Reviewerly/exHarmony huggingface dataset:
A Deep Learning based Customer Sentiment Analysis Model to Enhance Customer Retention and Loyalty in the Payment Industry

Both the industry and academia agree on the immense contribution of big data analytics and machine learning to competitive businesses. The payment industry would benefit from big data analytics and machine learning capabilities to harness their customers’ opinions through sentiment analysis, thereby customizing their services and products to fit their customers’ preferences. However, the challenge is implementing this competitive edge in small and medium-sized payment solution providers. This paper proposes a deep learning-based customer sentiment analysis model and a related (SaMS-PSP) algorithm that implements sentiment analysis within SaMS-PSP. Through experiments, we have demonstrated that our model has a super performance advantage over conventional machine learning methods and is more suited to handle "big data" applications such as customer sentiment analysis. This research has demonstrated that the sentiment analysis emotional polarity score can be used in a value-added customer orientation tool to promote customer retention and loyalty within SaMS-PSP.
Discarded Labor:

This pictorial critically explores the role of visual media representations in the deployment of automated and artificially intelligent (AI) technologies within essential work sectors. We draw on an exhaustive review of local and national newspaper articles about automation in two waste labor industries (cleaning and recycling) over the last five years. We highlight a set of common visual tropes and move to challenge these representations by taking up the lens of countervisuality. Our analysis reveals that press photographs tend to focus on machines and the decision-makers who champion them, overlooking the work that it takes to integrate technology on the ground. Through our countervisuals, we depict the extensive efforts of waste workers to maintain AI technologies, and their potential for surveillance. Through visualizing under-recognized forms of labor that come after the design process ends, we highlight how an outsized emphasis on invention ignores waste workers’ expertise and needs over time.
Synthesising augmentation and automation

Historically, there has been a tendency to consider hypertext as a type of system, perhaps characterised by provision of links or other structure to users. In this article, we consider hypertext as a method of inquiry, a way of viewing arbitrary systems. In this view, what are traditionally called “navigational hypertext systems” might be considered as information retrieval systems. This opens the hypertext field to various other types of systems that traditionally would not be considered as part of the field. The change of view enables a deeper fusion of human and machine. In particular, today's AI-based, intelligent systems open the demand of synthesising automation (on the machine's side) and augmentation (on the user's side). This article is not about researching AI systems; it is about extending the view of hypertext systems to synthesise augmentation and automation. We specifically apply this view to intelligent systems, asking the question about how hypertext can act as a common medium between human and machine, particularly for knowledge intensive tasks. We propose spatial hypertext as a medium that enables users to create cognitive maps. Along these lines, we provide examples from multiple projects and examine how these applications can be productive.
Detection of Mispronunciation in Non-native Speech Using Acoustic Model and Convolutional Recurrent Neural Networks

Abstract Computer-Aided Language Learning (CALL) frameworks have gained much popularity because of their adaptability, enabling students to refine their language abilities at their own pace. Much research has been done to help improve CALL systems and dig out the more suitable features for targeted Mandarin mispronunciation detection is still an open research area. The acoustic model of convolutional recurrent neural networks (CRNN) (CNN + LSTM) + connectivity time series classification (CTC) model is used to convert acoustic signals into pinyin label sequences. As many Chinese speech data sets were trained using this model, the initial and vowel pronunciation error rates were finally obtained. Therefore, through linguistic classification, mainly four pronunciation errors related to the native Spanish pronunciation habits are further discovered. Moreover, based on the final analysis obtained, some corresponding instructive suggestions for further international Chinese teaching from different aspects are also put forward. Apart from proposing practical suggestions from different perspectives for further Mandarin CALL international teaching according to the experiment results’ evaluations, this system still has room for further improvement.
The Classification System of Literary Works Based on <i>K</i>-Means Clustering

The plots in certain literary works are very complicated and hinder readers from understanding them. Therefore tools should be proposed to support readers; comprehension of complex literary works supports their understanding by providing the most important information to readers. A human reader must capture multiple levels of abstraction and meaning to formulate an understanding of a document. Hence, in this paper, an Improved K-means clustering algorithm (IKCA) has been proposed for literary word classification. For text data, the words that can express exact semantic in a class are generally better features. This paper uses the proposed technique to capture numerous cluster centroids for every class and then select the high-frequency words in centroids the text features for classification. Furthermore, neural networks have been used to classify text documents and k-mean to cluster text documents. To develop the model based on unsupervised and supervised techniques to meet and identify the similarity between documents. The numerical results show that the suggested model will enhance to increases quality comparison of the existing Algorithm and K-means algorithm, accuracy comparison of ALA and IKCA (95.2%), time is taken for clustering is less than 2 hours, success rate (97.4%) and performance ratio (98.1%).
Scalable and Highly Available Multi-Objective Neural Architecture Search in Bare Metal Kubernetes Cluster

The interest in deep neural networks for solving computer vision task has dramatically increased. Due to the heavy influence of the neural networks architecture on its predictive accuracy, neural architecture search has gained much attention in recent years. This research area typically implies a high computational burden and thus, requires high scalability as well as availability to ensure no data loss or waist of computational power. Moreover, the thinking of developing applications has changed from monolithic once to microservices. Hence, we developed a highly scalable and available multi-objective neural architecture search and adopted to the modern thinking of developing application by subdividing an already existing, monolithic neural architecture search - based on a genetic algorithm - into microservices. Furthermore, we adopted the initial population creation by 1,000 mutations of each individual, extended the approach by inception layers, implemented it as island model to facilitate scalability and achieved on MNIST, Fashion-MNIST and CIFAR-10 dataset 99.75%, 94.35% and 89.90% test accuracy respectively. Besides, our model is strongly focused on high availability empowered by the deployment in our bare-metal Kubernetes cluster. Our results show that the introduced multi-objective neural architecture search can easily handle even the loss of nodes and proceed the algorithm within seconds on another node without any loss of results or the necessity of human interaction.
Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution

In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM). SAAM works on top of traditional neural networks and is designed to address the problem of multi-aspect sentiment classification and sentiment regression. The framework works by exploiting the correlations between sentence-level embedding features and variations of document-level aspect rating scores. We demonstrate several variations of our framework on top of CNN and RNN based models. Experiments on a hotel review dataset and a beer review dataset have shown SAAM can improve sentiment analysis performance over corresponding base models. Moreover, because of the way our framework intuitively combines sentence-level scores into document-level scores, it is able to provide a deeper insight into data (e.g., semi-supervised sentence aspect labeling). Hence, we end the paper with a detailed analysis that shows the potential of our models for other applications such as sentiment snippet extraction.
Towards a Cloud-Based University Accelerated By the Pandemic

The coronavirus pandemic has accelerated the digital transformation of society, and in particular of university teaching. Professors who were reluctant or unwilling to take advantage of what digital technologies offered suddenly were forced to teach with technologies. In this paper, we describe teaching during the two main phases during the pandemic at Universidad Carlos III de Madrid (UC3M): the first phase during the complete lockdown and the second where students were allowed on campus with limitations. We also explain how the investment done during these two phases influences teaching in the future and how digital cloud-based technologies promote active learning at the university.
Construction and Analysis on College EFL Learners’ Autonomous Learning System Based on Mobile-aided Language Learning Pattern

With the rapid development of big data, cloud computing, artificial intelligence, and other technologies, the "Internet+" era has arrived. In this era, mobile device (also called as "handheld device" or "handheld computer") is widely used. Therefore, mobile-aided language learning (MALL) is extensively made use of in college EFL learners' autonomous learning. But the research in this area is still in its infancy period. The paper makes a survey of this area through questionnaire, analyzes the statistics through SPSS software and applies the research findings to construct a MALL pattern in college EFL learners' autonomous learning, and in hope of integrate the learning pattern with college English education.
Adiabatic Quantum Feature Selection for Sparse Linear Regression

Linear regression is a popular machine learning approach to learn and predict real valued outputs or dependent variables from independent variables or features. In many real world problems, its beneficial to perform sparse linear regression to identify important features helpful in predicting the dependent variable. It not only helps in getting interpretable results but also avoids overfitting when the number of features is large, and the amount of data is small. The most natural way to achieve this is by using 'best subset selection' which penalizes non-zero model parameters by adding $$\ell _0$$ norm over parameters to the least squares loss. However, this makes the objective function non-convex and intractable even for a small number of features. This paper aims to address the intractability of sparse linear regression with $$\ell _0$$ norm using adiabatic quantum computing, a quantum computing paradigm that is particularly useful for solving optimization problems faster. We formulate the $$\ell _0$$ optimization problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using the D-Wave adiabatic quantum computer. We study and compare the quality of QUBO solution on synthetic and real world datasets. The results demonstrate the effectiveness of the proposed adiabatic quantum computing approach in finding the optimal solution. The QUBO solution matches the optimal solution for a wide range of sparsity penalty values across the datasets.
Dynamic Process in Threshold Weighted Indecisive-Voting Systems

To model threshold weighted voting systems with multifailure modes, we analyze system performance in terms of system reliability by focusing on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">indecisive effect</i> . The measurement of system reliability is generated by the decision rule where we apply dynamic process on two major aspects including the indecisive parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> and voters’ errors resulting from various situations such as weights replacement and inputs discrepancy. The motivation is to make the existing model more applicable via the dynamic process. In this article, two major contributions are addressed as follows. First, the continuous distributed indecisive parameter gives an intuitive idea of how the model adapts from the decision rule, which provides the flexibility of adjustment to satisfy different applications. Second, the time delay degradation process on voters’ errors illuminates the opposite system performance for two types of inputs by considering different indecisive effects, which leads to further investigation of model sensitivity of input types.
Initial Limit Datalog: a New Extensible Class of Decidable Constrained Horn Clauses

We present initial limit Datalog, a new extensible class of constrained Horn clauses for which the satisfiability problem is decidable. The class may be viewed as a generalisation to higher-order logic (with a simple restriction on types) of the first-order language limit DatalogZ (a fragment of Datalog modulo linear integer arithmetic), but can be instantiated with any suitable background theory. For example, the fragment is decidable over any countable well-quasi-order with a decidable first-order theory, such as natural number vectors under componentwise linear arithmetic, and words of a bounded, context-free language ordered by the subword relation. Formulas of initial limit Datalog have the property that, under some assumptions on the background theory, their satisfiability can be witnessed by a new kind of term model which we call entwined structures. Whilst the set of all models is typically uncountable, the set of all entwined structures is recursively enumerable, and model checking is decidable.
New Arabic Medical Dataset for Diseases Classification

The Arabic language suffers from a great shortage of datasets suitable for training deep learning models, and the existing ones include general non-specialized classifications.
Spatial image segmentation based on Beta-Liouville mixture models and Markov Random Field

Finite mixture models are one of the most widely used probabilistic methods for image segmentation. In this paper, we propose and investigate a mixture model based on Beta-Liouville distributions, which offers more flexibility than previously proposed models. The proposed approach is based on integration of mixture models with Markov Random Field (MRF) with a novel factor that is induced to reduce noise and illumination in images. The model is learned using Expectation Maximization (EM) algorithm based on Newton-Raphson approach. The proposed approach is compared with mixtures of Gaussian, Dirichlet and generalized Dirichlet distributions with integrated MRF. The experimental results demonstrate that proposed segmentation framework gives better performance and better results as compared to mixtures of Gaussian, Dirichlet and generalized Dirichlet with MRF.
Spam Review Detection Using K-Means Artificial Bee Colony

The current businesses which use Internet for marketing depend on online reviews,Saini, Prateek and these online reviews can direct a customer toward or away from a product and service.Shringi, Sakshi Sharma, Nirmala Sharma, Harish This effect of online reviews is the reason that business uses spam reviews to either benefit their business or hinder their rival’s business. In this paper, a novel solution k-means artificial bee colony for feature selection and optimized clusters using artificial bee colony to detect spam reviews is presented. We report the testing of our novel method on three different datasets. The findings of our testing are encouraging and show a respectable performance on all three datasets.
Cross-Active Connection for Image-Text Multimodal Feature Fusion

Recent research fields tackle high-level machine learning tasks which often deal with multiplex datasets. Image-text multimodal learning is one of the comparatively challenging domains in Natural Language Processing. In this paper, we suggest a novel method for fusing and training the image-text multimodal feature. The proposed architecture follows a multi-step training scheme to train a neural network for image-text multimodal classification. In the training process, different groups of weights in the network are updated hierarchically in order to reflect the importance of each single modality as well as their mutual relationship. The effectiveness of Cross-Active Connection in image-text multimodal NLP tasks was verified through extensive experiments on the task of multimodal hashtag prediction and image-text feature fusion.
Fishing for interactions

Computational swarm intelligence has been demonstrably shown to efficiently solve high-dimensional optimization problems due to its flexibility, robustness, and (low) computational cost. Despite these features, swarm-based algorithms are black boxes whose dynamics may be hard to understand. In this paper, we delve into the Fish School Search (FSS) algorithm by looking at how fish interact within the fish school. We find that the network emerging from these interactions is structurally invariant to the optimization of three benchmark functions: Rastrigin, Rosenbrock and Schwefel. However, at the same time, our results also reveal that the level of social interactions among the fish depends on the problem. We show that the absence of highly-influential fish leads to a slow-paced convergence in FSS and that the changes in the intensity of social interactions enable good performance on both unimodal and multimodal problems. Finally, we examine two other swarm-based algorithms---the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms---and find that for the same three benchmark functions, the structural invariance characteristic only occurs in the FSS algorithm. We argue that FSS, ABC, and PSO have distinctive signatures of interaction structure and flow.
Image Classification with Knowledge-Based Systems on the Edge for Real-Time Danger Avoidance in Robots

Mobile robots are increasingly common in society and are increasingly being used for complex and high-stakes tasks such as search and rescue. The growing requirements for these robots demonstrate a need for systems which can review and react in real time to environmental hazards, which will allow robots to handle environments that are both dynamic and dangerous. We propose and test a system which allows mobile robots to reclassify environmental objects during operation in conjunction with an edge system. We train an image classification model with 99 percent accuracy and deploy it in conjunction with an edge server and JSON-based ruleset to allow robots to react to and avoid hazards.
Dependency-Based Embedding for Distinguishing Between Hate Speech and Offensive Language

The task of detecting online textual hate speech focuses on various objectives with one of the most important being distinguishing between hate speech and generic offensive language. This distinction is very blurry since the two classes are strongly overlapped. Using only keywords for identification falls short because of this overlap, hence the context in which each textual instance exists in, needs to be extracted for increased effectiveness. To achieve this, we investigate the use of embeddings learned with syntactic dependency context (words that have a grammatical relationship with the target word) as opposed to linear context (words that precede and succeed the target word as determined by the window size) in various forms as features for the classification task on a multi-class dataset. Our results and analysis show that for the downstream task of hate speech detection, specifically for distinguishing between hateful and offensive language, the dependency-based embedding has a very comparable performance with its linear-based counterpart even outperforming it in some settings. Moreover, compared to the state-of-the-art (BERT), it demonstrates competitive performance especially when in an ensemble with linear-based embedding. Also, we observed that for a specialised task such as hate speech detection, a domain-specific embedding is probably more important than a large out-of-domain embedding with a larger vocabulary size.
Stochastic performance modeling for practical byzantine fault tolerance consensus in the blockchain

The practical Byzantine fault tolerant (PBFT) consensus mechanism is one of the most basic consensus algorithms (or protocols) in blockchain technologies. Thus its performance evaluation is an interesting and challenging topic due to the higher complexity of its consensus work in a peer-to-peer network. This study describes a simple stochastic performance model of the PBFT consensus mechanism. This model is refined not only as a queuing system with complicated service times but also as a level-independent quasi-birth-and-death (QBD) process. With regard to the level-independent QBD process, we apply the matrix-geometric solution to obtain the necessary and sufficient condition under which the PBFT consensus system is stable and then numerically compute the stationary probability vector of the QBD process. Thus, we provide four useful performance measures for the PBFT consensus mechanism, and we can numerically calculate these performance measures. Finally, we use numerical examples to verify the validity of our theoretical results and demonstrate how the four performance measures are influenced by certain key parameters of the PBFT consensus. Considering theory of multi-dimensional Markov processes, we are optimistic that the methodology and results presented in this study are applicable to a wide range of PBFT consensus mechanism and even other types of consensus mechanisms.
GGTAN: Graph Gated Talking-Heads Attention Networks for Traveling Salesman Problem

Traveling Salesman Problem (TSP) is one of the most typical NP-hard combinatorial optimization problems with a variety of real-life applications. In this paper, we propose a Graph Gated Talking-Heads Attention Networks (GGTAN) trained with reinforcement learning (RL) for tackling TSP. GGTAN can learn characteristic structure information better by introducing talking-heads attention mechanism and a gated convolutional sub-network, which make hidden information moving across between attention heads and control each attention head's importance respectively, unlike recently proposed models which use attention mechanism for solving TSP. Experimental results on TSP up to 100 nodes demonstrate that our model obtains shorter tour lengths than other learning-based methods under the same solve strategy for problem instances of fixed graph sizes, and achieves better generalization on variable graph sizes compared with recent state-of-the-art models on the optimality gap.