Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence to Identify Ideas

Artificial Intelligence (AI) is now portrayed as a solution provider. Once considered to be an esoteric topic for being too math-heavy, AI has now become a household term with its presence in advertisements for commonly used items such as smartphones. AI devices fall under two fundamental categories – applied and general. Applied AI which is common in nature refers to systems designed to intelligently trade stocks and shares; whereas generalized AI, less common in nature, refers to systems that are capable of handling any task. Generalized AI, an emerging field, aims at functioning as a human with an ability to deal with complex situations with analytical thinking and depth of knowledge so that it can deal with diverse topics.
VEGAS: A Variable Length-Based Genetic Algorithm for Ensemble Selection in Deep Ensemble Learning

In this study, we introduce an ensemble selection method for deep ensemble systems called VEGAS. The deep ensemble models include multiple layers of the ensemble of classifiers (EoC). At each layer, we train the EoC and generates training data for the next layer by concatenating the predictions for training observations and the original training data. The predictions of the classifiers in the last layer are combined by a combining method to obtain the final collaborated prediction. We further improve the prediction accuracy of a deep ensemble model by searching for its optimal configuration, i.e., the optimal set of classifiers in each layer. The optimal configuration is obtained using the Variable-Length Genetic Algorithm (VLGA) to maximize the prediction accuracy of the deep ensemble model on the validation set. We developed three operators of VLGA: roulette wheel selection for breeding, a chunk-based crossover based on the number of classifiers to generate new offsprings, and multiple random points-based mutation on each offspring. The experiments on 20 datasets show that VEGAS outperforms selected benchmark algorithms, including two well-known ensemble methods (Random Forest and XgBoost) and three deep learning methods (Multiple Layer Perceptron, gcForest, and MULES).
Natural interfaces in VR - comparative analysis

The article presents the results of a comparative analysis of contemporary virtual reality devices. The analysis focuses on both the analysis of technical parameters of the goggles as well as comparison of natural interfaces. The following devices were tested: HTC Vive, Oculus Rift, PlayStation VR, Samsung Gear VR. The most ergonomic and user-friendly interface turned out to be Oculus Rift, while goggles Samsung Gear VR were the worst from tested devices.
Design High-Frequency and Low-Power 2-D DWT Based on 9/7 and 5/3 Coefficient Using Complex Multiplier

The distinctive 1-D and 2-D discrete wavelet transform (DWT) models that subsist in the composing are scull-section, equal channel, crumbled, flipping, and iterative skeleton. The plans vary with approbation to the competition and gear essential, the memory reacquired to provision the data picture and widely appealing coefficients. The guideline focus of this investigation striving is to decide capable VLSI structures, for the gear use of the 9/7 and 5/3 DWT, using complex multiplier (CM) and improving the speed and hardware complicities of existing plans.
Study on information system infrastructure in Probolinggo by using Self Organizing Map

Abstract The infrastructure of information systems becomes one of the main components that support government managements system. This study is addressed to achieve the infrastructure information mapping in District of Probolinggo. The proposed of the research is getting an infrastructure information map of 29 districts administered that become its reliability recommendation. The system information as website is developed for operational purpose. The Self Organizing Map (SOM) as one of neural networks is used. The clustering results by in field observation data show that the infrastructure information system of 29 districts administered are defined by 3 clusters, specifically 3 districts administered have good infrastructure, 20 have fair infrastructure and 6 districts administered have less infrastructure. Furthermore, the clustering by using SOM shows that have 1 good, 22 fair and 5 less, respectively. As a result, the accuracy of both clustering is less than 70 %. That means, SOM still need in field observation data to evaluate the recommendation.
The Design of E-C Parallel Corpus in Marine Environment and the Translation Difficulties of World Ocean Review

Abstract The construction of marine environment parallel corpus is based on the translation of The World Ocean Review. The text translation has been completed, and a term list has been built. The next step is to construct the parallel corpus, and the researchers should design the corpus in order to avoid some problems before starting. During the translation process, the researchers also find some problems and try to solve them which could supply some suggestion to the future E-C translation of scientific text.
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks

In an attempt to better understand structural benefits and generalization power of deep neural networks, we first present a novel graph theoretical formulation of neural network models, including fully connected, residual network (ResNet) and densely connected networks (DenseNet). Second, we extend the error analysis of the population risk for a two-layer network [W. E., C. Ma, and L. Wu, Commun. Math. Sci., 17 (2019), pp. 1407--1425] and ResNet [W. E., C. Ma, and Q. Wang, Commun. Math. Sci., 18 (2020), pp. 1755--1774] to DenseNet, and show further that for neural networks satisfying certain mild conditions, similar estimates can be obtained. These estimates are a priori in nature since they depend solely on the information prior to the training process, in particular, the bounds for the estimation errors do not suffer from the curse of dimensionality.
Topological Interpretable Multi-scale Sequential Recommendation

Sequential recommendation attempts to predict next items based on user historical sequences. However, items to be predicted next depend on user’s long, short or mid-term interest. The multi-scale modeling of user interest in an interpretable way poses a great challenge in sequential recommendation. Hence, we propose a topological data analysis based framework to model target items’ explicit dependency on previous items or item chunks with different time scales, which are easily changed into sequential patterns. First, we propose a topological transformation layer to map each user interaction sequence into persistent homology organized in a multi-scale interest tree. Then, this multi-scale interest tree is encoded to represent natural inclusion relations across scales through an recurrent aggregation process, namely tree aggregation block. Next, we add this block to the vanilla transformer, referred to as recurrent tree transformer, and utilize this new transformer to generate a unified user interest representation. The last fully connected layer is utilized to model the interaction between this unified representation and item embedding. Comprehensive experiments are conducted on two public benchmark datasets. Performance improvement on both datasets is averagely $$5\%$$ over state-of-the-art baselines.
Introduction to Cloud Computing

Cloud computing is an online platform where we can easily get our data from database with the help of network services such as 'Internet'.It provides on demand service to the client.Data storage is among one of the primary services of the cloud.Cloud computing is a paradigm for delivering the IT where rapid provisioning is an important factor for computing resources and data application in an IT sector.It provides several cloud services such as "SaaS, PaaS, IaaS" to the clients.It allows using application without access and installation their personal files on computer with internet access because of which data storage and processing become more efficient.
Socializing the web

Though today we think of the web and social media as nearly synonymous, the technology of the early web made social interaction difficult. The author discusses her work creating some of the web's earliest social applications and asks why our interfaces for seeing and communicating with each other online are still so primitive.
Organization and conducting the state final certification using distancelearning technologies

The classical procedure for conducting state final certification of students in critical conditions, such as a pandemic in 2020, is impossible without observing all measures to ensure the safety of participants in the procedure. Educational organizations faced legal, technical and organizational challenges when organizing the state final certification in June 2020. The implementation of basic educational programs both in the second academic semester of the academic year and during the final certification was carried out remotely through electronic technologies. The educational institutions were not ready for the situation, but in general they coped with it successfully. The purpose of the work is to study the experience of an educational organization in adapting to remote technologies in a pandemic. The experience of organizing the educational process in the context of a pandemic, technical and technological training of teaching staff, ensuring the operation of an electronic information and educational environment, or ganizing preparatory stages and directly conducting the state final certification in the SamGUPS branch in Nizhny Novgorod according to secondary vocational education programs. Legal aspects, issues of technical organization of the procedure and related processes, modernization of workflow and work of the commission online are considered. The results of the work of the state examination commission in 2020 were summed up and recommendations for the use of this experience in the future were proposed.
esearch activities as a creative component of training and professionalization of specialists

The paper deals with the problem of professionalization of University students by involving them in active research activities. The authors consider the development of the concept of higher science education and the model of training specialists to be an important element of professional competence education. The review analyzes the regularities of the formation of professionalism and the possibility of their use in higher education. According to the authors, the conception of scientific education, developed on the basis of a system-synergetic approach, could serve as a theoretical basis for the preparation of a new extended conception of natural science education for training specialists in physics, chemistry, biology, synergetics, ecology, geography, and biotechnology. The article shows that natural science education provides an opportunity for modern young people to use the methods of modern science, actively work in the relevant fields, and create. Such education, in addition to the educational component, includes an educational and scientific innovation environment and the necessary material and technical resources. Research activities of bachelors and masters are considered by us as a creative component of the formation of professionalism, innovative methods of training and collective cooperation are analyzed, and interesting work experience is promoted.
Unsupervised learning of depth and ego-motion with absolutely global scale recovery from visual and inertial data sequences

In this paper, we propose an unsupervised learning method for jointly estimating monocular depth and ego-motion, which is capable to recover the absolute scale of global camera trajectory. In order to solve the general problems of scale drift and scale ambiguity of monocular camera, we fuse geometric movement data from inertial measurement unit (IMU), and use Bi-directional Long Short-Term Memory (BiLSTM) to extract temporal features. Besides, we add a lightweight and efficient attention mechanism, Convolutional Block Attention Module (CBAM), to Convolutional Neural Networks (CNNs) to complete the extraction of image features. Considering the scenes with severe illumination changes, ambiguous structures, moving objects and occlusions, especially scenes with progressively-variant textures, the geometric features can provide adaptive estimation results in the case of the degeneration of visual features. Experiments on the KITTI driving dataset reveal that our scheme achieves promising results in the estimation of camera pose and depth. Moreover, the absolute scale recovery for the global camera trajectory is effective.
Hybrid Algorithm for Naïve Bayes-Based Credit Card Fraud Detection

In today’s world, the most widely means of payment in online and offline is the credit card [cc] which provides a method to provide cashless shopping. Credit cards being used are escalating and the fraudulent activity is also increasing due to which a huge amount of money is lost. Thus, it is very much essential to have mechanisms to overcome fraudulent activities. The existing systems used the users spending criteria to find out whether a fraud is happening. Even when multiple algorithms were used to predict fraudulent activity, the usage of a specific attribute led to the failure of few systems. The proposed system detects fradulent activity of cc using ML. When a user makes a transaction, a combination of standard algorithms like logistic regression and decision trees along with hybrid algorithms like majority voting method and Adaboost are used to stop fraudulent transactions. The results of the hybrid algorithms form clusters by using the k-means algorithm and finally the Naive Bayes is used to do the final prediction whether it is fraud or not. Based on the resultsk it can be analyzed that the prediction is not accurate when the trained dataset is small and the accuracy increases with increase in the dataset.
Optimization of Design Problems Using TLBO and mTLBO Algorithms

Every design problem requires optimization. There are many traditional and advanced optimization techniques available. Teaching-learning-based optimization algorithm has been proved beneficial in many engineering applications. This algorithm is free from any algorithm-specific parameters and can be adapted to all types of design problems. However, there are some limitations like convergence to local optimal solution, large computational time and slow convergence rate for complex functions. Some modifications were introduced to overcome these drawbacks in mTLBO algorithm. This paper gives better idea of both algorithms by applying them to optimize standard engineering design problems. Their performance was compared with differential evolution algorithm and some hybrid approaches of this algorithm. Best solution was obtained for all the testing problems using mTLBO algorithm. Also the solutions were obtained using less number of evaluations than other algorithms. Therefore, from present work, it can be inferred that mTLBO algorithm gives global optimum solution and requires less computational efforts.
THE PARADIGM OF EDUCATION IN THE ERA OF THE FOURTH INDUSTRIAL REVOLUTION

The goal is to substantiate the possibility of using the culture-centered paradigm of education as the basis for the development of education in the era of the fourth industrial revolution, which forms a person with a technocratic mindset, aimed at forming a person not only as a carrier of knowledge, but also as a carrier of values and traditions. Methods. The structural and functional approach was used as the methodological basis of the research, which made it possible to identify the main trends in the development of modern civilization, which actualize the need to form a culture-centered paradigm of education. The use of structural-functional and systemic approaches made it possible to identify the dialectical relationship between education, economy and quality of life. Results. The fourth industrial revolution, based on the digital economy, actualizes the problem of forming a new person. One of the mechanisms for solving this problem is education, the main development trends of which must meet the realities of our time. The main task of education is to prepare a person with creative thinking who has innovative professional competencies - the competencies of the XXI century. In connection with the above-mentioned modern trends, it is necessary to raise the question of the paradigm of education development. Each epoch in the history of mankind creates its own paradigms for the development of education. However, it should be borne in mind that different educational paradigms may exist in the same era, replacing each other. Conclusions. The analysis of existing paradigms allows the authors to prove that the basis of education in the era of the fourth industrial revolution should be a culture-centered paradigm as a reflection of various aspects of human life, which implements its content and functions in the "values-positions-knowledge-skills" scheme.
Extending User Coverage on Virtual Campus Using Web3D Technology: A Case Study

Abstract Web3D site which uses standard format has begun to elicit difficulties for some users, coupled with browser compatibility that is increasingly narrowing. This also happened on the sites which represented an institution in the form of a virtual college campus. For the campus Web3D to keep providing its services and facilitate access to more prospective users in the future, an update is needed that requires major changes. This can be done by the use of different technology approach which has wider features and possibilities of use. In this research, an experiment was first carried out to see the prospect of using it on the campus Web3D site. Furthermore, the prototype world using newer format was developed and then tested in a variety of browsers that are currently widely used. The resulting world was able to be displayed on more browsers and platforms, and it kept the same complexity which leads to similar appearance and functionality as the previous one. Performance decrease did occur, so further optimization was needed.
Handling Industrial Consumer Rights by Using Blockchain

The main objective of this research paper is to propose a business model to deal with warranties of products bought by clients and present a prototype implementation for a vehicle supply chain. Many customers have fake warranties which reduce customer loyalty to large companies. Therefore, the blockchain features can be used here as a technical solution. It can connect each device data and product lifetime to preserve consumer rights, by arranging companies with the highest guarantee under the blockchain. Furthermore, a proposed blockchain business model (BBM) is presented that regulates the life cycle of any product, the manufacturer under the control of the blockchain. The paper provided a case study for vehicle industry. After implementing blockchain structures, it was proven and observed that the blockchain is scalable to huge number of devices. This leads to obvious results and transparency among the giant companies in the interest of the client, and eliminating consumer rights issues such as black market and over prices.
An Obfs-based Tor Anonymous Communication Anline Identification Method

Obfs-based Tor is currently the most widely used type of anonymous communication network. The identification of this type of Tor communication can directly help block a large number of Tor anonymous communication. The existing recognition technology for this type of Tor communication extracts the characteristics of the overall data stream in the Tor communication process and combines the SVM classifier, which has low recognition efficiency and is difficult to adapt to online rapid detection. Therefore, this manuscript proposes an Obfs-based Tor communication online identification method. Different from the detection technology based on two-stage filtering that requires multiple data packets for identification, the proposed method only requires one data packet to identify this type of Tor communication during the communication process, thereby significantly improving the identification efficiency. At the same time, considering that the communication data packet has been encrypted for many times, it is difficult to manually extract the format features, and deep learning is introduced to extract the hidden features of the data packet, and the recognition model is constructed to maintain the accuracy of the recognition of this type of Tor communication. In a real Internet environment, compared to real-time identification of three Tor pluggable transports using machine learning techniques, this method increases the recognition speed of this type of Tor communication by four times, while increasing the recognition accuracy from 98 % to 99.9%. Experiments show that this method has the advantages of high recognition efficiency and high accuracy, and can quickly identify Obfs-based Tor traffic in a large amount of confused traffic.
SQE-GAN: A Supervised Query Expansion Scheme via GAN

Existing Supervised Query Expansion (SQE) spends much time in term feature extraction but generates sub-optimal expanded terms. In this paper, we introduce Generative Adversarial Nets (GANs) and propose a GAN-based SQE method (SQE-GAN) to get helpful query expansion terms. We unify two types of models in query expansion: the generative model and the discriminative one. The generative (resp., discriminative) model focuses on predicting relevant terms (resp., relevancy) given a query (resp., a query-term pair). We iteratively optimize both models with a game between them. Besides, a BiLSTM layer is adopted to encode the utility of a term with respect to the query. As a result, the costly feature calculation in SQE schemes is avoided, such that the efficiency can be significantly improved. Moreover, by introducing GAN into expansion, the expanded terms are possible to be more effective with respect to the eventual needs of the user. Our experimental results demonstrate that SQE-GAN can be 37.3% faster than state-of-the-art SQE solutions while outperforming some recently proposed neural models in the retrieval quality.
Development of information system based on mobile stock management in outlet

Abstract Garut is a regency in West Java that has various potentials that can be developed. Now many industries have been established. The food or culinary industry is one of the special characteristics of Garut Regency. One of them is a company engaged in the dodol food industry, PT. Herlinah Cipta Pratama, which has several outlets in terms of consignment delivery of dodol. Problems for the delivery of dodol occur because of the shipping process by PT. Herlinah Cipta Pratama which does not match the stock management at the outlet. Therefore, it will need a system that can manage dodol stock in outlets that can be set by PT. Herlinah Cipta Pratama. With the Unified Software Development Process design method with the stages of Requirement, Analysis, Design and Implementation. The results of this study in the form of dodol stock information system design at PT. Herlinah is expected to be able to help the problems in the stock management with the aim that both parties can know the consignment process.