Reviewerly/exHarmony huggingface dataset:
Efficient algorithm for sparse symmetric nonnegative matrix factorization

Symmetric Nonnegative Matrix Factorization (symNMF) is a special case of the standard Nonnegative Matrix Factorization (NMF) method which is the most popular linear dimensionality reduction technique for analyzing nonnegative data. Examples of symmetric matrices that arise in real-life applications include covariance matrices in finance, adjacency matrices associated with undirected graphs, and distance matrices arising from video and other media summarization technologies. The advantages of having sparse factors in symNMF include saving a great deal of storage space and enhancing the extraction of localized basis features that better represent the latent features of the original data. In order to obtain sparser basis factors, we formulate a new sparse symNMF model by imposing an l1-norm based sparsity constraint on the symNMF problem. We use the concept of rank-one nonnegative matrix approximation to decouple this non-convex optimization problem into convex nonnegative least squares sub-problems which are easier to solve. We develop a new and efficient coordinate-descent based algorithm and test it on four well-known databases of facial images. The proposed algorithm is shown to have fast convergence, well-suited for solving the sparse symNMF problem, and extract localized basis features of image datasets that facilitate interpretation.
How Does Connected Components Labeling with Decision Trees Perform on GPUs?

In this paper the problem of Connected Components Labeling (CCL) in binary images using Graphic Processing Units (GPUs) is tackled by a different perspective. In the last decade, many novel algorithms have been released, specifically designed for GPUs. Because CCL literature concerning sequential algorithms is very rich, and includes many efficient solutions, designers of parallel algorithms were often inspired by techniques that had already proved successful in a sequential environment, such as the Union-Find paradigm for solving equivalences between provisional labels. However, the use of decision trees to minimize memory accesses, which is one of the main feature of the best performing sequential algorithms, was never taken into account when designing parallel CCL solutions. In fact, branches in the code tend to cause thread divergence, which usually leads to inefficiency. Anyway, this consideration does not necessarily apply to every possible scenario. Are we sure that the advantages of decision trees do not compensate for the cost of thread divergence? In order to answer this question, we chose three well-known sequential CCL algorithms, which employ decision trees as the cornerstone of their strategy, and we built a data-parallel version of each of them. Experimental tests on real case datasets show that, in most cases, these solutions outperform state-of-the-art algorithms, thus demonstrating the effectiveness of decision trees also in a parallel environment.
A Validity Index for Fuzzy Clustering Based on Bipartite Modularity

Because traditional fuzzy clustering validity indices need to specify the number of clusters and are sensitive to noise data, we propose a validity index for fuzzy clustering, named CSBM (compactness separateness bipartite modularity), based on bipartite modularity. CSBM enhances the robustness by combining intraclass compactness and interclass separateness and can automatically determine the optimal number of clusters. In order to estimate the performance of CSBM, we carried out experiments on six real datasets and compared CSBM with other six prominent indices. Experimental results show that the CSBM index performs the best in terms of robustness while accurately detecting the number of clusters.
Why Do Readers Fail to Change Their Beliefs After Reading Persuasive Text?

Aristotle introduced the study of persuasion with advice to orators. Since, rhetoricians have turned to written persuasion as well. The content characteristics of persuasive text are as important in persuading the reader as the text’s structure. Aristotle proposed a taxonomy of content types for persuasion, which continues to influence the advice given to budding authors learning to design persuasive texts. Persuasive text, such as Pharmaceuticals, is predominately a presentation of evidence that can be characterized by the Toulmin argument model. Presumably, the authors assume that readers will carefully weigh the evidence and that some readers will change their beliefs after reading the text. D. Kuhn suggests an ideal model whereby readers compare the evidence they have for their prior beliefs with the evidence in a persuasive text, consider the relative merits of the opposing evidence, and either retain or change their beliefs accordingly.
Mobile Assisted Language Learning (MALL): Trends from 2010 to 2020 Using Text Analysis Techniques

Abstract Throughout the decade of 2010-2020, the widespread use of mobile devices of any type (smartphones, tablets) has encouraged and strengthened their use in different learning processes and in different ways. Latest improvements in devices’ processing power, in storage capacity, in memory allocation, in wireless connectivity, in GPS and in Bluetooth capabilities, has led to their wider integration and smoother use in the field of learning and in the field of language learning as well. This study examined the trend of a large number of academic studies concerning Mobile Assisted Language Learning (MALL) using text analysis techniques and tools, published in the decade 2010-2020. Over three hundred and forty (340) publications such as journal articles, conferences proceedings papers, books chapters and books were retrieved and analyzed. The preliminary analysis presents the main characteristics and the research trends of this decade and discuss how the field of mobile assisted language learning has evolved in these years.
Movie Recommendation based on User Similarity of Consumption Pattern Change

The recurrent neural network(RNN) deep learning algorithm, which mainly learns and predicts sequence data and time series data, is mainly used in language modeling, stock price prediction, and chat bot. In this paper, we propose a method of predicting and recommending a movie by considering movie consumption patterns of users. We measure the similarity between users based on movie rating data, classify users with similar movie preferences, and learn the consumption pattern of each similar user group to improve the prediction accuracy by considering the change of preference over time. In order to show the effectiveness of the proposed method, we apply the collaborative filtering algorithm, the simple RNN and our modified RNN and compare their prediction accuracies.
YouTube as a Learning Media to Improve the Student’s Speaking Ability in 21st Century

&lt;p&gt;&lt;em&gt;The development of students' skills&lt;/em&gt;&lt;em&gt;, especially&lt;/em&gt;&lt;em&gt; in speaking&lt;/em&gt;&lt;em&gt;,&lt;/em&gt;&lt;em&gt; &lt;/em&gt;&lt;em&gt;is considered important to &lt;/em&gt;&lt;em&gt;get more attention because speaking skill &lt;/em&gt;&lt;em&gt;is &lt;/em&gt;&lt;em&gt;included &lt;/em&gt;&lt;em&gt;as one of the &lt;/em&gt;&lt;em&gt;productive skills that require&lt;/em&gt;&lt;em&gt;s&lt;/em&gt;&lt;em&gt; a lot of practice rather than &lt;/em&gt;&lt;em&gt;only learning the&lt;/em&gt;&lt;em&gt; theory. In mastering speaking skil&lt;/em&gt;&lt;em&gt;l, it is regarded necessary to&lt;/em&gt;&lt;em&gt; not only understand what others are talking about but also to respond and communicate effectively&lt;/em&gt;&lt;em&gt; with others&lt;/em&gt;&lt;em&gt;. &lt;/em&gt;&lt;em&gt;Thus, a m&lt;/em&gt;&lt;em&gt;aximum use of instructional media, especially YouTube, is considered necessary &lt;/em&gt;&lt;em&gt;in enhancing&lt;/em&gt;&lt;em&gt; the development of student&lt;/em&gt;&lt;em&gt;s’ speaking&lt;/em&gt;&lt;em&gt; skill&lt;/em&gt;&lt;em&gt; as a crucial capability to be possessed &lt;/em&gt;&lt;em&gt;in the 21&lt;sup&gt;st&lt;/sup&gt; century. The purpose of this study is&lt;/em&gt;&lt;em&gt; mainly&lt;/em&gt;&lt;em&gt; to describe YouTube's maximum use in terms of practice&lt;/em&gt;&lt;em&gt; as well as disclose &lt;/em&gt;&lt;em&gt;the benefits of using YouTube as a media learning.&lt;/em&gt;&lt;/p&gt;
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN~(Adaboosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors of current nodes and then integrates knowledge from different hops of neighbors into the network in an Adaboost way. Different from other graph neural networks that directly stack many graph convolution layers, AdaGCN shares the same base neural network architecture among all ``layers'' and is recursively optimized, which is similar to an RNN. Besides, We also theoretically established the connection between AdaGCN and existing graph convolutional methods, presenting the benefits of our proposal. Finally, extensive experiments demonstrate the consistent state-of-the-art prediction performance on graphs across different label rates and the computational advantage of our approach AdaGCN~\footnote{Code is available at \url{https://github.com/datake/AdaGCN}.}
Strong admissibility revisited: Theory and applications

In the current paper, we re-examine the concept of strong admissibility, as was originally introduced by Baroni and Giacomin. We examine the formal properties of strong admissibility, both in its extension-based and in its labelling-based form, and analyse the computational complexity of the relevant decision problems. Moreover, we show that strong admissibility plays a vital role in discussion-based proof procedures for grounded semantics. In particular it allows one to compare the performance of alternative dialectical proof procedures for grounded semantics, and obtain some remarkable differences between the Standard Grounded Game and the Grounded Discussion Game.
Lightweight and Privacy-Preserving Data Aggregation for Mobile Multimedia Security

With the continuous development of multimedia technology, a growing number of multimedia applications have emerged. The demand for multimedia services continues to grow. But the development of multimedia services is still hampered by inherent security. Privacy and integrity of multimedia data are two key issues for implementing mobile multimedia security. At present, most data privacy-preserving solutions have a trusted third party, which may become the bottleneck of the system. And, the computational efficiency of the client is inefficient. In this paper, a lightweight and privacy-preserving data aggregation for mobile multimedia is proposed. In the proposed scheme, the terminal calculation is lightweight and there is no trusted third party in our scheme. Besides, multimedia big data and personal multimedia data are balanced by creating virtual aggregation areas and the system performance is improved by adopting batch verification in our scheme. Security analysis shows that the presented scheme can guarantee the privacy, confidentiality and integrity of the personal multimedia data. The performance analysis indicates the proposed scheme is lightweight.
Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning

Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential phenomena and hence fail to evaluate the ability of models to resolve coreference. We present a new crowdsourced dataset containing more than 24K span-selection questions that require resolving coreference among entities in over 4.7K English paragraphs from Wikipedia. Obtaining questions focused on such phenomena is challenging, because it is hard to avoid lexical cues that shortcut complex reasoning. We deal with this issue by using a strong baseline model as an adversary in the crowdsourcing loop, which helps crowdworkers avoid writing questions with exploitable surface cues. We show that state-of-the-art reading comprehension models perform significantly worse than humans on this benchmark---the best model performance is 70.5 F1, while the estimated human performance is 93.4 F1.
Combining Convolutional Neural Network and Photometric Refinement for Accurate Homography Estimation

Homography estimation refers to the problem of computing a 3 × 3 matrix which transfers image points between two images of a planar scene or two images captured from the same location.While existing algorithms exploiting hand-crafted sparse image features are well-established and efficient, recent methods based on convolutional neural networks (CNNs) achieve promising results especially for low-texture scenes.This work proposes to solve homography estimation using a hybrid framework HomoN-etComb which incorporates deep learning method and energy minimization.In particular, a customized light-weight CNN named HomoNetSim is designed to calculate an initial estimation of homography, where the network is trained in an end-to-end fashion using large amount of image pairs generated from a publicly available dataset.Due to the tiny size of the employed network, the computation time of both training and inference for HomoNetSim can be reduced significantly compared with existing CNN-based homography estimation method.The initial estimate is then refined via gradient-decent algorithm by minimizing the masked pixel-level photometric discrepancy between the warped image and the destination image in a parallel fashion.Extensive experiments on the large scale synthetic dataset demonstrate that the proposed HomoNetComb improves robustness of homography estimation significantly compared with traditional methods based on sparse image features, and meanwhile HomoNetComb achieves a mean average corner error (MACE) of 0.58 pixels which outperforms previous state-of-the-art CNN-based method.Moreover, the usefulness and applicability of the proposed method is demonstrated by applying it to solve a real-world image stitching problem.
Augmented Reality: Solusi Pembelajaran IPA di Era Revolusi Industri 4.0

Nowadays, the changes of the world are entering era of industrial revolution 4.0, which is all aspects human life depend on digital technology. Preparing qualified graduates, gaining to compete globally, mastering technological developments are important for learning especially for the future of education in Indonesia. Science is one of the subjects that is related to technology which includes aspects of products, processes, scientific attitudes, and applications. Science as a product in the form a body of knowledge consist of concepts, principles, laws, and theories must be able to contribute in creative human formation for industrial generation 4.0. Thus, the direction of development and utilization of technology in science learning should be able to help students become a superior generation, innovative, and able to compete in all fields. Augmented Reality (AR), is one of the latest technologies in the 4.0 industrial revolution that is applied in the world of education. Augmented Reality is an application of combining the real world with cyberspace in two-dimensional and three-dimensional forms projected in a real environment at the same time and is very relevant to be applied in science learning. This research method uses literature review by searching for sources that are relevant to the object of this research. Thus Augmented Reality (AR) is one solution to fix the problem of science learning in the industrial revolution era 4.0 that is able to accommodate science learning.
A Temporally Irreversible Visual Attention Model Inspired by Motion Sensitive Neurons

When a human perceives videos composed of the same images in various orders, such as normal order, reverse order, and random order, the human visual attention system perceives them as different visual inputs. This means that the temporal change in the image sequence exerts a considerable influence on the human visual system. However, most state-of-the-art computational visual attention models have not considered the temporal cues adequately. Motivated by this deficiency, we propose a novel temporally irreversible visual attention model considering the following three aspects. First, the central bias of human dynamic vision is incorporated into the model to manifest this tendency. Second, the depth and directional motion-sensitive neurons are fused to discern different motion patterns. Third, the rarity factor is integrated into the model to mimic the attention shift when human observer perceives new emerging motion cues. The proposed model demonstrates its competitiveness to select attentive events in our experiments, in both laboratory setting and real driving video clips. When compared with recent visual attention models, the proposed model achieves the highest score in similarity with human dynamic vision. The proposed model could be one of the fundamental building blocks for any visual attention systems coping with dynamic scenes.
Hyperentangled Time-Bin and Polarization Quantum Key Distribution

Fiber-based quantum communication networks are currently limited without quantum repeaters. Satellite-based quantum links have been proposed to extend the network domain. We develop a quantum communication system, suitable for realistic satellite-to-ground communication. With this system, we execute an entanglement-based quantum key distribution (QKD) protocol developed by Bennett, Brassard, and Mermin (BBM92), achieving quantum bit-error rates (QBERs) below 2% in all bases. More importantly, we demonstrate low-QBER execution of a higher-dimensional hyperentanglement-based QKD protocol, using photons simultaneously entangled in polarization and time bin, leading to significantly higher secure key rates, at the cost of increased technical complexity and system size. We show that our protocol is suitable for a space-to-ground link, after incorporating Doppler-shift compensation, and verify its security using a rigorous finite-key analysis. Additionally, we discuss system-engineering considerations relevant to those and other quantum communication protocols and their dependence on what photonic degrees of freedom are utilized.
Poster

In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR). SR entails the upscaling of a single low-resolution image in order to meet application-specific image quality demands and plays a key role in mobile devices. To comply with privacy regulations and reduce the overhead of cloud computing, executing SR models locally on-device constitutes a key alternative approach. Nevertheless, the excessive compute and memory requirements of SR workloads pose a challenge in mapping SR networks on resource-constrained mobile platforms. This work presents MobiSR, a novel framework for performing efficient super-resolution on-device. Given a target mobile platform, the proposed framework considers popular model compression techniques and traverses the design space to reach the highest performing trade-off between image quality and processing speed. At run time, a novel scheduler dispatches incoming image patches to the appropriate model-engine pair based on the patch's estimated upscaling difficulty in order to meet the required image quality with minimum processing latency. Quantitative evaluation shows that the proposed framework yields on-device SR designs that achieve an average speedup of 2.13x over highly-optimized parallel difficulty-unaware mappings and 4.79x over highly-optimized single compute engine implementations.
The Hybrid Invasive Weed Optimization-Shufﬂed Frog-leaping Algorithm Applied to Optimal Design of Frame Structures

In this article, an efficient hybrid optimization algorithm based on invasive weed optimization algorithm and shufﬂed frog-leaping algorithm is utilized for optimum design of skeletal frame structures. The shufﬂed frog-leaping algorithm is a population-based cooperative search metaphor inspired by natural memetic, and the invasive weed optimization algorithm is an optimization method based on dynamic growth of weeds colony. In the proposed algorithm, shufﬂed frog-leaping algorithm works to find optimal solution region rapidly, and invasive weed optimization performs the global search. Different benchmark frame structures are optimized using the new hybrid algorithm. Three design examples are tested using the new method. This algorithm converges to better or at least the same solutions compared the utilized methods with a smaller number of analyses. The outcomes are compared to those obtained previously using other recently developed meta-heuristic optimization methods.
X-Ray Stereo Digital Image Correlation

Digital Image Correlation (DIC) is a well-established, non-contact diagnostic technique used to measure shape, displacement and strain of a solid specimen subjected to loading or deformation. However, measurements using standard DIC can have significant errors or be completely infeasible in challenging experiments, such as explosive, combustion, or fluid-structure interaction applications, where beam-steering due to index of refraction variation biases measurements or where the sample is engulfed in flames or soot. To address these challenges, we propose using X-ray imaging instead of visible light imaging for stereo-DIC, since refraction of X-rays is negligible in many situations, and X-rays can penetrate occluding material. Two methods of creating an appropriate pattern for X-ray DIC are presented, both based on adding a dense material in a random speckle pattern on top of a less-dense specimen. A standard dot-calibration target is adapted for X-ray imaging, allowing the common bundle-adjustment calibration process in commercial stereo-DIC software to be used. High-quality X-ray images with sufficient signal-to-noise ratios for DIC are obtained for aluminum specimens with thickness up to 22.2 mm, with a speckle pattern thickness of only 80 μm of tantalum. The accuracy and precision of X-ray DIC measurements are verified through simultaneous optical and X-ray stereo-DIC measurements during rigid in-plane and out-of-plane translations, where errors in the X-ray DIC displacements were approximately 2–10 μm for applied displacements up to 20 mm. Finally, a vast reduction in measurement error—5–20 times reduction of displacement error and 2–3 times reduction of strain error—is demonstrated, by comparing X-ray and optical DIC when a hot plate induced a heterogeneous index of refraction field in the air between the specimen and the imaging systems. Collectively, these results show the feasibility of using X-ray-based stereo-DIC for non-contact measurements in exacting experimental conditions, where optical DIC cannot be used.