Reviewerly/exHarmony huggingface dataset:
A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space

Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space.
Adaptive Serendipity for Recommender Systems: Let It Find You

Recommender systems are nowadays widely implemented in order to predict the potential objects of interest for the user. With the wide world of the internet, these systems are necessary to limit the problem of information overload and make the user’s internet surfing a more agreeable experience. However, a very accurate recommender system creates a problem of over-personalization where there is no place for adventure and unexpected discoveries: the user will be trapped in filter bubbles and echo rooms. Serendipity is a beneficial discovery that happens by accident. Used alone, serendipity can be easily confused with randomness; this takes us back to the original problem of information overload. Hypothetically, combining accurate and serendipitous recommendations will result in a higher user satisfaction. The aim of this paper is to prove the following concept: including some serendipity at the cost of profile accuracy will result in a higher user satisfaction and is, therefore, more favourable to implement. We will be testing a first measure implementation of serendipity on an offline dataset that lacks serendipity implementation. By varying the ratio of accuracy and serendipity in the recommendation list, we will reach the optimal number of serendipitous recommendations to be included in an accurate list.
Actividades Kahoot! en el aula y satisfacción del alumnado

espanolEl presente estudio observa la relacion entre la implementacion de actividades basadas en herramientas TIC y la consiguiente satisfaccion del alumnado participante. Especificamente, utilizamos la plataforma Kahoot! para generar preguntas relacionadas con la materia impartida, que son contestadas en linea por los estudiantes a traves de smartphones u otros dispositivos digitales. Tras realizar este tipo de actividades en el aula universitaria durante varias sesiones docentes, solicitamos a los participantes que rellenaran una encuesta para medir el grado de satisfaccion de los mismos. Nuestros resultados confirman que el uso de este tipo de actividades motiva al alumnado a participar activamente en la clase. EnglishThe present study observes the relationship between the implementation of activities based on ICT tools and the consequent satisfaction of the participating students. Specifically, we use the Kahoot platform! to generate questions related to the subject taught, which are answered online by students through smartphones or other digital devices. After carrying out this type of activities in the university classroom during several teaching sessions, we asked the participants to fill out a survey to measure their degree of satisfaction. Our results confirm that the use of this type of activities motivates students to participate actively in the class.
Design Science Research Strengthened: Integrating Co-creation and Co-design

Design science research (DSR) is a well-known methodology that uses design as a tool for the development of both practical research resulting in an artefact solution, and theoretical scientific knowledge resulting in improved design processes. In this paper, we advocate strengthening the DSR methodology by including elements of co-creation and co-design in order to produce meaningfully contextualised solutions and to foster a stronger sense of ownership and social acceptance of a developed technological artefact solution within ICT4D. In our work, the inclusion of co-creation and co-design within DSR takes place in all of the stages of the design cycle, influencing also the relevance and rigour cycles as well as the impact of the artefact in the broader socio-technical context. Here we illustrate the practical implementation of these ideas through the involvement of women entrepreneurs from rural Tanzania in the development of a mobile application. This paper contributes to the body of research on the meaningful application of DSR processes to ICT4D.
Graph partition–based data and task co‐scheduling of scientific workflow in geo‐distributed datacenters

Summary Most large‐scale scientific workflows take place in multiple collaborative datacenters for access to community‐wide resources, while adhering to each datacenter's non‐uniform resource limits. However, moving both initial input datasets with predetermined locations and intermediate datasets needing placement decisions across geo‐distributed datacenters hinders efficient execution of large‐scale data‐intensive scientific workflows. Thus, scientific workflow's data and task co‐scheduling deal with situations such as pre‐placed initial input datasets, placement of intermediate datasets and each datacenter's non‐uniform computation and storage constraint, while minimizing the cross‐datacenter data transfer. Since this scheduling problem is known to be NP‐hard, here, we propose a novel approach, based on the multilevel graph coarsening and uncoarsening framework, together with a specialized hybrid genetic algorithm having distinctive graph partition driven features of repair and local improvement, for scheduling data‐intensive scientific workflows in geo‐distributed datacenters and optimizing the cross‐datacenter data transfer volume. Extensive simulations, based on four real‐world workflow traces, show that our algorithm significantly reduces the overall geo‐distributed data transfer and demonstrate its effectiveness.
Visual Analysis of a Smart City’s Energy Consumption

Through the use of open data portals, cities, districts and countries are increasingly making available energy consumption data. These data have the potential to inform both policymakers and local communities. At the same time, however, these datasets are large and complicated to analyze. We present the activity-centered-design, from requirements to evaluation, of a web-based visual analysis tool to explore energy consumption in Chicago. The resulting application integrates energy consumption data and census data, making it possible for both amateurs and experts to analyze disaggregated datasets at multiple levels of spatial aggregation and to compare temporal and spatial differences. An evaluation through case studies and qualitative feedback demonstrates that this visual analysis application successfully meets the goals of integrating large, disaggregated urban energy consumption datasets and of supporting analysis by both lay users and experts.
Open-Endedness for the Sake of Open-Endedness

Natural evolution keeps inventing new complex and intricate forms and behaviors. Digital evolution and genetic algorithms fail to create the same kind of complexity, not just because we still lack the computational resources to rival nature, but because (it has been argued) we have not understood in principle how to create open-ended evolving systems. Much effort has been made to define such open-endedness so as to create forms of increasing complexity indefinitely. Here, however, a simple evolving computational system that satisfies all such requirements is presented. Doing so reveals a shortcoming in the definitions for open-ended evolution. The goal to create models that rival biological complexity remains. This work suggests that our current definitions allow for even simple models to pass as open-ended, and that our definitions of complexity and diversity are more important for the quest of open-ended evolution than the fact that something runs indefinitely.
The State of Open Monographs

This report addresses the question of how we integrate and value monographs in the increasingly open digital scholarly network.Analysis from industry experts looks at the open monograph landscape in 2019, the impact and role of monographs in the scholarly record, the move towards open access and the nuances in funding.The set of contributions, which includes a foreword from Michael Elliott, Dean of Emory College of Arts and Sciences, Emory University, carefully outline the critical challenges that must be met if the open monograph is going to thrive and expand in the scholarly landscape.
Improving Distantly Supervised Relation Classification With Attention and Semantic Weight

Distantly supervised relation classification aims at identifying the relationship between two given entities and plays an essential part in natural language processing (NLP). Although distant supervision is able to generate labeled data automatically, it is facing with the problem of noisy data due to the wrong labeling problems. The attention mechanism is one of the most popular methods to reduce the influence of mislabeled data. However, regardless of the correlation among relations, the most existing methods treat all relationships as independent classes. In general, the definitions of relations contain rich semantic information, which improves the performance of the model, especially when classifying long-tail relations which lacks training data. Based on this idea, we propose a novel neural network architecture with an attention mechanism in this paper. First, we use bidirectional GRU to encode relation definitions as the context representations of relations. Then, we use the merge attention mechanism to make full use of the hidden states obtained by the GRU. To help the model make full use of the context of the entities, we also introduce semantic weights, calculated by the length of the shortest path between entities and words in the dependency tree. We conduct experiments on the widely used New York Times relation extraction corpus, and the results demonstrate that our model outperforms most of the state-of-the-art models.
Accelerating Exact Inner Product Retrieval by CPU-GPU Systems

Recommender systems are widely used in many applications, e.g., social network, e-commerce. Inner product retrieval IPR is the core subroutine in Matrix Factorization (MF) based recommender systems. It consists of two phases: i) inner product computation and ii) top-k items retrieval. The performance bottleneck of existing solutions is inner product computation phase. Exploiting Graphics Processing Units (GPUs) to accelerate the computation intensive workloads is the gold standard in data mining and machine learning communities. However, it is not trivial to apply CPU-GPU systems to boost the performance of IPR solutions due to the nature complex of the IPR problem. In this work, we analyze the time cost of each phase in IPR solutions at first. Second, we exploit the characteristics of CPU-GPU systems to improve performance. Specifically, the computation tasks of IPR solution are heterogeneously processed in CPU-GPU systems. Third, we demonstrate the efficiency of our proposal on four standard real datasets.
Precise Ship Location With CNN Filter Selection From Optical Aerial Images

This paper presents a method that can be used for the efficient detection of small maritime objects. The proposed method employs aerial images in the visible spectrum as inputs to train a categorical convolutional neural network for the classification of ships. A subset of those filters that make the greatest contribution to the classification of the target class is selected from the inner layers of the CNN. The gradients with respect to the input image are then calculated on these filters, which are subsequently normalized and combined. Thresholding and a morphological operation are then applied in order to eventually obtain the localization. One of the advantages of the proposed approach with regard to previous object detection methods is that it is only required to label a few images with bounding boxes of the targets to be trained for localization. The method was evaluated with an extended version of the MASATI (MAritime SATellite Imagery) dataset. This new dataset has more than 7 000 images, 4 157 of which contain ships. Using only 14 training images, the proposed approach achieves better results for small targets than other well-known object detection methods, which also require many more training images.
Tree-REX: SAT-Based Tree Exploration for Efficient and High-Quality HTN Planning

In this paper, we propose a novel SAT-based planning approach to solve totally ordered hierarchical planning problems. Our approach called “Tree-like Reduction Exploration” (Tree-REX) makes two contributions: (1) it allows to rapidly solve hierarchical planning problems by making effective use of incremental SAT solving, and (2) it implements an anytime approach that gradually improves plan quality (makespan) as time resources are allotted. Incremental SAT solving is important as it reduces the encoding volume of planning problems, it builds on information obtained from previous search iterations and speeds up the search for plans. We show that Tree-REX outperforms state-of-the-art SAT-based HTN planning with respect to run times and plan quality on most of the considered IPC domains.
Representation Learning on Graphs: A Reinforcement Learning Application

In this work, we study value function approximation in reinforcement learning (RL) problems with high dimensional state or action spaces via a generalized version of representation policy iteration (RPI).We consider the limitations of proto-value functions (PVFs) at accurately approximating the value function in low dimensions and we highlight the importance of features learning for an improved low-dimensional value function approximation. Then, we adopt different representation learning algorithms on graphs to learn the basis functions that best represent the value function. We empirically show that node2vec, an algorithm for scalable feature learning in networks, and Graph Auto-Encoder constantly outperform the commonly used smooth proto-value functions in low-dimensional feature space.
Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar

A usage-based Construction Grammar (CxG) posits that slot-constraints generalize from common exemplar constructions. But what is the best model of constraint generalization? This paper evaluates competing frequency-based and association-based models across eight languages using a metric derived from the Minimum Description Length paradigm. The experiments show that association-based models produce better generalizations across all languages by a significant margin.
Exploring to learn visual saliency: The RL-IAC approach

The problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual saliency directly on a robot. This model, learned and improved on-the-fly during the robot’s exploration provides an efficient tool for localizing relevant objects within their environment. The proposed approach includes two intertwined components. On the one hand, we describe a method for learning and incrementally updating a model of visual saliency from a depth-based object detector. This model of saliency can also be exploited to produce bounding box proposals around objects of interest. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, called Reinforcement Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot’s exploration so that samples selected by the robot are likely to improve the current model of saliency. We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that RL-IAC can drastically decrease the required time for learning a reliable saliency model.
Dual-View Variational Autoencoders for Semi-Supervised Text Matching

Semantically matching two text sequences (usually two sentences) is a fundamental problem in NLP. Most previous methods either encode each of the two sentences into a vector representation (sentence-level embedding) or leverage word-level interaction features between the two sentences. In this study, we propose to take the sentence-level embedding features and the word-level interaction features as two distinct views of a sentence pair, and unify them with a framework of Variational Autoencoders such that the sentence pair is matched in a semi-supervised manner. The proposed model is referred to as Dual-View Variational AutoEncoder (DV-VAE), where the optimization of the variational lower bound can be interpreted as an implicit Co-Training mechanism for two matching models over distinct views. Experiments on SNLI, Quora and a Community Question Answering dataset demonstrate the superiority of our DV-VAE over several strong semi-supervised and supervised text matching models.
Teaching Materials Developed Using QR Code Technology in Science Classes

The present study aims to investigate the opinions of prospective science teachers about the use of QR codes in the teaching materials that they prepared in the “Instructional Technologies and Material Development” course. The study group consists of 38 (32 female, 6 male) 3rd-year prospective science teachers who attended the “Instructional Technologies and Material Development” course in the fall semester of the 2018-2019 academic year, and who chose to use QR codes academic year, and who chose to use QR codes in the teaching materials. In this case study, the data were collected through semi-structured interviews. The data were analyzed by the content analysis method. The study revealed the perspectives of prospective science teachers on the use of QR codes in the teaching materials they prepared, in the learning process, its advantages, disadvantages, and effects of QR codes on the materials prepared.
An overview of the fundamental approaches that yield several image denoising techniques

Digital image is considered as a powerful tool to carry and transmit information between people. Thus, it attracts the attention of large number of researchers, among them those interested in preserving the image features from any factors that may reduce the image quality. One of these factors is the noise which affects the visual aspect of the image and makes others image processing more difficult. Thus far, solving this noise problem remains a challenge for the researchers in this field. A lot of image denoising techniques have been introduced in order to remove the noise by taking care of the image features; in other words, getting the best similarity to the original image from the noisy one. However, the findings are still inconclusive. Beside the enormous amount of researches and studies which adopt several mathematical concepts (statistics, probabilities, modeling, PDEs, wavelet, fuzzy logic, etc.), there is also the scarcity of review papers which carry an important role in the development and progress of research. Thus, this review paper intorduce an overview of the different fundamental approaches that yield the several image-denoising techniques, presented with a new classification. Furthermore, the paper presents the different evaluation tools needed on the comparison between these techniques in order to facilitate the processing of this noise problem, among a great diversity of techniques and concepts.
Improving Cloud Computing Performance Using Task Scheduling Method Based on VMs Grouping

The incredible rise of virtualization technology in cloud environments results the fostering workload which needs services provided by cloud resources. Task scheduling and Load balancing amongst the VMs and minimizing the makespan of the tasks are stimulating research concerns. In this paper, a method was introduced for scheduling workload based on VM grouping in cloud environments. The aim of the proposed method is improving cloud computing performance by reducing makespan and response time, and also through increasing VMs utilization. We evaluated the proposed algorithm with existing methods using various performance metrics. Evaluation results show that our proposed algorithm outperforms similar methods.
Discrete logarithms in quasi-polynomial time in finite fields of fixed characteristic

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