Reviewerly/exHarmony huggingface dataset:
Parameter settings in particle swarm optimisation algorithms: a survey

In swarm intelligence, 'fair comparison' is critical for the performance evaluation of algorithms. In this paper, the setting of parameters in particle swarm optimisation (PSO) algorithms, which include the population size S, topology structure (number of neighbours k), inertia weight w, acceleration coefficient c1, c2, velocity constraint Vmax, and the boundary constraint strategy, are reviewed and analysed. Based on the analysis and discussion of parameters and the variants of PSO algorithms, a list of parameter settings of PSO algorithms and a recommendation of PSO comparison are given. To compare variants of PSO algorithms, a recommended solution maybe that all compared algorithms have the same number of population size and the maximum number of fitness evaluations, and the inertia weight w, acceleration coefficient c1, c2 are the same settings as its original version.
A Zero-Shot Learning Approach to Classifying Requirements: A Preliminary Study

Context and motivation: Advances in Machine Learning (ML) and Deep Learning (DL) technologies have transformed the field of Natural Language Processing (NLP), making NLP more practical and accessible. Motivated by these exciting developments, Requirements Engineering (RE) researchers have been experimenting ML/DL based approaches for a range of RE tasks, such as requirements classification, requirements tracing, ambiguity detection, and modelling. Question/problem: Most of today’s ML/DL approaches are based on supervised learning techniques, meaning that they need to be trained using annotated datasets to learn how to assign a class label to examples from an application domain. This requirement poses an enormous challenge to RE researchers, as the lack of requirements datasets in general and annotated datasets in particular, makes it difficult for them to fully exploit the benefit of the advanced ML/DL technologies. Principal ideas/results: To address this challenge, this paper proposes a novel approach that employs the Zero-Shot Learning (ZSL) technique to perform requirements classification. We build several classification models using ZSL. We focus on the classification task because many RE tasks can be solved as classification problems by a large number of available ML/DL methods. In this preliminary study, we demonstrate our approach by classifying non-functional requirements (NFRs) into two categories: Usability and Security. ZSL supports learning without domain-specific training data, thus solving the lack of annotated datasets typical of RE. The study shows that our approach achieves an average of 82% recall and F-score. Contribution: This study demonstrates the potential of ZSL for requirements classification. The promising results of this study pave the way for further investigations and large-scale studies. An important implication is that it is possible to have very little or no training data to perform requirements classification. The proposed approach thus contributes to the solution of the long-standing problem of data shortage in RE.
A novel approach for attendance monitoring system with face mask detection

The major challenge faced during the pandemic is safety. Hence, this paper aims to ensure safety among the people working in populated areas organisations. The paper introduces face mask detection and an automatic attendance system. This real-time system aims to keep track of the users by a novel proposed algorithm. The system's flow begins with collecting the pre-integrated and the system's newly generated data, followed by face mask recognition, face recognition, causing the datasheet and notifying the administrator. The advanced techniques used are Graphic User Interference (GUI), a deep learning technique to extract targeted class features, Open CV to execute real-time operations, speech instructions, and the MobileNetV2 approach. The experimental results with detailed discussion and elaboration has been provided in the results section.
Concise fractional Fourier transform based on a non-uniform order searching method for estimating physical parameters from Newton’s rings

Newton’s ring pattern is very common in interferometry. By analyzing it, the physical parameters can be estimated, such as the curvature radius and the rings’ center. However, parameter estimation from large images using fractional Fourier transform (FRFT) consumes considerable time. A concise FRFT based on a non-uniform order searching method is proposed to reduce the computational time without decreasing the accuracy. Experimental results show that the time of the proposed method is about 2.7 s, which is about 1/600 of that of the traditional FRFT-based method and 1/5 of that of the Fast FRFT-based method when processing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>960</mml:mn> </mml:mrow> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>960</mml:mn> </mml:mrow> </mml:mrow> </mml:math> pixel images.
Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

Blood cell malignantly growth has been accounted for to be one of the most transcendent types of disease maladies. ALL (Acute Lymphoblastic Leukaemias) is the malignant types of blood cancer and their detection and classification in earlier stage is biggest issue. Automatic detection and classification of ALL from microscopic images is a challenging and intellectual assignment in medical science. Existing techniques for ALL detection and classification are an understandable alternative for real-time dermoscopic data analysis. Existing microscopic image processing approaches are unable to analyze the ALL data with non-stationary nature. In this perspective, the focus of this research is to design hybrid Convolutional Neural Network (CNN) architecture by utilizing Firefly Optimization Algorithm (FOA/FFA) to detect the ALL from microscopic images of human blood cell into malignant or normal blood cell. Methods: For training and testing of proposed ALL Detection and Classification (ALL-DC) Model, Standard ALL-IDB (Acute-Lymphoblastic-Leukaemias Image Database for Image Processing) is used with hybrid CNN architecture based on the FOA. Here, Histogram of Oriented Gradients (HOG) descriptor with FOA is used as feature extraction and selection mechanism from the Region of Blood Cell (ROBC).Feature extraction approach plays an important responsibility to classify lots of blood diseases. On the way to achieve this goal, we proposed ALL-DC model that combines recent developments in deep learning with fuzzy based CNN structure and for ROBC segmentation, hybridization of K-means segmentation algorithm with FOA that are capable to segment the accurate blood cell region from microscopic images. Using k-means segmentation technique, the foreground and background component is separated into two regions and after that to improve the segmentation results; FOA is used with the novel concept of image enhancement approach. Results: The proposed ALL-DC system is evaluated using the largest publicly accessible standard ALL-IDB dataset, containing 600 training and 400 testing microscopic images. When the evaluation parameters of proposed work is compared with a number of other state-of-art schemes, the proposed scheme achieves the most excellent performance of 98.5% in terms of accuracy which also known as area under the curve (AUC) in differentiating ALL from benign cell using only the extracted and optimized HOG feature. Conclusion: When the proposed model is tested on different microscopic images, evaluation parameters is calculated and compared with a few other state-of-art methods and we obtained the proposed method achieves the best performance in terms of classification accuracy. ALL-DC model is implemented and constructed using the concept of Image Processing and Neural Network Toolbox within MATLAB Software.
Modelling and analysis of real-time and reliability for WSN-based CPS

WSN-based CPS can be used to collect information from remote environment and control it. Delay and dependability directly affect the running quality of WSN-based CPS. In this paper, we present a specified process calculus to model the running of the system, including its real-time and reliability character. Then we design a group of algorithms to calculate the reliability of the system under certain responsible time constraint. Using these methods, we can formally depict the running of WSN-based CPS, and can assess real-time and reliability of WSN-based CPS quantitatively. We illustrate our ideas through bank night automatic monitoring and emergency system, and we can improve the system's reliability from 0.58 to 0.87 and higher gradually.
Parallel context-aware multi-agent tourism recommender system

The presence of millions and millions of users and items makes real time filtering a time consuming process in recommender systems (RS). In context aware recommender systems (CARS), choices of users depend on the contextual information as well as available items. The rapid change in interests of a user under different contexts puts extra load on RS. To address this problem, we present a parallel approach for CARS using a multi-agent system which accelerates the processing time drastically using General Purpose Graphic Processing Unit (GPGPU). Contextual pre-filtering and multi-agent environment update the system with the user context. A prototype of the system is developed using JCuda, JADE and Java technologies for tourism domain. The performance of the presented system is compared with the CARS without parallel processing with respect to processing time, scalability as well as precision, recall and F-measure. The results show a significant speedup for presented system over non-parallel CARS.
Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling

Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph convolutions in an encoder-core-decoder architecture, extracting high-level representations from graph nodes and their neighbors. Furthermore, we efficiently aggregate intermediate features from different hierarchies to enhance the proposed model's representation capability and labeling performance. We perform extensive experiments on public datasets, in which the results prove the superiority of our approach over baselines by a clear margin. Clinical relevance- The graph convolutions and feature fusion in our approach effectively extract graph information, which provides more accurate intracranial artery label predictions than existing methods and better facilitates medical research and disease diagnosis.
Ultrasound Image Enhancement using Super Resolution

Within today's day and age, amongst women who fall under the bracket of being of reproductive age, when discussion about Gynecologic tumors comes forward, Uterine Fibroids are found to be abundant. Since early detection for tumors is statutory for ensuring recovery, ultrasound detection remains prominent for being one of the most convenient Image-capturing methodologies. Although given the convenience of the method, the quality of the images could be deemed sub-optimal, a need to enhance the image arises for successful detection by appropriate algorithms. This all facilitates the development of a technique with a harmonious combination of filters and Super-Resolution Convolutional neural network (SRCNN). Super Resolution technique in itself is altered to make it feasible for monochromatic medical images, which essentially highlights the contagious aspect, i.e., the tumor region of the image. Thus, firstly the captured noisy image is denoised by making use of Spatial filters, which ensures denoising while preserving the essential edges. Then, this output of the filtered image is treated as an input for the Super-resolution technique, as described above. Within the said technique, multiple low-resolution images are combined in Super-Resolution to regenerate a target input image with higher resolution and quality. Furthermore, Subjective, quantitative, and visual execution evaluations were conducted using Peak Signal-to-Noise-Ratio (PSNR) for quantitative and Universal Quality Index (UQI) and structural similarity index metric (SSIM). Upon completion, these results of comparing the quality of super-resolved images on various data sets are provided in numerical form. These comparisons state that the experimental results have shown a substantial improvement in the results by the fusion technique of hybrid filters with Super Resolution convolutional neural network, for instance, the PSNR value achieved is in the range of 40% to 50%. As a result, the proposed technique has demonstrated outstanding performance in all cases, as evidenced by the findings that the proposed algorithms are robust and ultrasound images taken under various lighting conditions and parameters work efficiently and without egregious errors.
Challenges of contact tracing in college students during COVID-19 pandemic

Contact tracing is essential to help monitor and control the spread of the highly contagious COVID-19 virus. Many universities across the United States have developed and implemented contact tracing programs during the COVID-19 pandemic. In this viewpoint article, we characterized and reflected on the unique challenges of contact tracing in college students based on our extensive experience at a large urban private university. Three challenges of contact tracing were identified, including 1) contact tracers’ difficulty building trust with students, 2) students’ unwillingness to disclose information due to fear of consequences, and 3) students acting carelessly and not following guidelines. We made recommendations to address these challenges to enhance the practice of contact tracing to protect the health of this young adult population.
Detecting damaged buildings using real-time crowdsourced images and transfer learning

After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~ 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision.
Cycle Mutation: Evolving Permutations via Cycle Induction

Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems like the traveling salesperson problem (TSP), as well as assignment problems like the quadratic assignment problem (QAP) and largest common subgraph (LCS). We propose cycle mutation, a new mutation operator whose inspiration is the well known cycle crossover operator, and the concept of a permutation cycle. We use fitness landscape analysis to explore the problem characteristics for which cycle mutation works best. As a prerequisite, we develop new permutation distance measures: cycle distance, $k$-cycle distance, and cycle edit distance. The fitness landscape analysis predicts that cycle mutation is better suited for assignment and mapping problems than it is for ordering problems. We experimentally validate these findings showing cycle mutation's strengths on problems like QAP and LCS, and its limitations on problems like the TSP, while also showing that it is less prone to local optima than commonly used alternatives. We integrate cycle mutation into the open-source Chips-n-Salsa library, and the new distance metrics into the open-source JavaPermutationTools library.
HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction plays an important role in online advertising and recommendation systems, which aims at estimating the probability of a user clicking on a specific item. Feature interaction modeling and user interest modeling methods are two popular domains in CTR prediction, and they have been studied extensively in recent years. However, these methods still suffer from two limitations. First, traditional methods regard item attributes as ID features, while neglecting structure information and relation dependencies among attributes. Second, when mining user interests from user-item interactions, current models ignore user intents and item intents for different attributes, which lacks interpretability. Based on this observation, in this paper, we propose a novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph. HIEN also captures user intents for different item attributes as well as item intents based on our proposed hierarchical attention mechanism. Extensive experiments on both public and production datasets show that the proposed model significantly outperforms the state-of-the-art methods. In addition, HIEN can be applied as an input module to state-of-the-art CTR prediction methods, bringing further performance lift for these existing models that might already be intensively used in real systems.
Training a Feedforward Neural Network Using Hybrid Gravitational Search Algorithm with Dynamic Multiswarm Particle Swarm Optimization

One of the most well-known methods for solving real-world and complex optimization problems is the gravitational search algorithm (GSA). The gravitational search technique suffers from a sluggish convergence rate and weak local search capabilities while solving complicated optimization problems. A unique hybrid population-based strategy is designed to tackle the problem by combining dynamic multiswarm particle swarm optimization with gravitational search algorithm (GSADMSPSO). In this manuscript, GSADMSPSO is used as novel training techniques for Feedforward Neural Networks (FNNs) in order to test the algorithm's efficiency in decreasing the issues of local minima trapping and existing evolutionary learning methods' poor convergence rate. A novel method GSADMSPSO distributes the primary population of masses into smaller subswarms, according to the proposed algorithm, and also stabilizes them by offering a new neighborhood plan. At this time, each agent (particle) increases its position and velocity by using the suggested algorithm's global search capability. The fundamental concept is to combine GSA's ability with DMSPSO's to improve the performance of a given algorithm's exploration and exploitation. The suggested algorithm's performance on a range of well-known benchmark test functions, GSA, and its variations is compared. The results of the experiments suggest that the proposed method outperforms the other variants in terms of convergence speed and avoiding local minima; FNNs are being trained.
Multilingual Detection of Personal Employment Status on Twitter

Detecting disclosures of individuals' employment status on social media can provide valuable information to match job seekers with suitable vacancies, offer social protection, or measure labor market flows. However, identifying such personal disclosures is a challenging task due to their rarity in a sea of social media content and the variety of linguistic forms used to describe them. Here, we examine three Active Learning (AL) strategies in real-world settings of extreme class imbalance, and identify five types of disclosures about individuals' employment status (e.g. job loss) in three languages using BERT-based classification models. Our findings show that, even under extreme imbalance settings, a small number of AL iterations is sufficient to obtain large and significant gains in precision, recall, and diversity of results compared to a supervised baseline with the same number of labels. We also find that no AL strategy consistently outperforms the rest. Qualitative analysis suggests that AL helps focus the attention mechanism of BERT on core terms and adjust the boundaries of semantic expansion, highlighting the importance of interpretable models to provide greater control and visibility into this dynamic learning process.
Quantum neural network autoencoder and classifier applied to an industrial case study

Abstract Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers, it is relevant to develop algorithms that are useful for actual industrial processes. In this work, we propose a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e., a machine used in one of Eni’s Oil Treatment Plants. This work represents one of the first attempts to integrate quantum computing procedures in a real-case scenario of an industrial pipeline, in particular using actual data coming from physical machines, rather than pedagogical data from benchmark datasets.
Decoupled Federated Learning for ASR with Non-IID Data

Automatic speech recognition (ASR) with federated learning (FL) makes it possible to leverage data from multiple clients without compromising privacy. The quality of FL-based ASR could be measured by recognition performance, communication and computation costs. When data among different clients are not independently and identically distributed (non-IID), the performance could degrade significantly. In this work, we tackle the non-IID issue in FL-based ASR with personalized FL, which learns personalized models for each client. Concretely, we propose two types of personalized FL approaches for ASR. Firstly, we adapt the personalization layer based FL for ASR, which keeps some layers locally to learn personalization models. Secondly, to reduce the communication and computation costs, we propose decoupled federated learning (DecoupleFL). On one hand, DecoupleFL moves the computation burden to the server, thus decreasing the computation on clients. On the other hand, DecoupleFL communicates secure high-level features instead of model parameters, thus reducing communication cost when models are large. Experiments demonstrate two proposed personalized FL-based ASR approaches could reduce WER by 2.3% - 3.4% compared with FedAvg. Among them, DecoupleFL has only 11.4% communication and 75% computation cost compared with FedAvg, which is also significantly less than the personalization layer based FL.