Reviewerly/exHarmony huggingface dataset:
Towards Robust Graph Convolution Network via Stochastic Activation

Graph Neural Networks (GNNs for short), a generalization of neural networks to graph-structured data, performance good in closed setting with perfect data for variety tasks, including node classification, link prediction and graph classification. However, GNNs are vulnerable to adversarial attacks, i.e., a small perturbation to the graph structure and node features in wild setting can lead to non-trivial performance degradation. Non-robustness is one of the main obstacle to applying GNNs in the wild. In this work, we focus on one of the most popular GNNs, Graph Convolutional Networks (GCN for short), and propose Stochastic Activation GCN (SA-GCN for short) to improve the robustness of GCN models. More specifically, we propose building a roust model by directly introducing a regularization term to the objective function and maximizing the feature distribution variance. Extensive experiments show that this simple design makes SA-GCN achieving significantly improved robustness against adversarial attacks. Moreover, our approach generalizes well and can be equipped with various models. Conducted empirical experiments demonstrate the effectiveness of SA-GCN.
Linear Regression for Car Sales Prediction in Indian Automobile Industry

The automobile industry is one of the leading industries in our economy. Sudden up rise in the demand for automobile vehicle and also the growth in profits are the leading factors for this industry to become one of the major and important ones. This industry is also coming up with various financial aids and schemes for the general population which is why people buying vehicles is causing a ripple effect and maximizing their profits and the growth of industry. This industry has been a great force and a contributor to our economy. That is why this is of important significance for us to accurately predict the sales of automobile. That is why every industry or organization wants to predict the result by using their own past data and various learning algorithms of machine learning. This will help them visualize past data and help them to determine their future goals and plan accordingly and, thus, making sales prediction the current trend in the market. Current study helps to get the prediction of sales in automobile industry using machine learning techniques.
DHA: Product Title Generation with Discriminative Hierarchical Attention for E-commerce

Product titles play an important role in E-Commerce sites. However, manually crafting product titles needs tremendous time and human effort. It is expected that product titles can be automatically generated, but existing generation methods usually require densely labeled data that are unavailable in the real world. We formulate a novel product title generation task that generates the title from the image and auxiliary information (e.g., category) to address the gap. To generate titles that are consistent with search queries, we construct the first large-scale dataset (AEPro) and propose a Discriminative Hierarchical Attention (DHA) model. The DHA model first identifies the image regions related to the product of interest (POI) with a POI attention module. Then, based on the title context, the identified image regions are further revised by a generation attention module. Finally, the titles are generated by dynamically attending to these image regions. Experiments on the AEPro dataset demonstrate the effectiveness of the DHA model. Besides, online A/B testing results show that $$61.8\%$$ of the titles generated by the DHA model are accepted directly or with minor modifications. The exposure rate of the products with machine-generated titles is improved by $$40\%$$ .
Recommender System Based on the Friendship Between Social Network Users in a Cold-Start Scenario

Recommender systems have gained importance and visibility mainly in e-commerce applications and the transmission of music and videos. In these platforms, the user can choose vast items, and recommender systems facilitate the users’ choice by reducing the options to items of most significant interest. However, cold-start situations (new users in the system) make the recommendation difficult due to the lack of information about users’ preferences. Social networking data can be used as information to reduce the cold-start impact. In this scenario, identifying the best and most influential friends can improve the recommendation by placing the group of friends with the most excellent affinity. Thus, using data from social networks as the primary source of external information to recommend items to cold-start users, a recommendation model was proposed based on the strength of friendship and the degree of influence between individuals. More specifically, with the new user’s access into the system through the his/her social network credentials, we can identify his/her friends’ groups and, among these, his/her most influential friends. The preference information of these significant users is used to recommend items (tracks) to the cold-start user. The proposal was validated using a controlled experiment in which 20 users effectively participated. A social network, built especially for the proposal, retained information about the interaction between friends on the social network and their access to a music streaming service. Users evaluated the recommender system, giving scores from 1 to 5 for each recommended song. The assertiveness of the model was computed using the Root Mean Squared Error (RMSE) metric, presenting a result of 1.57, which shows that the recommendation prediction was very close to the values given by users. The results also showed that the proposed model could be used to improve the recommendation of any user and not just cold-starts. Thus, the proposed model is quite adequate to improve the recommendation.
Uncertainty Regularized Multi-Task Learning

By sharing parameters and providing taskindependent shared features, multi-task deep neural networks are considered one of the most interesting ways for parallel learning from different tasks and domains.However, fine-tuning on one task may compromise the performance of other tasks or restrict the generalization of the shared learned features.To address this issue, we propose to use task uncertainty to gauge the effect of the shared feature changes on other tasks and prevent the model from overfitting or over-generalizing.We conducted an experiment on 16 text classification tasks, and findings showed that the proposed method consistently improves the performance of the baseline, facilitates the knowledge transfer of learned features to unseen data, and provides explicit control over the generalization of the shared model.
Imperceptible and Reliable Adversarial Attack

Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them add the perturbation in a “sparse” or “global” way. Since the number of pixels perturbed by the “sparse" method and the perturbation intensity of each pixel added by the “global" method are both small, the adversarial property can be destroyed easily. Finally, it makes the adversarial attack and the adversarial training based on these samples unreliable. To address this issue, we present an “pixel-wise" method which is somewhere in between the “sparse” or “global” way. First, the perception of human eyes to the error of different image regions is different. Second, image processing methods have different effects on the different areas of the image. Based on these two considerations, we propose an imperceptible and reliable adversarial attack method, which projects the perturbation to the different areas differently. Extensive experiments demonstrate our method can preserve the attack ability while maintaining good view quality. More importantly, the proposed projection can be combined with existing attack methods to generate a stronger generation algorithm which improves the robustness of adversarial examples. Based on the proposed method, the reliability of adversarial attacks can be greatly improved.
Why We Might Augment Reality: Art’s Role in the Development of Cognition

In our investigations, here and elsewhere, art serves a neurological role, shaped by evolution. Art, not as a thing that is, but as a function that occurs, which we will call Behavioral Art (BA). An important aspect of BA is ‘borrowing intelligence’ from a humanly organized source, such as a painting, applied to a computer process. The resulting artifact of this auto-creative process might easily be mistaken for an object de (computer) art. But we must look further into the larger dynamic system, one that includes the audience as well. As we will discuss, the machine itself is incapable of meaningful organization. (i.e., alphabetical order is an arbitrary scheme to a machine.) A human (often the programmer) must supply the organizational paradigm to the input, and a human must recognize one in the output. However, by integrating resources from the environment via machine, a process we can now call augmented reality. We might imbue whatever quality triggered an interpretation of ‘potentially meaningful’ in audience members regarding the off-screen image to our computed output. In this chapter, we address how and why humans tend to employ this subtle particular form of nonverbal expression.
Solving Problems That Have Dual Solution Characteristics

Many real-world delivery problems must take into account multiple characteristics when searching for the desired solution (e.g. costs, delivery time or environmental impact). The techniques for finding solutions to the Vehicle Routing Problem discussed previously (e.g. Clarke and Wright Chap. 2 and Evolutionary Algorithms Chap. 3 ), two solution characteristics became apparent; the number of vehicles used and the distance travelled. The approaches discussed previously searched for solutions based on the minimising of a single objective value. This single objective value could be distance, the quantity of vehicles or some utility value based on a combination of both. For many real-world problems, a solution that focuses on one characteristic at the expense of the other may not be appropriate, nor may be desirable to present a solution based on a combination of the two—which may produce a solution that does not satisfactorily address either characteristic. To address this problem, this chapter introduces a theme that recurs through this book; incorporating user choice into the problem-solving process. We encourage user choiceUser Choice by presenting the user with a structured set of solutions rather than one solution created by the optimiser.
Exploring the Continuance Intentions in Agile Software Development Methods: The Case of Large-Scale Projects in the Banking Sector

The purpose of this study is to examine the factors that influence team members of software development projects to continue using agile methodologies after their initial adoption. The research focuses on large-scale projects in the banking sector and uses the expectation-confirmation model (ECM) as a conceptual framework. The research model is tested by employing partial least square structural equation modeling (PLS-SEM). The findings validate the model and report statistically significant positive associations between all constructs. The results suggest that confirmation of expectations regarding perceived usefulness and satisfaction are key determinants of agile continuance intentions in software development large-scale projects. The study has both theoretical and practical implications and calls for further research in the field of agile post-implementation.
A Relative Study on Machine Learning-Based Algorithm Used to Develop Recommender Systems

The research paper focuses on review of methods and techniques utilized for designing a recommending system using machine algorithms for e-commerce platforms. It also discussed developing innovative machine learning-based algorithms for e-commerce platforms. The paper gives a critical review and finds some scientific gaps in the literature related to machine learning-based recommendation systems and their designing methods. It then provides a suitable methodology to design and develop a machine learning algorithm for e-commerce platforms by using an improved approach of recommendation systems.
Feature Evolvable Learning with Image Streams

Feature Evolvable Stream Learning (FESL) has drawn extensive attentions during the past few years, where old features would vanish and new features would appear when learning with streaming data. Existing FESL algorithms are mainly designed for simple datasets with low-dimension features, however, they are ineffective to deal with complex streams such as image sequences. The shallow model, which is expected to be feasible for the low-dimension streams, fails to reveal the complex nonlinear patterns of images, and the linear mapping performs poorly when recovering the vanished features of image streams. To tackle this issue, in this paper, a new online learning paradigm, i.e., Feature Evolvable Learning with Image Streams (FELIS) is proposed to make the online learners less restrictive and more applicable. We present a novel ensemble residual network (ERN), in which the prediction is the weighted combination of classifiers learnt by the feature representations from several residual blocks. This facilitates fast convergence since the learning process starts with a shallow network and then gradually switches to a deeper model when more data has been received. In addition, to characterize the relationship between the previous and current feature space of the image streams, a latent representation mapping (LRM) approach is proposed through minimizing the discrepancy of the latent representations from the two different feature spaces. Extensive experimental results on both virtual and real scenarios datasets demonstrate the effectiveness of the proposed method.
Solving Tough Feedback Problems (Without Hair Loss) [Shop Talk: What You Didn’t Learn in School]

You don&#x2019;t admit it to your colleagues. You may not even admit it to yourself. But deep down, feedback scares you a little bit. Not the everyday dominant pole stuff with generous phase margins and plenty of loop gain. That&#x2019;s easy. No, it&#x2019;s that occasional &#x201C;special&#x201D; circuit with some peculiar element in the loop, or too many poles lurking around crossover (i.e., unity-gain frequency). In that weird territory, you don&#x2019;t trust your intuition, your mental constructs don&#x2019;t hold up, and you find yourself on shaky ground. The sensible response is just to retreat to more familiar surroundings: take the alien elements out of the loop. Crush the bandwidth with a monster capacitor. Run away. Take a management job&#x0021
ARISE: A Multitask Weak Supervision Framework for Network Measurements

The application of machine learning (ML) to mitigate network-related problems poses significant challenges for researchers and operators alike. For one, there is a general lack of labeled training data in networking, and labeling techniques popular in other domains are ill-suited due to the scarcity of operators’ domain expertise. Second, network problems are typically multi-tasked in nature, requiring multiple ML models (one per task) and resulting in multiplicative increases in training times as the number of tasks increases. Third, the adoption of ML by network operators hinges on the models’ ability to provide basic reasoning about their decision-making procedures. To address these challenges, we propose ARISE, a multi-task weak supervision framework for network measurements. ARISE uses weak supervision-based data programming to label network data at scale and applies learning paradigms such as multi-task learning (MTL) and meta-learning to facilitate information sharing between tasks as well as reduce overall training time. Using community datasets, we show that ARISE can generate MTL models with improved classification accuracy compared to multiple single-task learning (STL) models. We also report findings that show the promise of MTL models for providing a means for reasoning about their decision-making process, at least at the level of individual tasks.
Educational Potential of 3D Multi-User Virtual Environments

The text focuses on the issue of 3D multi-user virtual environments and their use in education. In the wake of the global Covid-19 pandemic, there was a worldwide need for a rapid transition in education at all levels of schooling and in lifelong learning to the online space. As this was a rapid organisational change, schools and lifelong learning institutions often found themselves in situations where tools not previously tested in the school were used, or online tools were used that did not lead to the desired effect. Therefore, it is necessary to investigate which types of online tools are most appropriate for education, depending on the age of the learners and the learning topic. This text analyses the educational potential of 3D multi-user virtual environments, which hold significant benefits for the application of basic didactic principles that bring significant advantages in terms of learning outcomes, in particular the principle of illustration, learning from simulated virtual experiences as well as direct contact with the learning community. The text concludes by discussing current perspectives on the effectiveness of these environments in the educational process.
Euler Totient Function and Fermat-Eulller Theorem Based on Optimized Key Management Scheme for Securing Mobile Agents Migration

As mobile agents move automatically on distributed networks, the security of agents and platforms is of prime concern. Over the years, mobile agents-based software applications have grown exceptionally. It also has increased the threats to the security of such applications. In the mobile agent paradigm, most of the protection schemes discuss the protection of platforms only and provide fewer directions on the security of mobile agents which is still a complicated issue. In this paper, a mathematical key management technique has been presented to solve the problem of agent security and its authentication during the hopping of agents in different platforms. Here, a novel agent security approach based on the Euler totient function and Fermat-Euler theorem is developed to secure a secret key among ‘n’ number of mobile agents. At the time of execution, the mobile agent reconstructs the secret key based on the proposed technique. An evaluative judgment, comparing with various agent security schemes, has been presented, along with their complex nature. The proposed approach has been implemented and its various features are tested. The results indicate that the computing here is much more secure and easier in comparison to traditional client–server and code on-demand paradigms.
Non-causality Influences on Uncertainties: A Brief Review

Uncertainty is everywhere, not only on the level of quantum mechanics. In reality, we do not have any exact measures or observations regardless of the advanced technologies that we are developing. There will always be an unresolved part of uncertainties due to noise, interference, modeling bandwidth, accuracy limitations, measuring device influences, distortions, etc. However, the reason for our uncertainties can be justified as due to lack of perfect and complete knowledge. Nevertheless, scientists claim that there are other kinds of uncertainties that are inherently random (blind) and are not due to the lack of knowledge. This article presents a brief and conceptual overview of most types of uncertainties and then proposes a new possible one. The proposed reason for some kinds of uncertainties is based on the non-causality principle, i.e., dynamic systems that could start to respond even before the excitation. In uncertain systems with a non-causal behavior, it is not feasible to resolve the associated uncertainties. The reason is that the response can be influenced by another process happening in the future (i.e., hidden by time) compared to the observed process time-scale. This article reviews different possible uncertainties in a gradual increase of complexity. The discussion levels have been kept mainly on the concept level and avoid mathematical proofs.
A Variational AutoEncoder-Based Relational Model for Cost-Effective Automatic Medical Fraud Detection

This work aims to develop a framework of automatic medical fraud detection (AMFD) which can be deployed in healthcare industry. To address the issue that the medical fraud labels are insufficient in both size and classes for training a good AMFD model, this work proposes a novel Variational AutoEncoder-based Relational Model (VAERM) which can simultaneously exploit Patient-Doctor relational network and one-class fraud labels to improve the fraud detection. Then, the proposed VAERM coupled with active learning strategy can assist healthcare industry experts to conduct cost-effective fraud investigation. Finally, we propose an online model updating method to reduce the computation and memory requirement while preserving the predictive performance. The proposed framework is tested in a real world dataset and it empirically outperforms the state-of-the-art methods in both automatic fraud detection and fraud investigation tasks.
Machine Learning-Based Channel State Estimators for 5G Wireless Communication Systems

For a 5G wireless communication system, a convolutional deep neural network (CNN) is employed to synthesize a robust channel state estimator (CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state information.Also, it utilizes pilots to offer more helpful information about the communication channel.The proposed CNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory (BiLSTM/LSTM) NNs-based CSEs.The CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based estimators.Using three different loss function-based classification layers and the Adam optimization algorithm, a comparative study was conducted to assess the performance of the presented DNNs-based CSEs.The BiLSTM-CSE outperforms LSTM, CNN, conventional least squares (LS), and minimum mean square error (MMSE) CSEs.In addition, the computational and learning time complexities for DNN-CSEs are provided.These estimators are promising for 5G and future communication systems because they can analyze large amounts of data, discover statistical dependencies, learn correlations between features, and generalize the gotten knowledge.
SBHDetector

Mining Software Repositories in collaborative environment during software evolution or maintenance faces challenges due to creation of larger than necessary slices or unnecessary splitting of Revision History and detection of edge level changes. Due to these limitations, GIT and Diff &amp; Merge Tools does not accurately detect the similarities and changes between versions due to renaming or shifting. Detection of these similarities accurately helps to detect code clones and change patterns that improves understandability, knowledge transfer and tracking changes. Therefore, the authors proposed fuzzy based hybrid technique to detect the similarities/changes between versions considering RS by enriching the Revision History at three granularities- File, Class and Method level. 30% more entities have been found similar/change by deriving Classification model with f-score and ROC Area more than 0.985 and .994 respectively for all applications. Hence, proposed technique improves productivity, reusability and maintainability with respect to VCA.