Reviewerly/exHarmony huggingface dataset:
Towards a theory of shared understanding of non-functional requirements in continuous software engineering

Building shared understanding of requirements is key to ensuring downstream software activities are efficient and effective. Nonfunctional requirements (NFR), which include performance, availability, and maintainability, are vitally important to overall software quality. Research has shown NFRs are, in practice, poorly defined and difficult to verify, especially in agile environments. Continuous software engineering (CSE) practices, which extend agile practices, emphasize fast paced, automated, and rapid release of software that poses additional challenges to NFRs. However, the level of shared understanding achieved across an organization is not well-understood. This dissertation builds the foundations towards a theory of the complex and intricate relationship between shared understanding of NFRs and CSE.
Image Augmentation based on Cross Domain Image Style Transfer

Data is the cornerstone of various existing deep learning methods. The basic paradigm of existing deep learning methods is to build a deep neural network model, and make the model parameters fit the internal representation of datasets through back-propagation and gradient descent. Data augmentation is a technology that uses existing data in the small datasets to expand the overall datasets scale. Image style transfer is a research hot spot in the field of computer vision. It can endow the image to different styles while keeping the image content unchanged. This paper proposes an image augmentation method based on cross domain image style transfer, it is divided into two parts: cross domain image style transfer and smooth. In the cross domain style transfer stage, the input image is transfer to the domain of the style image, and the smooth stage can eliminate the artifacts in the generated image. Experiments show that this method is effective, it can expand large-scale datasets on the basis of existing data, and enhance the ability of neural network model to obtain better performance in the train process.
Adversarial Contrastive Learning via Asymmetric InfoNCE

Contrastive learning (CL) has recently been applied to adversarial learning tasks. Such practice considers adversarial samples as additional positive views of an instance, and by maximizing their agreements with each other, yields better adversarial robustness. However, this mechanism can be potentially flawed, since adversarial perturbations may cause instance-level identity confusion, which can impede CL performance by pulling together different instances with separate identities. To address this issue, we propose to treat adversarial samples unequally when contrasted, with an asymmetric InfoNCE objective (A-InfoNCE) that allows discriminating considerations of adversarial samples. Specifically, adversaries are viewed as inferior positives that induce weaker learning signals, or as hard negatives exhibiting higher contrast to other negative samples. In the asymmetric fashion, the adverse impacts of conflicting objectives between CL and adversarial learning can be effectively mitigated. Experiments show that our approach consistently outperforms existing Adversarial CL methods across different finetuning schemes without additional computational cost. The proposed A-InfoNCE is also a generic form that can be readily extended to other CL methods. Code is available at https://github.com/yqy2001/A-InfoNCE .
Perfecting matching of an undirected dense based on geometric multiplicity

One of the important concepts of Graph Theory is Matching Theory. Several Concepts on Matching Theory has been dealt in [1, 2, 3]. The technique of maximum matching on directed graphs have been studied in [1]. In this paper, a new approach for finding maximum matching of an undirected dense graph based on largest geometric multiplicity of Eigen values using exact controllability network is studied. It will be interest to further study on their properties.
New Model to Store and Manage Private Healthcare Records Securely Using Block Chain Technologies

Electronic health records and data maintained require innovation. With help of various innovative advancements in technology the records of patients we can store in more secured and effective way. Till the day most of the hospitals are using the older data management schemes to store and retrieve the patients’ health records. It’s because of the strict medical data privacy and security rules. Which have prevented the use of the latest and innovative techniques in managing the health care data. With the use of new techniques, we can bring more transparency and security in the health care records management which will be much useful to the patients and to the doctors. To overcome this problem, we developed new model using block chain technology
Identifying and Addressing Ethical Challenges in Recommender Systems

Simultaneously with the internet and the artificial intelligence technologies evolution, recommender systems have also evolved and their use has been established across the spectrum of digital life. All content, (information, people, objects etc.) which we process, buy or study, reaches us through a recommender system filter. After all, internet browsing would be impossible without them, since information overload would paralyze any attempt. Nevertheless, many concerning issues identified over the last few years have - with more or less certainty - been connected with the recommender systems activity. Issues of privacy, personal data, fairness and transparency, but also issues concerning personal identity and the proper functioning of society and democracy have risen. In the publication at hand, the recommender systems technology is analyzed with a focus on the different techniques and effectiveness measures employed to address the aforementioned issues. Furthermore, different problematic sides of recommender systems operations are identified and categorized, and a measure of a recommender system’s criticality is developed. Different case studies are presented, under the perspective of criticality, based on the proposed measure. Finally, a series of regulatory actions is proposed. The goal of the latter is the betterment of the systems’ function and the harmonization of their progress with social prosperity.
Visualizing Online Education in the COVID-19 Pandemic Based on the Bibliometric Method

Online education has become the mainstream since the outbreak of COVID-19 pandemic. This paper did a bibliographic review based on VOSviewer and CiteSpace. The author reviewed the definitions, pros and cons for teachers and students, and factors affecting the learning effectiveness of related literature. The author analyzed the mapping of countries, keywords, organizations, authors, and sources of literature about the online education during the COVID-19 pandemic in the Web of Science Core Collection database. The USA and China were the most active countries. Central China Normal University was the most productive institution. Hodges, C. B. and Garrison, D. R. were the authors most cited. Education Sciences, Education, and Information Technologies published the majority of papers. Research hotspot topics were teachers' or students' perceptions, students' satisfaction, students' performance, and students' motivation and engagement. Future educators can explore innovative theoretical paradigms and conduct relevant empirical studies in the post-pandemic era based on this research.
Part Based Interaction Learning for Group Activity Recognition

Group activity recognition is a subject with broad applications, and its main challenge is to model the interactions between individuals. Existing algorithms mostly model the interactions merely based on holistic features of persons, which completely ignore the local details and local interactions that could be significant for recognition. In this paper, we propose a novel part based interaction learning algorithm for group activity recognition. Our proposed algorithm introduces both the physical structural information and fine-grained contextual information into representations, through exploring the intraand inter-actor part interactions. Specifically, a dual-branch framework is adopted to extract the appearance and motion features respectively. For each branch, we utilize the key point detection technique for proper part division and then extract the part features. The part features are further enhanced by the transformers for intra- and inter-actor part interactions, and are lastly used for group activity recognition. Comparison with the state-of-the-arts on two public datasets demonstrate the effectiveness of our proposed algorithm.
A Systematic Review on Phishing Detection: A Perspective Beyond a High Accuracy in Phishing Detection

Phishing is one of the cyberattacks most feared by users who use transactional services over the Internet, although there are a lot of studies focused on detecting phishing attacks showing high accuracy, those have problems acting with the effectiveness required to prevent people to fall into these attacks in the early stages. In this article, a state-of-the-art overview of phishing detection is shown using a systematic literature review methodology for studies addressed between 2016 and 2022, such as other survey papers between 2020 and 2022, focused on the different detection stages, information sources, phishing characterization, and different methods used in the literature. Found that 83% of applications works selected are focused on the mitigation stage, where the methodologies act in reactive ways using statics features that provides high accuracy but turn the models fail through time. Finally, conclusions will be presented to highlight the importance of using brand information and mixing different methods to improve stage detection and assure durability in the detection model. The article’s contribution is focused on establishing another perspective that encourages future research and future related works to consider their models beyond a high accuracy and start thinking about how these models can to provide effective solutions that could be integrated into production environments to protect the users.
3D Human Skeleton Estimation from Monocular Single RGB Image based on Multiple Virtual-View Skeleton Generation

3D human skeleton estimation from a single RGB image is one of the challenging problems in computer vision. Motivated by the advantage of the multi-views approach, we proposed a new two-stage approach. The 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> stage estimates a set of 3D heatmaps, by which 2D image coordinates and relative depth for each joint of a set of 2D skeletons can be derived. It consists of multi-streams, where 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> stream is to predict 2D+depth skeleton for the real-view and the other streams are to predict skeleton counterparts for virtual-views, thus called Multiple Virtual-View Skeleton Generator (MVSG) network. The 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> stage contains: depth-denoising and fusion network, where the outputs of MVSG network are depth-denoised and then fused by concatenation for regression into the final 3D skeleton. Experiments show that our technique has achieved a performance of MPJPE=46.75 mm, which is comparable to the state-of-the-art methods.
Semi-parametric Approach to Random Forests for High-Dimensional Bayesian Optimisation

Calibration of simulation models and hyperparameter optimisation of machine learning and deep learning methods are computationally demanding optimisation problems, for which many state-of-the-art optimisation methods are adopted and applied in various studies. However, their performances come to a test when the parameter optimisation problems exhibit high-dimensional spaces and expensive evaluation of models’ or methods’ settings. Population-based (evolutionary) methods work well for the former but not suitable for expensive evaluation functions. On the opposite, Bayesian optimisation eliminates the necessity of frequent simulations to find the global optima. However, the computational demand rises significantly as the number of parameters increases. Bayesian optimisation with random forests has overcome issues of its state-of-the-art counterparts. Still, due to the non-parametric output, it fails to utilise the capabilities of available acquisition functions. We propose a semi-parametric approach to overcome such limitations to random forests by identifying a mixture of parametric components in their outcomes. The proposed approach is evaluated empirically on four optimisation benchmark functions with varying dimensionality, confirming the improvement in guiding the search process. Finally, in terms of running time, it scales linearly with respect to the dimensionality of the search space.
Image Decluttering Techniques and Its Impact on YOLOv4 Performance

Object detection and specifically face detection are challenging computer vision problems. The purpose of this study is to explore the effect of data augmentation and image decluttering technique on performance of YoloV4 model. In this work, we proposed the idea of image decluttering technique and evaluated its effect on the face detection. We have also investigated Mosaic augmentation technique and identified some drawbacks of using that and suggested an enhancement to the existing Mosaic augmentation to address the drawbacks and showed the impact of the new proposed mosaic augmentation technique on performance of face detection using YoloV4 model. This study is structured to find the effect of the proposed techniques on various images with diverse backgrounds, illumination, occlusions, and viewpoints. We achieved promising results that prove the effectiveness of the proposed techniques on detection probability, specifically in the challenging conditions.
Prioritized Sampling with Intrinsic Motivation in Multi-Task Reinforcement Learning

Deep Reinforcement Learning (RL) promises to lead the next advances towards the development of coveted future intelligent agents. However, the unprecedented representational power of deep function approximators, e.g. deep neural networks, comes at the cost of demanding a huge amount of experience, making deep RL impractical for applications requiring interactions with the real world. We study the problem of making use of samples in deep RL more efficiently, exploiting the desirable properties of knowledge generalization resulting from learning multiple tasks together. The outcome of our work is the coupling of multi-task RL algorithms with a task-sampling policy based on the well-known intrinsic motivation paradigm. In particular, we leverage on the notion of TD-error of Bellman updates, as an effective measure of learning progress, to prioritize sampling from the tasks contributing the most to the learning of the agent. This sampling strategy speeds up the learning of tasks for which the agent is showing progress, and postpones the learning of the remaining ones, resulting in an optimized collection of samples. Our method is supported by experimental evaluations on well-known RL control tasks, for which our approach shows superior sample-efficiency and performance compared to representative baselines. We eventually evaluate our approach on simulated control tasks based on Quanser robotics systems, confirming the advantages over the baselines also in more realistic applications.
Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT

Multilingual BERT (mBERT), a language model pre-trained on large multilingual corpora, has impressive zeroshot cross-lingual transfer capabilities and performs surprisingly well on zero-shot POS tagging and Named Entity Recognition (NER), as well as on cross-lingual model transfer. At present, the mainstream methods to solve the cross-lingual downstream tasks are always using the last transformer layer’s output of mBERT as the representation of linguistic information. In this work, we explore the complementary property of lower layers to the last transformer layer of mBERT. A feature aggregation module based on an attention mechanism is proposed to fuse the information contained in different layers of mBERT. The experiments are conducted on four zero-shot cross-lingual transfer datasets, and the proposed method obtains performance improvements on key multilingual benchmark tasks XNLI (+1.5 %), PAWS-X (+2.4 %), NER (+1.2 F1), and POS (+1.5 F1). Through the analysis of the experimental results, we prove that the layers before the last layer of mBERT can provide extra useful information for cross-lingual downstream tasks and explore the interpretability of mBERT empirically.
Automatic Image Generation of Peking Opera Face using StyleGAN2

Image generation technology, which is often used in various applications of intelligent image generation, can learn the feature distribution of real images and sample from the distribution to obtain the generated images with high fidelity. This paper focuses on the feature extraction and intelligent generation techniques of Peking opera face with Chinese cultural characteristics. Based on the creation of a Peking opera face dataset, this paper compares the impact of different variants of a Style-based generator architecture for Generative Adversarial Networks (StyleGAN2) and different sizes of datasets on the quality of face generation. The experimental results verify that the synthetic images generated by StyleGAN2 with the addition of the Adaptive Discriminator Augmentation (ADA) module are visually better and have good local randomness when the dataset is small and unbalanced in distribution.
Design of Trademark Recommendation System Based on Knowledge Graph

Today, knowledge graphs are used in more and more places. Especially in the field of recommendation systems, the knowledge graph shines brightly. In the past, the core of recommendation system is algorithm. Now, the emergence of the knowledge graph improves the working efficiency of the recommendation system as well as its accuracy. The system to be designed in this paper is about the design of trademark recommendation system based on knowledge graph. The steps to set up the system are as follows: The first step is to set up the working environment required by the system; The second step is to conduct a simple data preprocessing on the collected trademark data sets according to their classification to form a reliable CSV file importing knowledge map of trademark data. The third step is to connect the online Neo4j graph database and import the obtained CSV file into the graph database to form the knowledge graph about trademarks. The fourth step is to find interested trademarks and their classification according to the trademark knowledge map, and output their names, classification and other information. Step 4, use content-based recommendation algorithm to test the output trademark and get the recommendation result; The fifth step is to use PyQt5 plug-in to make a platform to display pictures and display the recommended trademarks. In general, this project is to make a trademark recommendation system with simple visualization function based on knowledge graph.
Semi-Automatic Detection and Measurement of Fetal Parameters from Ultrasound Images and the Scope Automatic System Using LabVIEW

The idea of developing an automated fetal parameter detection and measurement system using image processing techniques available in LabVIEW is proposed in this research work. The six fetal parameters to be measured from the ultrasound images are crown rump length, biparietal diameter, femur length, head circumference, abdominal circumference and humerus length. We propose the semi-automatic and automatic process using the vision assistant tool in LabVIEW. The semi-automatic process is wherein the parameters are segregated in to six SUB VI’s and are programmed into one single MAIN VI. This is then followed by manually dragging the region of interest ROI which could be either circle or line annotation and the values of the result is obtained. The automatic process is being performed only for four parameters namely AC, HC, HL and FL. The trimester fetal scan image is loaded and by applying certain threshold levels and performing convolution as well as morphological operations using different icons such as contour extract, the search direction and the contour selection are used to locate the images from left to right, top to bottom, maximum length, etc., for automation. Here, it selects the appropriate ROI and the values of the result are obtained. The main advantage of this work is that all the conventional methods of fetal anatomical parameter detections are improved with customized LabVIEW and the vision assistant tool. The conventional procedure consumes 20–30 min for a single subject scanning whereas in case of these proposed techniques the time taken for automatic detection is less than 5 min based on the system used and the manual intervention also be reduced in diagnostic procedures.
Wireless Device Authentication Using LSTM-Based Autoencoders

In wireless communication systems, security attacks to the network is a major concern. One of the common threats is when an attacker attempts to interact with a wireless network without being authorized through spoofed communications. This rogue device may attempt to interact with the system through forged packets, forcing the authentication mechanism or executing a man-in-the-middle attack. Authentication of a device through characteristics present in its signal itself (i.e., physical layer) offers an additional layer of security when the other fails. Radio Frequency Distinct Native Attributes (RF-DNA) has shown good efficiency in the rejection of spoofing attackers from attaining access to the network. In this chapter, we propose an approach leveraging a deep learning model combining a long short-term memory (LSTM)-based autoencoder and supervised fully connected layers learning to improve device classification at the physical layer.
Dark Image Fusion-Based Contrast Enhancement using a weighted combination of Bright Channel Prior and Dark Channel Prior

The photos are acquired in the presence of poor lighting conditions. Commonly these photos affect the quality of the image. The details present in such images taken in poor lighting conditions are neither perceived by human vision nor by machines that try to interpret image details in an automated way for various applications. These poorly illuminated images need contrast enhancement to better understand and perceive details. Related to this field, research has introduced a few methods for the contrast enhancement of the low light (dark) image. These methods do suffer from drawbacks like the enhanced image becoming either blurry or brighter as these methods do the contrast enhancement uniformly on all pixels of the darker image even if the partial region of the image is properly illuminated. To get over such impediments, the contrast enhancement technique for darker images using a weighted Bright_Channel_Prior (BCP) and Dark-Channel-Prior (DCP) blend is provided here. The image entropy is accounted for in the assessment of the experiments performed. The given technique is performed on the Exclusively dark image dataset (ExDark). The outcome of these experiments determined that the proposed technique gives more reliable results over the existing individual BCP and DCP methods as well as over other contrast enhancement methods.