense against Adversarial Attacks on Deep Neural Networks

The vulnerability of deep neural networks to adversarial attacks has posed significant threats to real-world applications, especially security-critical ones. Given a well-trained model, slight modifications to the input samples can cause drastic changes in the predictions of the model. Many methods have been proposed to mitigate the issue. However, the majority of these defenses have proven to fail to resist all the adversarial attacks. This is mainly because the knowledge advantage of the attacker can help to either easily customize the information of the target model or create a surrogate model as a substitute to successfully construct the corresponding adversarial examples. In this paper, we propose a new defense mechanism that creates a knowledge gap between attackers and defenders by imposing a designed watermarking system into standard deep neural networks. The embedded watermark is data-independent and non-reproducible to an attacker, which improves randomization and security of the defense model without compromising performance on clean data, and thus yields knowledge disadvantage to prevent an attacker from crafting effective adversarial examples targeting the defensive model. We evaluate the performance of our watermarking defense using a wide range of watermarking algorithms against four state-of-the-art attacks on different datasets, and the experimental results validate its effectiveness.
Research on outlier detection of data based on machine learning

With the increasing magnitude of data, the accuracy of single data cannot be guaranteed. In order to improve the accuracy of model prediction data, and further for the subsequent data processing, this paper focuses on the detection of data on outliers. This paper introduces the density clustering and outlier detection methods of Isolation Forest in outlier recognition, and mainly describes the principle and process of outlier recognition using density clustering and Isolation Forest. Based on this, according to the features of data, an improved algorithm combining density clustering and Isolation Forest is proposed. Finally, through the existing common outlier detection data set, the statistical outlier recognition method, the existing machine learning algorithm and the improved algorithm proposed in this paper are compared to identify outliers, which verifies the stability of the proposed method and the effective improvement compared with the existing outlier detection.
Arabic text detection in news video using RetinaNet

Embedded text in video frames represents an interesting source of high level information especially for semantic indexing and retrieval applications. In this paper, we propose a novel approach for Arabic text detection in news videos using a deep learning method called RetinaNet. It is based in two steps. The first one aims to extract features using residual network (Res-Net) and a pyramidal feature network (FPN). In the second step, we use two fully convolutional networks (FCN), one is for the classification task and the other for the bounding box regression task. Experiments show that the proposed method can detect text regions effectively and provides a more satisfactory results compared to other existing methods.
Application of Federated Learning in Industrial Internet with Device Identifier

With the accelerated integration of the Internet and traditional industries, it is essential to study the Industrial Internet of Things (IIoT) based on device identification. Among the organizational elements of the IIoT, the device identifier is the key technology to realize the rapid development of the IIoT. However, IIoT faces many challenges (equipment security, communication security, data sharing, data security, etc.). This paper proposes a framework to realize the problem of data sharing and ensures the reliability of communication between network nodes. In the framework, the AI model is designed according to the device identifiers in the network and federated learning (FL) technology is used to realize the problem of data sharing. However, there are some limitations in FL. In this paper, we employ blockchain technology to ensure the security of data and proposed an algorithm to realize the reliability of communication.
Testing incompatibility of quantum devices with few states

When observations must come from incompatible devices and cannot be produced by compatible devices? This question motivates two integer valued quantifications of incompatibility, called incompatibility dimension and compatibility dimension. The first one quantifies how many states are minimally needed to detect incompatibility if the test states are chosen carefully, whereas the second one quantifies how many states one may have to use if they are randomly chosen. With concrete examples we show that these quantities have unexpected behaviour with respect to noise.
Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization

Existing differentiable neural architecture search approaches simply assume the architectural distribution on each edge is independent of each other, which conflicts with the intrinsic properties of architecture. In this paper, we view the architectural distribution as the latent representation of specific data points. Then we propose Variational Information Maximization Neural Architecture Search (VIM-NAS) to leverage a simple yet effective convolutional neural network to model the latent representation, and optimize for a tractable variational lower bound to the mutual information between the data points and the latent representations. VIM-NAS automatically learns a nearly one-hot distribution from a continuous distribution with extremely fast convergence speed, e.g., converging with one epoch. Experimental results demonstrate VIM-NAS achieves state-of-the-art performance on various search spaces, including DARTS search space, NAS-Bench-1shot1, NAS-Bench-201, and simplified search spaces S1-S4. Specifically, VIM-NAS achieves a top-1 error rate of 2.45% and 15.80% within 10 minutes on CIFAR-10 and CIFAR-100, respectively, and a top-1 error rate of 24.0% when transferred to ImageNet.
Semantically Stealthy Adversarial Attacks against Segmentation Models

Segmentation models have been found to be vulnerable to targeted and non-targeted adversarial attacks. However, the resulting segmentation outputs are often so damaged that it is easy to spot an attack. In this paper, we propose semantically stealthy adversarial attacks which can manipulate targeted labels while preserving non-targeted labels at the same time. One challenge is making semantically meaningful manipulations across datasets and models. Another challenge is avoiding damaging non-targeted labels. To solve these challenges, we consider each input image as prior knowledge to generate perturbations. We also design a special regularizer to help extract features. To evaluate our model’s performance, we design three basic attack types, namely ‘vanishing into the context,’ ‘embedding fake labels,’ and ‘displacing target objects.’ Our experiments show that our stealthy adversarial model can attack segmentation models with a relatively high success rate on Cityscapes, Mapillary, and BDD100K. Our framework shows good empirical generalization across datasets and models.
Image Denoising Using an Improved Generative Adversarial Network with Wasserstein Distance

The image denoising discriminant model has received extensive attention in recent years due to its good denoising performance. In order to solve the problems of denoising of traditional generative adversarial networks, which are difficult to train and easy to collapse, traditional denoising methods will damage the visibility of important structural details after the image is denoised, this paper proposes an improved generative adversarial network algorithm. The novel algorithm obtains more image features by adding multi-level convolution of the generative network, and adds multiple residual blocks and global residuals to extract and learn the features of the input noisy image to avoid the loss of features. The network uses the weighted sum of feature loss and perceptual loss, which can effectively retain the detailed information of the image while removing noise. In the experiments, we selected PSNR and SSIM as the indicators, and the experimental results show that the novel algorithm can effectively remove image noise and improve the visual perception of the image.
Bankrupting Sybil Despite Churn

A Sybil attack occurs when an adversary pretends to be multiple identities (IDs). Limiting the number of Sybil (bad) IDs to a minority is critical to the use of well-established tools for tolerating malicious behavior, such as Byzantine agreement and secure multiparty computation. A popular technique for enforcing a Sybil minority is resource burning: verifiable consumption of a network resource, such as computational power, bandwidth, or memory. Unfortunately, typical defenses based on resource burning require non-Sybil (good) IDs to consume at least as many resources as the adversary. Additionally, they have a high cost, even when the system membership is relatively stable. Here, we present a new Sybil defense, ERGO, that guarantees (1) there is always a minority of Sybil IDs; and (2) when the system is under significant attack, the good IDs consume asymptotically less than the bad. In particular, for churn rate that can vary exponentially, the resource burning rate of ERGO is, where is the resource burning rate of the adversary, and is the join rate of good IDs. We empirically evaluate ERGO alongside prior Sybil defenses. Unlike other Sybil defense, ERGO can be combined with machine learning techniques for identifying Sybil IDs, in a way that maintains its theoretical guarantees. Based on our experiments comparing ERGO with two state-of-the-art Sybil defenses, we show that ERGO improves by up to 2 orders of magnitude without machine learning, and up to 3 orders of magnitude using machine learning.
Deep Learning Based Automatic Detection of Adequately Positioned Mammograms

Screening mammograms are a routine imaging exam performed to detect breast cancer in its early stages to reduce morbidity and mortality attributed to this disease. In the United States alone, approximately 40 million mammograms are performed each year. In order to maximize the efficacy of breast cancer screening programs proper mammographic positioning is paramount. Proper positioning ensures adequate visualization of breast tissue and is necessary for breast cancer detection. Therefore, breast imaging radiologists must assess each mammogram for the adequacy of positioning before providing a final interpretation of the exam. In this paper we propose a method that mimics and automates the decision-making process performed by breast imaging radiologists to identify adequately positioned mammograms. Our objective is to improve the quality of mammographic positioning and performance, and ultimately reduce repeat visits for patients whose imaging is technically inadequate. If the method is not able to identify the adequately positioned mammogram, the patient will be scanned again. This AI driven method will be useful in reducing the cost and anxiety associated with mammogram scanning. The method can be very useful in serving the needs in developing countries, where mammogram scanning is not considered a routine procedure due to increased cost to the patient. In addition, the methodology can also be used for training the mammogram techs by providing actionable feedback during scan. The proposed method has a true positive rate of 91.4% for detecting a correctly positioned mediolateral oblique view. For detecting a correctly positioned craniocaudal view, the true positive rate is 95.11%.
An Image Stitching Algorithm Based on SIFT and DAISY Descriptor

Correspondences matching is essential to image stitching and greatly influences the stitching quality as the homography matrix is calculated from the correspondences. Mismatching would be generated by using SIFT alone when dealing images with repeat similar structures. To improve the matching accuracy, SIFT and DAISY are combined to extract and match feature points. Then the homography matrix is computed by least square, RANSAC and bundle adjust methods. Experiments show that the matching accuracy is improved and the stitching results are well and robust.
A Blockchain-Based Secure Car Hiring System

Currently, the blockchain technology is a boom in creating a secure and distributed environment for many real-life applications. Here, the focus is to introduce a car hiring system so that the car owners will get the fare without any significant loss and consumers can hire the car with variety of preferences. The proposed system is designed with blockchain technology; anyone can join the system by entering user’s information and is added to the specified block, computing hash for that required block by applying proof of work and storing hash of previous block in it. Thus, the system works on the complexity of blockchain with proof of work. The proposed model is implemented in Python and aims at removal of organizational role from car hiring system using blockchain technology.
BERT Pre-training Acceleration Algorithm Based on MASK Mechanism

Abstract With the development of natural language processing (NLP) science, machines can understand human language better and better. Especially with the emergence of pre-training models in recent years, such as ELMO, GPT, BERT, etc. Researchers can easily transfer model knowledge and apply them to related fields, but at the same time they often ignore the pretraining process. Due to the huge number of parameters and calculations of the pre-training model, it often takes a lot of resources and much time to get a good pre-training model. So many people are daunted by the pre-training process. From a different perspective, this article mainly focuses on the pre-training process of BERT, and conducts in-depth research and related improvements on its own mask mechanism, so as to accelerate the training process. The experimental results show that our algorithm can get better result than the original scheme which the BERT use. Also it is better than the original BERT in terms of training speed and can better understand the training text input. What is more, we have also conducted a detailed analysis of the experimental result.
Teaching Exploratory Tests through PBL and JiTT: an experience report in a context of distributed teams

There is no specific professional responsible for quality assurance in agile teams, such as the Test Engineer. Thus, the skills, competencies, and attributions inherent to this professional are the responsibility of all team members. Due to Exploratory Testing (ET) benefits in agile development, there is a need to train agile professionals. In this sense, this paper aims to investigate the contributions and limitations of adopting Problem-Based Learning (PBL) and Just-in-Time Teaching (JiTT) in ET teaching-learning. For this, we conducted a course in remote learning format with agile developers, distributed geographically. Data were collected through an online questionnaire and examined with quantitative and qualitative analysis at the end of the course. Our main findings are that (1) the collaboration between the participants and the adoption of a real problem, along with (2) activities and resources made available before the class, and (3) the existence of specific tool support for ET sessions optimized learning in the context of remote learning.