Reviewerly/exHarmony huggingface dataset:
Test Input Selection for Deep Neural Network Enhancement Based on Multiple-Objective Optimization

Deep Neural Networks (DNNs) have been applied in many domains, such as autonomous driving and image recognition. However, due to the lower-than-expected performance of the DNN models in the real application, researchers are committed to sampling a test subset from test data with the limited labeling effort to retrain the DNN models for enhancement. Existing test input selection methods aim at selecting the test inputs according to the probability that is classified incorrectly by the DNN model. However, the test inputs selected by using existing methods might have similar features, making the DNN model unable to learn more diverse features when retraining. To address this limitation, this paper proposes Multiple-Objective Optimization-Based Test Input Selection (MOTS) to select more effective test subset to retrain the DNN model for enhancement. Different from existing works, this work not only considers the uncertainty of the test input but also takes the diversity of the test subset into account. Then MOTS uses a multiple-objective optimization algorithm NSGA-II to solve this problem, which ensures the test subset has more diverse features and is more helpful for retraining DNN models. This paper conducts the experiment on two popular DNN models and three widely-used datasets. The experiment results indicate that MOTS achieves 114%, 72%, 55%, 41% average accuracy improvement under four different sampling ratios 1%, 3%, 5%, 10% compared with five baseline methods. Therefore, MOTS is very effective in improving the quality of the DNN models compared with the state-of-the-art methods and the diversity of the test subset contributes greatly to the effectiveness of retraining DNN models.
A Comparative Study of Code Clone Genealogies in Test Code and Production Code

Recent research has shown that clones occur far more often in test code than in production code. This is explained by the typical template of unit test code (the setup-stimulate-verify-teardown cycle), a template which lends itself well to parameterisation of proven solutions. However, little is known on how these clones evolve over time. In this paper, we compare the evolution of clones in test code with those in production code. Based on a quantitative and qualitative study of eight representative open-source systems, we conclude that clones in test code are inherently different than clones in production code throughout development. As a consequence, consistent maintenance and tracking of test clones becomes more important, hence the need for special purpose clone tracking and management tools.
A Cross-Language Name Binding Recognition and Discrimination Approach for Identifiers

Software developers usually rename identifiers and propagate the renaming based on the name binding of identifiers. Currently, software applications are usually developed using more than one language to enhance their functions and behaviors. Hence, when an identifier renaming is performed, it frequently affects more than one language in the multiple-language software applications. However, existing name binding approaches for identifiers only focus on a specific single language without considering the cross-language scenario. In this paper, we propose a cross-language name binding approach for the Java framework based on the deep learning model. Specifically, we first detect the potential name binding pairs via string matching. By analyzing the name binding pairs, the context, and the framework rules of identifiers, we extract several deep semantic features of identifiers and employ the BERT pre-trained model to recognize the name binding for unique identifiers, and further combine several classifiers to discriminate the name binding for duplicate identifiers. Our approach is evaluated on a manually constructed experimental dataset from 10 multiple-language projects. Experimental results demonstrate that our approach can achieve the average F-Measure of 85.14% in unique identifiers and 86.57% in duplicate identifiers, which significantly outperforms the baseline approaches. We also compare the performance of our approach against IntelliJ IDEA to further show its usefulness for developers in the real scenario.
Optimizing Continuous Integration by Dynamic Test Proportion Selection

Continuous integration is widely used in modern software engineering. However, it is an expensive practice. The proposed approaches focus on either intra- or inter-build cost reduction. Test case prioritization and selection (TCPS) techniques, typical intra-build techniques, are designed to save the high cost of CI by identifying failed test cases for failed builds. However, existing works are inadequate to distinguish characteristics of builds, but to apply an identical test selection proportion to different builds. Build-prediction techniques, typical inter-build techniques, are designed to save CI cost at the build level. If a build is deemed likely to pass based on the prediction of machine learning models, the whole test suite is skipped for it. Apparently, build in such a manner may miss some realistic failed test cases, if a machine learning model provides a false negative result. In this paper, we propose a dynamic test proportion selection technique DTPS, which incorporates intra- and inter-build cost reduction techniques. DTPS uses build features to construct machine learning models to predict the probability of a specific build failure and transform the probability into the necessary test proportion, with respect to a selected test case prioritization technique. Based on the output of machine learning model, it thus selects a prioritized test suite and a variable proportion of test cases with respect to a build. We constructed a large-scale dataset with approximately 115,000 builds, and conducted a controlled experiment using the dataset. The experiment shows that DTPS outperforms existing techniques significantly. It detects 19.9% to 32.5% more failed test cases, compared with state-of-the-art techniques evaluated in the experiment. At the same time, DTPS performs better than all three existing peer techniques on approximately 47% of projects. Moreover, the experiment also shows that our failure prediction model has an improvement of 0.15 in Area Under Curve (AUC), compared to prior machine learning models.
Constructing Temporal Networks of OSS Programming Language Ecosystems

One of the primary factors that encourage developers to contribute to open source software (OSS) projects is the collaborative nature of OSS development. However, the collaborative structure of these communities largely remains unclear, partly due to the enormous scale of data to be gathered, processed, and analyzed.In this work, we utilize the World Of Code dataset, which contains commit activity data for millions of OSS projects, to build collaboration networks for ten popular programming language ecosystems, containing in total over 290M commits across over 18M projects. We build a collaboration graph representation for each language ecosystem, having authors and projects as nodes, which enables various forms of social network analysis on the scale of language ecosystems. Moreover, we capture the information on the ecosystems’ evolution by slicing each network into 30 historical snapshots. Additionally, we calculate multiple collaboration metrics that characterise the ecosystems’ states.We make the resulting dataset publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , including the constructed graphs and the pipeline enabling the analysis of more ecosystems.
Prediction of Vulnerability Characteristics Based on Vulnerability Description and Prompt Learning

Identifying which vulnerabilities need to be prioritized is a long-term challenge in IT security, especially as the number of vulnerabilities grows. Faced with a large number of vulnerability reports, there is an urgent need for automated tools or models to assess the potential severity and exploitability of vulnerabilities. This will help security experts screen vulnerabilities that should be focused on. In this study, we aim to predict vulnerability severity and exploitability characteristics using only vulnerability descriptions. Some previous studies are based on traditional deep learning models, and their performance is relatively backward in the current era of pre-trained language models (PLMs). Therefore, we introduce a prompt learning method based on PLMs to predict vulnerability characteristics. The conventional fine-tuning PLMs method is difficult to make full use of the domain knowledge in PLMs and performs poorly with less training data. Unlike the fine-tuning paradigm, prompt learning imitates the pre-training process of PLM by reconstructing the task input and adding prompts, and uses the output of PLM itself as the prediction output. Combined with prompt ensembling and transfer learning, the performance of prompt learning in the above tasks is further improved. Our experiments show that prompt learning can make more effective use of the knowledge in PLMs. Compared with fine-tuning PLMs and other deep learning models, prompt learning based on BERT or RoBERTa achieves better performance in the above tasks. This advantage is more significant in predicting exploitability with few samples, which proves the ability of prompt learning in few-sample scenarios. In addition, prompt learning also shows the transferability between different tasks in the domain.
Multimodal Sentiment Analysis under modality deficiency with prototype-Augmentation in software engineering

Sentiment analysis has a wide range of promising applications in software engineering, and the development of deep learning has demonstrated that the uniform representation of different modalities can improve the model performance of sentiment analysis. However, in practical applications, multimodal sentiment analysis always faces unsatisfactory situations, especially when the modality has missing samples, most models may fail. For example, social dynamics of technicians in developer communities can face modality unavailability due to privacy settings. Several existing works based on deep learning and regularization methods have explored the modal missing problem, but these works cannot balance the cases of modal general missing (rate < 50%) and severe missing (rate ≥ 50%), and do not consider the resource consumption during model inference. Therefore, in this paper, we proposed a prototype augmented multimodal teacher-student network (PAMD) to address the above issues. Specifically, a multi-level and multi-origin distillation strategy is used to minimize the required resources and inference time, and prototype augmentation is used to guarantee the performance of the model when a modality is severely missing. Extensive experiments are conducted on different benchmark datasets to explore a network that balances performance and resource consumption. And It achieves good results in different modalities of missing cases.
Multi-scale Network and Ghost Attention Head for Semantic Segmentation

In view of the existing problems in semantic segmentation, such as insufficient utilization of information at different scales, excessive computing costs for generating redundant information, and ignoring spatial information. In this end, we propose a multi-scale convolution and ghost attention head. The former is inserted into the encoder, which increases the receptive field of the network and makes the network more effective to obtain context information. At the same time, a Ghost Attention Head is designed in the process of decoding, which adopts cheap operation and separable attention to guide the network, so as to solve the problems such as computational cost and ignoring spatial information. Experimental results on CamVid show that the mIoU of the proposed module reaches 71.32%, which is 9.7% higher than that of the traditional semantic segmentation network FCN. Ablation experiments were also carried out to further verify the effectiveness of the added module.
Distribution-Matching Stack Object Counting Based on Depth Information

Object counting is one of the crucial research fields of computer vision. Most of the existing counting methods are based on the plane counting of a single image. In the application field of industrial intelligent management, the counting of materials is very imperative, but the materials are usually placed in a stacked manner, and the existing counting technology is unintelligible to solve this problem. This paper proposes a distribution matching planar object counting method based on depth information. While optimizing the planar density map, depth information is introduced for the first time to assist in counting stacked objects. Experimental results show that our method remarkably reduces the error in the generation of planar density maps, and is effective in solving the problem of counting stacked objects.
Application of Personalized Emotion Speech Synthesis Technology in Human-Computer Interaction

Aiming at the problem that machine speech synthesis technology does not have emotion in human-computer interaction scenes at present, we propose a framework for personalized speech synthesis with emotion in human-computer interaction. Firstly, the emotion that the machine needs to convey is determined by the spoken text that has returned during the interaction process. Next, the Fastspeech 2 speech synthesis model is used to train the relevant individualized voice with emotion. The customized voice with emotion is then synthesized according to the emotion inferred from the text. In real-world scenarios involving emotional human-computer interactions, this technology has worked well.
Research on Combat Effectiveness Based on Internet of Things

With the rapid growth of new generation information and communication technologies such as Big Data, Cloud Computing, 5G Communication, Advanced Sensors, Unmanned Aerial Vehicles and Artificial Intelligence, the Internet of Things (IoT) technology has made continuous breakthroughs and process in many fields. The IoT technology is gradually playing an unpredictable role in the means of victory in the future battlefield and also has received high attention in the process of modern war. This paper focuses on the development and application of the IoT technology in the battlefield to research the mechanism and means of victory via studying the supporting factors in the whole process from sensors to weapon attacks and accelerating the OODA loop with IoT technology. It pays special attention on the IoT technology in Intelligence Reconnaissance, Command-Control-Fire Attack, which is of great significance to play an effective role in the overall operational effectiveness of weapons and equipment in future battlefield, as well as to explore the mechanism of helicopters.
Comparative Study and Performance Analysis of Deep Neural Networks for Sign Language Recognition using Transfer Learning

Sign Language is the primary source of communication for people with hearing disabilities. Communicating with people with such a disability is a challenge. An Automatic Sign Language Recognition system will reduce this communication gap and facilitate ease of conversing with people with hearing disabilities. In this work, we have compared the performance of 6 popular and recent deep neural network architectures for sign language recognition, namely DarkNet19, DarkNet53, InceptionResNetv2, ResNet50, DenseNet201 and VGG-19. Among these, DarkNet53 performed the best with our self-created dataset of 16,000 images from 60 classes with an average accuracy of 98.02%.
An Offline Signature Verification Using Deep Convolutional Neural Networks

When a biometric authentication technology verifies a handwritten offline signature. Due to the time-variant character of signatures, signature verification is a difficult process. There are two primary forms of signatures, such as the dynamic signature used online. A static signature is one that is not currently active. The phrase intra-personal variability is used when an offline signature cannot be made in the same way even by a skilled signer. To prevent fraudulent signatures in this instance, we use a highly deep learning (DL) offline signature verification algorithm. The Convolution Neural Network (CNN) is a component of deep learning (DL). CNN was created and educated for two distinct. For two distinct models, such WI and WD, the writer-independent (WI) and writer-dependent (WD) approaches are the most crucial elements in the signature verification process.
A Study on Statistical Analysis for Performance Evaluation of Digital Watermarking

Performance evaluation for image quality assessment plays a very vital role in digital image watermarking. The main objective of this paper is to study the process of statistical analysis of image quality metrics for performance evaluation testing in digital image watermarking. This review gives the basic idea of statistical method ANOVA, which will help the researchers to assess the quality of their methods not only related to image watermarking but also to different fields of research of their own interests. The comprehensive study on some existing methods is also presented in this paper to show the effectiveness as well as the limitations of the performance evaluation metrics in the domain of digital image watermarking.
Prediction Analysis of Hypothyroidism by Association

According to clinical data, ‘1 in 10’ Indians suffer from thyroid disease. Hypothyroidism / Hypothyroidism is an endocrine disorder or condition in which the thyroid gland does not produce enough thyroid hormone. Predicting hypothyroidism in each individual based on profile and behavior is a challenging task in the current field. The current system is a scientific study in which experts suspect hypothyroidism based on symptoms. Hypothyroidism can only be confirmed after a battery of diagnostic tests. There are some populations that require more accurate testing to determine their thyroid status. Approximately 42 million people in India suffer from thyroid disease and hypothyroidism. There is no automation to predict hypothyroidism. System uses the concept of association-rule mining approach to find out the patterns of hypothyroidism. Frequent item set mining in dataset are used for application. The main focus includes minimizing the scanning of dataset and a better time complexity than the popular algorithms. The research tries to weigh all the hypothyroidism attributes indivually and calculate to predict the accuracy of the hypothyroisim. It evolves by analyzing the association that the attribute can contribute towards the occurrence of hypothyroidism.
Overview and an Approach to AI based Healthcare System by using NLP

This paper presents the analysis and comparison of existing literature relevant to various s that act as virtual medical assistance and the mechanisms associated with it. Though, the literature consists of a lot many research contributions, but, here, we have critically and exhaustively analysed recent research and review papers that ae pertinent to AI based health care s. Based on the basic concepts used in their mechanisms, the existing approaches are categorized. The emphasis is on the concept used by the concerned authors, the methodology used for experimentations and the performance evaluation parameters. The claims of the researchers are also highlighted. Our findings from the exhaustive literature review are mentioned along with the identified problems. This paper is very important for the comparative study of various healthcare s approaches which is prerequisite for solving remote health issues. In the end, we've proposed our own pertaining to the health care system. We have blended AI with NLP and ML. We have implemented the idea wherein one can easily and readily diagnose the disease or illness and the details can be provided remotely before consulting a medical practitioner or visiting a doctor. This can inherently reduce the healthcare expenses and can be easily accessible at any time and place. Also, our proposed approach looks consistent and accurate.
A Bengali Word Identification and Verification Using Machine Learning Approach

The research project is mainly based on image classification. We also showed a path to Optical Character Recognition. Optical Character Recognition (OCR) is basically a technology which can change over a computerized image of content to editable content i.e. it can change digital image to editable text. An OCR by using image processing and classification for Bangla dialect is proposed here. The primary step of this project is that to recognize and detect ‘text’, the usage of image classification and Convolutional Neural Network (CNN) is implemented. Characters are separated from the Corpus and then the most decisive and challenging of the project step is implemented which is the verification of the word or corpus. The Bengali word or corpus is then extracted from the digital image and then it is converted to digital font and then the digital font is compared with our accumulated data set to verify whether the word is absolute or not. To enrich the diversity of Bengali language in the field of technology the project plays a very significant role from its own view.
In Search of a Suitable Epoch Value for the Optimum Result in Image Captioning

The proposed work is carried out on retrieving the content of an image using machine learning based approach. In order to gain a deeper insight of this topic, the current state-of-the-art image caption generation technique has been considered as the baseline. The goal of this work is to find a suitable Epoch value during the training phase which can maximize the performance of a system. The proposed model is composed of six major components: 1) Data pre-processing unit; 2) Convolutional Neural Network (CNN) as an encoder; 3) At-tention mechanism; 4) Recurrent Neural Network (RNN) as a decoder; 5) Beam search to find most optimal caption; and 6) Sentence Generation and evaluation. The model is trained to maximize the likelihood of the description sentence of a given training image. Experiments on the test dataset show the accuracy of the model and the fluency of the language it learns solely from image descriptions. The challenges of this work are mentioned in detail at the last of this paper.
Person Re-identification from Video using Hybrid Approach

Because of advances in technology, it is now feasible to employ massive picture collections for a variety of purposes. This trend is expected to continue. The process of maintaining and recovering face data from a video may be improved with the use of the person reidentification approach. In order to scan and collect the photos, machine learning methods such as OpenCV are used. The goal of this phase is to split the picture into components or areas of interest for the purpose of conducting a more in-depth examination of one or more of these parts. For the purpose of carrying out face detection, the Haar Cascade algorithm is being evaluated for the project. Image processing is the primary use of OpenCV, which is a library of programming functions. Picture segmentation may also be employed as a preprocessing step in order to produce a superior image before moving on to denoising or deblurring, both of which can be accomplished in a subsequent phase of image processing. When it comes to retrieving information, some of the most crucial features are feature extraction, image representation, and image matching. Picture processing is done with the intention of assisting a computer in comprehending the subject matter of an image.