Reviewerly/exHarmony huggingface dataset:
Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization

Particle Swarm Optimization (PSO) is a simple yet efficient population based evolutionary algorithm, which has been widely applied to solve global optimization problems. Similar to other evolutionary algorithms (EAs), PSO algorithm still suffers from loss of diversity and slow convergence caused by the contradiction between exploration and exploitation abilities. In order to address the drawbacks, we propose Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization (SAHLPSO) to improve optimization performance by using exploration-role and exploitation-role learning strategies with self-adaptively updating parameters manner. The exploration-role learning strategies with external archive adopt comprehensive learning to generate the learning exemplars, which can effectively maintain population diversity and thus avoid premature. On the other hand, the exploitation-role learning strategies utilize elitism learning to generate the learning exemplars so as to improve convergence performance. Considering that the performance improvement of PSO depends on not only the adaptation of different learning strategies but also the reasonable parameter settings, we also develop a self-adaptive parameter updating manner based on success history information. This manner can automatically update the learning parameters to appropriate values and avoid a user’s prior knowledge about the relationship between the parameter settings and the characteristics of optimization problems. In addition, the usage of the linear population size reduction is also helpful to well balance the exploration and exploitation along with the process of evolution. Simulation results on some classical and CEC2013 benchmark functions show that LSAHLPSO is better than other PSO variants or at least comparable to other state-of-the-art evolutionary algorithms.
Multiattention Generative Adversarial Network for Remote Sensing Image Super-Resolution

Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost of acquisition equipment, thereby providing a feasible way to improve the quality of remote sensing images. Clearly, image SR is a severe ill-posed problem. With the development of deep learning, the powerful fitting ability of deep neural networks has solved this problem to some extent. Since the texture information of various remote sensing images are totally different from each other, in this paper, we proposed a network based on generative adversarial network (GAN) to achieve high resolution remote sensing images, named multi-attention generative adversarial network (MA-GAN). The main body of the generator in MA-GAN contains three blocks: pyramid-convolutional residualdense (PCRD) block, attention-based upsampling (AUP) block and attention-based fusion (AF) block. Specifically, the developed attention-pyramid convolutional (AttPConv) operator in PCRD block combines multi-scale convolution and channel attention (CA) to automatically learn and adjust the scale of residuals for better representation. The established AUP block utilizes pixel attention (PA) to perform arbitrary scales of upsampling. And the AF block employs branch attention (BA) to integrate upsampled low-resolution images with high-level features. Besides, the loss function takes both adversarial loss and feature loss into consideration to guide the learning procedure of generator. We have compared our MA-GAN approach with several state-of-the-art methods on a number of remote sensing scenes, and experimental results consistently demonstrate the effectiveness of the proposed MA-GAN. For study replication, the source code will be released at: https://github.com/ZhihaoWang1997/MA-GAN.
Gender and Age Estimation Methods Based on Speech Using Deep Neural Networks

The speech signal contains a vast spectrum of information about the speaker such as speakers’ gender, age, accent, or health state. In this paper, we explored different approaches to automatic speaker’s gender classification and age estimation system using speech signals. We applied various Deep Neural Network-based embedder architectures such as x-vector and d-vector to age estimation and gender classification tasks. Furthermore, we have applied a transfer learning-based training scheme with pre-training the embedder network for a speaker recognition task using the Vox-Celeb1 dataset and then fine-tuning it for the joint age estimation and gender classification task. The best performing system achieves new state-of-the-art results on the age estimation task using popular TIMIT dataset with a mean absolute error (MAE) of 5.12 years for male and 5.29 years for female speakers and a root-mean square error (RMSE) of 7.24 and 8.12 years for male and female speakers, respectively, and an overall gender recognition accuracy of 99.60%.
Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction

Monocular depth prediction plays a crucial role in understanding 3D scene geometry. Although recent methods have achieved impressive progress in the evaluation metrics such as the pixel-wise relative error, most methods neglect the geometric constraints in the 3D space. In this work, we show the importance of the high-order 3D geometric constraints for depth prediction. By designing a loss term that enforces a simple geometric constraint, namely, virtual normal directions determined by randomly sampled three points in the reconstructed 3D space, we significantly improve the accuracy and robustness of monocular depth estimation. Importantly, the virtual normal loss can not only improve the performance of learning metric depth, but also disentangle the scale information and enrich the model with better shape information. Therefore, when not having access to absolute metric depth training data, we can use virtual normal to learn a robust affine-invariant depth generated on diverse scenes. Our experiments demonstrate state-of-the-art results of learning metric depth on NYU Depth-V2 and KITTI. From the high-quality predicted depth, we are now able to recover good 3D structures of the scene such as the point cloud and surface normal directly, eliminating the necessity of relying on additional models as was previously done. To demonstrate the excellent generalization capability of learning affine-invariant depth on diverse data with the virtual normal loss, we construct a large-scale and diverse dataset for training affine-invariant depth, termed Diverse Scene Depth dataset (DiverseDepth), and test on five datasets with the zero-shot test setting. Code is available at: https://git.io/Depth.
Memory-aware curriculum federated learning for breast cancer classification

Background and Objective: For early breast cancer detection, regular screening with mammography imaging is recommended. Routine examinations result in datasets with a predominant amount of negative samples. The limited representativeness of positive cases can be problematic for learning Computer-Aided Diagnosis (CAD) systems. Collecting data from multiple institutions is a potential solution to mitigate this problem. Recently, federated learning has emerged as an effective tool for collaborative learning. In this setting, local models perform computation on their private data to update the global model. The order and the frequency of local updates influence the final global model. In the context of federated adversarial learning to improve multi-site breast cancer classification, we investigate the role of the order in which samples are locally presented to the optimizers. Methods: We define a novel memory-aware curriculum learning method for the federated setting. We aim to improve the consistency of the local models penalizing inconsistent predictions, i.e., forgotten samples. Our curriculum controls the order of the training samples prioritizing those that are forgotten after the deployment of the global model. Our approach is combined with unsupervised domain adaptation to deal with domain shift while preserving data privacy. Results: Two classification metrics: area under the receiver operating characteristic curve (ROC-AUC) and area under the curve for the precision-recall curve (PR-AUC) are used to evaluate the performance of the proposed method. Our method is evaluated with three clinical datasets from different vendors. An ablation study showed the improvement of each component of our method. The AUC and PR-AUC are improved on average by 5% and 6%, respectively, compared to the conventional federated setting. Conclusions: We demonstrated the benefits of curriculum learning for the first time in a federated setting. Our results verified the effectiveness of the memory-aware curriculum federated learning for the multi-site breast cancer classification. Our code is publicly available at: https://github.com/ameliajimenez/curriculum-federated-learning.
Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation

The ever-increasing number of internet-connected devices, along with the continuous evolution of cyber-attacks, in terms of volume and ingenuity, has led to a widened cyber-threat landscape, rendering infrastructures prone to malicious attacks. Towards addressing systems’ vulnerabilities and alleviating the impact of these threats, this paper presents a machine learning based situational awareness framework that detects existing and newly introduced network-enabled entities, utilizing the real-time awareness feature provided by the SDN paradigm, assesses them against known vulnerabilities, and assigns them to a connectivity-appropriate network slice. The assessed entities are continuously monitored by an ML-based IDS, which is trained with an enhanced dataset. Our endeavor aims to demonstrate that a neural network, trained with heterogeneous data stemming from the operational environment (common vulnerability enumeration IDs that correlate attacks with existing vulnerabilities), can achieve more accurate prediction rates than a conventional one, thus addressing some aspects of the situational awareness paradigm. The proposed framework was evaluated within a real-life environment and the results revealed an increase of more than 4% in the overall prediction accuracy.
Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains. We identify two key aspects that can help to alleviate multiple domain-shifts in the MTDA: feature aggregation and curriculum learning. To this end, we propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier head, with one of them being a graph convolutional network (GCN) which aggregates features from similar samples across the domains. To prevent the classifiers from over-fitting on its own noisy pseudo-labels we develop a co-teaching strategy with the dual classifier head that is assisted by curriculum learning to obtain more reliable pseudo-labels. Furthermore, when the domain labels are available, we propose Domain-aware Curriculum Learning (DCL), a sequential adaptation strategy that first adapts on the easier target domains, followed by the harder ones. We experimentally demonstrate the effectiveness of our proposed frameworks on several benchmarks and advance the state-of-the-art in the MTDA by large margins (e.g. +5.6% on the DomainNet).
An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has 2N feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%.
The Agile Success Model

Organizations are increasingly adopting Agile frameworks for their internal software development. Cost reduction, rapid deployment, requirements and mental model alignment are typical reasons for an Agile transformation. This article presents an in-depth field study of a large-scale Agile transformation in a mission-critical environment, where stakeholders’ commitment was a critical success factor. The goal of such a transformation was to implement mission-oriented features, reducing costs and time to operate in critical scenarios. The project lasted several years and involved over 40 professionals. We report how a hierarchical and plan-driven organization exploited Agile methods to develop a Command &amp; Control (C2) system. Accordingly, we first abstract our experience, inducing a success model of general use for other comparable organizations by performing a post-mortem study. The goal of the inductive research process was to identify critical success factors and their relations. Finally, we validated and generalized our model through Partial Least Squares - Structural Equation Modelling, surveying 200 software engineers involved in similar projects. We conclude the article with data-driven recommendations concerning the management of Agile projects.
Energetic cost of Hamiltonian quantum gates

Landauer's principle laid the main foundation for the development of modern thermodynamics of information. However, in its original inception the principle relies on semiformal arguments and dissipative dynamics. Hence, if and how Landauer's principle applies to unitary quantum computing is less than obvious. Here, we prove an inequality bounding the change of Shannon information encoded in the logical quantum states by quantifying the energetic cost of Hamiltonian gate operations. The utility of this bound is demonstrated by outlining how it can be applied to identify energetically optimal quantum gates in theory and experiment. The analysis is concluded by discussing the energetic cost of quantum error correcting codes with non-interacting qubits, such as Shor's code.
Participatory design approach to address water crisis in the village of Karkatta, Jharkhand, India

Several Indian rural communities are marginalized due to the multi-dimensional sustainability challenges prevalent in their communities. The interrelationships between these challenges are also very complex. Previous remedial measures were unable to address the intricacies of the interconnected challenges. This study reveals how water scarcity during the summer months, experienced by a community residing in an Indian village named Karkatta, obligates the community to abandon agricultural practices during summer, culminating in significant risk of poverty. Students and faculty of the Live-in-Labs® program undertook diverse approaches to experience and engage in solving the challenges of this community. This work proposed a Human-Centered Design approach to analyze the water scarcity problem and elucidate potential solutions. The participatory methods, including co-design, were marshalled to get a handle of the multidimensional challenges from the stakeholder perspective, and develop the design requirements for the recommended solution. Meteorology, hydrology, geology, agriculture and mechanical engineering disciplines were unified in the derivation of a solution to ensure multi-dimensional sustainability of water, agriculture, and livelihood in Karkatta. The proffered design exploits advanced knowledge from these multiple domains towards combining rainwater harvesting, irrigation strategies, and less water-intensive crop-based agricultural choices while converting a water-scarce region to one of a water surplus.
Body2Hands: Learning to Infer 3D Hands from Conversational Gesture Body Dynamics

We propose a novel learned deep prior of body motion for 3D hand shape synthesis and estimation in the domain of conversational gestures. Our model builds upon the insight that body motion and hand gestures are strongly correlated in non-verbal communication settings. We formulate the learning of this prior as a prediction task of 3D hand shape over time given body motion input alone. Trained with 3D pose estimations obtained from a large-scale dataset of internet videos, our hand prediction model produces convincing 3D hand gestures given only the 3D motion of the speaker’s arms as input. We demonstrate the efficacy of our method on hand gesture synthesis from body motion input, and as a strong body prior for single-view image-based 3D hand pose estimation. We demonstrate that our method outperforms previous state-of-the-art approaches and can generalize beyond the monologue-based training data to multiperson conversations.
Keras and TensorFlow: A Hands-On Experience

This chapter provides a hands-on training experience on Keras in the TensorFlow library used in Jupyter Notebooks for Python. The main objective of this chapter’s content is to provide both theoretical and practical aspects of Keras and TensorFlow. Theoretical contents provide the fundamentals to understand neural networks, deep learning, convolutional neural networks, etc. Apart from this, the architectures of TensorFlow and Keras are explained to simplify the prior knowledge needed to work with Keras.
Analyzing and Evaluating Critical Challenges and Practices for Software Vendor Organizations to Secure Big Data on Cloud Computing: An AHP-Based Systematic Approach

Recently, its becomes easy to track down the data due to its availability in a large number.Although for data management, processing, and obtainability, cloud computing is considered a well-known approach for organizational development on the internet.Despite many advantages, cloud computing has still numerous security challenges that can affect the big-data usage on cloud computing.To find the security issues/challenges that are faced by software vendors' organizations we conducted a systematic literature review (SLR) through which we have find out 103 relevant research publications by developing a search string that is inspired by the research questions.This relevant data was comprised from different databases e.g.Google Scholar, IEEE Explore, ScienceDirect, ACM Digital Library, and SpringerLink.Furthermore, for the detailed literature review, we have accomplished all the steps in SLR, for example, development of SLR protocol, Initials and final assortment of the relevant data, data extraction, data quality assessment, and data synthesis.We identified fifteen (15) critical security challenges which are: data secrecy, geographical data location, unauthorized data access, lack of control, lack of data management, network-level issues, data integrity, data recovery, lack of trust, data sharing, data availability, asset issues, legal amenabilities, lack of quality, and lack of consistency.Furthermore, sixty four (64) standard practices are identified for these critical security challenges using the proposed SLR that could help vendor organizations to overcome the security challenges for big data.The findings of our research study demonstrate the resemblances and divergences in the identified security challenges in different periods, continents, databases, and methods.The proposed SLR will also support software vendor organizations for securing big data on the cloud computing platforms.This paper has the following content: in Section II, we have describe the Literature review; in Section III, research methodology is specified; in Section IV, the findings of the SLR and the analysis of result are discussed; in Section V, the limitations of this research are given; in Section VI, we discussed our conclusions and future work.
Shot Contrastive Self-Supervised Learning for Scene Boundary Detection

Scenes play a crucial role in breaking the storyline of movies and TV episodes into semantically cohesive parts. However, given their complex temporal structure, finding scene boundaries can be a challenging task requiring large amounts of labeled training data. To address this challenge, we present a self-supervised shot contrastive learning approach (ShotCoL) to learn a shot representation that maximizes the similarity between nearby shots compared to randomly selected shots. We show how to apply our learned shot representation for the task of scene boundary detection to offer state-of-the-art performance on the MovieNet [33] dataset while requiring only ~25% of the training labels, using 9× fewer model parameters and offering 7× faster runtime. To assess the effectiveness of ShotCoL on novel applications of scene boundary detection, we take on the problem of finding timestamps in movies and TV episodes where video-ads can be inserted while offering a minimally disruptive viewing experience. To this end, we collected a new dataset called AdCuepoints with 3, 975 movies and TV episodes, 2.2 million shots and 19, 119 minimally disruptive ad cue-point labels. We present a thorough empirical analysis on this dataset demonstrating the effectiveness of ShotCoL for ad cue-points detection.
Genetic programming with local search to evolve priority rules for scheduling jobs on a machine with time-varying capacity

Priority rules combined with schedule generation schemes are a usual approach to online scheduling. These rules are commonly designed by experts on the problem domain. However, some automatic method may be better as it could capture some characteristics of the problem that are not evident to the human eye. Furthermore, automatic methods could devise priority rules adapted to particular sets of instances of the problem at hand. In this paper we propose a Memetic Algorithm, which combines a Genetic Program and a Local Search algorithm, to evolve priority rules for the problem of scheduling a set of jobs on a machine with time-varying capacity. We propose a number of neighbourhood structures that are specifically designed to this problem. These structures were analyzed theoretically and also experimentally on the version of the problem with tardiness minimization, which provided interesting insights on this problem. The results of the experimental study show that a proper selection and combination of neighbourhood structures allows the Memetic Algorithm to outperform previous approaches to the same problem.