Reviewerly/exHarmony huggingface dataset:
Properties and Styles of Software Technology Tutorials

Authors:
raw_author_name: Deeksha M. Arya
raw_affiliation_string: McGill University, Montreal, Canada
raw_author_name: Jin L.C. Guo
raw_affiliation_string: McGill University, Montreal, Canada
raw_author_name: Martin P. Robillard
raw_affiliation_string: McGill University, Montreal, Canada

Abstract: A large number of tutorials for popular software development technologies are available online, and those about the same technology vary widely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resources</i> , differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distinguishing attributes</i> , i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resource style</i> , i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.
Early analysis of requirements using NLP and Petri-nets

Authors:
raw_author_name: Edgar Sarmiento-Calisaya
raw_affiliation_string: Universidad Nacional de San Agustín de Arequipa, Arequipa, Peru
raw_author_name: Julio Cesar Sampaio do Prado Leite
raw_affiliation_string: Instituto de Computação, Universidade Federal da Bahia, Salvador, Brazil

Abstract: Scenario-based approaches are widely used for software requirements specification. Since scenarios are usually written using natural language, specifications may have statements that are ambiguous, unnecessarily complicated, missing, duplicated, or conflicting. Requirements quality is challenging since it is hard to achieve consistency in requirements products. Unfortunately, if done manually, analysis of textual scenarios can be an arduous, time-consuming, and error-prone activity. This work rethinks the unambiguity, completeness, consistency, and correctness properties of scenario-based specifications; and how static and dynamic analysis strategies could automatically evaluate them. To do so, we introduce an automated requirements analysis approach to check both structural and behavioral aspects of scenarios, which combines natural language processing, Petri-nets, and visualization techniques for: (i) identifying certain types of defects and their indicators; (ii) highlighting scenario statements or relationships among scenarios that can lead to defects; and (iii) foreseeing scenario execution paths that can lead to inconsistencies. We show the feasibility of the proposed approach through the analysis of four projects specified as scenario-based descriptions. Overall, our approach produced reasonable results, with precision greater than 89% and recall greater than 98%. Our work allows researchers, as well as practitioners, to improve the quality of scenarios through an automated analysis approach.
Dynamically retrieving knowledge via query generation for informative dialogue generation

Authors:
raw_author_name: Zhongtian Hu
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Lifang Wang
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Yushuang Liu
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Ronghan Li
raw_affiliation_string: Xidian University, China
raw_author_name: Meng Zhao
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Xinyu Lu
raw_affiliation_string: Northwestern Polytechnical University, China
raw_author_name: Zejun Jiang
raw_affiliation_string: Northwestern Polytechnical University, China

Abstract: Knowledge-driven dialogue systems have recently made remarkable breakthroughs. Compared with general dialogue systems, superior knowledge-driven dialogue systems can generate more informative and knowledgeable responses with pre-provided knowledge. However, in practical applications, the knowledge-driven dialogue systems cannot be supplied with relevant knowledge beforehand. Hence, to enhance the practicality of the knowledge-driven dialogue systems, it is crucial to devise a method to dynamically retrieve pertinent knowledge based on the context. In addressing this challenge, we introduce a knowledge-driven dialogue system called DRKQG (Dynamically Retrieving Knowledge via Query Generation for informative dialogue response). Specifically, this system is composed of two main modules: a query generation module and a response generation module. Initially, a time-aware mechanism is employed to capture contextual information, enabling the generation of a query for knowledge retrieval through a search engine. Subsequently, we incorporate the copy mechanism and transformers, empowering the response generation module to create responses based on both the context and retrieved knowledge. Experimental results at LIC2022, Language and Intelligence Technology Competition, show that our module outperforms the baseline model by a large margin on automatic evaluation metrics, while human evaluation by the Baidu Linguistics team shows that our system achieves impressive results in Factually Correct and Knowledgeable.
Teacher–student complementary sample contrastive distillation

Authors:
raw_author_name: Zhiqiang Bao
raw_affiliation_string: School of Computer Science, South China Normal University, South China Normal University, Guangzhou, 510631, Guangdong, China
raw_author_name: Zhenhua Huang
raw_affiliation_string: School of Computer Science, South China Normal University, South China Normal University, Guangzhou, 510631, Guangdong, China
raw_author_name: Jianping Gou
raw_affiliation_string: College of Computer and Information Science, College of Software, Southwest University, Chongqing, 400715, Chongqing, China
raw_author_name: Lan Du
raw_affiliation_string: Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Victoria, Australia
raw_author_name: Kang Liu
raw_affiliation_string: School of Computer Science, South China Normal University, South China Normal University, Guangzhou, 510631, Guangdong, China
raw_author_name: Jingtao Zhou
raw_affiliation_string: School of Computer Science, South China Normal University, South China Normal University, Guangzhou, 510631, Guangdong, China
raw_author_name: Yunwen Chen
raw_affiliation_string: Research and Development Department, DataGrand Inc., Shanghai, 201203, China

Abstract: Knowledge distillation (KD) is a widely adopted model compression technique for improving the performance of compact student models, by utilizing the "dark knowledge" of a large teacher model. However, previous studies have not adequately investigated the effectiveness of supervision from the teacher model, and overconfident predictions in the student model may degrade its performance. In this work, we propose a novel framework, Teacher-Student Complementary Sample Contrastive Distillation (TSCSCD), that alleviate these challenges. TSCSCD consists of three key components: Contrastive Sample Hardness (CSH), Supervision Signal Correction (SSC), and Student Self-Learning (SSL). Specifically, CSH evaluates the teacher's supervision for each sample by comparing the predictions of two compact models, one distilled from the teacher and the other trained from scratch. SSC corrects weak supervision according to CSH, while SSL employs integrated learning among multi-classifiers to regularize overconfident predictions. Extensive experiments on four real-world datasets demonstrate that TSCSCD outperforms recent state-of-the-art knowledge distillation techniques.
Optimized hybrid deep learning pipelines for processing heterogeneous facial expression datasets

Authors:
raw_author_name: B. Subramani
raw_affiliation_string: Shri Nehru Maha Vidyalaya College of Arts & Science, Coimbatore, India

Abstract: Emotions, central to human experience, have been categorized through various theories. Biological theories, such as the James-Lange model, suggest that physiological reactions are the precursors to emotions. Cognitive theories, as highlighted by the Schachter-Singer model, argue that emotions emerge from a combination of physiological responses and cognitive appraisal. Evolutionary theories, rooted in Darwin's work, view emotions as adaptive mechanisms that have evolved to promote survival. In the realm of psychology, expressing emotions via facial expressions is termed face reading, a vital communication method. With the swift advancement of Deep Neural Networks (DNN) across various fields, they are increasingly used to derive exclusive features for automated Facial Expression Prediction and Recognition (FEPR). This research employs a Three-staged Deep Learning (TsDL) strategy for FEPR. Recognizing the pivotal role of datasets in determining system performance, we incorporate both laboratory-controlled and real-world data. Initially, the Generative Adversarial Networks (GAN) model, leveraging datasets like CK+ and Radboud Faces Database (RFD), is introduced. This model successfully identifies expressions across seven categories with an accuracy of 92.15 %. Subsequently, another model based on the Attention-Long Short Term Memory (A-LSTM) is trained using the Real-world Affective Face Database, given its capability to manage spatio-temporal features. The TsDL approach predicts and classifies six primary emotions, achieving an 89.22 % accuracy rate. In the culmination of our work, we harness Ensemble Learning Strategies to craft a refined hybrid model, ensuring systematic performance and optimal accuracy. The diverse theoretical perspectives on emotions greatly enrich our comprehension, especially when developing our FEPR model.
Introducing shape priors in Siamese networks for image classification

Authors:
raw_author_name: Hiba Alqasir
raw_affiliation_string: Université Jean Monnet Saint-Etienne, CNRS, Institut d'Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France
raw_author_name: Damien Muselet
raw_affiliation_string: Université Jean Monnet Saint-Etienne, CNRS, Institut d'Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France
raw_author_name: Christophe Ducottet
raw_affiliation_string: Université Jean Monnet Saint-Etienne, CNRS, Institut d'Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France

Abstract: The efficiency of deep neural networks is increasing, and so is the amount of annotated data required for training them. We propose a solution improving the learning process of a classification network with less labeled data. Our approach is to inform the classifier of the elements it should focus on to make its decision by supplying it with some shape priors. These shape priors are expressed as binary masks, giving a rough idea of the shape of the relevant elements for a given class. We resort to Siamese architecture and feed it with image/mask pairs. By inserting shape priors, only the relevant features are retained. This provides the network with significant generalization power without requiring a specific domain adaptation step. This solution is tested on some standard cross-domain digit classification tasks and on a real-world video surveillance application. Extensive tests show that our approach outperforms the classical classifier by generating a good latent space with less training data. Code is available at https://github.com/halqasir/MG-Siamese.
Rotation-equivariant correspondence matching based on a dual-activation mixer

Authors:
raw_author_name: Shuai Su
raw_affiliation_string: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China
raw_author_name: Ronghao Dang
raw_affiliation_string: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China
raw_author_name: Rui Fan
raw_affiliation_string: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China
raw_author_name: Chengju Liu
raw_affiliation_string: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China; Tongji Research Institute of Artificial Intelligence (Suzhou), Jiangsu, 215131, China
raw_author_name: Qijun Chen
raw_affiliation_string: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

Abstract: Learning-based correspondence matching methods have become the mainstream techniques in many computer vision and robotics applications due to their robustness to large illumination and viewpoint changes. However, it is difficult for conventional convolutional neural networks (CNNs) to extract rotation-equivariant local features. Recent work has shown that CNNs combined with group-equivariant architectures are surprisingly effective at matching correspondences even when the images are rotated to a dramatic extent. However, the inherent shape (square) of convolution kernels causes the performance bottleneck of such rotation-equivariant CNNs. To address this issue, we propose an adaptive dual rotation-equivariant correspondence matching algorithm, which performs stably at all angles. We mathematically analyze the effectiveness of our proposed rotation-equivariant correspondence matching approach and its performance with respect to different convolution kernels. Extensive experiments on the Rotated-HPatches, SIM2E, and MegaDepth datasets demonstrate the effectiveness of our proposed algorithm.
Influence of cost/loss functions on classification rate: A comparative study across diverse classifiers and domains

Authors:
raw_author_name: Mehdi Khashei
raw_affiliation_string: Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, 84156˗83111, Iran

Abstract: The classification rate is the most significant factor affecting the selection of an appropriate classification approach for achieving the desired quality of decisions. Several researchers have evaluated the influence of different features on the performance of classification approaches, but cost/loss functions, despite their theoretical impact on the classification rate, have been less considered in the comparative literature review. In this paper, a comparative study has been presented on the influence of different cost/loss functions on the classification rate of different classifiers. For this purpose, five of the most well-known and widely used categories of cost/loss functions and three of the most popular main categories of classification approaches are considered. Also, 23 benchmark datasets from six different domains are selected. In this paper, all modeling of the statistical and intelligent classifiers are run in the Eviews and MATLAB package software, respectively. In addition, all mathematical modeling of models run in the GAMS package software. Empirical results indicate that cost/loss functions can significantly affect the classification rate. Numerical results show that the discrete cost/loss function, by average 3.91% and 8.31% improvement rather than semi-continuous and continuous cost/loss functions, respectively, is the most effective one. This clearly illustrates that the consistency of the cost/loss function with the goal function of the classification approach has a positive direct relationship with the classification rate. Furthermore, nonlinear versions of semi-continuous and continuous cost/loss functions are on average slightly more efficient than linear ones. Numerical results indicate that nonlinear forms can averagely yield an 88.99% classification rate that is overall 0.98% higher than linear ones, which achieved an 88.13% classification rate. Although the type of cost/loss function can affect the classification rate of different classifiers, the improvement amount varies based on the type of classifier used. In general, the improvement amount in statistical classifiers, i.e., 15.07% is significantly higher than others, and intelligent by 7.86% and deep learning by 5.20% are in the second and third places, respectively. This overall demonstrates that the complexity of the used classifiers has a negative relationship with the improvement of the classification rate in changing cost/loss function. Finally, numerical results indicate that the difference between improvement amounts achieved by varying cost/loss functions, in general, is not significant and is independent of the type of classifier, as well as the type and domain/area of data.
A sentiment analysis method for COVID-19 network comments integrated with semantic concept

Authors:
raw_author_name: Jun Li
raw_affiliation_string: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
raw_author_name: Lanlan Jiang
raw_affiliation_string: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
raw_author_name: Guimin Huang
raw_affiliation_string: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

Abstract: In recent years, the new coronavirus COVID-19 has brought great disaster and loss to the world and is still spreading around the world. The situation in China is generally well controlled, and the lockdown has been removed, but the comments and messages about the epidemic persist online. For people working and living normally in China, their attitudes and views toward COVID-19 directly reflect the current situation of the pandemic. This paper collected Chinese microblogs, forums, and online comments, identified the latest comments about COVID-19, and conducted a sentiment analysis of them. Specifically, we proposed a new sentiment analysis method that integrated the semantics of words with the text analyzed. Different from the traditional sentiment analysis method which only relied on sentiment words, the proposed method extended the semantic concepts of affective words by integrating the semantic conceptual information about the affective words from the context of the comments and thus, provided information to support the final judgment of the affective opinions. The proposed approach incorporated the part-of-speech embedding information along with word embedding and relies on semantic concepts to enhance the emotional expression of words in context. The experimental results showed that by integrating the semantics of words, the accuracy of sentiment analysis is substantially improved, and it also reflected that different semantics of the same word have different influences on sentiment analysis. On several benchmark datasets, there was a 3–6% improvement in accuracy.
Multilinear subspace learning for Person Re-Identification based fusion of high order tensor features

Authors:
raw_author_name: Ammar Chouchane
raw_affiliation_string: LI3C Laboratory, University of Biskra, University of Biskra, Algeria; University Center of Barika, Amdoukal Road, Barika, 05001, Algeria
raw_author_name: Mohcene Bessaoudi
raw_affiliation_string: LI3C Laboratory, University of Biskra, University of Biskra, Algeria
raw_author_name: Hamza Kheddar
raw_affiliation_string: Department of Electrical Engineering, University of MEDEA, Algeria
raw_author_name: Abdelmalik Ouamane
raw_affiliation_string: LI3C Laboratory, University of Biskra, University of Biskra, Algeria
raw_author_name: Tiago Vieira
raw_affiliation_string: Institute of Computing, Federal University of Alagoas, Brazil
raw_author_name: Mahmoud Hassaballah
raw_affiliation_string: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, AlKharj, 16278, Saudi Arabia; Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt

Abstract: Video surveillance image analysis and processing is an important field in computer vision and among the challenging tasks for Person Re-Identification (PRe-ID). The latter aims at finding a target person who has already been identified and appeared on a camera network using a powerful description of their pedestrian images. The success of recent research on person PRe-ID is largely backed into the effective features extraction and representation with a powerful learning of these features to correctly discriminate pedestrian images. To this end, two powerful features, Convolutional Neural Network (CNN) and Local Maximal Occurrence (LOMO) are modeled on a multidimensional data in the proposed method, High-Dimensional Feature Fusion (HDFF). Specifically, a new tensor fusion scheme is introduced to take advantage and combine two types of features in the same tensor data even if its dimensions are not the same. To improve the accuracy, we use Tensor Cross-View Quadratic Analysis (TXQDA) to perform multilinear subspace learning followed by the Cosine similarity for matching. TXQDA efficiently ensures the learning ability and reduces the high dimensionality resulting from high-order tensor data. The effectiveness of the proposed method is verified through experiments on three challenging widely-used PRe-ID datasets namely, VIPeR, GRID, and PRID450S. Extensive experiments show that the proposed method performs very well when compared with recent state-of-the-art methods.
A fast planning approach for 3D short trajectory with a parallel framework

Authors:
raw_author_name: Shengyang Chen
raw_affiliation_string: Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
raw_author_name: Chih-Yung Wen
raw_affiliation_string: Department of Aeronautical and Aviation Engineering, Hong Kong Polytechnic University, Hong Kong, China
raw_author_name: Peng Lu
raw_affiliation_string: Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China

Abstract: For real applications of unmanned aerial vehicles, the capability of navigating with full autonomy in unknown environments is a crucial requirement. However, planning a shorter path with less computing time is contradictory. To address this problem, we present a framework with the map planner and point cloud planner running in parallel in this paper. The map planner determines the initial path using the improved jump point search method on the 2D map, and then it tries to optimize the path by considering a possible shorter 3D path. The point cloud planner is executed at a high frequency to generate the motion primitives. It makes the drone follow the solved path and avoid the suddenly appearing obstacles nearby. Thus, vehicles can achieve a short trajectory while reacting quickly to the intruding obstacles. We demonstrate fully autonomous quadrotor flight tests in unknown and complex environments with static and dynamic obstacles to validate the proposed method. In simulation and hardware experiments, the proposed framework shows satisfactorily comprehensive performance.