Reviewerly/exHarmony huggingface dataset:
Ocean ship detection and recognition algorithm based on aerial image

Because the image of UAV aerial photography is easy to be affected by light, sea area and other conditions, there are many kinds of ships. Under different conditions, the characteristics of ships are different, which makes the target recognition more difficult. In order to improve the efficiency of sea surface supervision and make the sea surface management more intelligent, an ocean ship detection algorithm based on aerial photography image is proposed. In this paper, the improved Yolo algorithm is mainly used for high-efficiency ship detection of aerial video, which can achieve real-time performance and detection speed of 23fps. In order to improve the accuracy, this paper proposes a standardized mechanism of fixed frame length detection results, which uses deep learning mask RCNN algorithm for fine detection of specific frame images, and the detection map is 85%, which improves the detection speed without affecting the detection speed The accuracy of the algorithm forms an efficient and accurate algorithm for the detection of ships on the sea, which brings convenience to the management of the sea.
Plan-Space Explanation via Plan-Property Dependencies: Faster Algorithms &amp; More Powerful Properties

Justifying a plan to a user requires answering questions about the space of possible plans. Recent work introduced a framework for doing so via plan-property dependencies, where plan properties p are Boolean functions on plans, and p entails q if all plans that satisfy p also satisfy q. We extend this work in two ways. First, we introduce new algorithms for computing plan-property dependencies, leveraging symbolic search and devising pruning methods for this purpose. Second, while the properties p were previously limited to goal facts and so-called action-set (AS) properties, here we extend them to LTL. Our new algorithms vastly outperform the previous ones, and our methods for LTL cause little overhead on AS properties.
The Importance of the Test Set Size in Quantification Assessment

Quantification is a task similar to classification in the sense that it learns from a labeled training set. However, quantification is not interested in predicting the class of each observation, but rather measure the class distribution in the test set. The community has developed performance measures and experimental setups tailored to quantification tasks. Nonetheless, we argue that a critical variable, the size of the test sets, remains ignored. Such disregard has three main detrimental effects. First, it implicitly assumes that quantifiers will perform equally well for different test set sizes. Second, it increases the risk of cherry-picking by selecting a test set size for which a particular proposal performs best. Finally, it disregards the importance of designing methods that are suitable for different test set sizes. We discuss these issues with the support of one of the broadest experimental evaluations ever performed, with three main outcomes. (i) We empirically demonstrate the importance of the test set size to assess quantifiers. (ii) We show that current quantifiers generally have a mediocre performance on the smallest test sets. (iii) We propose a metalearning scheme to select the best quantifier based on the test size that can outperform the best single quantification method.
Research on Oral English Teaching System Based on VR in the Background of AI

Abstract 2016 is a year when AI (Artificial Intelligence) technology has made a significant breakthrough. The language service products of AI and VR (virtual reality) technology are increasingly appearing in the commercial market, which has brought a huge impact on the language service industry. Based on the analysis of the current principles and development status of the language service products of artificial intelligence and VR (virtual reality), this paper attempts to explore the enlightenment of new technology to language service, especially in oral English teaching, while oral English Corpus needs to be built. This research designs a set of oral English training system based on artificial intelligence and VR (virtual reality) technology, which makes oral English teaching conform to the requirements of the times. The research result is that it can improve the efficiency of oral English training.
Population sizing of cellular evolutionary algorithms

Cellular evolutionary algorithms (cEAs) are a particular type of EAs in which a communication structure is imposed to the population and mating restricted to topographically nearby individuals. In general, these algorithms have longer takeover times than panmictic EAs and previous investigations argue that they are more efficient in escaping local optima of multimodal and deceptive functions. However, most of those studies are not primarily concerned with population size, despite being one of the design decisions with a greater impact in the accuracy and convergence speed of population-based metaheuristics. In this paper, optimal population size for cEAs structured by regular and random graphs with different degree is estimated. Selecto-recombinative cEAs and standard cEAs with mutation and different types of crossover were tested on a class of functions with tunable degrees of difficulty. Results and statistical tests demonstrate the importance of setting an appropriate population size. Event Takeover Values (ETV) were also studied and previous assumptions on their distribution were not confirmed: although ETV distributions of panmictic EAs are heavy-tailed, log-log plots of complementary cumulative distribution functions display no linearity. Furthermore, statistical tests on ETVs generated by several instances of the problems conclude that power law models cannot be favored over log-normal. On the other hand, results confirm that cEAs impose deviations to distribution tails and that large ETVs are less probable when the population is structured by graphs with low connectivity degree. Finally, results suggest that for panmictic EAs the ETVs' upper bounds are approximately equal to the optimal population size.
What more than a hundred project groups reveal about teaching visualization

Abstract The growing number of students can be a challenge for teaching visualization lectures, supervision, evaluation, and grading. Moreover, designing visualization courses by matching the different experiences and skills of the students is a major goal in order to find a common solvable task for all of them. Particularly, the given task is important to follow a common project goal, to collaborate in small project groups, but also to further experience, learn, or extend programming skills. In this article, we survey our experiences from teaching 116 student project groups of 6 bachelor courses on information visualization with varying topics. Moreover, two teaching strategies were tried: 2 courses were held without lectures and assignments but with weekly scrum sessions (further denoted by TS1) and 4 courses were guided by weekly lectures and assignments (further denoted by TS2). A total number of 687 students took part in all of these 6 courses. Managing the ever growing number of students in computer and data science is a big challenge in these days, i.e., the students typically apply a design-based active learning scenario while being supported by weekly lectures, assignments, or scrum sessions. As a major outcome, we identified a regular supervision either by lectures and assignments or by regular scrum sessions as important due to the fact that the students were relatively unexperienced bachelor students with a wide range of programming skills, but nearly no visualization background. In this article, we explain different subsequent stages to successfully handle the upcoming problems and describe how much supervision was involved in the development of the visualization project. The project task description is given in a way that it has a minimal number of requirements but can be extended in many directions while most of the decisions are up to the students like programming languages, visualization approaches, or interaction techniques. Finally, we discuss the benefits and drawbacks of both teaching strategies. Graphic abstract
Motion Planning for Convertible Indoor Scene Layout Design

We present a system for designing indoor scenes with convertible furniture layouts. Such layouts are useful for scenarios where an indoor scene has multiple purposes and requires layout conversion, such as merging multiple small furniture objects into a larger one or changing the locus of the furniture. We aim at planning the motion for the convertible layouts of a scene with the most efficient conversion process. To achieve this, our system first establishes object-level correspondences between the layout of a given source and that of a reference to compute a target layout, where the objects are re-arranged in the source layout with respect to the reference layout. After that, our system initializes the movement paths of objects between the source and target layouts based on various mechanical constraints. A joint space-time optimization is then performed to program a control stream of object translations, rotations, and stops, under which the movements of all objects are efficient and the potential object collisions are avoided. We demonstrate the effectiveness of our system through various design examples of multi-purpose, indoor scenes with convertible layouts.
$ \mathbb{Z}_{4}\mathbb{Z}_{4}[u] $-additive cyclic and constacyclic codes

&lt;p style='text-indent:20px;'&gt;We study mixed alphabet cyclic and constacyclic codes over the two alphabets &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \mathbb{Z}_{4}, $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; the ring of integers modulo &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ 4 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, and its quadratic extension &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ \mathbb{Z}_{4}[u] = \mathbb{Z}_{4}+u\mathbb{Z}_{4}, u^{2} = 0. $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; Their generator polynomials and minimal spanning sets are obtained. Further, under new Gray maps, we find cyclic, quasi-cyclic codes over &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ \mathbb{Z}_{4} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; as the Gray images of both &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ \lambda $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-constacyclic and skew &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ \lambda $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-constacyclic codes over &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ \mathbb{Z}_{4}[u] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Moreover, it is proved that the Gray images of &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ \mathbb{Z}_{4}\mathbb{Z}_{4}[u] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-additive constacyclic and skew &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ \mathbb{Z}_{4}\mathbb{Z}_{4}[u] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-additive constacyclic codes are generalized quasi-cyclic codes over &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ \mathbb{Z}_{4} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Finally, several new quaternary linear codes are obtained from these cyclic and constacyclic codes.&lt;/p&gt;
Dynamic countermeasures selection for multi-path attacks

Multi-step attacks have been widely adopted by attackers, resulting in privacy leakage. Although many cost-sensitive approaches have been proposed to respond to the multi-step attacks, most studies have lack global optimization and ignored the fact that attackers may take multiple paths to launch multi-step attacks, which may lead to an over-response or an under-response. To address this problem, we formulate a response to multi-path attacks as an optimization problem and prove it is NP-hard. To obtain a feasible solution to the problem, we first identify suspicious attack paths and evaluate several metrics (i.e., security benefit, deployment cost, and negative impact on the quality of services) of the countermeasures. Specifically, by considering the compositions and cover degrees of atomic attacks, we define Attacks Surface Coverage to accurately evaluate the security benefit of countermeasures. Then, we propose an improved greedy algorithm to select reasonable countermeasures. Experimental results demonstrate the effectiveness and feasibility of our approach.
Single Image-Based Food Volume Estimation Using Monocular Depth-Prediction Networks

In this work, we present a system that can estimate food volume from a single input image, by utilizing the latest advancements in monocular depth estimation. We employ a state-of-the-art, monocular depth prediction network architecture, trained exclusively on videos, which we obtain from the publicly available EPIC-KITCHENS and our own collected food videos datasets. Alongside it, an instance segmentation network is trained on the UNIMIB2016 food-image dataset, to detect and produce segmentation masks for each of the different foods depicted in the given image. Combining the predicted depth map, segmentation masks and known camera intrinsic parameters, we generate three-dimensional (3D) point cloud representations of the target food objects and approximate their volumes with our point cloud-to-volume algorithm. We evaluate our system on a test set, consisting of images portraying various foods and their respective measured volumes, as well as combinations of foods placed in a single image.
Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-objective Optimization

Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes (defective or non-defective). (2) we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network—Extreme learning machine (ELM) based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE. We mainly consider two objectives. One objective is to maximize the performance of ELM, which refers to the benefit of the SMONGE model. Another objective is to minimize the output weight norm of ELM, which is related to the cost of the SMONGE model. We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects. The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.
Research Data Management in Universities: A Comparative Study from the Perspectives of Librarians and Management

With the research landscape tilting toward a more intensive research, propelled by the power of information and communication technologies, the research community is overwhelmed with tons of resear...
Distributional Reinforcement Learning with Maximum Mean Discrepancy.

Distributional reinforcement learning (RL) has achieved state-of-the-art performance in Atari games by recasting the traditional RL into a distribution estimation problem, explicitly estimating the probability distribution instead of the expectation of a total return. The bottleneck in distributional RL lies in the estimation of this distribution where one must resort to an approximate representation of the return distributions which are infinite-dimensional. Most existing methods focus on learning a set of predefined statistic functionals of the return distributions requiring involved projections to maintain the order statistics. We take a different perspective using deterministic sampling wherein we approximate the return distributions with a set of deterministic particles that are not attached to any predefined statistic functional, allowing us to freely approximate the return distributions. The learning is then interpreted as evolution of these particles so that a distance between the return distribution and its target distribution is minimized. This learning aim is realized via maximum mean discrepancy (MMD) distance which in turn leads to a simpler loss amenable to backpropagation. Experiments on the suite of Atari 2600 games show that our algorithm outperforms the standard distributional RL baselines and sets a new record in the Atari games for non-distributed agents.
THE IMPACT OF EMPLOYEE TRAINING PROGRAMS ON JOB PERFORMANCE AND JOB SATISFACTION IN THE TELECOMMUNICATION COMPANIES IN MALAYSIA

&lt;p&gt;Currently, many organizations are facing trials when competing in the turbulent business environment; this is coupled with the current Covid-19 pandemic, which has worsened the strength of the organizations. The telecommunication companies in Malaysia was also not spared in this situation. Therefore, a crucial strategy for the telecommunication companies to gain competitive advantage is by conducting training to its employees at all levels to overcome this current drawback. This research aims to discover if training impacts job performance and job satisfaction which are two crucial variables that leads to the telecommunication company’s survival and growth. Therefore, in order to achieve the objective of this research, the survey procedure is used to develop a thorough profile, which is gathered from 316 out of 500 formal standardized questionnaires sent to employees at three major telecommunication companies in Malaysia. This research utilized the quantitative methods to produce empirical outcomes and substantiations that answer the research questions. The literature examined the areas of training, job performance, and job satisfaction to fill the gap and to determine the significance of training programs conducted on job performance and job satisfaction. The analysis shows that training is statistically significant and has a strong relationship with job performance and job satisfaction. The hypothesis depicted that training programs conducted in the telecommunication companies have a significant relationship with job performance and job satisfaction of the employees. Thus, the findings of this study could aid as an example to other companies in Malaysia to not take granted of the impact of training on job performance and job satisfaction. By undertaking continuous training, companies could rise against the current economic problems caused by the Covid-19 pandemic and continue to survive and flourish as well as maintain its competitive strength.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0628/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Adaptive Fusion of Heterogeneous Manifolds for Subspace Clustering

Multiview clustering (MVC) has recently received great interest due to its pleasing efficacy in combining the abundant and complementary information to improve clustering performance, which overcomes the drawbacks of view limitation existed in the standard single-view clustering. However, the existing MVC methods are mostly designed for vectorial data from linear spaces and, thus, are not suitable for multiple dimensional data with intrinsic nonlinear manifold structures, e.g., videos or image sets. Some works have introduced manifolds' representation methods of data into MVC and obtained considerable improvements, but how to fuse multiple manifolds efficiently for clustering is still a challenging problem. Particularly, for heterogeneous manifolds, it is an entirely new problem. In this article, we propose to represent the complicated multiviews' data as heterogeneous manifolds and a fusion framework of heterogeneous manifolds for clustering. Different from the empirical weighting methods, an adaptive fusion strategy is designed to weight the importance of different manifolds in a data-driven manner. In addition, the low-rank representation is generalized onto the fused heterogeneous manifolds to explore the low-dimensional subspace structures embedded in data for clustering. We assessed the proposed method on several public data sets, including human action video, facial image, and traffic scenario video. The experimental results show that our method obviously outperforms a number of state-of-the-art clustering methods.
Copy Move Forgery Detection with Quadtree Decomposition Segmentation

The increasing usage of digital images and the wide availability of easy-to-use image editors have made the authenticity of images questionable. Copy-move is one of the most applied forgery types. The keypoint-based copy move forgery detection methods in the literature cannot detect forgeries with smooth regions. In this paper to overcome this problem, a new copy-move forgery detection technique is proposed. In the proposed scheme, the input image is segmented into two sub-image via Quadtree Decomposition. The sub-images are labeled as smooth and textured. The textural form of the smooth labeled segment is obtained based on LBPROT approach. Then the keypoints are extracted from both textured segment and textural form of the smooth segment. After that, they are matched with each other to obtain forgery clues. By matching the extracted keypoints, it is revealed whether the image is forged or not. The experimental results show that the proposed method is robust under scaling and rotation attacks.