Reviewerly/exHarmony huggingface dataset:
Follow-the-Leader Control Strategy for Azimuth Propulsion System on Surface Vessels

This paper presents an effective autonomous follow-the-leader strategy for Azimuthal Stern Drive vessels. The control logic has been investigated from a theoretical point of view. A line-of-sight algorithm is exploited to ensure yaw-check ability, while a speed-check feature is implemented to track the velocity of the target along the path. For this purpose, a linearised manoeuvrability model for azimuthal drive surface vessels is presented. A model-based control synthesis is proposed to ensure the stability of the closed-loop system and robust PID controllers are designed by using Linear Matrix Inequalities technique. The control strategy has been successively validated in two steps, initially by using simulation techniques, and then experimentally using an outdoor scenario with model scale tugs. The path planning, navigation, guidance and control modules are studied, detailed, and digitally implemented on-board of the model scale tugs. The models are supplied with GNSS+INS navigation system. Low-level management and control of Azimuthals angles and shaft revolutions is implemented on-board. High-level decentralised path planning, guidance, and control sequence evaluation are dealt with at a remote ground station. In particular, the presented follow-the-leader strategy meets the most generic needs of platooning convoys, and, in the specific instance, of Escort convoy tugs. The operative profile of the latter concerns long-lasting and routine chases with the continuous demand of tuning heading and speed to track the target vessels, until the rare occurrence of an emergency event. In a realistic scenario, the proposed control system would be beneficial for the tug master’s lucidity and alertness, while reducing avoidable risks. At the end of the paper, the results of the experimental campaign are shown to demonstrate the effectiveness of the proposed control logic.
Towards Explainable AI: Significance Tests for Neural Networks

Neural networks underpin many of the best-performing AI systems. Theirsuccess is largely due to their strong approximation properties, superiorpredictive performance, and scalability. However, a major caveat isexplainability: neural networks are often perceived as black boxes that permitlittle insight into how predictions are being made. We tackle this issue bydeveloping a pivotal test to assess the statistical significance of the featurevariables of a neural network. We propose a gradient-based test statistic andstudy its asymptotics using nonparametric techniques. The limiting distributionis given by a mixture of chi-square distributions. The tests enable one todiscern the impact of individual variables on the prediction of a neuralnetwork. The test statistic can be used to rank variables according to theirinfluence. Simulation results illustrate the computational efficiency and theperformance of the test. An empirical application to house price valuationhighlights the behavior of the test using actual data.
Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization

We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks without any interactions with the environments, making RL truly practical in many real-world applications. This problem is still not fully understood, for which two major challenges need to be addressed. First, offline RL usually suffers from bootstrapping errors of out-of-distribution state-actions which leads to divergence of value functions. Second, meta-RL requires efficient and robust task inference learned jointly with control policy. In this work, we enforce behavior regularization on learned policy as a general approach to offline RL, combined with a deterministic context encoder for efficient task inference. We propose a novel negative-power distance metric on bounded context embedding space, whose gradients propagation is detached from the Bellman backup. We provide analysis and insight showing that some simple design choices can yield substantial improvements over recent approaches involving meta-RL and distance metric learning. To the best of our knowledge, our method is the first model-free and end-to-end OMRL algorithm, which is computationally efficient and demonstrated to outperform prior algorithms on several meta-RL benchmarks.
Hard Masking for Explaining Graph Neural Networks

Graph Neural Networks (GNNs) are a flexible and powerful family of models that build nodes' representations on irregular graph-structured data. This paper focuses on explaining or interpreting the rationale underlying a given prediction of already trained graph neural networks for the node classification task. Existing approaches for interpreting GNNs try to find subsets of important features and nodes by learning a continuous mask. Our objective is to find discrete masks that are arguably more interpretable while minimizing the expected deviation from the underlying model's prediction. We empirically show that our explanations are both more predictive and sparse. Additionally, we find that multiple diverse explanations are possible, which sufficiently explain a prediction. Finally, we analyze the explanations to find the effect of network homophily on the decision-making process of GNNs.
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This paper presents a new fractal search space decomposition-based algorithm to address the issue of scaling up the divide and conquer approach to deal with large scale problems (up to 50 continuous decision variables). The proposed algorithm, called polyFrac, fractally decomposes the search space using hyper-polytopes. It allows moving throughout different granularity levels by only computing the average of vertices of a hyper-polytope to obtain the coordinates of the centroids. Only the most promising hyper-polytopes are decomposed into child-polytopes. Then, a simple deterministic local search (single solution-based metaheuristic) is used to perform the intensification process to find the best solution within the selected lowest hyper-polytope. The proposed algorithm performance is evaluated on the well-known SOCO 2011, CEC 2013, and CEC 2017 benchmarks and compared with 26 states of the art algorithms. A real-world optimization problem is also used to calibrate its performance. The obtained results show that polyFrac outperforms all the algorithms. Moreover, experimental results and analysis suggest that polyFrac is a highly competitive optimization algorithm for solving large-scale and complex optimization problems.
Cryptocurrency and the Nigerian Economy

In this paper, we discuss the impact of cryptocurrency on some selected sectors of the Nigerian economy. The cultural presence and persistent market of bitcoin’s prompt researchers and policymakers to ask questions on how cryptocurrencies would impact on the economy, most especially on the monetary policy. There has been debate whether it would positively and/or negatively affect the economy. We give our opinion on how cryptocurrencies might impact on some selected sectors of an economy, most especially in the case of Nigeria
Controlled Vocabularies: Past, Present and Future of Subject Access

Controlled vocabularies are a foundational concept in library science and provide a framework for consistency in cataloging practices. Subject headings provide valuable access points to library resources during search and discovery for patrons. Many librarians will be familiar with the more widely used controlled vocabularies, like those maintained by national libraries or major professional organizations. More recently, there has been an increasing shift toward specialized vocabularies maintained by independent entities intended for much narrower use. While there is valid criticism of the nature or content of controlled vocabularies, they will likely continue to be an important feature in information organization.
Capturing Label Characteristics in VAEs

We present a principled approach to incorporating labels in variational autoencoders (VAEs) that captures the rich characteristic information associated with those labels. While prior work has typically conflated these by learning latent variables that directly correspond to label values, we argue this is contrary to the intended effect of supervision in VAEs—capturing rich label characteristics with the latents. For example, we may want to capture the characteristics of a face that make it look young, rather than just the age of the person. To this end, we develop a novel VAE model, the characteristic capturing VAE (CCVAE), which “reparameterizes” supervision through auxiliary variables and a concomitant variational objective. Through judicious structuring of mappings between latent and auxiliary variables, we show that the CCVAE can effectively learn meaningful representations of the characteristics of interest across a variety of supervision schemes. In particular, we show that the CCVAE allows for more effective and more general interventions to be performed, such as smooth traversals within the characteristics for a given label, diverse conditional generation, and transferring characteristics across datapoints.
DisCont: Self-Supervised Visual Attribute Disentanglement Using Context Vectors

Disentangling underlying feature attributes within an image with no prior supervision is a challenging task. Models that can disentangle attributes well, provide greater interpretability and control. In this paper, we propose a self-supervised framework: DisCont to disentangle multiple attributes by exploiting structural inductive biases within images. Motivated by a recent surge in contrastive learning frameworks, our model bridges the gap between self-supervised contrastive learning algorithms and unsupervised disentanglement. We evaluate the efficacy of our approach, qualitatively and quantitatively, on four benchmark datasets. The code is available at https://github.com/ sarthak268/DisCont .
The Abandonment of the Assignment of Subject Headings and Classification Codes in University Libraries Due to the Massive Emergence of Electronic Books

The massive and unstoppable emergence of electronic books in libraries has altered their organization. This disruptive technology has led to structural changes. Currently, an e-book exists only if its metadata exists. The objective of this article is to analyse the impact that the massive incorporation of electronic books in university library systems is having in the processes of assignment of subject headings and classification codes. We carried out a survey of more than six hundred libraries, which means almost all the university libraries in Portugal, Spain, England, United States, Brazil, Sweden, Norway, Finland and Australia. From the results obtained, it is deduced that: 1) librarians expect e-books to be provided with descriptive metadata related to the subject headings and classification codes; 2) the bibliographic records provided by publishers/providers seem to be improvable; 3) the quality of the metadata provided by the providers does not seem to be taken into account when selecting publishers for the purchase; 4) the discovery tools are also clearly improvable; 5) it seems that there is no “frustration” or “stress” among librarians about the changes produced in relation to technical processes; and, 6) it does not seem that we are facing a paradigm shift motivated by these issues.
Identification and Mitigation Tool for Sql Injection Attacks (SQLIA)

Structured Query Language Injection Attack (SQLIA) is a very frequent web security vulnerability. The attacker adds a malicious Structured Query Language (SQL) code to the input field of a web form, so that he can gain access to data or make unauthorized changes to data. A successful malicious SQL injection cause serious consequence to the victimized organization such as financial loss, reputation loss, compliance, and regulatory breaches. There have been several research works on detection and prevention of SQL injection attacks. However, still there is an absence of an advanced single tools for both identification and mitigation of SQL injection attacks. We have proposed an approach to identify and mitigate SQL injection attacks using a single tool and it allows software testers to identify the SQL injection vulnerabilities of their web applications during the testing stages. The proposed approach is based on parameterized queries and user input validation. Our results show that the tool provides 100% accurate and efficient results on identification and mitigation of SQL vulnerabilities.
Combining the Band-Limited Parameterization and Semi-Lagrangian Runge–Kutta Integration for Efficient PDE-Constrained LDDMM

The family of PDE-constrained Large Deformation Diffeomorphic Metric Mapping (LDDMM) methods is emerging as a particularly interesting approach for physically meaningful diffeomorphic transformations. The original combination of Gauss–Newton–Krylov optimization and Runge–Kutta integration shows excellent numerical accuracy and fast convergence rate. However, its most significant limitation is the huge computational complexity, hindering its extensive use in Computational Anatomy applied studies. This limitation has been treated independently by the problem formulation in the space of band-limited vector fields and semi-Lagrangian integration. The purpose of this work is to combine both in three variants of band-limited PDE-constrained LDDMM for further increasing their computational efficiency. The accuracy of the resulting methods is evaluated extensively. For all the variants, the proposed combined approach shows a significant increment of the computational efficiency. In addition, the variant based on the deformation state equation is positioned consistently as the best performing method across all the evaluation frameworks in terms of accuracy and efficiency.
Audio compression using transforms and high order entropy encoding

&lt;span&gt;Digital audio is required to transmit large sizes of audio information through the most common communication systems; in turn this leads to more challenges in both storage and archieving. In this paper, an efficient audio compressive scheme is proposed, it depends on combined transform coding scheme; it is consist of i) bi-orthogonal (tab 9/7) wavelet transform to decompose the audio signal into low &amp;amp; multi high sub-bands, ii) then the produced sub-bands passed through DCT to de-correlate the signal, iii) the product of the combined transform stage is passed through progressive hierarchical quantization, then traditional run-length encoding (RLE), iv) and finally LZW coding to generate the output mate bitstream. The measures Peak signal-to-noise ratio (PSNR) and compression ratio (CR) were used to conduct a comparative analysis for the performance of the whole system. Many audio test samples were utilized to test the performance behavior; the used samples have various sizes and vary in features. The simulation results appear the efficiency of these combined transforms when using LZW within the domain of data compression. The compression results are encouraging and show a remarkable reduction in audio file size with good fidelity.&lt;/span&gt;
Off-diagonal symmetric nonnegative matrix factorization

Symmetric nonnegative matrix factorization (symNMF) is a variant of nonnegative matrix factorization (NMF) that allows handling symmetric input matrices and has been shown to be particularly well suited for clustering tasks. In this paper, we present a new model, dubbed off-diagonal symNMF (ODsymNMF), that does not take into account the diagonal entries of the input matrix in the objective function. ODsymNMF has three key advantages compared to symNMF. First, ODsymNMF is theoretically much more sound as there always exists an exact factorization of size at most n(n − 1)/2 where n is the dimension of the input matrix. Second, it makes more sense in practice as diagonal entries of the input matrix typically correspond to the similarity between an item and itself, not bringing much information. Third, it makes the optimization problem much easier to solve. In particular, it will allow us to design an algorithm based on coordinate descent that minimizes the component-wise ℓ1 norm between the input matrix and its approximation. We prove that this norm is much better suited for binary input matrices often encountered in practice. We also derive a coordinate descent method for the component-wise ℓ2 norm, and compare the two approaches with symNMF on synthetic and document datasets.
Motion Segmentation using Frequency Domain Transformer Networks

Self-supervised prediction is a powerful mechanism to learn representations that capture the underlying structure of the data. Despite recent progress, the self-supervised video prediction task is still challenging. One of the critical factors that make the task hard is motion segmentation, which is segmenting individual objects and the background and estimating their motion separately. In video prediction, the shape, appearance, and transformation of each object should be understood only by predicting the next frame in pixel space. To address this task, we propose a novel end-to-end learnable architecture that predicts the next frame by modeling foreground and background separately while simultaneously estimating and predicting the foreground motion using Frequency Domain Transformer Networks. Experimental evaluations show that this yields interpretable representations and that our approach can outperform some widely used video prediction methods like Video Ladder Network and Predictive Gated Pyramids on synthetic data.
The simultaneous conjugacy problem in the symmetric group

The transitive simultaneous conjugacy problem asks whether there exists a permutation $\tau \in S_n$ such that $b_j = \tau ^{-1} a_j \tau$ holds for all $j = 1,2, \ldots , d$, where $a_1, a_2, \ldots , a_d$ and $b_1, b_2, \ldots , b_d$ are given sequences of $d$ permutations in $S_n$, each of which generates a transitive subgroup of $S_n$. As from mid 70â it has been known that the problem can be solved in $O(dn^2)$ time. An algorithm with running time $O(dn \log (dn))$, proposed in late 80â, does not work correctly on all input data. In this paper we solve the transitive simultaneous conjugacy problem in $O(n^2 \log d / \log n + dn\log n)$ time and $O(n^{3/ 2} + dn)$ space. Experimental evaluation on random instances shows that the expected running time of our algorithm is considerably better, perhaps even nearly linear in $n$ at given $d$.
Semantically Adaptive Image-to-image Translation for Domain Adaptation of Semantic Segmentation.

Domain shift is a very challenging problem for semantic segmentation. Any model can be easily trained on synthetic data, where images and labels are artificially generated, but it will perform poorly when deployed on real environments. In this paper, we address the problem of domain adaptation for semantic segmentation of street scenes. Many state-of-the-art approaches focus on translating the source image while imposing that the result should be semantically consistent with the input. However, we advocate that the image semantics can also be exploited to guide the translation algorithm. To this end, we rethink the generative model to enforce this assumption and strengthen the connection between pixel-level and feature-level domain alignment. We conduct extensive experiments by training common semantic segmentation models with our method and show that the results we obtain on the synthetic-to-real benchmarks surpass the state-of-the-art.
A Recommendation Approach in Social Learning Based on K-Means Clustering

E-learning, among the most prominent modes of learning, offers learners the opportunity to attend online courses.To improve the quality of online learning, social learning through social networks promotes interaction and collaboration among learners.As part of the learning process management in these environments, the implementation of recommendation systems facilitates the provision of content adapted to the needs and requirements of learners and generates recommendations likely to arouse their interest.Many researchers have been involved in several recommendation techniques such as the development of Machine Learning algorithms and the incorporation of social interactions between learners.However, the behavior within a learning environment can diverge from one learner to another.This must therefore be taken into consideration when generating recommendations, i.e., it is initially important to form groups of homogeneous learners prior to proposing recommendations.In this respect, the recommendations generated will be more appropriate to the learners' profiles and level of interaction.On this basis, we raise an important issue which is the importance of grouping learners into homogeneous groups in a recommendation system.In the recommendation system we advocate, we group learners based on the degree of interaction within the learning environment before generating the recommendation list based on a hybrid approach for each cluster.The overall system is, therefore, based on the identification of communities based on the k-means algorithm and the generation of recommendations list for each community separately.Finally, we compare the results of the system integrating the classification of learners as a preliminary step to the system excluding the k-means algorithm.The results reveal that the integration of the clustering algorithm leads to improvements in terms of performance and accuracy.
VERGE in VBS 2021

This paper presents VERGE, an interactive video search engine that supports efficient browsing and searching into a collection of images or videos. The framework involves a variety of retrieval approaches as well as reranking and fusion capabilities. A Web application enables users to create queries and view the results in a fast and friendly manner.
Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images

A novel high-resolution method for forecasting cloud motion from all-sky images using deep learning is presented. A convolutional neural network (CNN) was created and trained with more than two years of all-sky images, recorded by a hemispheric sky imager (HSI) at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Using the haze indexpostprocessing algorithm, cloud characteristics were found, and the deformation vector of each cloud was performed and used as ground truth. The CNN training process was built to predict cloud motion up to 10 min ahead, in a sequence of HSI images, tracking clouds frame by frame. The first two simulated minutes show a strong similarity between simulated and measured cloud motion, which allows photovoltaic (PV) companies to make accurate horizon time predictions and better marketing decisions for primary and secondary control reserves. This cloud motion algorithm principally targets global irradiance predictions as an application for electrical engineering and in PV output predictions. Comparisons between the results of the predicted region of interest of a cloud by the proposed method and real cloud position show a mean Sørensen–Dice similarity coefficient (SD) of 94 ± 2.6% (mean ± standard deviation) for the first minute, outperforming the persistence model (89 ± 3.8%). As the forecast time window increased the index decreased to 44.4 ± 12.3% for the CNN and 37.8 ± 16.4% for the persistence model for 10 min ahead forecast. In addition, up to 10 min global horizontal irradiance was also derived using a feed-forward artificial neural network technique for each CNN forecasted image. Therefore, the new algorithm presented here increases the SD approximately 15% compared to the reference persistence model.