Reviewerly/exHarmony huggingface dataset:
‘Choice’ of social media platform or encrypted messaging app to buy and sell illegal drugs

Social media is increasingly being utilized to facilitate the buying and selling of illegal drugs, particularly by young people. However, the range of social media and messaging platforms means people must make 'choices' around which platforms they will use to conduct drug transactions. Polymedia theory suggests that people use different media to manage their social relationships, 'choosing' appropriate channels or platforms based on platform features and the social context of the relationship. This paper uses polymedia theory to explore how buyers and sellers navigate platform 'choice' for drug trading, and how this bears similarities to the ways in which they move between different platforms in their daily lives. We conducted anonymous online interviews with thirty-three people who buy and/or sell drugs via social media and encrypted messaging apps in New Zealand to explore the factors shaping their selection of platforms for drug transactions. Our findings highlight the importance of the relationship between the buyer and seller in shaping how interviewees weighted the relative importance of platform security and convenience. Though more commercial drug sellers exercised considerable influence in directing buyers of drugs towards a given platform, this pattern was less characteristic of how platform 'choice' was navigated in contexts of drug supply between friends. In social supply contexts, platform 'choices' were often not explicitly made, but rather exchanges were channelled through platforms already in use within the social group. We highlight the tensions that arise between seller preferences for a more secure platform and buyer preferences for greater convenience in contexts of friendship. Our findings highlight the importance of understanding social media drug market engagement as shaped by broader patterns of social media engagement, and the distinctions between different social contexts and personal relationships, consistent with polymedia theory.
State observer-based fuzzy echo state network sliding mode control for uncertain strict-feedback chaotic systems without backstepping

To control uncertain strict-feedback chaotic systems, the adaptive backstepping technique is a popular method, yet this method requires repeatedly differentiating virtual control inputs, which will result in the “explosion of complexity” problem. In this paper, an alternative control method for uncertain strict-feedback chaotic systems without using backstepping technique is presented. We first translate the uncertain strict-feedback chaotic system into a new straightforward normative system whose states are unmeasurable, and then, an observer is designed to estimate the unknown states of the transformed system. A new recurrent neural network , namely fuzzy echo state network (FESN), is constructed to approximate the lumped uncertainty of the normative system. The semi-globally stability of the closed-loop system can be guaranteed by the FESN sliding mode controller that only uses one FESN and one adaptation law. Comparative simulations are put forward to verify the derived theoretical results.
Valuating requirements arguments in the online user's forum for requirements decision‐making: The CrowdRE‐VArg framework

Abstract User forums enable a large population of crowd‐users to publicly share their experience, useful thoughts, and concerns about the software applications in the form of user reviews. Recent research studies have revealed that end‐user reviews contain rich and pivotal sources of information for the software vendors and developers that can help undertake software evolution and maintenance tasks. However, such user‐generated information is often fragmented, with multiple viewpoints from various stakeholders involved in the ongoing discussions in the Reddit forum. In this article, we proposed a crowd‐based requirements engineering by valuation argumentation (CrowdRE‐VArg) approach that analyzes the end‐users discussion in the Reddit forum and identifies conflict‐free new features, design alternatives, or issues, and reach a rationale‐based requirements decision by gradually valuating the relative strength of their supporting and attacking arguments. The proposed approach helps to negotiate the conflict over the new features or issues between the different crowd‐users on the run by finding a settlement that satisfies the involved crowd‐users in the ongoing discussion in the Reddit forum using argumentation theory. For this purpose, we adopted the bipolar gradual valuation argumentation framework, extended from the abstract argumentation framework and abstract valuation framework. The automated CrowdRE‐VArg approach is illustrated through a sample crowd‐users conversation topic adopted from the Reddit forum about Google Map mobile application. Finally, we applied natural language processing and different machine learning algorithms to support the automated execution of the CrowdRE‐VArg approach. The results demonstrate that the proposed CrowdRE‐VArg approach works as a proof‐of‐concept and automatically identifies prioritized requirements‐related information for software engineers.
Quadratic-Kalman-Filter-Based Sensor Fault Detection Approach for Unmanned Aerial Vehicles

Sensors are crucial for the control of unmanned aerial vehicles (UAVs). However, sensor faults will inevitably appear over time. Therefore, it is important to develop a sensor fault detection approach for the reliability of UAV. This article presents a novel model-based UAV fault detection approach based on quadratic Kalman filter (QKF). First, an accurate kinematic and dynamic model of UAVs is established, where the model is linearized and discretized for Kalman filter (KF). Second, the first KF is used for denoising, the secondKF is used to detrend, and residuals are calculated for detection. It is worth mentioning that the second KF is a modified Sage–Husa adaptive KF, which can automatically estimate the measurement noise variance. Compared with traditional approaches, this approach has the advantages of noise reduction, self-adaptation, divergence avoidance, and high detection rate. Simulation and experimental results show the effectiveness of the proposed approach, which can accurately detect the abrupt and incipient fault of an inertial measurement unit (IMU) sensor. At the same time, it can get the higher fault detection rates (FDRs) compared with conventional KF. Furthermore, this approach also provides the leading information and foundation for UAV fault-tolerant control.
Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification

Hate speech has become a hot research topic in the area of natural language processing (NLP) due to the tremendous increase in the usage of social media platforms like Instagram, Twitter, Facebook, etc. The facelessness and flexibility provided through the Internet have made it easier for people to interact aggressively. Furthermore, the massive quantity of increasing hate speech on social media with heterogeneous sources makes it a challenging task. With this motivation, this study presents an Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification (ESGONLP-HSC) model. The major intention of the presented ESGONLP-HSC model is to identify and classify the occurrence of hate speech on social media websites. To accomplish this, the presented ESGONLP-HSC model involves data pre-processing at several stages, such as tokenization, vectorization, etc. Additionally, the Glove technique is applied for the feature extraction process. In addition, an attention-based bidirectional long short-term memory (ABLSTM) model is utilized for the classification of social media text into three classes such as neutral, offensive, and hate language. Moreover, the ESGO algorithm is utilized as a hyperparameter optimizer to adjust the hyperparameters related to the ABLSTM model, which shows the novelty of the work. The experimental validation of the ESGONLP-HSC model is carried out, and the results are examined under diverse aspects. The experimentation outcomes reported the promising performance of the ESGONLP-HSC model over recent state of art approaches.
MANomaly: Mutual adversarial networks for semi-supervised anomaly detection

In network intrusion detection, since the available attack traffic is much less than normal traffic, detecting attacks and intrusions from these unbalanced traffic can be a problem of semi-supervised learning, i.e., finding outliers (anomalies) from a data population that obeys a certain distribution. In this paper, we design a novel network model named the mutual adversarial network (MAN), which has two identical reconstruction autoencoder (RecAE) subnetworks. In training, these two subnetworks use the proposed mutual adversarial training to learn the data distribution of normal traffic samples. In detection, we identify anomalies based on the residual values obtained after different samples are reconstructed by MAN. In addition, we devise a novel method to identify anomalies from anomaly scores named the high anomaly suppression (HAS) determination mechanism, which uses the mean values to suppress the effect of noisy data in the test sample. Then, we construct a novel semi-supervised reconstruction anomaly detection framework named MANomaly by combining MAN with the HAS determination mechanism. Meanwhile, we design three different mutual adversarial training approaches to MANomaly and evaluate them on two publicly available network traffic datasets: NSL-KDD and UNSW-NB15. Experimental results show that our method achieves excellent performance by using only 5% of normal training data.
A Deep Learning Framework for Grocery Product Detection and Recognition

Object detection and recognition are the most important and challenging problems in computer vision. The remarkable advancements in deep learning techniques have significantly accelerated the momentum of object detection/recognition in recent years. Meanwhile, text detection/recognition is also a critical task in computer vision and has gotten more attention from many researchers due to its wide range of applications. This work focuses on detecting and recognizing multiple retail products stacked on the shelves and off the shelves in the grocery stores by identifying the label texts. In this paper, we proposed a new framework is composed of three modules: (a) retail product detection, (b) product-text detection, (c) product-text recognition. In the first module, on-the-shelf and off-the-shelf retail products are detected using the YOLOv5 object detection algorithm. In the second module, we improve the performance of the state-of-the-art text detection algorithm by replacing the backbone network with ResNet50 + FPN and by introducing a new post-processing technique, Width Height based Bounding Box Reconstruction, to mitigate the problem of inaccurate text detection. In the final module, we used a state-of-the-art text recognition model to recognize the retail product's text information. The YOLOv5 algorithm accurately detects both on-the-shelf and off-the-shelf grocery products from the video frames and the static images. The experimental results show that the proposed post-processing approach improves the performance of the existing methods on both regular and irregular text. The robust text detection and text recognition methods greatly support our proposed framework to recognize the on-the-shelf retail products by extracting product information such as product name, brand name, price, and expiring date. The recognized text contexts around the retail products can be used as the identifier to distinguish the product.
Sentiment Analysis of Covid19 Vaccines Tweets Using NLP and Machine Learning Classifiers

Sentiment Analysis (SA) is an approach for detecting subjective information such as thoughts, outlooks, reactions, and emotional state. The majority of previous SA work treats it as a text-classification problem that requires labelled input to train the model. However, obtaining a tagged dataset is difficult. We will have to do it by hand the majority of the time. Another concern is that the absence of sufficient cross-domain portability creates challenging situation to reuse same-labelled data across applications. As a result, we will have to manually classify data for each domain. This research work applies sentiment analysis to evaluate the entire vaccine twitter dataset. The work involves the lexicon analysis using NLP libraries like neattext, textblob and multi class classification using BERT. This word evaluates and compares the results of the machine learning algorithms.
Towards cost-benefit evaluation for continuous software engineering activities

Abstract Context: Software companies must become better at delivering software to remain relevant in the market. Continuous integration and delivery practices promise to streamline software deliveries to end-users by implementing an automated software development and delivery pipeline. However, implementing or retrofitting an organization with such a pipeline is a substantial investment, while the reporting on benefits and their relevance in specific contexts/domains are vague. Aim: In this study, we explore continuous software engineering practices from an investment-benefit perspective. We identify what benefits can be attained by adopting continuous practices, what the associated investments and risks are, and analyze what parameters determine their relevance. Method: We perform a multiple case study to understand state-of-practice, organizational aims, and challenges in adopting continuous software engineering practices. We compare state-of-practice with state-of-the-art to validate the best practices and identify relevant gaps for further investigation. Results: We found that companies start the CI/CD adoption by automating and streamlining the internal development process with clear and immediate benefits. However, upgrading customers to continuous deliveries is a major obstacle due to existing agreements and customer push-back. Renegotiating existing agreements comes with a risk of losing customers and disrupting the whole organization. Conclusions: We conclude that the benefits of CI/CD are overstated in literature without considering the contextual and domain complexities rendering some benefits infeasible. We identify the need to understand the customer and organizational perspectives further and understand the contextual requirements towards the CI/CD.
Pedestrian Detection Using MB-CSP Model and Boosted Identity Aware Non-Maximum Suppression

Pedestrian detection is an important task in autonomous surveillance systems. Despite the rapid progress in pedestrian detection field, detecting occluded pedestrians remains a challenging task due to the great variations in occluded pedestrians appearance and the drastic loss of pedestrian information in some severe cases. In this paper, we tackle the occlusion problem by proposing a multi-branch pedestrian detection model based on center and scale prediction framework. The proposed model employs features extracted from full pedestrian’s body as well as its upper, middle, and lower body parts using four detection branches. This multi-branch approach ensures that data representing the true pedestrian appearances, whether they are partially or completely visible, can dominate the final decision-making, minimizing the interference of non-pedestrian data in the detection. Furthermore, to implement the proposed model, the visibility of different pedestrian parts is appropriately annotated, which facilitates the training process. The final decision is made based on the four MB-CSP branches outputs, using a proposed fusing method, named Boosted Identity Aware-Non Maximum Suppression. On heavy occlusion settings, the proposed model resulted in the miss rates of 27.83%, 47.29% and 33.3% for Caltech-USA, Citypersons and EuroCity Persons datasets, respectively.
Multi-Type Object Tracking Based on Residual Neural Network Model

In this paper, a tracking algorithm based on the residual neural network model and machine learning is proposed. Compared with the widely used VGG network, the residual neural network has deeper characteristic layers and special additional layer structure, which break the symmetry of the network and reduce the degradation of the neural network. The additional layer and convolution layer are used for feature fusion to represent the target. The multi-features of the object can be captured by using the developed algorithm, so that the accuracy of tracking can be improved in some complex scenarios. In addition, we defined a new measure to calculate the similarity of different image regions and find the optimal matched region. The search area is delimited according to the continuity of the target motion, which improves the real-time performance of tracking. The experimental results illustrate that the proposed algorithm achieved a higher accuracy while taking into account the real time performance, especially in dealing with some complex scenarios such as deformation, rotation changes and background clutters, in comparison with the Multi-Domain Network (MDNet) algorithm based on a convolutional neural network.
Secured information sharing in supply chain management: Modified data sanitization with optimal key generation via hybrid algorithm

In the majority of commercial scenarios, supply chain management (SCM) is turning out to be a fundamental focus area. In SCM, a hopeful solution for secured information sharing (IS) is blockchain technology. Nevertheless, it is a bit crucial to maintain security at all levels of blockchain, and therefore private and public-key cryptography is often favored. This study improves the security and privacy of a suggested "blockchain assisted supply chain management (SCM)," in which "Modified Data Sanitization and Data Restoration" is performed with an ideal key generation technique to retain sensitive data in each block. The major challenge that must be overcome to make data transmission security is optimal key selection. Significantly, the proposed hybridized algorithm is used to generate the optimal key in both processes. The proposed hybrid model is known as Whale Updated Butterfly Optimization (WU-BO) and is a conceptual mixture of "Butterfly optimization algorithm (BOA) and Whale Optimization Algorithm (WOA)."Eventually, the examination is performed for verifying the enhancement of the developed model.
On genetic programming representations and fitness functions for interpretable dimensionality reduction

Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. However, most of the main DR methods are either linear (e.g., PCA), do not provide an explicit mapping between the original data and its lower-dimensional representation (e.g., MDS, t-SNE, isomap), or produce mappings that cannot be easily interpreted (e.g., kernel PCA, neural-based autoencoder). Recently genetic programming (GP) has been used to evolve interpretable DR mappings in the form of symbolic expressions. There exists a number of ways in which GP can be used to this end and no study exists that performs a comparison. In this paper, we fill this gap by comparing existing GP methods as well as devising new ones. We evaluate our methods on several benchmark datasets based on predictive accuracy and on how well the original features can be reconstructed using the lower-dimensional representation only. Finally we qualitatively assess the resulting expressions and their complexity. We find that various GP methods can be competitive with state-of-the-art DR algorithms and that they have the potential to produce interpretable DR mappings.
Semi-supervised region-connectivity-based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image

Deep-learning-based methods have achieved state-of-the-art results in cerebrovascular segmentation. However, it is costly and time-consuming to acquire labeled data because of the complex structure of cerebral vessels. In this paper, we propose a novel semi-supervised cerebrovascular segmentation with a region-connectivity-based mean teacher model (RC-MT) from time-of-flight magnetic resonance angiography (TOF-MRA), where unlabeled data is introduced into the training. Concretely, the RC-MT framework consists of a mean teachers (MT) model and a region-connectivity-based model. The region-connectivity-based model dynamically controls the balance between the supervised loss and unsupervised consistency loss by taking into account that the predicted vessel voxels should be continuous in the underlying anatomy of the brain. Meanwhile, we design a novel multi-scale channel attention fusion Unet (MSCAF-Unet) as a backbone for the student model and the teacher model. The MSCAF-Unet is a multi-scale channel attention fusion layer used to construct an image pyramid input and achieve multi-level receptive field fusion. The proposed method is evaluated on diverse TOF-MRA datasets (three clinical datasets and a public dataset). Experimental results show that the proposed method achieves high-performance gains by incorporating the unlabeled data and outperforms competing semi-supervised-based methods. The code will be openly available at https://github.com/IPIS-XieLei/RC-MT.
A new multi-objective optimization algorithm based on combined swarm intelligence and Monte Carlo simulation

Currently, multi-objective optimization is an important problem in various fields. This paper proposes an innovative multi-objective flower pollination algorithm combined with Monte Carlo simulation (called MFPAMC). MFPAMC incorporates the following three technologies: the flower pollination algorithm (FPA), the Monte Carlo method (MC), and a sorting function. Initially, the FPA is used to search for the optimal solution, which improves the search efficiency. When the solution obtained by the FPA is no longer updated, MC simulation is adopted to further find the optimal solution. The sorting function prevents the loss of objective function information, and the result obtained is objective and practical. A test function is used to verify whether the combination of the FPA and MC simulation can improve the search accuracy. The results confirm that the search accuracy is improved. Finally, MFPAMC is used for empirical analysis, and the experimental results are compared with those of four other methods. The comparison results confirm that the optimized results are ideal and further indicate that MFPAMC has significant potential for practical application.
Modal Analysis Using Digital Image Correlation Technique

The present paper discusses a new approach for the experimental determination of modal parameters (resonant frequencies, modal shapes and damping coefficients) based on measured displacement values, using the non-contact optical method of digital image correlation (DIC). The output is a newly developed application module that, based on a three-dimensional displacement matrix from the experimental measurement results, can construct a frequency response function (FRF) for the purpose of experimental and operational modal analysis. From this frequency response function, the modal parameters of interest are able to be determined. The application module has been designed for practical use in Scilab 6.1.0, and its code interfaces directly with the ISTRA4D high-speed camera software. The module was built on measurements of a steel plate excited by an impact hammer to simulate experimental modal analysis. Verification of the correctness of the computational algorithm or the obtained modal parameters of the excited sheet metal plate was performed by simulation in the numerical software Abaqus, whose modal shapes and resonant frequencies showed high agreement with the results of the newly developed application.