Reviewerly/exHarmony huggingface dataset:
Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density

Many computer vision algorithms which are not robust to noise incorporate a noise removal stage in their workflow to avoid distortions in the final result. In the last decade, many filters for salt-and-pepper noise removal have been proposed. In this paper, a novel filter based on the weighted arithmetic mean aggregation function and the fuzzy mathematical morphology is proposed. The performance of the proposed filter is highly competitive when compared with other state-of-the-art filters regardless of the amount of salt-and-pepper noise present in the image, achieving notable results for any noise density from 5% to 98%. A statistical analysis based on some objective restoration measures supports that this filter surpasses several state-of-the-art filters for most of the noise levels considered in the comparison experiments.
Search for Appropriate Textual Information Sources

In this paper, we deal with the support in the search for appropriate textual sources. Users ask for an atomic concept that is explicated using machine learning methods applied to different textual sources. Next, we deal with the so-obtained explications to provide even more useful information. To this end, we apply the method of computing association rules. The method is one of the data-mining methods used for information retrieval. Our background theory is the system of Transparent Intensional Logic (TIL); all the concepts are formalised as TIL constructions.
Internet-Memes als multimodale Sprechakte in öffentlichen Diskursen anhand von Beispielen aus Antwortthreads von @realDonaldTrump

Internet memes are highly relevant for digital communications but there is not much re-search about their pragmatic uses. Besides being texts, memes are also multimodal speech acts. They are very similar to the speech acts Searle described, but there are some differ-ences. The communicative function of memes is in accord with Keller’s principle for social success, which is aimed for by all speakers. It is shown that people on Twitter use memes as speech acts when replying to Donald Trump’s Tweets. The difference between meme speech acts and traditional speech acts is their emphasis on the expressive function. While the meme speech acts can take any of Searle’s described functions, there is almost always an expressive element to be found.
Merge and Recognize: A Geometry and 2D Context Aware Graph Model for Named Entity Recognition from Visual Documents

Named entity recognition (NER) from visual documents, such as invoices, receipts or business cards, is a critical task for visual document understanding.Most classical approaches use a sequence-based model (typically BiLSTM-CRF framework) without considering document structure.Recent work on graph-based model using graph convolutional networks to encode visual and textual features have achieved promising performance on the task.However, few attempts take geometry information of text segments (text in bounding box) in visual documents into account.Meanwhile, existing methods do not consider that related text segments which need to be merged to form a complete entity in many real-world situations.In this paper, we present GraphNEMR, a graph-based model that uses graph convolutional networks to jointly merge text segments and recognize named entities.By incorporating geometry information from visual documents into our model, richer 2D context information is generated to improve document representations.To merge text segments, we introduce a novel mechanism that captures both geometry information as well as semantic information based on pre-trained language model.Experimental results show that the proposed GraphNEMR model outperforms both sequence-based and graph-based SOTA methods significantly.
Fine-grained image inpainting with scale-enhanced generative adversarial network

With the emergence of Generative Adversarial Networks, great progress has been made in image inpainting. However, most existing methods can produce plausible results, but fail to generate finer textures and structures. This is mainly due to the fact that (1) the generation of finer content in the masked region of an image is not constrained enough during network training, and (2) many different alternative pixels are exist to fill in the masked regions, making it very difficult for the inpainting network to generate reasonable sharp edges. To address these issues, we propose a Scale Enhanced GAN (SE-GAN) model which combines the constraints of large- and small-scale receptive fields of our tailor-made discriminators to achieve fine-grained constraint on image details, a novel edge loss to further ensure the sharpness of the generated image. Experiments on multiple datasets including faces(CelebA-HQ), textures(DTD), buildings(Facade) and natural images(ImageNet, Places2) show that our approach can generate higher quality inpainting results with more details than previous methods.
A Link Quality Estimation Method Based on Improved Weighted Extreme Learning Machine

The link quality of wireless sensor networks is the basis for selecting communication links in routing protocols.Effective link quality estimation is helpful to select high-quality links for communication and to improve network stability.The correlation of link quality parameter and packet reception rate (PRR) is calculated by the Pearson correlation coefficient.According to Pearson coefficient values, the averages of the link quality indication, received signal strength indication, and signal-to-noise are selected as the parameters of the link quality.The link quality grade is taken as a metric of the link quality estimation.Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of the weighted extreme learning machine (WELM), including the number of hidden nodes, weights, and the normalization factor.A link quality estimator (LQE) based on the improved weighted extreme learning machine (LQE-IWELM) is constructed.In different scenarios, experiment results show that the improved weighted extreme learning machine (IWELM) is more effective than extreme learning machine (ELM) and WELM.Compared with the other three link quality estimation models, LQE-IWELM has better precision and G_mean.
Development of Indonesian Language Text Books with Multiculturalism and Character Education to Improve Traditional Poetry Writing Skills

&lt;p style="text-align:justify"&gt;This research aims to develop Indonesian Language textbooks with multiculturalism and character education for traditional poetry writing learning. This research uses a research and development approach until the discovery of a proper textbook. The subjects involved in this study were 101 elementary school fourth-graders, 33 teachers, and 2 Indonesian language and literature learning experts. Data analysis techniques at the need analysis stage through interactive models, and data analysis at the product testing stage using quantitative techniques with non-independent t-test statistics. Preliminary studies found that teachers and students needed creative writing skills textbooks developed by integrating the values of multiculturalism and character education. Textbook development referred to aspects of content, language, presentation, and graphics. Limited textbook product testing using t-test showed that t-obtained= -4,265 and t-table on α = 5 % is 2,064, so H0 is rejected. Broad testing also resulted in t-obtained= -6,426 and t-table = 2,004, so it can be decided that H0 is rejected. Students' traditional poetry writing skills after learning using textbooks are better than before learning using textbooks. Thus it is concluded that the textbooks developed are inferred worthy of use by teachers and students to improve their writing skills in elementary school.&lt;/p&gt;
PAVI: Bootstrapping Accountability and Privacy to IPv6 Internet

Accountability and privacy are considered valuable but conflicting properties in the Internet, which at present does not provide native support for either. Past efforts to balance accountability and privacy in the Internet have unsatisfactory deployability due to the introduction of new communication identifiers, and because of large-scale modifications to fully deployed infrastructures and protocols. The IPv6 is being deployed around the world and this trend will accelerate. In this paper, we propose a private and accountable proposal based on IPv6 called PAVI that seeks to bootstrap accountability and privacy to the IPv6 Internet without introducing new communication identifiers and large-scale modifications to the deployed base. A dedicated quantitative analysis shows that the proposed PAVI achieves satisfactory levels of accountability and privacy. The results of the evaluation of a PAVI prototype show that it incurs little performance overhead, and is widely deployable.
Comparison of different configuration space representations for path planning under combinatorial method

&lt;span&gt;The use of autonomous vehicle/robot has been adopted widely to replace human beings in performing dangerous missions in adverse environments. Keeping this in mind, path planning ensures that the autonomous vehicle must safely arrive to its destination with required criteria like lower computation time, shortest travelled path and completeness. There are few kinds of path planning strategies, such as combinatorial method, sampling based method and bio-inspired method. Among them, combinatorial method can accomplish couple of criteria without further adjustment in conventional algorithm. Configuration space provides detailed information about the position of all points in the system and it is the space for all configurations. Therefore, C-space denotes the actual free space zone for the movement of robot and guarantees that the vehicle or robot must not collide with the obstacle. This paper analyses different C-Space representation techniques under combinatorial method based on the past researches and their findings with different criteria such as optimality, completeness, safety, memory uses, real time and computational time etc. Visibility Graph has optimality which is a unique from other&lt;/span&gt;
ReLU Network with Bounded Width Is a Universal Approximator in View of an Approximate Identity

Deep neural networks have shown very successful performance in a wide range of tasks, but a theory of why they work so well is in the early stage. Recently, the expressive power of neural networks, important for understanding deep learning, has received considerable attention. Classic results, provided by Cybenko, Barron, etc., state that a network with a single hidden layer and suitable activation functions is a universal approximator. A few years ago, one started to study how width affects the expressiveness of neural networks, i.e., a universal approximation theorem for a deep neural network with a Rectified Linear Unit (ReLU) activation function and bounded width. Here, we show how any continuous function on a compact set of Rnin,nin∈N can be approximated by a ReLU network having hidden layers with at most nin+5 nodes in view of an approximate identity.
Bringing AI to the edge: a formal M&amp;S specification to deploy effective IoT architectures

The Internet of Things is transforming our society, providing new services that improve the quality of life and resource management. These applications are based on ubiquitous networks of multiple distributed devices, with limited computing resources and power, capable of collecting and storing data from heterogeneous sources in real-time. To avoid network saturation and high delays, new architectures such as fog computing are emerging to bring computing infrastructure closer to data sources. Additionally, new data centers are needed to provide real-time Big Data and data analytics capabilities at the edge of the network, where energy efficiency needs to be considered to ensure a sustainable and effective deployment in areas of human activity. In this research, we present an IoT model based on the principles of Model-Based Systems Engineering defined using the Discrete Event System Specification formalism. The provided mathematical formalism covers the description of the entire architecture, from IoT devices to the processing units in edge data centers. Our work includes the location-awareness of user equipment, network, and computing infrastructures to optimize federated resource management in terms of delay and power consumption. We present an effective framework to assist the dimensioning and the dynamic operation of IoT data stream analytics applications, demonstrating our contributions through a driving assistance use case based on real traces and data.
Teaching Biochemistry and Molecular Biology with Virtual Reality — Lesson Creation and Student Response

The commercialization of Virtual Reality (VR) hardware has enabled the use of VR as an educational tool. We describe how a VR platform was used to create molecular visualizations using standard PDB files with the purpose of delivering biochemistry and cellular biology lessons for undergraduates. Specifically, we describe two new software modules, one for rendering molecules generated from PDB files and one for creating interactive lectures. These modules were used to create two VR labs, one on DNA and collagen and the other on hemoglobin. We then describe how these labs were delivered, along with surveys and quizzes, to over 100 students in undergraduate biology courses. Student response to the labs are found to be extremely positive and the labs themselves are found to satisfy their educational objectives.
Two Models Based on Social Relations and SVD++ Method for Recommendation System

Recently, Recommender Systems (RSs) have attracted many researchers whose goal is to improve the performance of the prediction accuracy of recommendation systems by alleviating RSs drawbacks. The most common limitations are sparsity and the cold-start problem. This article proposes two models to mitigate the effects of these limitations. The proposed models exploit five sources of information: rating information, which involves two sources, namely explicit and implicit, which can be extracted via users’ ratings, and two types of social relations: explicit and implicit relations, the last source is confidence values that are included in the first model only. The whole sources are combined into the Singular Value Decomposition plus (SVD++) method. First, to extract implicit relations, each non-friend pair of users, the Multi-Steps Resource Allocation (MSRA) method is adopted to compute the probability of being friends. If the probability has accepted value which exceeds a threshold, an implicit relationship will be created. Second, the similarity of explicit and implicit social relationships for each pair of users is computed. Regarding the first model, a confidence value between each pair of users is computed by dividing the number of common items by the total number of items which have also rated by the first user of this pair. The confidence values are combined with the similarity values to produce the weight factor. Furthermore, the weight factor, explicit, and implicit feedback information are integrated into the SVD++ method to compute the missing prediction values. Additionally, three standard datasets are utilized in this study, namely Last.Fm, Ciao, and FilmTrust, to evaluate our models. The experimental results have revealed that the proposed models outperformed state-of-the-art approaches in terms of accuracy.
Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation

Determining the plausibility of causal relations between clauses is a commonsense reasoning task that requires complex inference ability. The general approach to this task is to train a large pretrained language model on a specific dataset. However, the available training data for the task is often scarce, which leads to instability of model training or reliance on the shallow features of the dataset. This paper presents a number of techniques for making models more robust in the domain of causal reasoning. Firstly, we perform adversarial training by generating perturbed inputs through synonym substitution. Secondly, based on a linguistic theory of discourse connectives, we perform data augmentation using a discourse parser for detecting causally linked clauses in large text, and a generative language model for generating distractors. Both methods boost model performance on the Choice of Plausible Alternatives (COPA) dataset, as well as on a Balanced COPA dataset, which is a modified version of the original data that has been developed to avoid superficial cues, leading to a more challenging benchmark. We show a statistically significant improvement in performance and robustness on both datasets, even with only a small number of additionally generated data points.
Finite-key analysis of asymmetric phase-matching quantum key distribution with unstable sources

The twin-field quantum key distribution (TF-QKD) protocol has been studied widely to overcome the linear bound, and several variations of the TF-QKD protocol have been proposed to improve security and practicality. One variation called the phase-matching QKD (PM-QKD) protocol develops an optical-mode-based security; simultaneously, it has a quadratic improvement of key rate without either basis choice or phase locking. So far, the main research work of the PM-QKD protocol has focused on symmetric channels. However, it is quite common that the two channels have asymmetric transmittances in real life. In this paper, we discuss the effects of asymmetric channels on the PM-QKD protocol; to reach a much better performance and achieve a higher key rate, the intensity must be optimized to compensate for channel asymmetry. We also study the practical issues of finite-key effects and intensity fluctuations, which have significant effects on the key rate of PM-QKD.
A Fast Military Object Recognition using Extreme Learning Approach on CNN

Convolutional Neural Network (CNN) is an algorithm that can classify image data with very high accuracy but requires a long training time so that the required resources are quite large.One of the causes of the long training time is the existence of a backpropagation-based classification layer, which uses a slow gradient-based algorithm to perform learning, and all parameters on the network are determined iteratively.This paper proposes a combination of CNN and Extreme Learning Machine (ELM) to overcome these problems.Combination process is carried out using a convolution extraction layer on CNN, which then combines it with the classification layer using the ELM method.ELM method is Single Hidden Layer Feedforward Neural Networks (SLFNs) which was created to overcome traditional CNN's weaknesses, especially in terms of training speed of feedforward neural networks.The combination of CNN and ELM is expected to produce a model that has a faster training time, so that its resource usage can be smaller, but maintaining the accuracy as much as standard CNN.In the experiment, the military object classification problem was implemented, and it achieves smaller resources as much as 400 MB on GPU comparing to standard CNN.
The Development of E-Worksheet Using Kvisoft Flipbook Maker Software Based on Lesson Study to Improve Teacher’s Critical Thinking Ability

&lt;p class="0abstract"&gt;The effectivity and efficiency of the learning process is very dependent on the quality of teacher. Therefore must improve their quality by conducting various learning innovations, one of them by implementing ICT-based learning. The purpose of this study was to develop e-worksheet using kvisoft flipbook maker software based on lesson study to improve chemistry teacher’s critical thinking ability. This research used development model 4-D namely define, design, develop, and disseminate. The subject of this research was involved chemistry teachers and student of grade XI of Senior High School in Rokan Hulu, Riau, Indonesia. The draft e-worksheet was validated by experts using validation questionnaires and critical thinking ability instruments using essay tests. The data were analyzed using N-Gain formulation. Based on the result, the developed e-worksheet in chemistry material has exactly valid with score percentages 88.88% and average critical thinking ability about 60 with enough category. Therefore, it can be concluded that e-worksheets using kvisoft flipbook maker software based on lesson studies that have been developed can improve critical thinking ability of teacher and students.&lt;/p&gt;
Measurement-Device-Independent Entanglement Detection for Continuous-Variable Systems

We study the detection of continuous-variable entanglement, for which most of the existing methods designed so far require a full specification of the devices, and we present protocols for entanglement detection in a scenario where the measurement devices are completely uncharacterised. We first generalise, to the continuous variable regime, the seminal results by Buscemi [PRL 108, 200401(2012)] and Branciard et al. [PRL 110, 060405 (2013)], showing that all entangled states can bedetected in this scenario. Most importantly, we then describe a practical protocol that allows for the measurement-device-independent certification of entanglement of all two-mode entangled Gaussian states. This protocol is feasible with current technology as it makes only use of standard optical setups such as coherent states and homodyne measurements.
Human Action Recognition in Videos using a Robust CNN LSTM Approach

Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this paper we propose (1) Implement a CNN–LSTM architecture. First, a pre-trained VGG16 convolutional neural network extracts the features of the input video. Then, an LSTM classifies the video in a particular class. (2) Study how the number of LSTM units affects the performance of the system. To carry out the training and test phases, we used the KTH, UCF-11 and HMDB-51 datasets. (3) Evaluate the performance of our system using accuracy as evaluation metric. We obtain 93%, 91% and 47% accuracy respectively for each dataset.
Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching

&#x0D; &#x0D; &#x0D; &#x0D; In this short article we present our manual annotation of the eye movement events in a subset of the large-scale eye tracking data set Hollywood2. Our labels include fixations, saccades, and smooth pursuits, as well as a noise event type (the latter representing either blinks, loss of tracking, or physically implausible signals). In order to achieve more consistent annotations, the gaze samples were labelled by a novice rater based on rudimentary algorithmic suggestions, and subsequently corrected by an expert rater. Overall, we annotated eye movement events in the recordings corresponding to 50 randomly selected test set clips and 6 training set clips from Hollywood2, which were viewed by 16 observers and amount to a total of approximately 130 minutes of gaze data. In these labels, 62.4% of the samples were attributed to fixations, 9.1% – to saccades, and, notably, 24.2% – to pursuit (the remainder marked as noise). After evaluation of 15 published eye movement classification algorithms on our newly collected annotated data set, we found that the most recent algorithms perform very well on average, and even reach human-level labelling quality for fixations and saccades, but all have a much larger room for improvement when it comes to smooth pursuit classification. The data set is made available at https://gin.g- node.org/ioannis.agtzidis/hollywood2_em. &#x0D; &#x0D; &#x0D; &#x0D;