Reviewerly/exHarmony huggingface dataset:
Text Classification on Twitter Data

Sentiment analysis is a classification problem where the main focus is to predict the polarity of words and then classify them into positive or negative sentiment. Classifiers used are of mainly two types, namely lexicon-based and machine learning based. The former include SentiWordNet and Word Sense Disambiguation while the latter include Multinomial Naive Bayes(MNB), Logistic Regression(LR), Support Vector Machine(SVM) and RNN Classifier. In this paper, existing datasets have been used, the first one from "Sentiment140" from Stanford University, consisting of 1.6 million tweets and the other one originally came from "Crowdflower's Data for Everyone library", consisting of 13870 entries, and both datasets are already categorised as per the sentiments expressed in them. Textblob, Sentiwordnet, MNB, LR, SVM and RNN Classifier are applied on the above dataset and a comparison is drawn between the results obtained from above mentioned sentiment classifiers, classifying tweets according to the sentiment expressed in them, i.e. positive or negative. Also, along with the machine learning approaches, an ensemble form of MNB, LR and SVM has been performed on the datasets and compared with the above results. Further the above trained models can be used for sentiment prediction of a new data.
A systematic literature review and comparative study of different software quality models

AbstractAbstractEvery organization wants to run with profit only and it is a common trend to acquire the new technologies, methods and models to enhance the quality of software product. Quality of product is directly proportional to the value of the product and the profit of the organization. Different quality model are used by different organization foundation upon the requirements. Different thoughts of software quality attributes with various software quality models are evaluated and examined in this survey. Also researcher performed comparative study of different software quality models utilized by different organizations. Making software by using open source software (OSS) and commercial of the shelf (COTS) component is very useful for any organization. So as to quantify quality in software, quality models can be used to check the quality of existing COTS and OSS software components. The purpose of this research is to do the comparative study to check software quality prediction using COTS and OSS components. In this survey the researcher do study of more than 100 papers and identified that OSS as the development technique is more better then COTS development. Because COTS development is black box in nature, so some tradition quality metrics cannot be apply but in case of OSS as development methodology source code is available. So we can apply all software quality metrics to assess the OSS quality. But still there is no proper technique, model and tool is available by which we can evaluate the quality of OSS. In future the researcher can improve the software quality by creating some tools and models for estimation of quality characteristics according to the modified ISO-9126 quality assurance model for OSS.Subject Classification: 68N3068T35Keywords: Software QualityOpen Source Software -OSSSoftware Quality Model Commercial of the Shelf-(COTS)ReliabilityReusabilitySoftware Testing
Open Science and Radical Solutions for Diversity, Equity and Quality in Research: A Literature Review of Different Research Schools, Philosophies and Frameworks and Their Potential Impact on Science and Education

Open Science is a phenomenon that can be traced back to the Middle Ages. In the end of the twentieth century and the beginning of the twenty-first century, Open Science is strongly growing due to the worldwide internet and related new technologies, tools and communication channels. Two core objectives (reliability and trust) and three main characteristics (transparency, openness and reproducibility) of Open Science can be identified but it is still too early for a broad definition of this growing movement. Its growth is happening in many disciplines and in diverse facets. This article presents an overview of how Open Science is introduced and established in all three science dimensions of research design, processes and publications. For the future, the benefits are analysed that Open Science is offering, as well as the challenges that it is facing. It can be concluded that it is desirable that all researchers collaborate in Open Science. Open Science can improve the different science disciplines, research practices and science in general. In that way, Open Science can contribute to overcome the post-truth age through increasing objective and subjective credibility of science and research. And in the long-term perspective, Open Science can improve the whole research, education, as well as our society.
Bird Mating Optimizer for Combinatorial Optimization Problems

The bird mating optimizer is a new metaheuristic algorithm that was originally proposed to solve continuous optimization problems with a very promising performance.However, the algorithm has not yet been applied for solving combinatorial optimization problems.Thus, the formulation may not be able to generate a discrete feasible solution.Many continuous algorithms used random-key representation to represent the discrete solution using real numbers or a discrete variant of the algorithm is used to deal with the discrete solution of the problem.However, there is no evidence which methodology is better for solving combinatorial optimization problems.Therefore, this work proposes two variants of bird mating optimizer (random-key bird mating optimizer and the discrete bird mating optimizer), to identify which one is more efficient in solving combinatorial optimization problems.In the first one, we use a random-key encoding scheme, whilst, in the later one, we use crossover (multi-parent) and mutation operators to combine the components of the selected parents to generate new broods.The performance of these algorithms is tested on the travelling salesman problem and berth allocation problem, and are compared with the results of two well-known optimization algorithms: Genetic Algorithm and Particle Swarm Optimization.Experimental results show that the discrete bird mating optimizer is more efficient than the others on all tested benchmark instances.Indeed, it is able to attain the best-known results in some of the BAP benchmark instances.These indicate the applicability and the effectiveness of the proposed discrete bird mating optimizer in solving combinatorial optimization problems.
Analyzing the impact of soft errors in VGG networks implemented on GPUs

Convolutional Neural Networks (CNNs) models are widely implemented on Graphic Processing Units (GPUs) due to their computational powers, particularly, in object classification and detection. In safety-critical environments, reliability issues of these models induced by soft errors are a major concern. In this paper, we analyze the reliability of VGG model, a well-known CNN architecture, through two metrics: (1) the so-called Kernels Vulnerability Factor (KVF) used to identify vulnerable kernels and (2) the Layer Vulnerability Factor (LVF) used to determine track fault propagation through layers. As our model runs on an NVIDIA's GPU, our main evaluation tool in this study is the fault injector called SASSIFI. Our results show that Im2col presents the highest vulnerability among all kernels and hardening this kernel significantly reduces silent data corruption rate by 85.67% with 35.63% time penalty. The average of errors causing misclassification may reach up to 19.7% in some injection modes and this may be extremely high and unacceptable. Fault injection mode affects both KVF and LVF because RF mode is performed at architecture level, while IOV and IOA modes are at a different level (i.e., program level). Also, a thorough comparison among VGG, AlexNet, and ResNet indicates that LVF and KVF are determined by CNN network architecture. This is because layers of these networks, particularly convolutional layers, are implemented with the Im2col kernel, the most vulnerable and invoked in these CNN networks.
DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images

This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported regression based methods, the DeepDistance model proposes to approach its learning as a multi-task regression problem where multiple tasks are learned by using shared feature representations. To this end, it defines a secondary metric, normalized outer distance, to represent a different aspect of the problem and proposes to define its learning as complementary to the main cell detection task. In order to learn these two complementary tasks more effectively, the DeepDistance model designs a fully convolutional network (FCN) with a shared encoder path and end-to-end trains this FCN to concurrently learn the tasks in parallel. For further performance improvement on the main task, this paper also presents an extended version of the DeepDistance model that includes an auxiliary classification task and learns it in parallel to the two regression tasks by also sharing feature representations with them. DeepDistance uses the inner distances estimated by these FCNs in a detection algorithm to locate individual cells in a given image. In addition to this detection algorithm, this paper also suggests a cell segmentation algorithm that employs the estimated maps to find cell boundaries. Our experiments on three different human cell lines reveal that the proposed multi-task learning models, the DeepDistance model and its extended version, successfully identify the locations of cell as well as delineate their boundaries, even for the cell line that was not used in training, and improve the results of its counterparts.
A novel multi-objective genetic algorithm based error correcting output codes

Up to now, different genetic algorithm (GA) based error correcting output codes (ECOC) algorithms have been proposed by setting accuracy as the optimization objective. However, it was demonstrated that diversity among learners is of great significance to a robust ensemble. In this paper, we propose a multi-objective GA with setting accuracy and diversity as two objectives. To further promote diversity in an ensemble, a new individual structure is designed to accommodate heterogeneous dichotomizers. Three multi-objective ranking strategies are deployed to balance two objectives respectively. A novel genetic operator is designed to produce ECOC-compatible offspring in the evolutionary process, and a local improvement algorithm is designed to promote individuals’ fitness values. To verify the performance of our GA, a single objective ranking strategy and the design of homogeneous learner based GA are also adopted. Ten widely used ECOC algorithms and three famous ensemble algorithms are deployed for performance comparisons based on a set of the UCI data and microarray data sets. Results show that compared with other algorithms, our GA obtains higher performance in most cases due to the trade-off between performance and diversity. Besides, the accommodation of heterogeneous dichotomizers in an ensemble provides higher generalization ability compared with homogeneous ensembles.
Facial Age Estimation Using a Multi-task Network Combining Classification and Regression

Age estimation of facial images is very challenging because of the complexity of face aging process and the difficulty of collecting and labeling data.A holistic regression model is subject to imbalanced training data, while a divide-and-conquer method highly depends on the effect of the age classification, which usually has boundary effect due to cross-age correlations.This paper proposes a simple but effective multi-task learning (MTL) network combining classification and regression for age estimation called CR-MT net, where classification acts as an auxiliary task to regression.MTL can boost the generalization performance of the age regression task by shared information representation learning of the two tasks.Compared to divide-and-conquer methods, our method performs MTL for two tasks with an end-to-end training, and has no error propagation from classification to regression.The holistic regression model in CR-MT net does not meet the boundary effect, and can fit the heterogeneous or unbalanced age data more accurately with the aid of a good age data partition in classification.We evaluate two age grouping techniques to find a good data partition, and diagnose various factors which can influence the performance of the CR-MT net by extensive experiments.The CR-MT net is verified in three public datasets, and achieves state-of-art results.
Applications of Fuzzy Logic, Artificial Neural Network and Neuro-Fuzzy in Industrial Engineering

Artificial intelligence methods namely artificial neural network, fuzzy logic, and neuro-fuzzy have been effectively utilized in different applications like business, marketing, control engendering, health care, and social services. To demonstrate the usage of fuzzy set theory, artificial neural network, as well as neuro-fuzzy in industrial engineering and also for providing a basis for future investigation, a literature review of the artificial neural network, fuzzy logic, and neuro-fuzzy in industrial engineering is conducted in this paper.
Agent-based Testing: An Automated Approach toward Artificial Reactions to Human Behavior

Vast amounts of news are consumed through algorithmically curated media environments, such as search engines, social networking sites, or news aggregators. This renders algorithmic content curation with much societal relevance and highlights the urgent need for independent and resilient academic research. Therefore, a plethora of methodological approaches have been applied, such as case studies, expert interviews, observations, or agent-based approaches. The paper discusses the applicability of these methodological efforts for journalism studies, showing that all of these approaches face their limitations, especially with regard to external validity, recruitment difficulties, and data reliability. Thereby, agent-based testing represents one of the most promising approaches to overcome plenty of these methodological limitations. Agent-based testing is a systematic and experimental approach that emulates online human behavior to test algorithmically curated media environments under various conditions. For this to be achieved properly, this paper suggests a multitude of settings and requirements to adequately face the technological, legal, and ethical challenges, which come with the empirical investigation of algorithmic content curation. Ultimately, the paper presents both general considerations and practical instructions (using the "ScrapeBot") to employ agent-based testing for journalism studies.
Using Immersive Virtual Environments (IVEs) to Conduct Environmental Design Research: A Primer and Decision Framework

Purpose: We propose a methodological framework to use immersive virtual environments (IVEs) in head-mounted displays for environmental design research. Background: IVEs offer researchers with experimental control and realistic representations of environments with high levels of ecological validity. IVEs are also increasingly being used to visualize proposed designs. Despite these tremendous benefits, IVEs are underutilized in environmental design research. We are unaware of preexisting frameworks that synthesize the methodological decisions related to the use of IVEs to conduct environmental design research. Methods/Results: We define the concepts necessary to conduct IVE research (virtual reality [VR], IVEs, ecological validity, visual realism, behavioral realism, and contextual realism). We also summarize the available study designs (correlational studies, experimental studies, and performance evaluations) and software systems used to create IVEs. We conclude with a conceptual framework that describes how research questions and study designs inform IVE selection and ecological validity. This framework is accompanied by a workflow that operationalizes the creation and use of IVEs in research. Conclusions: VR provides a robust and innovative research strategy for environmental design research because of its high degree of experimental control, rich data collection options, and opportunities for systematic evaluation of alternative design configurations.
CryptoDL: Predicting Dyslexia Biomarkers from Encrypted Neuroimaging Dataset Using Energy-Efficient Residue Number System and Deep Convolutional Neural Network

The increasing availability of medical images generated via different imaging techniques necessitates the need for their remote analysis and diagnosis, especially when such datasets involve brain morphological biomarkers, an important biological symmetry concept. This development has made the privacy and confidentiality of patients’ medical records extremely important. In this study, an approach for a secure dyslexia biomarkers classification is proposed using a deep learning model and the concept of residue number system (RNS). A special moduli set of RNS was used to develop a pixel-bitstream encoder that encrypts the 7-bit binary value of each pixel present in the training and testing brain magnetic resonance imaging (MRI) dataset (neuroimaging dataset) prior to classification using cascaded deep convolutional neural network (CNN). Theoretical analysis of our encoder design shows that the proposed pixel-bitstream encoder is a combinational circuit that requires fewer fast adders, with area complexity of 4n AFA and time delay of (3n + 3) DFA for n ≥ 3. FPGA implementation of the proposed encoder shows 23.5% critical path delay improvement and saves up to 42.4% power. Our proposed cascaded deep CNN also shows promising classification outcomes, with the highest performance accuracy of 73.2% on the encrypted data. Specifically, this study has attempted to explore the potencies of CNN to discriminate cases of dyslexia from control subjects using encrypted dyslexia biomarkers neuroimaging dataset. This kind of research becomes expedient owing to the educational and medical importance of dyslexia.
Educational affordances of mobile social media for language teaching and learning: a chinese teacher’s perspective

The educational potential of mobile and social media technologies in combination is becoming increasingly salient with the advance of modern technology. However, there is a dearth in the research on educational use of mobile social media for language learning. This study adopted a qualitative single case study approach to investigate a language teacher’s integration of mobile social media in the context of higher education in mainland China, with the aim of exploring educational affordances of mobile social media for language teaching and learning and understanding the relationship between the teacher’s technology use and her private theories about technology integration. Data were collected from observation, interviews, as well as documents and artifacts provided by the teacher. Qualitative content analysis was conducted to analyze the data. Five educational affordances of mobile social media emerged from the results, including a motivating environment, resources accessing and sharing, evaluation and feedback offering, administration for learning, and content generation. Results also showed that the teacher’s private theories about technology integration changed with her adoption of these affordances. However, such changes did not lead to student-centered technology-based learning designs. Implications for teachers as well as teacher educators are provided. Directions for future research are suggested.
Joint identification–verification for person re-identification: A four stream deep learning approach with improved quartet loss function

A deep four-stream convolutional neural network (CNN) is proposed for person re-identification (re-ID) to overcome the poor generalisation of the traditional triplet loss function. Specifically, the proposed method is a four-stream network, taking four input images where two images are from the same identity and the other two are from different identities. The network uses dual identification and verification losses in a single framework to minimise the intra-class distance while maximising the inter-class distance. Extensive experiments illustrate the state-of-the-art performance of the proposed approach on seven challenging person re-ID datasets: VIPeR, CUHK03, CUHK01, PRID2011, i-LIDS, Market-1501, and DukeMTMC-reID. In addition, we build a five-stream network and a four-stream network with an alternate formulation of positive and negative pairs to further explore the performance of the proposed four-stream network. We also demonstrate promising performance when training and testing sets are from different domains, highlighting the real-world applicability of the approach.
Digitizing water management: Toward the innovative use of blockchain technologies to address sustainability

Water has always been considered as a physically scarce resource, particularly in North Africa, Central Asia, West Asia, among others. On the other hand, the current water management system is facing substantial difficulties due to the depletion of resources, the complexity of regulation, as well as the increasing demand of water in society. This article attempts to show the possibility of using blockchain technologies in managing scarce resources, such as water, to address environmental sustainability. Those applications could consolidate the seamless integration of the existing water management system through keen agreements which dwell on the blockchain and take into account automated work processes. It is expected that the implementation of blockchain technology will ensure trust, transparency, and accountability among individuals and other economic actors.
ELM-HTM guided bio-inspired unsupervised learning for anomalous trajectory classification

Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose a new bio-inspired learning model for a single-class classifier to detect abnormality in video object trajectories. The method uses a simple but dynamic extreme learning machine (ELM) and hierarchical temporal memory (HTM) together referred to as ELM-HTM in an unsupervised way to learn and classify time series patterns. The method has been tested on trajectory sequences in traffic surveillance to find abnormal behaviors such as high-speed, unusual stops, driving in wrong directions, loitering, etc. Experiments have also been performed with 3D air signatures captured using sensors and used for biometric authentication(forged/genuine). The results indicate a significant gain over training time and classification accuracy. The proposed method outperforms in predicting long-time patterns by observing small steps with an average accuracy gain of 15% as compared to the state-of-the-art HTM. The method has applications in detecting abnormal activities in videos by learning the movement patterns as well as in biometric authentication.