Reviewerly/exHarmony huggingface dataset:
A New Feature Selection in Email Spam Detection by Particle Swarm Optimization and Fruit Fly Optimization Algorithms

With the advent of the internet, along with email, and social networking, there are some new issues that have caused vulnerability of users against attackers. Internet users face a lot of undesirable emails and their data privacy and security is in danger. Spammers are often sent to users by intruders and sales markets, and most of the time they target spam, harassment, and abuse of user data. With increasing attacks on computer networks, attempts to rebuild computer networks and detect spam emails are important. Hackers use the identities of users by obtaining their personal information and account of users for malicious and subversive actions. Intruders are attempting to expose, remove, or change user information by opening encrypted information. Therefore, it is very important to detect spam in the early stages. In this paper, a new approach is proposed based on a hybridization of Particle Swarm Optimization (PSO) with Fruit Fly Optimization (FFO) to email spam detection. This paper shows a Feature Selection (FS) based on PSO, which decreases dimensionality and improves the accuracy of email spam classification. The PSO searches the feature space for the best feature subsets. Experiments results on the public spambase dataset show that the accuracy of the proposed model is 92.21%, which is better in comparison with others models, such as PSO, Genetic Algorithm (GA), and Ant Colony Optimization (ACO).
An Improved Methodology for Collaborative Construction of Reusable, Localized, and Shareable Ontology

Interlocking Institutional Worlds (IWs) is a concept explaining the need to interoperate between institutions (or players), to solve problems of common interest in a given domain. Managing knowledge in the IWs domain is complex; however, promoting knowledge sharing based on standards and common terms agreeable to all players is essential and is something that must be established. In this context, ontologies, as a conceptual tool and a key component of knowledge-based systems, have been used by organizations for effective knowledge management, better decision-making, and interoperability among diverse institutions of an IWs domain. The development of ontology involves structural and logical complexity, and requires a well-designed, mature, and widely accepted methodology, to ensure its reliability. Many methodologies for ontology development have been proposed by several researchers; however, most of the developed methodologies have not included several important phases. Furthermore, several methodologies have not provided the complete details of the techniques and activities involved in the ontology construction process. Fewer details make it difficult to follow a methodology for designing ontologies. This study aims to compare existing methodologies based on sixteen important criteria and proposes an improved methodology for ontology development for IWs domains. The proposed methodology has included several important phases such as the Estimation of Human Resources, Re-engineering and Re-using of Resources, Collaborative Ontology Construction, the Conceptualization of both Endurants and Perdurants, Localization of an Ontology, Ontology Integration and Merging, Support for Interoperability, Versioning of an Ontology, and Ontology Population. The proposed methodology has been applied to design an OntoWM domain ontology for the Waste Management (WM) domain. The evaluation of the proposed methodology has been made by utilizing the designed OntoWM domain ontology in an ontology-based web application called SmartBinAnalytics. SmartBinAnalytics has been built using OntoWM, Java Server Pages (JSP), Jena Application Programming Interface (API), and MySQL. The proposed methodology can be used for the construction of domain ontologies in any IWs domain.
Domain adaptation with feature and label adversarial networks

Learning a cross-domain representation from labeled source domains to unlabeled target domains is an important research problem in representation learning. Despite the success of traditional adversarial methods, they proposed to align features from each domain only while neglecting the importance of labels, when fooling a special domain discriminator network. Thus, the discriminator of these approaches merely distinguishes whether the generated features are in-domain or not, which may lead to less class-discriminative features. In this paper, by considering the joint distributions of features and labels in both domains, we present Feature and Label Adversarial Networks (FLAN). As a result, FLAN can generate more discriminative features in both domains. Experimental results on standard unsupervised domain adaptation benchmarks have demonstrated that FLAN can outperform the state-of-art domain invariant representation learning methods.
One-time-pad cipher algorithm based on confusion mapping and DNA storage technology

In order to solve the problems of low computational security in the encoding mapping and difficulty in practical operation of biological experiments in DNA-based one-time-pad cryptography, we proposed a one-time-pad cipher algorithm based on confusion mapping and DNA storage technology. In our constructed algorithm, the confusion mapping methods such as chaos map, encoding mapping, confusion encoding table and simulating biological operation process are used to increase the key space. Among them, the encoding mapping and the confusion encoding table provide the realization conditions for the transition of data and biological information. By selecting security parameters and confounding parameters, the algorithm realizes a more random dynamic encryption and decryption process than similar algorithms. In addition, the use of DNA storage technologies including DNA synthesis and high-throughput sequencing ensures a viable biological encryption process. Theoretical analysis and simulation experiments show that the algorithm provides both mathematical and biological security, which not only has the difficult advantage of cracking DNA biological experiments, but also provides relatively high computational security.
A Comparative Study of Particle Swarm Optimization and Genetic Algorithm

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.
An Enhanced Filtering-Based Information Granulation Procedure for Graph Embedding and Classification

Granular Computing is a powerful information processing paradigm for synthesizing advanced pattern recognition systems in non-conventional domains. In this article, a novel procedure for the automatic synthesis of suitable information granules is proposed. The procedure leverages a joint sensitivity-vs-specificity score that accounts the meaningfulness of candidate information granules for each class considered in the classification problem at hand. Only statistically relevant granules are retained for a graph embedding procedure towards a geometric space, in which standard classification systems can be used without alterations. Performance tests have been carried out by considering open access datasets of fully labelled graphs with arbitrarily complex nodes and/or edges attributes that, by definition, must rely on inexact graph matching procedures to quantify dissimilarities. Two variants of the procedure are investigated: a standard variant, which aims at automatically finding suitable information granules for solving the classification problem as a whole, and a class-specific metric learning variant, in which the optimization procedure is performed in a class-aware fashion. In the latter case, each class will have its own set of information granules, along with the corresponding parameters defining distinct instances of the dissimilarity measure. Computational results show that the proposed algorithm is able to outperform the vast majority of current approaches for graph classification, while at the same time returning a grey-box model, interpretable by field-experts.
Exploring Factors Leading to Quality Online Learning in the Era of Covid-19: A Correlation Model Study

The spark of Covid-19 spread in the world in the start of 2020.When it destroyed other segments of social life, it also impacted education negatively.The closure of educational institutes raised question mark on stakeholders in this age of technological advancement.When other counties of the world adopted alternative ways of education, Pakistan also adopted the policy of online education using available technological resources.The objective of the study was to explore the students' experience of online learning and satisfaction level among them.Using survey method to get the views of students studying in Foundation University of Islamabad, researchers designed well structure simple and easy to understand questionnaire.The questionnaire consisted of 23 close-ended questions and was distributed via email and Whatsapp groups designed by departments of the university.In the period of two weeks, n=273 students responded to the questionnaire.The findings of the study revealed that in online learning comfortability with computer usage, self-motivation, confidence and time spending on computer significant correlate to quality education and lead to better educational understanding while getting online education.The study concluded that students found satisfied with online learning.Familiarity with technological tools found positive aspect for students to get online education easily.
Self-Supervised Policy Adaptation during Deployment

In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal. Our work explores the use of self-supervision to allow the policy to continue training after deployment without using any rewards. While previous methods explicitly anticipate changes in the new environment, we assume no prior knowledge of those changes yet still obtain significant improvements. Empirical evaluations are performed on diverse simulation environments from DeepMind Control suite and ViZDoom, as well as real robotic manipulation tasks in continuously changing environments, taking observations from an uncalibrated camera. Our method improves generalization in 28 out of 32 environments across various tasks and outperforms domain randomization on a majority of environments. Videos are available at https://iclr2021submission.github.io/ICLR2021_Anonymized_PAD/.
l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences

Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & majordisadvantage.To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output.The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules & a l,r-PanoED-network, where each submodule is a robust ensemble of several deep-learning, computer-vision & image-handling techniques.This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoEDnetwork) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs.The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-featuremaps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction & featuremapping operations are performed simultaneously in the l,r-PanoED's encoder-network based on the split-encoding-network methodology.The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks' bottle-neck feature-maps.The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets.Multiple evaluation metrics (SSIM, PSNR, MSE, L α,β,γ , FM-rate, Average-latency-time) & wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs.
Efficient Multisource Remote Sensing Image Matching Using Dominant Orientation of Gradient

Image matching is the key step for image registration. Due to the existing nonlinear intensity differences between multisource images, their matching is still a challenging task. A fast matching approach based on dominant orientation of gradient (DOG) is proposed in this article, which is robust to nonlinear intensity variations. The DOG feature maps are constructed by extracting DOG feature of each pixel in the images in the first place. A template matching method is used to determine correspondences between images based on the feature representations. We define a similarity measurement, referred to as sum of cosine differences, which can be accelerated by fast Fourier transform. Subsequently, the subpixel accuracy can be achieved by fitting the similarity measurement using a quadratic polynomial modal. A new variable template matching (VTM) method has been developed to improve the matching performance. Experimental results confirm that the proposed matching approach is robust to nonlinear intensity differences and has time efficiency. The VTM method additionally improves the matching precision effectively.
When you hear /baksɛt/ do you think /baskɛt/? Evidence for transposed-phoneme effect with multisyllabic words.

In this study we asked whether nonwords created by transposing two phonemes (/biksɔt/) are perceived as being more similar to their base words (/biskɔt/) than nonwords created by substituting two phonemes (/bipfɔt/). Using the short-term phonological priming and a lexical-decision task, Experiment 1 showed that transposed-phoneme nonword primes lead to shorter RTs on the target base words than substituted-phoneme nonword primes. Using a single-presentation lexical-decision task, Experiment 2 showed that transposed-phoneme nonwords lead to longer "no" decision responses than substituted-phoneme nonwords. In both Experiments 1 and 2, the transposed-phoneme effect was observed when the transposed phonemes were adjacent (/biksɔt/-/biskɔt/) but not when they were distant (/ʃoloka/-/ʃokola/). Our findings suggest that nonwords created by transposing adjacent phonemes in real words generate more activation of the lexical representations associated with the base words than do matched control nonwords. More generally, our findings present a challenge for models of spoken word recognition that code for the precise order of speech segments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Detecting Duplicate Contributions in Pull-Based Model Combining Textual and Change Similarities

Communication and coordination between open source software (OSS) developers who do not work physically in the same location have always been the challenging issues. The pull-based development model, as the state-of-the-art collaborative development mechanism, provides high openness and transparency to improve the visibility of contributors’ work. However, duplicate contributions may still be submitted by more than one contributor to solve the same problem due to the parallel and uncoordinated nature of this model. If not detected in time, duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant work. In this paper, we propose an approach combining textual and change similarities to automatically detect duplicate contributions in the pull-based model at submission time. For a new-arriving contribution, we first compute textual similarity and change similarity between it and other existing contributions. And then our method returns a list of candidate duplicate contributions that are most similar to the new contribution in terms of the combined textual and change similarity. The evaluation shows that 83.4% of the duplicates can be found in average when we use the combined textual and change similarity compared with 54.8% using only textual similarity and 78.2% using only change similarity.
Intent Detection and Slot Filling with Capsule Net Architectures for a Romanian Home Assistant

As virtual home assistants are becoming more popular, there is an emerging need for supporting languages other than English. While more wide-spread or popular languages such as Spanish, French or Hindi are already integrated into existing home assistants like Google Home or Alexa, integration of other less-known languages such as Romanian is still missing. This paper explores the problem of Natural Language Understanding (NLU) applied to a Romanian home assistant. We propose a customized capsule neural network architecture that performs intent detection and slot filling in a joint manner and we evaluate how well it handles utterances containing various levels of complexity. The capsule network model shows a significant improvement in intent detection when compared to models built using the well-known Rasa NLU tool. Through error analysis, we observe clear error patterns that occur systematically. Variability in language when expressing one intent proves to be the biggest challenge encountered by the model.
A Proposed Architectural Framework for Generating Personalized Users’ Query Response

Personality mining is a vital approach which is currently been on focus for retaining customers as well as extending the customers’ segment. Data in its varied forms such as personal profile, social networks data, as well as transactional data could be considered as key for the success of industries. Therefore, successful data analysis by applying intelligent techniques, such as mining, natural language processing techniques, weighting methods, and others, drive competitiveness to a higher level and, further, to uniqueness. One of the main competitive advantages is the customer satisfaction, therefore, utilizing such successful techniques and successfully analyzing these sources of data with exploring their varieties of relationships lead to the successful personalization and consequently the user satisfaction. This research proposes an adaptive framework for documents’ personalization. The proposed framework builds the most suitable documents’ contents following user’s queries as well as the preferred style for content presentation with respect to users’ personality characteristics. On the other hand, the research highlights the importance of the Egyptian Knowledge Bank as a source of professional documents for replying to the users’ queries. A preliminary investigation has been applied to explore the importance of the proposed framework in the educational field through conducting a survey that included ten staff members and fifty students. The results revealed that the current research is a vital objective in the education field with 91% and 95% respectively.
BLVector: Fast BLAST-Like Algorithm for Manycore CPU With Vectorization

New High-Performance Computing architectures have been recently developed for commercial central processing unit (CPU). Yet, that has not improved the execution time of widely used bioinformatics applications, like BLAST+. This is due to a lack of optimization between the bases of the existing algorithms and the internals of the hardware that allows taking full advantage of the available CPU cores. To optimize the new architectures, algorithms must be revised and redesigned; usually rewritten from scratch. BLVector adapts the high-level concepts of BLAST+ to the x86 architectures with AVX-512, to harness their capabilities. A deep comprehensive study has been carried out to optimize the approach, with a significant reduction in time execution. BLVector reduces the execution time of BLAST+ when aligning up to mid-size protein sequences (∼750 amino acids). The gain in real scenario cases is 3.2-fold. When applied to longer proteins, BLVector consumes more time than BLAST+, but retrieves a much larger set of results. BLVector and BLAST+ are fine-tuned heuristics. Therefore, the relevant results returned by both are the same, although they behave differently specially when performing alignments with low scores. Hence, they can be considered complementary bioinformatics tools.
Blockchain: A Coordination Mechanism

Blockchain technology is firmly established in the public awareness as a revolutionary new technology underpinning cryptocurrency. However, its potential applications can be found across sectors and industries in providing a novel way of producing coordination necessary to transact online, making it a timely invention in the age of progressing digitalization and increasing demands for efficiency and security of online transactions, and a promising research topic addressing the growing academic interest in the coordination aspect of the contract scholarship. The aim of this conceptual paper is to model blockchain as a coordination mechanism for online transactions. Three key aspects of coordination with blockchains are identified and examined – (1) producing consensus about the facts relevant to a transaction, (2) coding contracts, and (3) autonomously executing transactions. They are argued to be integral parts of the mechanism, jointly enabling blockchains to function as a complete mechanism of coordination for online transactions. The model is intended to inform debates on the prospects for the blockchain technology and can be further used to integrate coordination and contract scholarship.
Online learning: Students’ autonomy and attitudes

This study aims at investigating students’ autonomy and attitude in learning TOEFL online which the program was organized by the Language Centre, Department of Education and Culture, West Nusa Tenggara during the pandemic Covid-19. A set of questionnaires was distributed to 134 students that came from a different geographical location in West Nusa Tenggara province, and were different in age. The participants in the present study were all who had been announced officially as the TOEFL course awardees for Mataram area. The findings indicated that albeit positive tenets on students’ autonomy and attitude. The results showed very close similarity as described by 2% difference in mean scores between students’ autonomy and attitude. The poor category results were found from students’ inability to evaluate their strength and weakness in learning autonomously and the students found it was hard to keep make correspondence immediately with teachers. This study ends with suggestions for the next TOEFL course programs