Reviewerly/exHarmony huggingface dataset:
Creating Classifier Ensembles through Meta-heuristic Algorithms for Aerial Scene Classification

Convolutional Neural Networks (CNN) have been being widely employed to solve the challenging remote sensing task of aerial scene classification. Nevertheless, it is not straightforward to find single CNN models that can solve all aerial scene classification tasks, allowing the development of a better alternative, which is to fuse CNN-based classifiers into an ensemble. However, an appropriate choice of the classifiers that will belong to the ensemble is a critical factor, as it is unfeasible to employ all the possible classifiers in the literature. Therefore, this work proposes a novel framework based on meta-heuristic optimization for creating optimized ensembles in the context of aerial scene classification. The experimental results were performed across nine meta-heuristic algorithms and three aerial scene literature datasets, being compared in terms of effectiveness (accuracy), efficiency (execution time), and behavioral performance in different scenarios. Our results suggest that the Univariate Marginal Distribution Algorithm shows more effective and efficient results than other commonly used meta-heuristic algorithms, such as Genetic Programming and Particle Swarm Optimization.
Heuristics for the Construction of Counterexamples to the Agrawal Conjecture

The time complexity of the Agrawal-Kayal-Saxena (AKS) primality test (Agrawal et al. in Annals Math. 781–793, 2004) can be reduced by a significantly large amount if the Bhattacharjee-Pandey conjecture (Bhattacharjee and Pandey in Primality testing, 2001) proves to be true. However, Lenstra and Pomerance gave heuristics to construct potential counterexamples for this conjecture (Lenstra and Pomerance in Future directions in algorithmic number theory, 2008). In this paper, we generalize and extend the methods used by Lenstra and Pomerance to provide heuristics for the construction of two additional classes of counterexamples to the Bhattacharjee-Pandey conjecture. We also use some methods of analytic number theory to provide an estimate of the number of such counterexamples within a large interval.
Science and Technology for Future Education

Abstract In this article, the five imminent trends of science and technology were proposed. Each presents some of the boundless exciting possibilities for future education. For each trend, the characteristics of the specific new technology and the benefited pedagogy of learning were discussed. The article also introduces the related on-going projects conducted in Taiwan. These projects describe how we prepared for future education in Taiwan.
MIMF: Mutual Information-Driven Multimodal Fusion

In this paper, we propose a novel adaptive multimodal fusion network MIMF that is driven by the mutual information between the input data and the target recognition pattern. Due to the variant weather and road conditions, the real scenes can be far more complicated than those in the training dataset. That constructs a non-ignorable challenge for multimodal fusion models that obey fixed fusion modes, especially for autonomous driving. To address the problem, we leverage mutual information for adaptive modal selection in fusion, which measures the relation between the input and target output. We therefore design a weight-fusion module based on MI, and integrate it into our feature fusion lane line segmentation network. We evaluate it with the KITTI and A2D2 datasets, in which we simulate the extreme malfunction of sensors like modality loss problem. The result demonstrates the benefit of our method in practical application, and informs the future research into development of multimodal fusion as well.
Linguistic Impact of Emojis in Online Communication

The cute little emojis that we use daily in our conversations online have become an inherent aspect of casual communication on digital platforms. Originally an evolution from emoticons made of punctuation marks, emojis as pictographs were aimed to make digital communications more interesting. Due to the increased usage, they have acquired a complex role beyond the depiction of emotions and feelings. Just like human language, they are developing organic rules as they become a part of online written conversations of more and more people around the world. The paper attempts to explore the usage of emojis, arguing that their complex usage has led to various impacts in online communication, viz. emotive, phatic and ironic. It also analyses to what extent they are universal and are affected by cultural contexts. This is done by using the published studies about emojis in various contexts and domains as sources.
A Context Enhanced Attention Network for Aspect-Based Sentiment Classification

Aspect-based sentiment classification is a fine-grained task in sentiment analysis, it aims to detect the sentiment polarity of an aspect in a given context. Existing approaches mostly focused on the processing of aspect words, which often fail to adequately model the context via aspect terms. However, the surrounding words in context usually have an important impact on the sentiment polarity of aspect words. This paper proposes a context enhanced attention network (CE-ATT) to enhance context modeling for aspect-based sentiment classification. Firstly, the text sequence was divided into left and right contexts by a given aspect. Then, the Bidirectional Long Short Term Memory (BiLSTM) was used to model the left context and aspect, the right context and aspect, and both sides context and aspect, respectively. After summing these contextual representations, an attention mechanism was used to concentrate on the parts that have an important influence on the aspect word. The experimental results on three benchmark datasets demonstrate the effectiveness of the CE-ATT method.
Accelerating Data-Parallel Neural Network Training with Weighted-Averaging Reparameterisation

Recent advances in artificial intelligence has shown a direct correlation between the performance of a network and the number of hidden layers within the network. The Compute Unified Device Architecture (CUDA) framework facilitates the movement of heavy computation from the CPU to the graphics processing unit (GPU) and is used to accelerate the training of neural networks. In this paper, we consider the problem of data-parallel neural network training. We compare the performance of training the same neural network on the GPU with and without data parallelism. When data parallelism is used, we compare with both the conventional averaging of coefficients and our proposed method. We set out to show that not all sub-networks are equal and thus, should not be treated as equals when normalising weight vectors. The proposed method achieved state of the art accuracy faster than conventional training along with better classification performance in some cases.
The Illusion of Free Choice in the Age of Augmented Decisions

Abstract In our augmented world, many decision situations are designed by smart technologies. Artificial intelligence helps reduce information overload, filter relevant information and limit an otherwise overwhelming abundance of choices. While such algorithms make our lives more convenient, they also fulfill various organizational objectives that users may not be aware of and that may not be in their best interest. We do not know whether algorithms truly optimize the benefits of their users or rather the return on investment of a company. They are not only designed for convenience but also to be addictive, and this opens the doors for manipulation. Therefore, augmented decision making undermines the freedom of choice. To limit the threats of augmented decisions and enable humans to be critical towards the outcomes of artificial intelligence–driven recommendations, everybody should develop “algorithmic literacy.” It involves a basic understanding of artificial intelligence and how algorithms work in the background. Algorithmic literacy also requires that users understand the role and value of the personal data they sacrifice in exchange for decision augmentation.
Machine Translation Problems at Discourse Level

This chapter introduces the novel discourse-processing methods in statistical machine translation (SMT) and neural machine translation (NMT) architectures. It describes our first attempt at investigating the potential for implicitly incorporating discourse information into machine translation (MT) and addresses the research question about MT problems at discourse level, regarding the influence of global context on SMT and NMT performance. Focusing on two typical discourse MT scenarios as pro-drop language and large-context NMT, the chapter presents some novel approaches to translation quality improvement targeting these problems. Experiment results show that it is crucial to identify the dropped pronouns to improve translation performance and demonstrate that the novel model significantly outperforms a strong attention-based large-context NMT baseline system.
On the Smoothing of Deep Networks

Many popular deep neural networks implement an input-output mapping that is non-smooth with respect to the network parameters. This non-smoothness may have contributed to the difficulty of analyzing deep learning theoretically. Sophisticated approaches have recently been proposed to address this specific difficulty. In this note, we explore a simple approach consisting instead in smoothing the input-output mapping. We show how to perform smoothing automatically within a differentiable programming framework. The impact of the smoothing on the convergence behavior can then be automatically controlled. We illustrate our approach with numerical examples using multilayer perceptrons.
A Time-Based Online Customer Behaviors Analysis Model

Abstract With the advent of the big data era, reviews are common when shopping online. To better understand the connection between reviews and the quality of the real product, we have to use mathematical language to simplify the problem. This relationship can help both the buyer to get what they want and the firms to make better products with attractive features. To help us understand how the indicators like star ratings can reflect the product’s popularity, we construct a series of models to illustrate them. We first use the NLP method to process text type data, such as product review information, and convert these text data into sentiment scores, with the help of the k-means++ algorithm, we cluster the data into 3 categories. These categories can help the company to track the performance of their products by simply calculating which category the product belongs to. Next, a Time-based Reputation Judgment Model combined with AHP and Markov Chain Model was established to describe the change of reputation over time. In this way, a company can easily predict the future trend of their merchandise sales.
Building cloud native application — analysis for multi-component application deployment

Cloud-native application is a term broadly used to classify applications that are built with a cloud first approach making use of cloud computing models by cloud providers (both on and off premise) to achieve increased speed, flexibility and quality, while reducing deployment risks. Container based application development has gained prominence as it provides businesses with the capability to build applications that are highly scalable and fault tolerant. This paper discusses the nature of multi-component applications, fundamental approaches of developing applications using container-based technologies, how they fair against virtual machine-based deployments. The paper provides a comparative study on virtual machine-based deployments and container-based deployments and evaluates the performance for multi-component application on these environments.
Aesthetics and Power

This chapter aims to illustrate the role of technology in providing access to and the inclusion of cultural diversity in education supported by information and communication technologies (ICT). The chapter provides an overview of some of the existing efforts on this topic in Peru, as in the case of the Aymara indigenous people. Examples are presented at three levels of the educational system—national, regional, and local schools. The analysis highlights several impacts of ICT on cultural content: 1) making indigenous languages visible in cyberspace; 2) promoting the inclusion of indigenous culture, values, and worldviews in a curriculum; and 3) giving ownership to indigenous people by allowing them to create their own digital content. Also provided is a discussion of the achievements and challenges faced in the implementation of government policies and initiatives that foster the inclusion of cultural content in ICT-supported education. These initiatives are critical for fostering cultural diversity and empowering indigenous culture.
Serious Mobile Games Development. Possibilities and Challenges for Teachers

The possibilities that digital games allow in a didactic proposal are not new, however, and in relation to the specific experiences of integration of digital games in teaching and learning scenarios, a limitation is observed given by the adaptability of them to specific contexts. Thus, it is important to advance in the search for tools that are preferably free and open access that allow teachers to create their own games or edit others created by third parties, based on profiles with different degrees of programming knowledge. This work presents the results obtained from the search, selection and analysis of tools, including web platforms, software applications, and/or frameworks, which allow the creation of serious games, particularly those considered mobile, and which include interactions using augmented reality through QR codes and/or user location, by users with different technical profiles.
PEMBELAJARAN BERCERITA DENGAN MODEL KOOPERATIF TIPE NUMBER HEAD TOGETHER DAN MEDIA BUKU DONGENG FABEL

The purpose of this study is to improve the ability to tell stories significantly through the use of cooperative model learning methods Number Number Together (NHT) in the form of fable fable books, which are expected to increase effectiveness, efficiency, and high attractiveness in providing a stimulus for students to dare to tell stories in front of the class or in the school environment. This study uses two data, namely quantitative data and qualitative data. From the results of the study it was obtained that the average post-test score was better than the pre-test which was 77.50&gt; 29.59. whereas in the calculation of the manual t test with a significance level of 0.05, the results of t arithmetic tabel t table are 3.731&gt; 1.717. meaning that Ho is rejected and Ha is accepted. So that there are significant differences in the learning media used. This shows that the learning methods and media used have high levels of validity qualifications, so the learning methods and media used are appropriate to be used and applied in learning.
Robust visual tracking via scale-aware localization and peak response strength

Existing regression-based deep trackers usually localize a target based on a response map, where the highest peak response corresponds to the predicted target location. Nevertheless, when the background distractors appear or the target scale changes frequently, the response map is prone to produce multiple sub-peak responses to interfere with model prediction. In this paper, we propose a robust online tracking method via Scale-Aware localization and Peak Response strength (SAPR), which can learn a discriminative model predictor to estimate a target state accurately. Specifically, to cope with large scale variations, we propose a Scale-Aware Localization (SAL) module to provide multi-scale response maps based on the scale pyramid scheme. Furthermore, to focus on the target response, we propose a simple yet effective Peak Response Strength (PRS) module to fuse the multi-scale response maps and the response maps generated by a correlation filter. According to the response map with the maximum classification score, the model predictor iteratively updates its filter weights for accurate target state estimation. Experimental results on three benchmark datasets, including OTB100, VOT2018 and LaSOT, demonstrate that the proposed SAPR accurately estimates the target state, achieving the favorable performance against several state-of-the-art trackers.
Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms: Why Success Rates Matter

Abstract Evolutionary algorithms (EAs) are general-purpose optimisers that come with several parameters like the sizes of parent and offspring populations or the mutation rate. It is well known that the performance of EAs may depend drastically on these parameters. Recent theoretical studies have shown that self-adjusting parameter control mechanisms that tune parameters during the algorithm run can provably outperform the best static parameters in EAs on discrete problems. However, the majority of these studies concerned elitist EAs and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist EAs. We study one of the best-known parameter control mechanisms, the one-fifth success rule, to control the offspring population size $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> in the non-elitist $${(1,\lambda )}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>λ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> EA. It is known that the $${(1,\lambda )}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>λ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> EA has a sharp threshold with respect to the choice of $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> where the expected runtime on the benchmark function OneMax changes from polynomial to exponential time. Hence, it is not clear whether parameter control mechanisms are able to find and maintain suitable values of $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> . For OneMax we show that the answer crucially depends on the success rate s (i. e. a one- $$(s+1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>s</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -th success rule). We prove that, if the success rate is appropriately small, the self-adjusting $${(1,\lambda )}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>λ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> EA optimises OneMax in O ( n ) expected generations and $$O(n \log n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> expected evaluations, the best possible runtime for any unary unbiased black-box algorithm. A small success rate is crucial: we also show that if the success rate is too large, the algorithm has an exponential runtime on OneMax and other functions with similar characteristics.
The THE IMPACT OF TECHNOLOGY-ENHANCED LEARNING ENVIRONMENT ON TEACHING CHINESE AS A SECOND LANGUAGE

Due to an increased number of economic and business relationships between Ukraine and China, the number of those who learn Chinese language multiplies each year. However, the main problem is that Ukrainian educational institutions do not possess enough resources and academic staff to meet the growing demand. The research sheds light on the current state of teaching Chinese as a second language (CSL) on the example of International Humanitarian University (Odesa, Ukraine) and shares the experience of technology-assisted curriculum that contributes to the Chinese teaching process. To do that, the paper compares the scores of the students who learn Chinese as a foreign language in experimental (EG) and the control (CG) groups. EG used a number of modern technologies to assist the educational process, while CG used only the traditional teaching approaches. The learning objectives and final test were unbiased and had same tasks for both groups. The results show that 64% of the students in EG got an A, while just 14% of those in the CG managed to perform the same. Moreover, the paper presents a SWOT analysis of the use of Edmodo (online educational platform) and some other applications for the teaching and learning foreign languages, namely Quizlet, Kahoot! and Duoliguo for Schools. And the last but not least, the study develops a student-centered model that helps a teacher to assist unlimited number of students and, therefore, minimize the academic expanses and resources on learning activities. The major limitations of the research are related to the small number of participants and insufficient development of the Ukrainian educational system in terms of the implementation of online educational platforms. Further research can focus on the potential ways of using technology-enriched curriculum for maximizing the academic outcomes among CSL students.
Based on WeChat Analysis of Social Platform Value Co-Creation Theory

In the Internet environment, more and more companies are beginning to operate on platforms. As far as the social field is concerned, a large number of outstanding companies have emerged in the past ten years. At present, WeChat is not only limited to a communication tool, it has become a way of life. Focusing on the core function of communication, WeChat has developed into an open platform integrating social interaction, information, and services. Its products and services are integrated into people's lives like water and electricity. Since its establishment in 2011, WeChat has achieved breakthrough growth many times, creating a historical miracle of China's Internet and becoming a textbook case. According to the business expansion process, the development process of WeChat can be summarized into three stages: the "social" platform stage, the "social + information" platform stage, and the "social ten information + service" platform stage.
A Study of Fall Detection System Using Context Cognition Method

Falls are one of the major causes of injury and death among elderly people aged 65 and industries too. A fall detection system is necessary to identify falling activities out of activities of daily lives. Even though new methods have shown on research paper, the number of studies in vision-based systems is still increasing. But existing vision-based fall detection systems have lot of weakness to be generalized mainly due to the difficulties such as variations in physical appearances, different camera viewpoints, occlusions, background clutter and darkness. Head and upper body location of human provides a critical information at the time of fall. This paper presents a vision-based fall tracking method where upper body joints are grouped into one segment to increase the fall classification ratio. Segment consist of Head and shoulders joints combinations represents the upper region and head region. Segment method can be beneficial to achieve an efficient tracking of human activities and provide strong technic to distinguish falls from activities of daily lives.