Reviewerly/exHarmony huggingface dataset:
Research on Parking Control of Bus Based on Improved Pure Pursuit Algorithms

Compared with small vehicles, the bus body is longer and the parking path is relatively shorter. If there are some errors in the path tracking process, there will be the risk of crossing the parking line. Therefore, the parking system of bus needs a high precision path tracking algorithm. Pure pursuit algorithm is widely used in vehicle or robot path tracking problem, but its performance depends on the selection of look-ahead distance which needs to be determined by scene in practical application. Aiming at the performance requirement of bus parking system, this paper improves the pure pursuit algorithm by analyzing the stability and error sources of the pure pursuit algorithm. In the improvement of the algorithm, speed is used as one of the parameters in the calculation of look-ahead distance to improve the stability of the system, and PI (Proportion Integration) control is added to the calculation of steering angle of pure pursuit algorithm to improve the tracking accuracy of the algorithm in the curve stage. The results of experiments show that the proposed algorithm can effectively improve the parking accuracy and stability of buses.
Deep Code-Comment Understanding and Assessment

Code comments are a key software component for program comprehension and software maintainability. High-quality code and comments are urgently needed by data-driven models widely used in tasks like code summarization. Many existing approaches for assessing the quality of comments are machine learning based classification algorithms or rely on heuristic rules. These approaches are difficult to capture the complicated features of text data and are often limited in accuracy, efficiency, and generalization ability. In this paper, we convert the quality assessment of code comments into a classification problem based on the multi-input neural network. We summarize the input, the code and comments, into vectors using the attention-based Bi-LSTM model and the weighted GloVe model, respectively, and concatenate the code vectors and the comment vectors as the input of the Multiple-Layer Perceptron classifier for the comment quality assessment. Experimental results show that our approach, in general, outperforms the previous technique, on both our labeled dataset and the public dataset, with the F1-score of 96.91% and 91.90%, respectively. Using the training set and the testing set from distinct sources, our approach can still achieve reasonable performance, which demonstrates its generalization ability.
Using Dynamic Adjusting NGHS-ANN for Predicting the Recidivism Rate of Commuted Prisoners

Commutation is a judicial policy that is implemented in most countries. The recidivism rate of commuted prisoners directly affects people’s perceptions and trust of commutation. Hence, if the recidivism rate of a commuted prisoner could be accurately predicted before the person returns to society, the number of reoffences could be reduced; thereby, enhancing trust in the process. Therefore, it is of considerable importance that the recidivism rates of commuted prisoners are accurately predicted. The dynamic adjusting novel global harmony search (DANGHS) algorithm, as proposed in 2018, is an improved algorithm that combines dynamic parameter adjustment strategies and the novel global harmony search (NGHS). The DANGHS algorithm improves the searching ability of the NGHS algorithm by using dynamic adjustment strategies for genetic mutation probability. In this paper, we combined the DANGHS algorithm and an artificial neural network (ANN) into a DANGHS-ANN forecasting system to predict the recidivism rate of commuted prisoners. To verify the prediction performance of the DANGHS-ANN algorithm, we compared the experimental results with five other forecasting systems. The results showed that the proposed DANGHS-ANN algorithm gave more accurate predictions. In addition, the use of the threshold linear posterior decreasing strategy with the DANGHS-ANN forecasting system resulted in more accurate predictions of recidivism. Finally, the metaheuristic algorithm performs better searches with the dynamic parameter adjustment strategy than without it.
Voice Activity Detection by Upper Body Motion Analysis and Unsupervised Domain Adaptation

We present a novel vision-based voice activity detection (VAD) method that relies only on automatic upper body motion (UBM) analysis.Traditionally, VAD is performed using audio features only, but the use of visual cues instead of audio can be desirable especially when audio is not available such as due to technical, ethical or legal issues.Psychology literature confirms that the way people move while speaking is different from while they are not speaking.This motivates us to claim that an effective representation of UBM can be used to detect "Who is Speaking and When".On the other hand, the way people move during their speech varies a lot from culture to culture, and even person to person in the same culture.This results in unrelated UBM representations, such that the distribution of training and test data becomes disparate.To overcome this, we combine stacked sparse autoencoders and simple subspace alignment methods while a classifier is jointly learned using the VAD labels of the training data only.This yields new domain invariant feature representations for training and test data, showing improved VAD results.Our approach is applicable to any person without requiring re-training.The tests applied on a publicly available real-life VAD dataset show better results as compared to the state-of-the-art video-only VAD methods.Moreover, the ablation study justifies the superiority of the proposed method and demonstrates the positive contribution of each component.
A framework for extractive text summarization using semantic graph based approach

Automatic extractive text summarization finds the subset of the data, which represent the most salient information of the entire document. Now-a-days in the era of Internet, it is a demand of the users to understand the huge amount of texts within a very short time, as there are lots of redundant unnecessary textual information in the webpages. Here comes the value of automatic extractive text summarization. Internet users get the idea of entire content of the document by reading summary generated by the summarizer within a very short time and also can decide whether to read the entire content or not. There are several methods for generating extractive summary such as clustering based, optimization based, information and itemset based, and word embedding based methods. These methods fail to determine the redundancy generated from the summary sentences. Therefore, in this paper, we propose a novel approach for generating extractive summary by considering semantic relationships among the sentences of the document.Here, we extract the Predicate Argument Structure (PAS) from each sentence and measure semantic distance among the sentences is measured from the PAS to PAS semantic relationship of the sentences. Then, we apply modified page rank algorithm to rank the sentences. Subsequently, we employ Maximum Marginal Relevance (MMR) method to rerank the sentences. Accordingly, we propose top n sentence selection algorithm is proposed to select summary sentences. To evaluate the performance of our proposed method, we adopt Rouge-1, Rouge-2 evaluation metrics for computing precision, recall, and f-score of the summary. Furthermore, we also employ cosine simillarity measurement based approach to determine redundancy in the summary sentences. Our evaluation results demonstrate that the proposed approach achieves improved performance compared to the other existing methods available for generating an the extractive summary.
Generating Semantically Similar and Human-Readable Summaries With Generative Adversarial Networks

The application of neural networks in natural language processing, including abstractive text summarization, is increasingly attractive in recent years. However, teaching a neural network to generate a human-readable summary that reflects the core idea of the original source text (i.e., semantically similar) remains a challenging problem. In this paper, we explore using generative adversarial networks to solve this problem. The proposed model contains three components: a generator that encodes the long input text into a shorter representation; a discriminator to teach the generator to create human-readable summaries and another discriminator to restrict the output of the generator to reflect the core idea of the input text. The main training process can be carried out in an adversarial learning process. To solve the non-differentiable problem caused by the words sampling process, we use the policy gradient algorithm to optimize the generator. We evaluate the proposed model on the CNN/Daily Mail summarization task. The experimental results show that the model outperforms previous state-of-the-art models.
A Multi-objective Examples Generation Approach to Fool the Deep Neural Networks in the Black-Box Scenario

Image classifiers have been proven to be easily fooled by perturbations, but it is still exceedingly challenging to generate imperceptible disturbances, especially without the internal knowledge of the classifiers. Imperceptibility and attack capability are two main evaluating indicators of this problem, while most existing methods, so far, can only either maximize misclassification or minimize the distortion. Although there are some algorithms to consider both of them via the weighted sum method, which is equivalent to solve the multiple optimization problems, it will doubtless enlarge the computation complexity, and the strategy of setting the weights cannot make sure both indicators the optimal solutions. In this paper, we proposed an innovative general algorithm named MOEA-APGA, which is based on multi-objective evolutionary algorithm, taking both factors as the optimization objective function. A set of perturbations with diversity is generated by population evolution, and then an appropriate perturbation is selected by the proposed filtering strategy to synthesize the adversarial example. It can achieve the goal of the targeted attack without the internal knowledge of the victim networks. We tried four perturbation strategies to generate adversarial examples. The experimental results on the MNIST datasets demonstrate the effectiveness of MOEA-APGA. In addition, we refer to a slice of indicators to evaluate the power of the algorithm and the vulnerability of different samples.
Learning Requirements Elicitation Interviews with Role-Playing, Self-Assessment and Peer-Review

Interviews are largely used in the practice of requirements elicitation. Nevertheless, performing an effective interview often depends on soft-skills, and on knowledge acquired through experience. When it comes to requirements engineering education and training (REET), limited resources and few well-founded pedagogical approaches are available to allow students to acquire and improve their skills as interviewers. This paper presents a novel pedagogical approach that combines role-playing, peer-review and self-assessment to enable students to reflect on their mistakes, and improve their interview skills. We evaluate the approach through a controlled quasi-experiment. The study shows that the approach significantly reduces the amount of mistakes made by the students. Feedback from the participants confirms the usefulness and easiness of the proposed training. This work contributes to the body of knowledge of REET with an empirically evaluated method for teaching inter-views. Furthermore, we share the pedagogical material used, to enable other educators to apply and possibly tailor the approach.
The Impact of Undergraduate Mentorship on Student Satisfaction and Engagement, Teamwork Performance, and Team Dysfunction in a Software Engineering Group Project

Mentorship schemes in software engineering education usually involve professional software engineers guiding and advising teams of undergraduate students working collaboratively to develop a software system. With or without mentorship, teams run the risk of experiencing team dysfunction: a situation where lack of engagement, internal conflicts, and/or poor team management lead to different assessment outcomes for individual team members and overall frustration and dissatisfaction within the team. The paper describes a mentorship scheme devised as part of a 33 week software engineering group project course, where the mentors were undergraduate students who had recently completed the course successfully and possessed at least a year's experience as professional software engineers. We measure and discuss the impact the scheme had on: (1) student satisfaction and engagement, (2) team performance, and (3) team dysfunction.
An Evolutionary Memetic Weighted Associative Classification Algorithm for Heart Disease Prediction

In recent years, many researchers are focusing on computational intelligence-based biomedical system to solve the problematic medical issues and to attain higher accuracy in predicting various diseases. Associative classification technique is one of the essential supervised learning methods that integrate association rule mining and classification to achieve higher accuracy. Heart disease is a major cause of death in many developing countries. So, this paper provides an efficient weighted decision support system for identifying heart disease by using an evolutionary memetic algorithm based on random walk algorithm. The experiments are carried out on heart disease data set, which has been collected from UCI repository. Various well-known validation techniques have been employed to evaluate the proposed system efficiency in the field of biomedical, which helps the physicians to diagnose the heart disease in the patient at early stage for starting further treatment.
Black box variational inference to adaptive kalman filter with unknown process noise covariance matrix

Adaptive Kalman filter (AKF) is concerned with jointly estimating the system state and the unknown parameters of the state-space models. In this paper, we treat the model uncertainty of the process noise covariance matrix (PNCM) from black box variational inference (BBVI) perspective. In order to lay the foundation for research, we prove that the probabilistic model for online Bayesian inference of the system state and PNCM is non-conjugate, so the traditional coordinate-ascent variational inference (CAVI) cannot deal with this problem. To fill this gap, we propose an AKF in the presence of unknown PNCM based on the BBVI method (which is recently introduced to conduct the approximate Bayesian inference for the non-conjugate probabilistic model). Firstly, we introduce a structured posterior model of the system state and PNCM, by which the posterior distributions of the system state and the PNCM can be calculated efficiently. Then, the BBVI online inference for the posterior distribution of the PNCM is derived. In what follows, we use the intrinsically Bayesian robust KF (IBR-KF) to calculate the state posterior distribution. In addition, a special case, when the structure of the PNCM is known, is explored. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed filters.
How Well Do You Know This Library?

Third-party libraries have been widely adopted in modern software projects due to several benefits, such as code reuse and software quality. Software development is increasingly complex and requires specialists with knowledge in several technologies, such as the nowadays libraries. Such complexity turns it extremely challenging to deliver quality software given the time pressure. For this purpose, it is necessary to identify and hire qualified developers, to obtain a good team, both in open source and proprietary systems. For these reasons, enterprise and open source projects try to build teams composed of highly skilled developers in specific libraries. Developers with expertise in specific libraries may reduce the time spent on software development tasks and improve the quality of the final product. However, their identification may not be trivial. In this paper, we first argue that source code activities can be used to identify library experts. We then evaluate a mining-based strategy to identify library experts. To achieve our goal, we selected the 9 most popular Java libraries and identified the top-10 experts in each library by analyzing commits in 16,703 Java projects on GitHub. We validated the results by applying a survey with 137 library expert candidates and observed, on average, 88% of precision for the applied strategy.
A Simple and Robust Deep Convolutional Approach to Blind Image Denoising

Image denoising, particularly Gaussian denoising, has achieved continuous success in the past decades. Although deep convolutional neural networks (CNNs) are also shown leading high-performance in Gaussian denoising just as in many other computer vision tasks, they are not competitive at all on real noisy photographs to representative classical methods such as BM3D and WNNM. In this paper, a simple yet robust method is proposed to improve the effectiveness and practicability of deep denoising models. In view of the difference between real-world noise in camera systems and additive white Gaussian noise (AWGN), the model learning has exploited clean-noisy image pairs newly produced built on a generalized signal dependent noise model. During the model inference, the proposed denoising model is not only blind to the noise type but also to the noise level. Meanwhile, in order to separate the noise from image content as full as possible, a new convolutional architecture is advocated for such a blind denoising task where a kind of lifting residual modules is specifically proposed for discriminative feature extraction. Experimental results on both simulated and real noisy images demonstrate that the proposed blind denoiser achieves fairly competitive or even better performance than state-of-the-art algorithms in terms of both quantitative and qualitative assessment. The codes of the proposed method are available at https://github.com/zhaohengyuan1/SDNet.
Storage Structure of Student Record based on Hyperledger Fabric Blockchain

Student records (SR) are needed to be regularly protected for safe access and security control. The blockchain system can support security, confidentiality, unchangeable, transparency and access control service. Although Hyperledger Fabric blockchain is a scalability modular architecture, various configuration elements affect the performance of transaction access time. Moreover, it cannot provide an efficient indexing capability on the transaction. In this paper, a storage structure that provides better access time is presented. Firstly, the suitable block size is proposed for transactions based on the configuring parameter and the transaction arrival rate to reduce the latency of transaction storage time. Secondly, the metadata of the SR block is replicated on an off-chain database to overcome the limitation of indexing capability and performance issue. SR data will be accessed transparently with greater performance and lower the costs of manpower and time.
Decentralized Trusted Computing Base for Blockchain Infrastructure Security

There is a growing interest today in blockchain technology as a possible foundation for the future global financial ecosystem. However, in order for this future financial ecosystem to be truly global, with a high degree of interoperability and stability, several challenges need to be addressed related to infrastructure security. One key aspect concerns the security and survivability of the systems that participate in the blockchain peer-to-peer networks. In this paper we discuss the notion of the decentralized trusted computing base as an extension of the TCB concept in trusted computing. We explore how a decentralized TCB can be useful to (i) harden individual nodes and systems in the blockchain infrastructure, and (ii) be the basis for secure group-oriented computations within the P2P network of nodes. We explore the application of the decentralized TCB for blockchain interoperability via blockchain gateways. Similar to border gateways in classical IP routing, blockchain gateways may provide several potential benefits in use-cases involving the transferal of virtual assets across different blockchain autonomous systems.
Dynamic Sign Language Recognition Based on Convolutional Neural Networks and Texture Maps

Sign language recognition (SLR) is a very challenging task due to the complexity of learning or developing descriptors to represent its primary parameters (location, movement, and hand configuration). In this paper, we propose a robust deep learning based method for sign language recognition. Our approach represents multimodal information (RGB-D) through texture maps to describe the hand location and movement. Moreover, we introduce an intuitive method to extract a representative frame that describes the hand shape. Next, we use this information as inputs to two three-stream and two-stream CNN models to learn robust features capable of recognizing a dynamic sign. We conduct our experiments on two sign language datasets, and the comparison with state-of-the-art SLR methods reveal the superiority of our approach which optimally combines texture maps and hand shape for SLR tasks.
Design of Virtual Reality Simulation-based Safety Training Workshop

Safety training is essential to high danger jobs. It is normally conducted in the traditional ways of classroom teaching or physical training at the workspace. In classroom teaching, safety procedures are theoretically described, the trainees cannot physically feel or see the machines or tools. Whereby safety training in the real-life workspace for high risk and high danger jobs often poses a risk by directly exposing the trainee to the danger area. In this project, a quiz-based interactive Virtual Reality (VR) simulated workshop was developed which allows training to be carried out in a realistic yet harmless environment, with the aims to improve the effectiveness of safety training and to reduce the incidents in the workshop. Studies were done to compare both the traditional classes and the VR system. Results show that the VR system is more effective, the trainees who took the VR training scored higher in the test compared to trainees who attended the normal classes.