Reviewerly/exHarmony huggingface dataset:
Ratio product model: A rank‐preserving normalization‐agnostic multi‐criteria decision‐making method

Abstract This paper presents a new multi‐criteria decision‐making (MCDM) method, namely the ratio product model (RPM). We first overview two popular aggregating models: the weighted sum model (WSM) and the weighted product model (WPM). Then, we argue that the two models suffer from some fundamental issues mainly due to ignoring the ratio nature of the alternatives' scores with respect to the criteria and the importance weights of the criteria. Building on the notion of compositional data analysis, the developed RPM regards performance scores and criteria weights as compositions, which solves the issues around the WSM and WPM. Using several examples, we show that the WSM and WPM could lead to erroneous conclusions, whereas the RPM could lead to fully reliable conclusions. Since many MCDM methods rely on some aggregation approaches, the proposed method is a significant contribution to the field and puts forward the correct way to analyze decision problems while respecting the nature and constraints of the input data.
Privacy-preserving data aggregation scheme against deletion and tampering attacks from aggregators

Because privacy-preserving data aggregation protocols provide data privacy and data compression, they have been extensively studied in smart grids. However, most of the existing data aggregation protocols are based on honest-but-curious aggregators, or adopt computationally intensive asymmetric homomorphic encryption, making these protocols only provably secure in weak security models, or resulting in high computational complexity for terminal users. To address the aforementioned issues, in this work, we propose a novel lightweight privacy-preserving data aggregation scheme against malicious aggregators based on our symmetric homomorphic encryption. The proposed scheme can reduce the computational cost of smart meters while also resisting tampering and deletion attacks from malicious aggregators. One of the highlights of the proposed scheme is its ability to withstand deletion attacks from malicious aggregators, which makes it unnecessary for the malicious aggregator to aggregate the readings of all smart meters instead of only a part of them. Therefore, our scheme is more secure than other schemes. It is the first privacy-preserving data aggregation scheme based on a stronger security model without multiple rounds of interaction. Finally, the detailed security analysis shows that the proposed scheme satisfies desirable security properties. Moreover, experimental results demonstrate that the proposed scheme is superior to the other four schemes in terms of computational complexity and communication overhead.
A Brief Tour of Deep Learning from a Statistical Perspective

We expose the statistical foundations of deep learning with the goal of facilitating conversation between the deep learning and statistics communities. We highlight core themes at the intersection; summarize key neural models, such as feedforward neural networks, sequential neural networks, and neural latent variable models; and link these ideas to their roots in probability and statistics. We also highlight research directions in deep learning where there are opportunities for statistical contributions.
On-site instrumental seismic intensity prediction for China via recurrent neural network and transfer learning

China is a seismically active country. Rapidly and accurately predicting instrumental seismic intensity at recording sites is important for China to mitigate earthquake disasters. According to the peak ground acceleration (PGA) and peak ground velocity (PGV) at recording stations, instrumental seismic intensity for China is measured. Here, for the robust and rapid on-site instrumental seismic intensity prediction, we propose a method combining recurrent neural network (RNN) and transfer learning to predict on-site PGA and PGV for China. For the same test dataset from China, our results indicate that at 3 s after P-wave arrival, the RNN models using transfer learning have better performance on PGA and PGV prediction than the baseline models, which include traditional methods based on the single parameter and RNN models without using transfer learning. Additionally, according to the predicted PGA and PGV of the RNN models using transfer learning, we statistically analyze the alarm performance based on the predicted on-site instrumental seismic intensity. Meanwhile, according to the proposed method in this paper, we test five destructive earthquake events (M ≥ 6.6) occurred in China. The results show that at 3 s after the P-wave arrival, the predicted instrumental seismic intensity is almost consistent with the observed instrumental seismic intensity, the predicted instrumental seismic intensity error is mainly within ± 1, and the mean absolute error is 0.78. Meanwhile, for the area near the epicenter, the percentage of successful alarms reaches 90%, and the percentage of false alarms is 0%.
A systematic literature review of capstone courses in software engineering

Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone courses are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of project-based software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experiences. In a systematic literature review for 2007–2022, we identified 127 articles describing real-world capstone courses. These articles were analysed based on their presented course characteristics and the reported course outcomes. The characteristics were synthesised into a taxonomy consisting of duration, team sizes, client and project sources, project implementation, and student assessment. We found out that capstone courses generally last one semester and divide students into groups of 4–5 where they work on a project for a client. For a slight majority of courses, the clients are external to the course staff and students are often expected to produce a proof-of-concept level software product as the main end deliverable. The courses generally include various forms of student assessment both during and at the end of the course. This paper provides researchers and educators with a classification of characteristics of software engineering capstone courses based on previous research. We also further synthesise insights on the reported course outcomes. Our review study aims to help educators to identify various ways of organising capstones and effectively plan and deliver their own capstone courses. The characterisation also helps researchers to conduct further studies on software engineering capstones.
Estimating Quantum Hamiltonians via Joint Measurements of Noisy Noncommuting Observables

Estimation of expectation values of incompatible observables is an essential practical task in quantum computing, especially for approximating energies of chemical and other many-body quantum systems. In this Letter, we introduce a method for this purpose based on performing a single joint measurement that can be implemented locally and whose marginals yield noisy (unsharp) versions of the target set of noncommuting Pauli observables. We derive bounds on the number of experimental repetitions required to estimate energies up to a certain precision. We compare this strategy to the classical shadow formalism and show that our method yields the same performance as the locally biased classical shadow protocol. We also highlight some general connections between the two approaches by showing that classical shadows can be used to construct joint measurements and vice versa. Finally, we adapt the joint measurement strategy to minimise the sample complexity when the implementation of measurements is assumed noisy. This can provide significant efficiency improvements compared to known generalizations of classical shadows to noisy scenarios.
AQSA: Aspect-Based Quality Sentiment Analysis for Multi-Labeling with Improved ResNet Hybrid Algorithm

Sentiment analysis (SA) is an area of study currently being investigated in text mining. SA is the computational handling of a text’s views, emotions, subjectivity, and subjective nature. The researchers realized that generating generic sentiment from textual material was inadequate, so they developed SA to extract expressions from textual information. The problem of removing emotional aspects through multi-labeling based on data from certain aspects may be resolved. This article proposes the swarm-based hybrid model residual networks with sand cat swarm optimization (ResNet-SCSO), a novel method for increasing the precision and variation of learning the text with the multi-labeling method. Contrary to existing multi-label training approaches, ResNet-SCSO highlights the diversity and accuracy of methodologies based on multi-labeling. Five distinct datasets were analyzed (movies, research articles, medical, birds, and proteins). To achieve accurate and improved data, we initially used preprocessing. Secondly, we used the GloVe and TF-IDF to extract features. Thirdly, a word association is created using the word2vec method. Additionally, the enhanced data are utilized for training and validating the ResNet model (tuned with SCSO). We tested the accuracy of ResNet-SCSO on research article, medical, birds, movie, and protein images using the aspect-based multi-labeling method. The accuracy was 95%, 96%, 97%, 92%, and 96%, respectively. With multi-label datasets of varying dimensions, our proposed model shows that ResNet-SCSO is significantly better than other commonly used techniques. Experimental findings confirm the implemented strategy’s success compared to existing benchmark methods.
Generative negative replay for continual learning

Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems. One of the most effective strategies to control catastrophic forgetting, the Achilles' heel of continual learning, is storing part of the old data and replaying them interleaved with new experiences (also known as the replay approach). Generative replay, which is using generative models to provide replay patterns on demand, is particularly intriguing, however, it was shown to be effective mainly under simplified assumptions, such as simple scenarios and low-dimensional data. In this paper, we show that, while the generated data are usually not able to improve the classification accuracy for the old classes, they can be effective as negative examples (or antagonists) to better learn the new classes, especially when the learning experiences are small and contain examples of just one or few classes. The proposed approach is validated on complex class-incremental and data-incremental continual learning scenarios (CORe50 and ImageNet-1000) composed of high-dimensional data and a large number of training experiences: a setup where existing generative replay approaches usually fail.
A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.
Real-time vehicle target detection in inclement weather conditions based on YOLOv4

As a crucial component of the autonomous driving task, the vehicle target detection algorithm directly impacts driving safety, particularly in inclement weather situations, where the detection precision and speed are significantly decreased. This paper investigated the You Only Look Once (YOLO) algorithm and proposed an enhanced YOLOv4 for real-time target detection in inclement weather conditions. The algorithm uses the Anchor-free approach to tackle the problem of YOLO preset anchor frame and poor fit. It better adapts to the detected target size, making it suitable for multi-scale target identification. The improved FPN network transmits feature maps to unanchored frames to expand the model's sensory field and maximize the utilization of model feature data. Decoupled head detecting head to increase the precision of target category and location prediction. The experimental dataset BDD-IW was created by extracting specific labeled photos from the BDD100K dataset and fogging some of them to test the proposed method's practical implications in terms of detection precision and speed in Inclement weather conditions. The proposed method is compared to advanced target detection algorithms in this dataset. Experimental results indicated that the proposed method achieved a mean average precision of 60.3%, which is 5.8 percentage points higher than the original YOLOv4; the inference speed of the algorithm is enhanced by 4.5 fps compared to the original, reaching a real-time detection speed of 69.44 fps. The robustness test results indicated that the proposed model has considerably improved the capacity to recognize targets in inclement weather conditions and has achieved high precision in real-time detection.
A Quasi-Oppositional Based Flamingo Search Algorithm Integrated with Generalized Ring Crossover for Effective Feature Selection

AbstractThe goal of this article is to present a new hybrid technique for solving the feature selection problem. Conventionally, the process of determining the most relevant subset based on the given criteria is known as feature selection. Specifically, feature selection is a real world issue that can be tackled by an optimization technique. In the proposed method, a novel hybrid Quasi oppositional based Flamingo search algorithm with Generalized Ring Crossover (QOFSA-GRC) model is introduced to pick the most relevant features from the dataset. Specifically, the Quasi oppositional based Flamingo search algorithm (QOFSA) generates two populations one by Quasi oppositional based learning and the other by the Flamingo search algorithm to resolve the issue known as the curse of dimensionality. Then, by utilizing generalized ring crossover, the multiple relevant features are selected from the UCI repository dataset. Finally, the Kernel Extreme Learning Machine (KELM) classifier validates the selected features. The performance of the proposed model is tested by 20 UCI benchmark datasets and the outcomes are compared with other models. Through experimental outcomes, it has been revealed that the suggested model produces the best accuracy and also selects fewer numbers of features that are more relevant from the dataset. In terms of other performance measures, the proposed model attains better outcomes for the majority of the datasets.KEYWORDS: Feature selectionFlamingo search algorithmgeneralized ring crossoverKernel extreme learning machinequasi oppositional learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRevathi DurgamDurgam Revathi Graduated in B Tech, computer science and engineering from JNT University Hyderabad. She completed her post graduate in M Tech, computer science and engineering from JNT University Hyderabad. She is pursuing doctor of philosophy at School of Computer Science and Engineering, Vellore Institute of Technology-AP., India. Her Interests Research areas include data engineering, soft computing, optimization techniques and machine learning. Corresponding author. Email: revathi.vitap@gmail.comNagaraju DevarakondaNagaraju Devarakonda is working as an associate professor in the School of Computing Science and Engineering at VITAP, India. He has got 16 years of teaching experience in various engineering institutions in Andhra Pradesh. His research interests include data mining, machine learning, soft computing and optimization techniques. Email: dnagaraj_dnr@yahoo.co.in
Classification of Elastic Wave for Non-Destructive Inspections Based on Self-Organizing Map

An arrival time of an elastic wave is the important parameter to visualize the locations of the failures and/or elastic wave velocity distributions in the field of non-destructive testing (NDT). The arrival time detection is conducted generally using automatic picking algorithms in a measured time-history waveform. According to automatic picking algorithms, it is expected that the detected arrival time from low S/N signals has low accuracy if low S/N signals are measured in elastic wave measurements. Thus, in order to accurately detect the arrival time for NDT, the classification of measured elastic waves is required. However, the classification of elastic waves based on algorithms has not been extensively conducted. In this study, a classification method based on self-organizing maps (SOMs) is applied to classify the measured waves. SOMs visualize relation of measured data wherein the number of classes is unknown. Therefore, using SOM selects high and low S/N signals adequately from the measured waves. SOM is validated on model tests using the pencil lead breaks (PLBs), and it was confirmed that SOM successfully visualize the classes consisted of high S/N signal. Moreover, classified high S/N signals were applied to the source localization and it was noteworthy that localized sources were more accurate in comparison with using all of the measured waves.
A uniform transformer-based structure for feature fusion and enhancement for RGB-D saliency detection

RGB-D saliency detection integrates information from both RGB images and depth maps to improve the prediction of salient regions under challenging conditions. The key to RGB-D saliency detection is to fully mine and fuse information at multiple scales across the two modalities. Previous approaches tend to apply the multi-scale and multi-modal fusion separately via local operations, which fails to capture long-range dependencies. Here we propose a transformer-based structure to address this issue. The proposed architecture is composed of two modules: an Intra-modality Feature Enhancement Module (IFEM) and an Inter-modality Feature Fusion Module (IFFM). IFFM conducts a sufficient feature fusion by integrating features from multiple scales and two modalities over all positions simultaneously. IFEM enhances feature on each scale by selecting and integrating complementary information from other scales within the same modality before IFFM. We show that transformer is a uniform operation which presents great efficacy in both feature fusion and feature enhancement, and simplifies the model design. Extensive experimental results on five benchmark datasets demonstrate that our proposed network performs favorably against most state-of-the-art RGB-D saliency detection methods. Furthermore, our model is efficient for having relatively smaller FLOPs and model size compared with other methods.
Multi-Target Tracking Based on a Combined Attention Mechanism and Occlusion Sensing in a Behavior-Analysis System

Multi-object tracking (MOT) is a topic of great interest in the field of computer vision, which is essential in smart behavior-analysis systems for healthcare, such as human-flow monitoring, crime analysis, and behavior warnings. Most MOT methods achieve stability by combining object-detection and re-identification networks. However, MOT requires high efficiency and accuracy in complex environments with occlusions and interference. This often increases the algorithm's complexity, affects the speed of tracking calculations, and reduces real-time performance. In this paper, we present an improved MOT method combining an attention mechanism and occlusion sensing as a solution. A convolutional block attention module (CBAM) calculates the weights of space and channel attention from the feature map. The attention weights are used to fuse the feature maps to extract adaptively robust object representations. An occlusion-sensing module detects an object's occlusion, and the appearance characteristics of an occluded object are not updated. This can enhance the model's ability to extract object features and improve appearance feature pollution caused by the short-term occlusion of an object. Experiments on public datasets demonstrate the competitive performance of the proposed method compared with the state-of-the-art MOT methods. The experimental results show that our method has powerful data association capability, e.g., 73.2% MOTA and 73.9% IDF1 on the MOT17 dataset.
Implicit and explicit attention mechanisms for zero-shot learning

Zero-Shot Learning (ZSL) aims to recognise unseen object classes which are not observed during the training phase. Most of the existing methods on ZSL focus on learning a compatibility function between the image representation and class semantic information. Few others concentrate on learning image representation by combining local and global features. However, the existing approaches still fail to address the bias issue towards the seen classes. This paper proposes implicit and explicit attention mechanisms to address the existing bias problem in generalised ZSL models. We formulate the implicit attention mechanism with a self-supervised image angle rotation task, which focuses on specific image features aiding in solving the task. On the other hand, the explicit attention mechanism is composed via the consideration of a multi-headed self-attention mechanism in the Vision Transformer model, which learns to attend important image locations and map global image features to semantic space during the training stage. We have conducted comprehensive experiments on three popular benchmarks: AWA2, CUB and SUN, where the effectiveness of our proposed attention mechanisms is shown in both discriminative and generative settings. Our extensive experiments show that our method has achieved state-of-the-art performance obtaining the highest harmonic mean on all three datasets, which is very encouraging to consider the ViT-based attention mechanisms for ZSL tasks in the future.