Reviewerly/exHarmony huggingface dataset:
Group-Buying Recommendation for Social E-Commerce

Group buying, as an emerging form of purchase in social e-commerce websites, such as Pinduoduo <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , has recently achieved great success. In this new business model, users, initiator, can launch a group and share products to their social networks, and when there are enough friends, participants, join it, the deal is clinched. Group-buying recommendation for social e-commerce, which recommends an item list when users want to launch a group, plays an important role in the group success ratio and sales. However, designing a personalized recommendation model for group buying is an entirely new problem that is seldom explored. In this work, we take the first step to approach the problem of group-buying recommendation for social e-commerce and develop a GBGCN method (short for Group-Buying Graph Convolutional Network). Considering there are multiple types of behaviors (launch and join) and structured social network data, we first propose to construct directed heterogeneous graphs to represent behavioral data and social networks. We then develop a graph convolutional network model with multi-view embedding propagation, which can extract the complicated high-order graph structure to learn the embeddings. Last, since a failed group-buying implies rich preferences of the initiator and participants, we design a double-pairwise loss function to distill such preference signals. We collect a real-world dataset of group-buying and conduct experiments to evaluate the performance. Empirical results demonstrate that our proposed GBGCN can significantly outperform baseline methods by 2.69%-7.36%. The codes and the dataset are released at https://github.com/Sweetnow/group-buying-recommendation.
Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection

Considering the fatality of phishing attacks, the data-driven approach using massive URL observations has been verified, especially in the field of cyber security. On the other hand, the supervised learning approach relying on known attacks has limitations in terms of robustness against zero-day phishing attacks. Moreover, it is known that it is critical for the phishing detection task to fully exploit the sequential features from the URL characters. Taken together, to ensure both sustainability and intelligibility, we propose the combination of a convolution operation to model the character-level URL features and a deep convolutional autoencoder (CAE) to consider the nature of zero-day attacks. Extensive experiments on three real-world datasets consisting of 222,541 URLs showed the highest performance among the latest deep-learning methods. We demonstrated the superiority of the proposed method by receiver-operating characteristic (ROC) curve analysis in addition to 10-fold cross-validation and confirmed that the sensitivity improved by 3.98% compared to the latest deep model.
Machine learning based deep job exploration and secure transactions in virtual private cloud systems

Virtual Private Cloud (VPC) is an emerging cloud environment used to provide more secure data communication. VPC provides authentic communication channel for secure communication between the cloud participants. The cloud jobs and the description of the runtime cloud events must be evaluated to provide flawless VPC service. Although VPC provides security in network services, it has to be enriched with internal and external platform level security features. In this regard, secure job service schemes ensure elimination of attacks, unauthorized jobs, improper accesses and intrusions in VPC. These irrelevant tasks (activities) can be isolated before initiating job scheduling process. Particularly, providing security for zero-trust cloud environment is more challenging task. Zero-trust cloud environment has completely vulnerable trust model on both internal and external circumstances. The proposed Machine Learning Based Secure Cloud Job Services (MLSCS) is implemented to provide multi-level security in this zero-trust cloud job servicing system. The proposed MLSCS develops Multi-Server Queue Management techniques, Reinforcement Learning based deep Q Matrix (RL-Q Matrix) techniques, Authentic VPC configuration and VPC Genetic Algorithm Network (VGAN) for establishing security practices in complex job handling system. MLSCS applies effective techniques for eliminating irrelevant cloud jobs to reduce the scheduler complexity and processor utilization. In this work, irrelevant jobs are considered as the jobs that are not appropriate for particular VPC scheduling policies and security principles (attacks). These jobs are identified through various key validation procedures and VPC policy determination procedures. These jobs are eliminated and prohibited in to job scheduler. Consequently, the legitimate jobs are securely forwarded in to job scheduler through multi-server queues. In the experimental setup, the proposed MLSCS is compared with existing schemes such as Reinforcement Learning Based Distributed Heterogeneous Servicing technique (RLDH), Cat Swarm Optimization Based Job Servicing technique (CSOS) and Fuzzy Based Security-Driven Servicing technique (FSDS). The results show the MLSCS delivers 5% to 8% optimal results than existing schemes.
Implementing bilinear interpolation with quantum images

A bilinear interpolation technique is proposed for flexible representations of quantum images (FRQIs). In this process, several quantum modules were developed, including assignment, increment, and quarter modules, for use in an interpolation circuit. The network structure of these circuits, capable of both up-sampling and down-sampling FRQIs, was investigated as part of the study. Additionally, the proposed method was verified using a series of simulation experiments in which test samples were enlarged and reduced, for comparison with nearest-neighbor interpolation. The up-scaled images produced using the proposed technique were of higher quality than those produced using nearest-neighbor interpolation, as measured using the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). Finally, with the extension of the FRQI model from square to rectangular shapes, the proposed algorithm is used to process rectangular FRQIs with special and arbitrary sizes.
Deep-Feature-Based Approach to Marine Debris Classification

The global community has recognized an increasing amount of pollutants entering oceans and other water bodies as a severe environmental, economic, and social issue. In addition to prevention, one of the key measures in addressing marine pollution is the cleanup of debris already present in marine environments. Deployment of machine learning (ML) and deep learning (DL) techniques can automate marine waste removal, making the cleanup process more efficient. This study examines the performance of six well-known deep convolutional neural networks (CNNs), namely VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNet121, and MobileNetV2, utilized as feature extractors according to three different extraction schemes for the identification and classification of underwater marine debris. We compare the performance of a neural network (NN) classifier trained on top of deep CNN feature extractors when the feature extractor is (1) fixed; (2) fine-tuned on the given task; (3) fixed during the first phase of training and fine-tuned afterward. In general, fine-tuning resulted in better-performing models but is much more computationally expensive. The overall best NN performance showed the fine-tuned Inception-ResNetV2 feature extractor with an accuracy of 91.40% and F1-score 92.08%, followed by fine-tuned InceptionV3 extractor. Furthermore, we analyze conventional ML classifiers’ performance when trained on features extracted with deep CNNs. Finally, we show that replacing NN with a conventional ML classifier, such as support vector machine (SVM) or logistic regression (LR), can further enhance the classification performance on new data.
Real Time Face Mask Detection System using Transfer Learning with Machine Learning Method in the Era of Covid-19 Pandemic

The rapid development of computer vision has attracted more attention to the global epidemic Covid-19 to enable human-computer interaction and improve public health services. Due to the rapid spread of the (Covid-19), various countries are facing a major health crisis. According to the World Health Organization (WHO) an effective way to protect people from Covid-19 is to wear medical masks in public areas. It is very difficult to manually monitor people in public places and detect the face mask in the video. which is mainly because the mask itself acts as an obstruction to the face detection algorithm, because there are no face signs in the mask area. Therefore, automatic face mask detection system helps authorities to identify people who may be susceptible to infections disease. This research aims to use deep learning to automatically detect face masks in videos. The proposed framework consists of two components. The first component is designed for face detection and tracking using OpenCV and machine learning, and in the second component, these facial frames are then processed into our proposed deep transfer learning model MobileNetV2 to identify the mask area. The proposed framework was tested on different videos and images using the smartphone camera. The purpose is to achieve high-precision real-time detection and classification. The model achieved 99.2% accuracy during training and 99.8% validation accuracy. which is better than other recently proposed methods. Experimental results show that the work proposed in this paper can effectively recognize face masks with multiple targets and provide effective personnel surveillance. This research is useful for controlling the spread of the virus and preventing exposure to the virus.
A Robust MTMC Tracking System for AI-City Challenge 2021

Multi-Target Multi-Camera tracking (MTMC) is an essential task in the intelligent city and traffic analysis. It is a great challenging task due to several problems such as heavy occlusions and appearance variance caused by various camera perspectives and congested vehicles. In this paper, we propose a practical framework for dealing with the MTMC problem. The proposed framework contains three stage. Firstly, in the vehicles detection and Re-ID stage, the proposed system leverages Cascade R-CNN to detect all vehicles and extract appearance features with a Re-ID module for all cameras. Secondly, in the Multi-Target Single-Camera tracking (MTSC) stage, on the basis of the detected boxes and appearance features, it tracks multiple vehicles to generate candidate trajectories within each single camera with Tracklet-Plane Matching (TPM) tracking algorithm. Finally, in the Inter-Camera Association (ICA) stage, it associates all candidate trajectories between two successive cameras using the established distance matrix, and combines all successively matching results for final submission. The established distance matrix is simply computed by the Re-ID features and refined by the constraints of traveling time, road structures, and traffic rules to accelerate matching time as well as reduce search space. Extensive experiments on the public track3 test set of NVIDIA AI CITY 2021 CHALLENGE demonstrate the effectiveness of our method, which achieves IDF1 of 77.87%.
Scene Text Retrieval via Joint Text Detection and Similarity Learning

Scene text retrieval aims to localize and search all text instances from an image gallery, which are the same or similar with a given query text. Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter. In this paper, we address this problem by directly learning a cross-modal similarity between a query text and each text instance from natural images. Specifically, we establish an end-to-end trainable network, jointly optimizing the procedures of scene text detection and cross-modal similarity learning. In this way, scene text retrieval can be simply performed by ranking the detected text instances with the learned similarity. Experiments on three benchmark datasets demonstrate our method consistently outperforms the state-of-the-art scene text spotting/retrieval approaches. In particular, the proposed framework of joint detection and similarity learning achieves significantly better performance than separated methods. Code is available at: https://github.com/lanfeng4659/STR-TDSL.
Investigating the impact of recommender systems on user-based and item-based popularity bias

Recommender Systems are decision support tools that adopt advanced algorithms in order to help users to find less-explored items that can be interesting for them. While recommender systems may offer a range of attractive benefits, they may also intensify undesired effects, such as the Popularity Bias, where a few popular users/items get more popular and many unpopular users/items get more unpopular. In this paper, we study the impact of different recommender algorithms on the popularity bias in different application domains and recommendation scenarios. We have designed a comprehensive evaluation methodology by considering two different recommendation scenarios, i.e., the user-based scenario (e.g., recommending users to users to follow), and the item-based scenario (e.g., recommending items to users to consume). We have used two large datasets, Twitter and Movielens, and compared a wide range of classical and modern recommender algorithms by considering a diverse range of metrics, such as PR-AUC, RCE, Gini index, and Entropy Score. The results have shown a substantial difference between different scenarios and different recommendation domains. According to our observations, while the recommendation of users to users may increase the popularity bias in the system, the recommendation of items to users may indeed decrease it. Moreover, while we have measured a different level of popularity bias in different languages (i.e., English, Spanish, Portuguese, and Japaneses), the above-noted phenomena has been consistently observed in all of these languages.
Exemplar-Based Open-Set Panoptic Segmentation Network

We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task. This task requires performing panoptic segmentation for not only known classes but also unknown ones that have not been acknowledged during training. We investigate the practical challenges of the task and construct a benchmark on top of an existing dataset, COCO. In addition, we propose a novel exemplar-based open-set panoptic segmentation network (EOPSN) inspired by exemplar theory. Our approach identifies a new class based on exemplars, which are identified by clustering and employed as pseudoground-truths. The size of each class increases by mining new exemplars based on the similarities to the existing ones associated with the class. We evaluate EOPSN on the proposed benchmark and demonstrate the effectiveness of our proposals. The primary goal of our work is to draw the attention of the community to the recognition in the open- world scenarios. The implementation of our algorithm is available on the project webpage <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
WOA-TLBO: Whale optimization algorithm with Teaching-learning-based optimization for global optimization and facial emotion recognition

The Whale Optimization Algorithm (WOA) is a recently developed algorithm that is based on the chasing mechanism of humpback whales. Benefiting from the unique structure, WOA has virtuous global search capability. One of the drawbacks of this algorithm is the slow convergence rate that limits its real-world application. In resolving complicated global optimization problems, without any exertion for adequate fine-tuning preliminary constraints, Teaching-learning-based optimization (TLBO) is smooth to plunge into local optimal, but it has a fast convergence speed. By given the features of WOA and TLBO, an active hybrid WOA-TLBO algorithm is proposed for resolving optimization difficulties. To explore the enactment of the proposed WOA-TLBO algorithm, several experimentations are accompanied by regular benchmark test functions and compared with six other algorithms. The investigational outcomes indicate the more magnificent concert of the proposed WOA-TLBO algorithm for the benchmark function results. The proposed method has also been applied to the Facial Emotion Recognition (FER) functional problem. FER is the thought-provoking investigation zone that empowers us to classify the expression of the human face in everyday life. Centered on the portions’ actions in the human face, the maximum of the standard approaches fail to distinguish the expressions precisely as the expressions. In this paper, we have proposed FER’s productive process using WOA-TLBO based MultiSVNN (Multi-Support Vector Neural Network). Investigational outcomes deliver an indication of the virtuous enactment of the proposed technique resolutions in terms of accurateness.
On the Use of Dependabot Security Pull Requests

Vulnerable dependencies are a major problem in modern software development. As software projects depend on multiple external dependencies, developers struggle to constantly track and check for corresponding security vulnerabilities that affect their project dependencies. To help mitigate this issue, Dependabot has been created, a bot that issues pull-requests to automatically update vulnerable dependencies. However, little is known about the degree to which developers adopt Dependabot to help them update vulnerable dependencies. In this paper, we investigate 2,904 JavaScript open-source GitHub projects that subscribed to Dependabot. Our results show that the vast majority (65.42%) of the created security-related pull-requests are accepted, often merged within a day. Through manual analysis, we identify 7 main reasons for Dependabot security pull-requests not being merged, mostly related to concurrent modifications of the affected dependencies rather than Dependabot failures. Interestingly, only 3.2% of the manually examined pull-requests suffered from build breakages. Finally, we model the time it takes to merge a Dependabot security pull-request using characteristics from projects, the fixed vulnerabilities and issued pull requests. Our model reveals 5 significant features to explain merge times, e.g., projects with relevant experience with Dependabot security pull-requests are most likely associated with rapid merges. Surprisingly, the severity of the dependency vulnerability and the potential risk of breaking changes are not strongly associated with the merge time. To the best of our knowledge, this study is the first to evaluate how developers receive Dependabot's security contributions. Our findings indicate that Dependabot provides an effective platform for increasing awareness of dependency vulnerabilities and helps developers mitigate vulnerability threats in JavaScript projects.
Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation

Deep neural networks are susceptible to catastrophic forgetting: when encountering a new task, they can only remember the new task and fail to preserve its ability to accomplish previously learned tasks. In this paper, we study the problem of lifelong learning for generative models and propose a novel and generic continual learning framework Hyper-LifelongGAN which is more scalable compared with state-of-the-art approaches. Given a sequence of tasks, the conventional convolutional filters are factorized into the dynamic base filters which are generated using task specific filter generators, and deterministic weight matrix which linearly combines the base filters and is shared across different tasks. Moreover, the shared weight matrix is multiplied by task specific coefficients to introduce more flexibility in combining task specific base filters differently for different tasks. Attributed to the novel architecture, the proposed method can preserve or even improve the generation quality at a low cost of parameters. We validate Hyper-LifelongGAN on diverse image-conditioned generation tasks, extensive ablation studies and comparisons with state-of-the-art models are carried out to show that the proposed approach can address catastrophic forgetting effectively.
Death mechanism-based moth–flame optimization with improved flame generation mechanism for global optimization tasks

Moth–Flame Optimization (MFO) algorithm is widely used to solve real parameter global optimization problems. However, the original MFO algorithm has some problems. First, the global search ability of the algorithm is insufficient due to the lack of high-quality flames or the lack of diversity of the population. Moreover, due to the reduction of the number of flames mechanism and the simple transverse orientation flight mode of moths, the algorithm is easy to fall into local optimum. To overcome these difficulties, a new MFO called ODSFMFO is proposed in this paper, which consists of an improved flame generation mechanism based on Opposition-Based Learning (OBL) and Differential Evolution (DE) algorithm, and an enhanced local search mechanism based on Shuffled Frog Leaping Algorithm (SFLA) and death mechanism. The ODSFMFO firstly obtains a high-quality population through opposition-based learning, then enhances the diversity of the population through differential evolution algorithm, to improve the global search ability of the algorithm, and then, employs an improved shuffled frog leaping algorithm as a local search algorithm, and uses the death mechanism to eliminate the individuals with low fitness value, which not only make the algorithm easy to jump out of the local optimum, but also further improve the convergence speed. To demonstrate the effectiveness of the proposed algorithm, the ODSFMFO and other fourteen well-known optimization algorithms are tested and compared on 28 benchmark tasks of CEC 2013 and 30 benchmark tasks of CEC 2017. Simulation results show that the proposed algorithm obtains better results compared with other competitive algorithms.
Entropic uncertainty relation and quantum coherence under Ising model with Dzyaloshinskii–Moriya interaction

Recently, Ming et al (Quantum Inf Process 17 267, 2018) have investigated the entropic uncertainty relation in the presence of quantum memory (EUR-QM) and quantum coherence in a thermal regime for a two-spin chain under the effect of Dzyaloshinskii–Moriya (DM) interaction. Here, we study thermal quantum coherence and EUR-QM for a three-spin chain in Ising model under the effect of different directions of DM interaction. Our results confirm the results for the two-qubit case. Increasing temperature can reduce the quantum coherence but oppositely the uncertainty will grow. We show that the coupling parameter and DM interaction have constructive effects on quantum coherence and they reduce uncertainty in measurement results of incompatible observables, and especially the y-direction of DM interaction has a better influence on decreasing the uncertainty than the x and z directions. Unfortunately, the effect of the third spin acts destructively on the uncertainty and its bound, as well as the quantum coherence between the first two spins. Notably, an explicit relationship between quantum coherence and the uncertainty lower bound is found.