Reviewerly/exHarmony huggingface dataset:
A Deep Swarm-Optimized Model for Leveraging Industrial Data Analytics in Cognitive Manufacturing

To compete in the current data-driven economy, it is essential that industrial manufacturers leverage real-time tangible information assets and embrace big data technologies. Data classification is one of the most proverbial analytical techniques within the cognitively capable manufacturing industries for finding the patterns in the structured and unstructured data at the plant, enterprise, and industry levels. This article presents a cognition-driven analytics model, CNN-WSADT, for the real-time data classification using three soft computing techniques, namely, deep learning [convolution neural network (CNN)], machine learning [decision tree (DT)], and swarm intelligence [wolf search algorithm (WSA)]. The proposed deep swarm-optimized classifier is a feature-boosted DT, which learns features using a deep convolution net and an optimal feature set built using a metaheuristic WSA. The performance of CNN-WSADT is studied on two benchmark datasets and the experimental results depict that the proposed cognition model outperforms the other considered algorithms in terms of the classification accuracy.
Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware

Abstract Artificial neural networks (ANNs) have emerged as hot topics in the research community. Despite the success of ANNs, it is challenging to train and deploy modern ANNs on commodity hardware due to the ever-increasing model size and the unprecedented growth in the data volumes. Particularly for microarray data, the very high dimensionality and the small number of samples make it difficult for machine learning techniques to handle. Furthermore, specialized hardware such as graphics processing unit (GPU) is expensive. Sparse neural networks are the leading approaches to address these challenges. However, off-the-shelf sparsity-inducing techniques either operate from a pretrained model or enforce the sparse structure via binary masks. The training efficiency of sparse neural networks cannot be obtained practically. In this paper, we introduce a technique allowing us to train truly sparse neural networks with fixed parameter count throughout training. Our experimental results demonstrate that our method can be applied directly to handle high-dimensional data, while achieving higher accuracy than the traditional two-phase approaches. Moreover, we have been able to create truly sparse multilayer perceptron models with over one million neurons and to train them on a typical laptop without GPU ( https://github.com/dcmocanu/sparse-evolutionary-artificial-neural-networks/tree/master/SET-MLP-Sparse-Python-Data-Structures ), this being way beyond what is possible with any state-of-the-art technique.
A Two-Stage Transformer-Based Approach for Variable-Length Abstractive Summarization

This study proposes a two-stage method for variable-length abstractive summarization. This is an improvement over previous models, in that the proposed approach can simultaneously achieve fluent and variable-length abstractive summarization. The proposed abstractive summarization model consists of a text segmentation module and a two-stage Transformer-based summarization module. First, the text segmentation module utilizes a pre-trained Bidirectional Encoder Representations from Transformers (BERT) and a bidirectional long short-term memory (LSTM) to divide the input text into segments. An extractive model based on the BERT-based summarization model (BERTSUM) is then constructed to extract the most important sentence from each segment. For training the two-stage summarization model, first, the extracted sentences are used to train the document summarization module in the second stage. Next, the segments are used to train the segment summarization module in the first stage by simultaneously considering the outputs of the segment summarization module and the pre-trained second-stage document summarization module. The parameters of the segment summarization module are updated by considering the loss scores of the document summarization module as well as the segment summarization module. Finally, collaborative training is applied to alternately train the segment summarization module and the document summarization module until convergence. For testing, the outputs of the segment summarization module are concatenated to provide the variable-length abstractive summarization result. For evaluation, the BERT-biLSTM-based text segmentation model is evaluated using ChWiki_181k database and obtains a good effect in capturing the relationship between sentences. Finally, the proposed variable-length abstractive summarization system achieved a maximum of 70.0% accuracy in human subjective evaluation on the LCSTS dataset.
Social Image Sentiment Analysis by Exploiting Multimodal Content and Heterogeneous Relations

In the circumstance of social big data, sentiment analysis is attracting increasing attention for its capacity in understanding individuals' attitudes and feelings. Traditional sentiment analysis methods focus on single modality and become ineffective as enormous data are emerging on the social websites with multiple manifestations. In this article, multimodal learning approaches are proposed to capture the relations between image and text, which only stay at the region level and ignore the fact that the channels are also closely correlated with the semantic information. In addition, social images in the social platforms are closely connected by various types of relations, which are also conducice to sentiment classification but neglected by most existing works. In this article, we propose an attention-based heterogeneous relational model to improve the multimodal sentiment analysis performance by incorporating rich social information. Specifically, we propose a progressive dual attention module to capture the correlations between image and text, and then learn the joint image-text representation from the perspective of content information. A channel attention schema is proposed here to highlight semantically rich image channels and a region attention schema is further designed to highlight the emotional regions based on the attended channels. After that, we construct a heterogeneous relation network and extend graph convolutional network to aggregate the content information from social contexts as complements to learn high-quality representations of social images. Our proposal is thoroughly evaluated on two benchmark datasets, and experimental results demonstrate the superiority of the proposed model.
Sign Language Recognition Using Wearable Electronics: Implementing k-Nearest Neighbors with Dynamic Time Warping and Convolutional Neural Network Algorithms

We propose a sign language recognition system based on wearable electronics and two different classification algorithms. The wearable electronics were made of a sensory glove and inertial measurement units to gather fingers, wrist, and arm/forearm movements. The classifiers were k-Nearest Neighbors with Dynamic Time Warping (that is a non-parametric method) and Convolutional Neural Networks (that is a parametric method). Ten sign-words were considered from the Italian Sign Language: cose, grazie, maestra, together with words with international meaning such as google, internet, jogging, pizza, television, twitter, and ciao. The signs were repeated one-hundred times each by seven people, five male and two females, aged 29–54 y ± 10.34 (SD). The adopted classifiers performed with an accuracy of 96.6% ± 3.4 (SD) for the k-Nearest Neighbors plus the Dynamic Time Warping and of 98.0% ± 2.0 (SD) for the Convolutional Neural Networks. Our system was made of wearable electronics among the most complete ones, and the classifiers top performed in comparison with other relevant works reported in the literature.
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-Modal Retrieval

Cross-modal retrieval aims to retrieve data in one modality by a query in another modality, which has been a very interesting research issue in the field of multimedia, information retrieval, and computer vision, and database. Most existing works focus on cross-modal retrieval between text-image, text-video, and lyrics-audio. Little research addresses cross-modal retrieval between audio and video due to limited audio-video paired datasets and semantic information. The main challenge of the audio-visual cross-modal retrieval task focuses on learning joint embeddings from a shared subspace for computing the similarity across different modalities, where generating new representations is to maximize the correlation between audio and visual modalities space. In this work, we propose TNN-C-CCA, a novel deep triplet neural network with cluster canonical correlation analysis, which is an end-to-end supervised learning architecture with an audio branch and a video branch. We not only consider the matching pairs in the common space but also compute the mismatching pairs when maximizing the correlation. In particular, two significant contributions are made. First, a better representation by constructing a deep triplet neural network with triplet loss for optimal projections can be generated to maximize correlation in the shared subspace. Second, positive examples and negative examples are used in the learning stage to improve the capability of embedding learning between audio and video. Our experiment is run over fivefold cross validation, where average performance is applied to demonstrate the performance of audio-video cross-modal retrieval. The experimental results achieved on two different audio-visual datasets show that the proposed learning architecture with two branches outperforms existing six canonical correlation analysis–based methods and four state-of-the-art-based cross-modal retrieval methods.
Multi-view subspace learning via bidirectional sparsity

With the improvement of multi-view data collection technology, multi-view learning has become a hot research area. How to deal with diverse and complex data is one of the challenging problems in multi-view learning. However, it is hard for traditional multi-view subspace learning methods to find an effective subspace dimension and deal with outliers simultaneously. In this paper, we propose a novel method, named as Multi-view Subspace Learning via Bidirectional Sparsity(SLBS), which is effective to overcome the above difficulties and learn a better representation. Specifically, we divide the shared subspace into two parts. One is a row sparse matrix to do a secondary extraction of features and the other is a column sparse matrix to reduce the influence of outliers. The proposed model is a non-convex problem which is difficult to be solved. To address this problem, we develop an efficient algorithm and analyze its convergence and computational complexity. Finally, compared with other multi-view subspace learning methods, the extensive experimental results on real-world datasets present the effectiveness of our SLBS.
An Empirical Taxonomy of DevOps in Practice

DevOps is described as a software engineering culture and philosophy that utilises cross-functional teams to build, test and release software faster and more reliably through automation. Research shows that its adoption speeds up software delivery time, improve quality, security, and collaboration in software development. One controversial issue has been whether DevOps is an organisation-wide culture or a job description. As DevOps is an emerging concept, its definitions and best practices are still hazy, making its implementation in practice less informed and somewhat risky. The rising trend of DevOps adoption among software development practitioners therefore heightens the need for in-depth investigation into its implementation.This paper seeks to contribute to the above by critically examining DevOps implementation in practice through an exploratory case study, based on interviews with 11 industry practitioners across nine organisations. Transcripts of interviews were coded and analysed using a method informed by Grounded Theory. This study provides an empirical taxonomy of DevOps implementation, describing developers' interaction with On-premises Ops, Outsourced Ops, DevOps teams, and DevOps bridge teams. We present a novel mapping of the approaches to on-premises and cloud-based deployments, and identified the facilitators of DevOps practices in the different modes. We further identified three distinct groups of activities in the fourth mode: provisioning and maintenance of physical systems, function virtualisation and creation of automated pipelines, and development, deployment and maintenance of applications, which may have given rise to the implementation of DevOps as bridge teams. Interviewees claimed these distinctions allowed developers to focus on delivering value for the business.
Finance, inequality and inclusive education in Sub-Saharan Africa

This research establishes inequality critical masses that should not be exceeded in order for financial access to promote gender parity inclusive education. The focus is on 42 countries in Sub-Saharan Africa and the data is for the period 2004–2014. The estimation approach is the Generalized Method of Moments. When remittances are involved in the conditioning information set: (i) the Palma ratio should not exceed 6.000 in order for financial access to promote gender parity inclusive “primary and secondary education” and (ii) the Atkinson index should not exceed 0.695 in order for financial access to promote inclusive tertiary education. However, when the internet is involved in the conditioning information set, it is established that in order for financial access to promote inclusive primary and secondary education, the: (i) Gini coefficient should not exceed 0.571; (ii) Atkinson index should not be above 0.750 and (iii) Palma ratio should be maintained below 8.000. Irrespective of variable in the conditioning information set, what is apparent is that inequality decreases the incidence of financial access on inclusive education. Hence, a common policy measure is to reduce inequality in order to promote inclusive education using the financial access mechanism. Policy implications are discussed in the light of Sustainable Development Goals.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si34.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>y</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>: Chaotic yolo for user intended image encryption and sharing in social media

Social media is an inseparable part of our daily life where we post and share photos and media related to our life and in some cases we intend to share them between specific people. This intended and cherry picked sharing of media needs a better solution rather than simply picking users. Some social media platforms do not restrict other users from sharing timeline posts of others; meaning, one can simply forward a post from another person to a third one and no data preservation has been applied. In most cases we do not intend to secure the whole media and only important parts of it are intended to be secured. In this work we propose a novel method based on YoloV3 object detection and chaotic image encryption to overcome the issue of user intended data preservation in social media platforms. Our proposed method is capable of both automatic image encryption on full or user selected regions. Statistical and cryptographic analysis show superiority of our method compared to other state-of-the-art methods while it keeps the speed as high as possible for online and realtime use cases.
Revocation Mechanisms for Academic Certificates Stored on a Blockchain

Applications built on blockchain technology face many challenges, namely when they perform corrective operations on their databases. Unlike immutability, which is characteristic of a blockchain, corrective operations must be done without altering the existing data and guaranteeing privacy. Such conditions, make this task more complex for this kind of system. This paper discusses an approach to this problem. This approach is applied in a real domain. We propose an application in the higher education area, which uses the blockchain to issue academic certificates and proposes a mechanism to revoke digital diplomas that may have been issued incorrectly.
On the Accuracy–Complexity Tradeoff of Fuzzy Broad Learning System

The fuzzy broad learning system (FBLS) is a recently proposed neuro-fuzzy model that shares the similar structure of a broad learning system (BLS). It shows high accuracy in both classification and regression tasks and inherits the fast computational nature of a BLS. However, the ensemble of several fuzzy subsystems in an FBLS decreases the possibility of understanding the fuzzy model since the fuzzy rules from different fuzzy systems are difficult to combine together while keeping the consistence. To balance the model accuracy and complexity, a synthetically simplified FBLS with better interpretability, named compact FBLS (CFBLS), is developed in this article, which can generate much fewer and more explainable fuzzy rules for understanding. In such a way, only one traditional Takagi–Sugeno–Kang fuzzy system is employed in the feature layer of a CFBLS, and the input universe of discourse is equally partitioned to obtain the fuzzy sets with proper linguistic labels accordingly. The random feature selection matrix and rule combination matrix are employed to reduce the total number of fuzzy rules and to avoid the “curse of dimensionality.” The enhancement layer is kept in the CFBLS that helps to add a nonlinear transformation of input features to the traditional first-order polynomial used in the consequent part of a fuzzy rule. The pseudoinverse is also used to determine the parameters of CFBLS guaranteeing its fast computational nature. The experiments on the popular UCI and KEEL datasets indicate that the CFBLS can generate a smaller set of comprehensible fuzzy rules and achieve much higher accuracy than some state-of-the-art neuro-fuzzy models. Moreover, the advantage of CFBLS is also verified in a real-world application.
NeedFull – a Tweet Analysis Platform to Study Human Needs During the COVID-19 Pandemic in New York State

Governments and municipalities need to understand their citizens' psychological needs in critical times and dangerous situations. COVID-19 brings lots of challenges to deal with. We propose NeedFull, an interactive and scalable tweet analysis platform, to help governments and municipalities to understand residents' real psychological needs during those periods. The platform mainly consists of four parts: data collection module, data storage module, data analysis module and data visualization module. The four parts interact with each other and provide users with a thorough human needs analysis based on their queries. We employed the proposed platform to investigate the reaction of people in New York State to the ongoing worldwide COVID-19 pandemic.
Hierarchical Human-Like Deep Neural Networks for Abstractive Text Summarization

Developing an abstractive text summarization (ATS) system that is capable of generating concise, appropriate, and plausible summaries for the source documents is a long-term goal of artificial intelligence (AI). Recent advances in ATS are overwhelmingly contributed by deep learning techniques, which have taken the state-of-the-art of ATS to a new level. Despite the significant success of previous methods, generating high-quality and human-like abstractive summaries remains a challenge in practice. The human reading cognition, which is essential for reading comprehension and logical thinking, is still relatively new territory and underexplored in deep neural networks. In this article, we propose a novel Hierarchical Human-like deep neural network for ATS (HH-ATS), inspired by the process of how humans comprehend an article and write the corresponding summary. Specifically, HH-ATS is composed of three primary components (i.e., a knowledge-aware hierarchical attention module, a multitask learning module, and a dual discriminator generative adversarial network), which mimic the three stages of human reading cognition (i.e., rough reading, active reading, and postediting). Experimental results on two benchmark data sets (CNN/Daily Mail and Gigaword) demonstrate that HH-ATS consistently and substantially outperforms the compared methods.
Cryptanalysis of an image block encryption algorithm based on chaotic maps

Recently, an image block encryption algorithm was proposed based on some well-known chaotic maps. The authors claim that the encryption algorithm achieves enough security level and high encryption speed at the same time. In this paper, we give a thorough security analysis on the algorithm from the perspective of modern cryptology and report some critical security defects on the algorithm. Given five chosen plain-images and the corresponding cipher-images, the attacker can obtain an equivalent secret key to successfully decrypt the other cipher-images encrypted with the same secret key. In addition, each security metric adopted in the security evaluation on the algorithm is questioned. The drawn lessons are generally applicable to many other image encryption algorithms.
Freely typed keystroke dynamics-based user authentication for mobile devices based on heterogeneous features

Keystroke dynamics-based authentication (KDA) is one of the human behavioral biometric-based user authentication methods based on the unique typing pattern of a person. Previous KDA studies on mobile devices primarily focused on fixed-length text-based KDA, such as passwords and personal identification numbers. This can strengthen the login system and prevent abnormal usage of impostors based on certain attack methods, such as shoulder surfing and smudge attacks. However, this method possesses a limitation that continuous monitoring is not possible after login. To solve this problem, KDA based on freely typed text was studied; however, there are only a few studies on this technique. Further, the performance authentication based on these studies is insufficient for a real-world implementation. In this paper, we propose a novel freely typed text-based KDA method for mobile devices named FACT, i.e., user authentication on mobile devices based on free text, accelerator, coordinate, and time. We collected data from three different smartphone sensors while typing in two languages (English and Korean), and 17 variables were extracted for a set of keystroke data. A total of six authentication methods were employed and the proposed FACT yielded an equal error rate lower than 1% with only one reference keystroke set; moreover, it demonstrated a perfect protection capability while using Korean when more than four reference keystroke sets were used. To contribute to the research and industrial community, we have publicized our collected keystroke dataset so that anyone who conducts a KDA study or develops a KDA-related mobile service can use the dataset without any restrictions.