Reviewerly/exHarmony huggingface dataset:
A self-adaptive and gradient-based cuckoo search algorithm for global optimization

The stochastic global optimization (SGO) methods like particle swarm optimization (PSO), genetic algorithm (GA), and cuckoo search (CS) have been widely used in a variety of optimization problems partly because of the ability to find the global optimum. Most existing SGO algorithms are designed for gradient-free problems and ignore the gradient information even if the gradient is readily available, resulting in low efficiency and high computational cost. In this paper, we introduce a hybrid self-adaptive gradient-based cuckoo search (HAGCS) to tackle this limitation. HAGCS first takes a gradient-based local random walk to explore the search space, and then uses gradient-based local optimization (GBLO) to find a local minimum near to the current best solution, which is more efficient and precise than standard CS. Additionally, in order to avoid premature convergence potentially being caused by the use of the gradient, we introduce two novel self-adaptation and diversity promotion strategies onto HAGCS. These help HAGCS find proper control parameters and prevent HAGCS from getting stuck at local minima or stationary points. Lastly, we compare HAGCS with PSO, GA, CS, and 5 refinements of CS on 12 benchmark functions. Compared to the other methods, the experiment results show that the proposed method HAGCS has about 2 times faster convergence speed, higher accuracy, and 27.5% higher success rate of finding the global minimum in high-dimension problems. Even when the dimension of the problem is 1000, HAGCS still offers a success rate of 64% to find the global minima accurately.
Dynamic Resource Allocation Using Improved Firefly Optimization Algorithm in Cloud Environment

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Unsupervised Multi-View CNN for Salient View Selection and 3D Interest Point Detection

We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named by us view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN instantiating it for salient view selection and interest point detection of 3D objects, which quintessentially cannot be handled by supervised learning due to the difficulty of collecting sufficient and consistent training data. Our unsupervised multi-view CNN, namely UMVCNN, branches off two channels which encode the knowledge within each 2D view and the 3D object respectively and also exploits both intra-view and inter-view knowledge of the object. It ends with a new loss layer which formulates the view-object consistency by impelling the two channels to generate consistent classification outcomes. The UMVCNN is then integrated with a global distinction adjustment scheme to incorporate global cues into salient view selection. We evaluate our method for salient view section both qualitatively and quantitatively, demonstrating its superiority over several state-of-the-art methods. In addition, we showcase that our method can be used to select salient views of 3D scenes containing multiple objects. We also develop a method based on the UMVCNN for 3D interest point detection and conduct comparative evaluations on a publicly available benchmark, which shows that the UMVCNN is amenable to different 3D shape understanding tasks.
Bridging the Gap: Evidence from the Return Migration of African Scientists

Although prior research has highlighted the benefits that accrue to brokers in innovation networks, much less attention is devoted to understanding who benefits from associating with a broker. This study focuses on the impact of associating with a specific kind of broker—a core/periphery bridge—that is, one that spans central and peripheral actors. I argue that actors associated with a core/periphery bridge benefit more when they have no central connection, or are outsiders in the network, due to their greater need for sponsorship from the broker. I explore this idea in the context of the return migration of American-trained scientists to African institutions, who span the core and the periphery of the global scientific network. I evaluate the impact of their return on the publication outcomes of nonmigrant scientists based in African institutions and find that following the arrival of a returnee in their institution, the nonmigrants who are not already connected to scientists in top global institutions have a greater publication output through improved access to central knowledge and connections. The findings contribute to a better understanding of how brokers can influence innovation systems more broadly. Supplemental Material: The online appendices available at https://doi.org/10.1287/orsc.2022.1580 .
eSPA+: Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems

Classification problems in the small data regime (with small data statistic T and relatively large feature space dimension D) impose challenges for the common machine learning (ML) and deep learning (DL) tools. The standard learning methods from these areas tend to show a lack of robustness when applied to data sets with significantly fewer data points than dimensions and quickly reach the overfitting bound, thus leading to poor performance beyond the training set. To tackle this issue, we propose eSPA+, a significant extension of the recently formulated entropy-optimal scalable probabilistic approximation algorithm (eSPA). Specifically, we propose to change the order of the optimization steps and replace the most computationally expensive subproblem of eSPA with its closed-form solution. We prove that with these two enhancements, eSPA+ moves from the polynomial to the linear class of complexity scaling algorithms. On several small data learning benchmarks, we show that the eSPA+ algorithm achieves a many-fold speed-up with respect to eSPA and even better performance results when compared to a wide array of ML and DL tools. In particular, we benchmark eSPA+ against the standard eSPA and the main classes of common learning algorithms in the small data regime: various forms of support vector machines, random forests, and long short-term memory algorithms. In all the considered applications, the common learning methods and eSPA are markedly outperformed by eSPA+, which achieves significantly higher prediction accuracy with an orders-of-magnitude lower computational cost.
Nipping in the bud

Since the proliferation of social media usage, hate speech has become a major crisis. Hateful content can spread quickly and create an environment of distress and hostility. Further, what can be considered hateful is contextual and varies with time. While online hate speech reduces the ability of already marginalised groups to participate in discussion freely, offline hate speech leads to hate crimes and violence against individuals and communities. The multifaceted nature of hate speech and its real-world impact have already piqued the interest of the data mining and machine learning communities. Despite our best efforts, hate speech remains an evasive issue for researchers and practitioners alike. This article presents methodological challenges that hinder building automated hate mitigation systems. These challenges inspired our work in the broader area of combating hateful content on the web. We discuss a series of our proposed solutions to limit the spread of hate speech on social media.
The Evaluation of Music Teaching in Colleges and Universities Based on Machine Learning

With the implementation of the strategic policy of rejuvenating the country through science and education, many innovative and practical teaching concepts and teaching models have been comprehensively developed. This breaks the backward teaching mode of traditional teaching activities. With the development of science and technology and Internet technology, deep learning is widely used in the field of education. Music teachers in colleges and universities constantly update their teaching methods and comprehensively use a variety of methods to carry out in-depth teaching in the classroom, and strive to stimulate students’ learning Interest and enthusiasm, and comprehensively enhance students’ music aesthetic ability. This article uses decision tree algorithms, support vector machines, Bayesian theory, and random forest four different classification techniques to evaluate the student curriculum evaluation dataset. Classification experiment: through the analysis of the experimental results, the performance of the four classifier models was compared, and the data showed the difference in accuracy, precision, recall, and F1 value of the four classifiers. At the same time, each of the classifier models was analyzed. This article verifies the effectiveness of machine learning models in curriculum evaluation and higher education mining, the importance of evaluation features.
Incentivizing Semisupervised Vehicular Federated Learning: A Multidimensional Contract Approach With Bounded Rationality

To facilitate the implementation of deep learning-based vehicular applications, vehicular federated learning is introduced by integrating vehicular edge computing with the newly emerged federated learning technology. In vehicular federated learning, it is widely considered that the raw data collected by vehicles have complete ground-truth labels. This, however, is not realistic and inconsistent with the current applications. To deal with the above dilemma, a semisupervised vehicular federated learning (Semi-VFL) framework is proposed. In the framework, each vehicular client uses labeled data shared by an application provider, and its own unlabeled data to cooperatively update a global deep neural network model. Furthermore, the application provider combines the multidimensional contract theory with prospect theory (PT) to design an incentive mechanism to stimulate appropriate vehicular clients to participate in Semi-VFL. Multidimensional contract theory is used to deal with the information asymmetry scenario where the application provider is not aware of vehicular clients’ 3-D cost information, while PT is used to model the application provider’s risk-aware behavior and make the incentive mechanism more acceptable in practice. After that, a closed-form solution for the optimal contract items under PT is derived. We present the real-world experimental results to demonstrate that Semi-VFL achieves the advantages in both the test accuracy and convergence speed, in comparison with existing baseline schemes. Based on the experimental results, we further perform the simulations to verify that our incentive mechanism is efficient.
Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples

Background and objectives The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a deep-learning approach could save time and provide more precise data. This work aimed to develop a free tool to automatically obtain kidney histologic prognostic features. Design, setting, participants, &amp; measurements In total, 241 samples of healthy kidney tissue were split into three independent cohorts. The “Training” cohort ( n =65) was used to train two convolutional neural networks: one to detect the cortex and a second to segment the kidney structures. The “Test” cohort ( n =50) assessed their performance by comparing manually outlined regions of interest to predicted ones. The “Application” cohort ( n =126) compared prognostic histologic data obtained manually or through the algorithm on the basis of the combination of the two convolutional neural networks. Results In the Test cohort, the networks isolated the cortex and segmented the elements of interest with good performances (&gt;90% of the cortex, healthy tubules, glomeruli, and even globally sclerotic glomeruli were detected). In the Application cohort, the expected and predicted prognostic data were significantly correlated. The correlation coefficients r were 0.85 for glomerular volume, 0.51 for glomerular density, 0.75 for interstitial fibrosis, 0.71 for tubular atrophy, and 0.73 for vascular intimal thickness, respectively. The algorithm had a good ability to predict significant (&gt;25%) tubular atrophy and interstitial fibrosis level (receiver operator characteristic curve with an area under the curve, 0.92 and 0.91, respectively) or a significant vascular luminal stenosis (&gt;50%) (area under the curve, 0.85). Conclusion This freely available tool enables the automated segmentation of kidney tissue to obtain prognostic histologic data in a fast, objective, reliable, and reproducible way.
CGRNet: Contour-guided graph reasoning network for ambiguous biomedical image segmentation

In this work, we propose to address the existing problem of biomedical image segmentation that often produces results, which fail to capture the exact contours of the target and suffer from ambiguity. Most previous techniques are suboptimal because they often simply concatenate contour information to alleviate this problem, while ignoring the correlation between regions and contours. As a matter of fact, the relationship between cross-domain features is an important clue for ambiguous pixel segmentation in biomedical images. To this end, we contribute a simple yet effective framework called Contour-Guided Graph Reasoning Network (CGRNet) for more accurate segmentation against ambiguity, which is capable of capturing the semantic relations between object regions and contours through graph reasoning. Specifically, we first perform a global graph representation of the low-level and high-level features extracted by the feature extractor, where clusters of pixels with similar features are mapped to each vertex. Further, we explicitly combine contour information as the geometric prior, which can aggregate features of contour pixels to graph vertices and focus on features along the boundaries. Then, the cross-domain features propagate information through the vertices on the graph to efficiently learn and reason about the semantic relations. Finally, the learned refinement graph features are projected back to the original pixel coordinate space for the final pixel-wise segmentation task. Extensive experiments on the three publicly available Kvasir, CVC-612, and COVID19-100 datasets show the effectiveness of our CGRNet with superior performance to existing state-of-the-art methods. Our code is publicly available at: https://github.com/DLWK/CGRNet.
Blockchain-based Multifactor Authentication for Future 6G Cellular Networks: A Systematic Review

There are continued advances in the internet and communication fields regarding the deployment of 5G-based applications. It is expected that by 2030, 6G applications will emerge as a continued evolution of the mobile network. Blockchain technology is one of the leading supporting technologies predicted to provide a secure and unique network to 6G-enabled devices, transactions, and applications. It is anticipated that the 6G mobile networks will be virtualized, have cloud-based systems, and aim to be the foundation for the Internet of Everything. However, along with the development of communication technologies, threats from malicious parties have become more sophisticated, making security a significant concern for the 6G era in the future. Despite enormous efforts by researchers to improve security and authentication protocols, systems still face novel intrusion and attacks. Recently, multifactor authentication techniques (MFA) have been deployed as potential solutions to attacks in blockchains. The 6G applications and the cellular network have specific vulnerabilities that need to be addressed using blockchain-based MFA technologies. The current paper is a systematic review that discusses the three technologies under consideration; then, several studies are reviewed that discuss MFA techniques in general and use blockchains as potential solutions to future security and authentication issues that may arise for 6G applications.
IE-Net: Information-Enhanced Binary Neural Networks for Accurate Classification

Binary neural networks (BNNs) have been proposed to reduce the heavy memory and computation burdens in deep neural networks. However, the binarized weights and activations in BNNs cause huge information loss, which leads to a severe accuracy decrease, and hinders the real-world applications of BNNs. To solve this problem, in this paper, we propose the information-enhanced network (IE-Net) to improve the performance of BNNs. Firstly, we design an information-enhanced binary convolution (IE-BC), which enriches the information of binary activations and boosts the representational power of the binary convolution. Secondly, we propose an information-enhanced estimator (IEE) to gradually approximate the sign function, which not only reduces the information loss caused by quantization error, but also retains the information of binary weights. Furthermore, by reducing the information loss in binary representations, the novel binary convolution and estimator gain large information compared with the previous work. The experimental results show that the IE-Net achieves accuracies of 88.5% (ResNet-20) and 61.4% (ResNet-18) on CIFAR-10 and ImageNet datasets respectively, which outperforms other SOTA methods. In conclusion, the performance of BNNs could be improved significantly with information enhancement on both weights and activations.
A 28nm 48KOPS 3.4µJ/Op Agile Crypto-Processor for Post-Quantum Cryptography on Multi-Mathematical Problems

In the post-quantum era, post-quantum cryptography (PQC) processors are required to ensure quantum-secure communication and e-commerce with high throughput, while maintaining adequate flexibility to execute different crypto-primitives, such as key encapsulation mechanism (KEM) and digital signature (DS) at multiple security levels with evolving modifications. The PQC standards, which are based on multiple mathematical problems, will be available around the end of 2021 in NIST's PQC standardization (Fig. 34.1.1). Crypto-agility, coupled with new mathematical calculations, high computing complexity, and large memory consumption brings challenges to the design of PQC processors considering flexibility, throughput, and energy efficiency.
A novel dependency-oriented mixed-attribute data classification method

How to design an efficient method to handle mixed-attribute data classification (MADC) problems has become a hot topic in data mining and machine learning. Current MADC methods mostly transform mixed-attribute data into discrete-attribute data or continuous-attribute data before classification algorithms are trained. The discretization of continuous-attribute data usually results in information loss, while the binarization of discrete-attribute data generally yield more discrete-attributes. To address these issues, this paper proposes a novel MADC method abbreviated as DO-RVFL-NBC, which is a Dependency-Oriented aggregation model of random vector functional link (RVFL) network and naive Bayes classifier (NBC). First, the method transforms the original mixed-attribute set into a dependent attribute set and an independent attribute set by considering the variation rates of dependence and independence, respectively. Second, a RVFL network is trained based on the dependent attribute set where each attribute has a weight to represent its dependence importance degree. Third, a weighted NBC is constructed by assigning the independence importance degrees as weights for the calculation of class-conditional probability. Finally, exhaustive experiments are conducted to validate the feasibility, rationality, and effectiveness of the DO-RVFL-NBC method using 22 benchmark mixed-attribute data sets. Experimental results show that (1) dependence and independence exist in the original mixed-attribute set and can be effectively explored; (2) changes of attribute dependences can improve the generalization capabilities of the RVFL network and NBC; and (3) a statistical analysis indicates that DO-RVFL-NBC can obtain considerably better testing accuracies on the benchmark mixed-attribute data sets in comparison with 13 other MADC methods. This demonstrates that DO-RVFL-NBC is a viable approach for MADC problems. • Dependency-Oriented aggregation of RVFL network and NBC was proposed. • The new feature dependence and independence exploration strategies were designed. • The exhaustive experiments were conducted to validate the effectiveness of new model. • The reasons why model can obtain the better prediction performances were revealed.
EKSL: An effective novel dynamic ensemble model for unbalanced datasets based on LR and SVM hyperplane-distances

Unbalanced data is considered in many real-world classification problems, where it is often costly in practice to sample and establish a homogeneous class distribution for a minority class. The choice of methods, diversity of datasets used for structuring, and the correct kernel decision are quite decisive in the success of the system. This study develops a powerful classifier algorithm that provides an alternative solution to kernel experiments with the choice of a general-purpose, fast, automatic linear kernel. Principally based on SVM, k-means clustering in partitions is used, and logistic regression is integrated into the ensemble system. To increase the success rate and deal with the maximum convergence problem, the soft margin value of the standard SVM is changed in an adaptive structure. In the experiments, it has been observed that accuracy performance showed an improvement of (1–8) %, even when the optimal k value in the k-means clustering stage of the proposed ensemble methodology has been compared to the best in other kernels measurements. In particular, when the unbalance property of the data gets an increase, it has been observed that better results are obtained in all evaluation metrics than in a single classifier.
A Learned Approach to Design Compressed Rank/Select Data Structures

We address the problem of designing, implementing, and experimenting with compressed data structures that support rank and select queries over a dictionary of integers. We shine a new light on this classical problem by showing a connection between the input integers and the geometry of a set of points in a Cartesian plane suitably derived from them. We then build upon some results in computational geometry to introduce the first compressed rank/select dictionary based on the idea of “learning” the distribution of such points via proper linear approximations (LA). We therefore call this novel data structure the la_vector . We prove time and space complexities of the la_vector in several scenarios: in the worst case, in the case of input distributions with finite mean and variance, and taking into account the k th order entropy of some of its building blocks. We also discuss improved hybrid data structures, namely, ones that suitably orchestrate known compressed rank/select dictionaries with the la_vector . We corroborate our theoretical results with a large set of experiments over datasets originating from a variety of applications (Web search, DNA sequencing, information retrieval, and natural language processing) and show that our approach provides new interesting space-time tradeoffs with respect to many well-established compressed rank/select dictionary implementations. In particular, we show that our select is the fastest, and our rank is on the space-time Pareto frontier.