Reviewerly/exHarmony huggingface dataset:
Enabling “Untact” Culture via Online Product Recommendations: An Optimized Graph-CNN based Approach

The COVID-19 pandemic is swiftly changing our behaviors toward online channels across the globe. Cultural patterns of working, thinking, shopping, and use of technology are changing accordingly. Customers are seeking convenience in online shopping. It is the peak time to assist the digital marketplace with right kind of tools and technologies that uses the strategy of click and collect. Session-based recommendation systems have the potential to be equally useful for both the customers and the service providers. These frameworks can foresee customer’s inclinations and interests, by investigating authentic information on their conduct and activities. Various methods exist and are pertinent in various situations. We propose a product recommendation system that uses a graph convolutional neural network (GCN)-based approach to recommend products to users by analyzing their previous interactions. Unlike other conventional techniques, GCN is not widely explored in recommendation systems. Therefore, we propose a variation of GCN that uses optimization strategy for better representation of graphs. Our model uses session-based data to generate patterns. The input patterns are encoded and passed to embedding layer. GCN uses the session graphs as input. The experiments on data show that the optimized GCN (OpGCN) was able to achieve higher prediction rate with around 93% accuracy as compared with simple GCN (around 88%).
R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

Abstract The paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis.
Modeling Prosodic Phrasing With Multi-Task Learning in Tacotron-Based TTS

Tacotron-based end-to-end speech synthesis has shown remarkable voice quality. However, the rendering of prosody in the synthesized speech remains to be improved, especially for long sentences, where prosodic phrasing errors can occur frequently. In this paper, we extend the Tacotron-based speech synthesis framework to explicitly model the prosodic phrase breaks. We propose a multi-task learning scheme for Tacotron training, that optimizes the system to predict both Mel spectrum and phrase breaks. To our best knowledge, this is the first implementation of multi-task learning for Tacotron based TTS with a prosodic phrasing model. Experiments show that our proposed training scheme consistently improves the voice quality for both Chinese and Mongolian systems.
A Zero-Shot Sketch-Based Intermodal Object Retrieval Scheme for Remote Sensing Images

Conventional existing retrieval methods in remote sensing (RS) are often based on a uni-modal data retrieval framework. In this work, we propose a novel inter-modal triplet-based zero-shot retrieval scheme utilizing a sketch-based representation of RS data. The proposed scheme performs efficiently even when the sketch representations are marginally prototypical of the image. We conducted experiments on a new bi-modal image-sketch dataset called Earth on Canvas (EoC) conceived during this study. We perform a thorough bench-marking of this dataset and demonstrate that the proposed network outperforms other state-of-the-art methods for zero-shot sketch-based retrieval framework in remote sensing.
A Conceptual Data Model and Its Automatic Implementation for IoT-Based Business Intelligence Applications

In the context of Industry 4.0, the analysis of Internet-of-Things (IoT) data with business intelligence (BI) technologies has acquired high relevance. However, designing and implementing IoT-based BI applications is hard for several reasons. Therefore, we propose a novel conceptual data model based on UML profiles and model-driven architecture (MDA) for modeling and implementing IoT-based BI applications. Our approach provides highly readable data models of IoT, which are also compatible with traditional BI data models. Furthermore, it could help in the implementation process of the IoT subsystem through automatic code generation.
The Role of Social Media in Development of English Language Writing Skill at School Level

Social media have been used for teaching and learning for quite some time.Social media as a learning platform making it possible for students to do self-study, exchange ideas, give comments, and submit the assignments in order to improve their grammar knowledge and writing ability.This study investigated to see how students perceived Social media in terms of ease of use, usefulness, and attitude toward the use of Social media for doing the activities and examined whether these factors were correlated.The sample consisted of 132 secondary school students selected via random sampling technique.The data was tabulated and analyzed by SPSS version 23 and applying descriptive statistical tools.It was found that social media plays a significant role in development of English language writing performance at school level.
Accelerated Zeroth-Order Momentum Methods from Mini to Minimax Optimization

In the paper, we propose a new accelerated zeroth-order momentum (Acc-ZOM) method to solve the non-convex stochastic mini-optimization problems. We prove that the Acc-ZOM method achieves a lower query complexity of $O(d^{3/4}\epsilon^{-3})$ for finding an $\epsilon$-stationary point, which improves the best known result by a factor of $O(d^{1/4})$ where $d$ denotes the parameter dimension. The Acc-ZOM does not require any batches compared to the large batches required in the existing zeroth-order stochastic algorithms. Further, we extend the Acc-ZOM method to solve the non-convex stochastic minimax-optimization problems and propose an accelerated zeroth-order momentum descent ascent (Acc-ZOMDA) method. We prove that the Acc-ZOMDA method reaches the best know query complexity of $\tilde{O}(\kappa_y^3(d_1+d_2)^{3/2}\epsilon^{-3})$ for finding an $\epsilon$-stationary point, where $d_1$ and $d_2$ denote dimensions of the mini and max optimization parameters respectively and $\kappa_y$ is condition number. In particular, our theoretical result does not rely on large batches required in the existing methods. Moreover, we propose a momentum-based accelerated framework for the minimax-optimization problems. At the same time, we present an accelerated momentum descent ascent (Acc-MDA) method for solving the white-box minimax problems, and prove that it achieves near the best known gradient complexity of $\tilde{O}(\kappa_y^{4}\epsilon^{-3})$ without large batches. Extensive experimental results on the black-box adversarial attack to deep neural networks (DNNs) and poisoning attack demonstrate the efficiency of our algorithms.
Google CoLaboratory as a Platform for Python Coding with Students

Google CoLaboratory (Google CoLab) is a powerful collaborative tool for coding in Python with students. This work presents a project to calculate the period of an eclipsing binary system that was completed by Stanford Online High School students using Google CoLaboratory. The Las Cumbres Observatory 0.4m telescopes were used to obtain images, and photometry from the Our Solar Siblings pipeline was imported into Google CoLaboratory using JSON (Javascript Object Notation) for analysis in Python. Some additional classroom applications of Google CoLaboratory are highlighted, such as converting between astronomical coordinate systems.
A Combination of Frequent Pattern Mining and Graph Traversal Approaches for Aspect Elicitation in Customer Reviews

Due to the remarkable increase in e-commerce transactions, people try to have an appropriate choice of purchase through considering other people's reflected experience in product's or service's reviews.Automatic analysis of such corpus requires enhanced developed algorithms based on natural language processing and opinion mining.Moreover, the linguistic differences make extending existing algorithms from one language to another challenging and in some cases impossible.Opinion mining focuses on different subjects of review analysis such as spam detection, aspect elicitation and polarity allocation.In this article, we focus on detection of explicit aspect and propose a methodology to overcome some difficult and problematic aspect compounds in the form of multi-words format in Persian language.Our approach proposes the construction of a directed weighted graph (ADG structure) based on some yielded information from FP-Growth frequent pattern identification algorithm on our corpus of Persian sentence.Traversing some special paths within the ADG graph according to our developed rules could lead us to the extraction of problematic multi-word aspects.We utilize Neo4j NoSQL graph database environment and its Cypher query language in order to create the ADG graph and access the desired paths that reflects our developed rules on the ADG structure which lead us to extract the multi-word aspects.The evaluation of our methodology with the existing approaches on the issue of aspect derivation in Persian language including ELDA, SAM, an MMI-based and an LRT-based algorithms indicates the robustness of our approach.
Hybrid Cryptography: Performance Analysis of Various Cryptographic Combinations for Secure Communication

The amount of data that is transmitted across the internet is continuously increasing.With the transmission of this huge volume of data, the need of an encryption algorithm that guarantees the data transmission speedily and in a secure manner is a must.Hence, to achieve security in wireless networks, cryptography plays a very important role.In this paper, several hybrid combinations, which combines both symmetric and asymmetric cryptographic techniques to offer high security with minimum key maintenance is presented.This hybrid combination offers several cryptographic primitives such as integrity, confidentiality and authentication, thereby enhancing the security.Various combinations of Advanced Encryption Standard (AES), Elliptical Curve Cryptography (ECC) and Rivest, Shamir and Adleman (RSA) algorithms are used to provide hybrid encryption.Secure Hash Algorithm (SHA-256) is also used to provide authentication and integrity.The experimental results show that the proposed hybrid combinations gives better performance in terms of computation time compared to individual cryptographic schemes.
An Effective Dense Co-Attention Networks for Visual Question Answering

At present, the state-of-the-art approaches of Visual Question Answering (VQA) mainly use the co-attention model to relate each visual object with text objects, which can achieve the coarse interactions between multimodalities. However, they ignore the dense self-attention within question modality. In order to solve this problem and improve the accuracy of VQA tasks, in the present paper, an effective Dense Co-Attention Networks (DCAN) is proposed. First, to better capture the relationship between words that are relatively far apart and make the extracted semantics more robust, the Bidirectional Long Short-Term Memory (Bi-LSTM) neural network is introduced to encode questions and answers; second, to realize the fine-grained interactions between the question words and image regions, a dense multimodal co-attention model is proposed. The model’s basic components include the self-attention unit and the guided-attention unit, which are cascaded in depth to form a hierarchical structure. The experimental results on the VQA-v2 dataset show that DCAN has obvious performance advantages, which makes VQA applicable to a wider range of AI scenarios.
Lattice Gauge Theory for a Quantum Computer

The quantum link [1] Hamiltonian was introduced two decades ago as an alternative to Wilson's Euclidean lattice QCD with gauge fields represented by bi-linear fermion/anti-fermion operators.When generalized this new microscopic representation of lattice field theories is referred as D-theory [2].Recast as a Hamiltonian in Minkowski space for real time evolution, D-theory leads naturally to quantum Qubit algorithms.Here to explore digital quantum computing for gauge theories, the simplest example of U(1) compact QED on triangular lattice is defined and gauge invariant kernels for the Suzuki-Trotter expansions are expressed as Qubit circuits capable of being tested on the IMB-Q and other existing Noisy Intermediate Scale Quantum (NISQ) hardware.This is a modest step in exploring the quantum complexity of D-theory to guide future applications to high energy physics and condensed matter quantum field theories.
Intelligent Tutoring Supported Collaborative Learning (ITSCL): A Hybrid Framework

Recently Intelligent Tutoring Systems (ITS) and Computer-Supported Collaborative Learning (CSCL) have got much attention in the field of computer science, artificial intelligence, cognitive psychology, and educational technologies.An ITS is a technologically intelligent system that provides an adaptive learning paradigm for an individual learner only, while CSCL is also a technology-driven learning paradigm that supports groups of learners in pertaining knowledge by collaboration.In a multidisciplinary research field-the Learning Sciences, both individual and collaborative learning have their own significance.This research aims to extend ITS for collaborative constructivist view of learning using CSCL.Integrating both design architecture of CSCL and ITS, this research model propose a new conceptual framework underpinning "Intelligent Tutoring Supported Collaborative Learning (ITSCL)".ITSCL extend ITS by supporting multiple learners interacting system.ITSCL support three different types of interaction levels.The first level of interaction supports individual learning by learner-tutor interaction.The second and third level of interaction support collaborative learning, by learner-learner interaction and tutor-group of collaborative learners' interactions, respectively.To evaluate ITSCL, a prototype model was implemented to conduct few experiments.The statistical results extrapolate the learning gains, measured from Paired T-Test and frequency analysis, contend a significant learning gain and improvement in the learning process with enhanced learning performance.
Information Technology Risk Management Using ISO 31000 Based on ISSAF Framework Penetration Testing (Case Study: Election Commission of X City)

Election Commission of X City is an institution that serves as the organizer of elections in the X City, which has a website as a medium in the delivery of information to the public and as a medium for the management and structuring of voter data in the domicile of X City.As a website that stores sensitive data, it is necessary to have risk management aimed at improving the security aspects of the website of Election Commission of X City.The Information System Security Assessment Framework (ISSAF) is a penetration testing standard used to test website resilience, with nine stages of attack testing which has several advantages over existing security controls against threats and security gaps, and serves as a bridge between technical and managerial views of penetration testing by applying the necessary controls on both aspects.Penetration testing is carried out to find security holes on the website, which can then be used for assessment on ISO 31000 risk management which includes the stages of risk identification, risk analysis, and risk evaluation.The main findings of this study are testing a combination of penetration testing using the ISSAF framework and ISO 31000 risk management to obtain the security risks posed by a website.Based on this research, obtained the results that there are 18 security gaps from penetration testing, which based on ISO 31000 risk management assessment there are two types of security risks with high level, eight risks of medium level security vulnerabilities, and eight risks of security vulnerability with low levels.Some recommendations are given to overcome the risk of gaps found on the website.
Features of perception and understanding of digital texts: interdisciplinary view

М. Ю. Лебедева, Т
Blockchain-based multi-party proof of assets with privacy preservation

There are a number of requirements associated with cryptocurrency transactions, and one such requirement is to preserve the privacy in proof of assets for multiple parties. Specifically, multi-party proof of assets assures one that a large number of guarantors control sufficient assets that satisfy the lender’s predefined value, for example in credit guarantee transactions. However, privacy-preserving multi-party proof of assets for cryptocurrency transactions remains challenging to achieve. This paper introduces a novel concept of privacy-preserving multi-party proof of assets for cryptocurrency transactions, where the proof process does not disclose their cryptocurrency addresses and individual holding. We formalize both the system model and the security model, and present a concrete protocol. Finally, we show that the proposed protocol is provably secure and efficient.
LAUN Improved StarGAN for Facial Emotion Recognition

In the field of facial expression recognition, deep learning is extensively used. However, insufficient and unbalanced facial training data in available public databases is a major challenge for improving the expression recognition rate. Generative Adversarial Networks (GANs) can produce more one-to-one faces with different expressions, which can be used to enhance databases. StarGAN can perform one-to-many translations for multiple expressions. Compared with original GANs, StarGAN can increase the efficiency of sample generation. Nevertheless, there are some defects in essential areas of the generated face, such as the mouth and the fuzzy side face image generation. To address these limitations, we improved StarGAN to alleviate the defects of images generation by modifying the reconstruction loss and adding the Contextual loss. Meanwhile, we added the Attention U-Net to StarGAN's generator, replacing StarGAN's original generator. Therefore, we proposed the Contextual loss and Attention U-Net (LAUN) improved StarGAN. The U-shape structure and skip connection in Attention U-Net can effectively integrate the details and semantic features of images. The network's attention structure can pay attention to the essential areas of the human face. The experimental results demonstrate that the improved model can alleviate some flaws in the face generated by the original StarGAN. Therefore, it can generate person images with better quality with different poses and expressions. The experiments were conducted on the Karolinska Directed Emotional Faces database, and the accuracy of facial expression recognition is 95.97%, 2.19% higher than that by using StarGAN. Meanwhile, the experiments were carried out on the MMI Facial Expression Database, and the accuracy of expression is 98.30%, 1.21% higher than that by using StarGAN. Moreover, experiment results have better performance based on the LAUN improved StarGAN enhanced databases than those without enhancement.
Hybridization of Grey Wolf Optimizer and Crow Search Algorithm Based on Dynamic Fuzzy Learning Strategy for Large-Scale Optimization

A novel optimization algorithm named hybrid grey wolf optimizer with crow search algorithm (GWO-CSA) is developed in this paper for handling large-scale numerical optimization problems.The proposed GWO-CSA algorithm combines the strong points of both grey wolf optimizer (GWO) and crow search algorithm (CSA) with the aim to escape from local optima with faster convergence than the standard GWO and CSA.In this algorithm, GWO operates in enhancing the exploration ability while CSA works as a local searching scheme to emphasize the exploitation capability to achieve global optimal solutions.In this sense, the movement direction and speed of leader grey wolf (alpha) is improved by incorporating the CSA phase.Also, a dynamic fuzzy learning strategy (DFLS) is introduced to enable the occurring of tiny changes in the neighborhood of the best solution to avoid the caught in the local optima and refine the quality of the obtained solution.The robustness and efficiency of the proposed GWO-CSA algorithm are investigated on fifteen CEC 2015 benchmark problems in addition to four large-scale problems and four real applications related to engineering design optimization taken from the literature.The comprehensive comparisons with other algorithms have demonstrated the effectiveness of GWO-CSA to address optimization tasks.
Attributes-Based Decision Making for Selection of Requirement Elicitation Techniques Using the Analytic Network Process

Requirement engineering is the first phase of software engineering. In requirement engineering, the first phase is requirement elicitation (RE), which is the most critical and error-prone activity. In this phase, the requirements are extracted from various sources; after extraction, they are analyzed and documented for a specific purpose of software development. In RE, process requirements from stakeholders are gathered, upon which the entire software product failure and success are dependent. In order to accomplish the goal of requirement elicitation, various techniques are used. However, the selection of these techniques is a very challenging task, as one technique may suit a situation but may not be suited for other situations. Besides this, project attributes such as documentation culture of organization, degree of relationship among stakeholders, and familiarity to domain also have a great impact on the process of technique selection. The reason is that there is no empirical value of the techniques that provide help in techniques selection to analyze the basis software project attributes. This study proposed the analytic network process, which is one of the multicriteria decision making processes for the elicitation technique selection process with respect to criterion attributes of project. The motivation toward the use of the ANP approach for the selection of requirement selection technique is that there are dependencies existing among attributes of the project elements. So, the ANP approach is capable of dealing with such situations where dependencies and complexity occur. Results of the proposed study demonstrate that the technique helps in complex situations where decision making is difficult based on the alternatives.