Reviewerly/exHarmony huggingface dataset:
Technology and Luxury in Tourism and Hospitality

This chapter intends to give insights into the use of technologies such as virtual reality (VR), augmented reality (AR) and artificial intelligence (AI) in tourism and hospitality. Thus, the objectives are to: (1) give an overview about the meaning of luxury the use of technologies in tourism and hospitality, (2) provide examples about the use of such technologies (e.g., VR, AR, AI in practice) and (3) point out suggestions about further research in the interface of luxury and technologies in tourism and hospitality. The chapter is of interest to both academics and practitioners since it presents luxury, VR, AR and AI concepts, gives examples of technologies applications and points out critical areas for future research.
Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM)

In order to further accurately predict gas emission of working face, this paper proposes a prediction model of gas emission of working face based on the combination of improved artificial bee colony algorithm and weighted least squares support vector machine (IABC-WLSSAVM). The research steps are as follows: Firstly, in order to obtain the sparse solution of LSSVM, a more reliable prediction model is realized by weighting the error value. Secondly, the chaotic sequence is introduced into the artificial bee colony algorithm to find a better initial honey source, which increases the diversity of the population, and combines the Levy flight to update the search step to avoid falling into the trap of local optimum. At the same time, the improved artificial bee colony algorithm is used to optimize the kernel width <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>σ</mi> </math> and regularization parameter <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>λ</mi> </math> of WLSSVM, which improves the prediction accuracy and convergence rate of WLSSVM. Finally, the quantitative analysis model of WLSSVM is reconstructed by using the optimized parameters, and the nine parameters of buried depth of coal seam, gas content of coal seam, coal thickness, interlayer lithology, production rate of working face, length of working face, inclination of coal seam, gas content of adjacent layer, and thickness of adjacent layer are used as the main influencing factors. After normalization, the nonlinear prediction model of gas emission is established. The simulation results based on the three indicators of determination coefficient, root mean square error, and average relative variance show that the IABC-WLSSVM prediction model proposed in this paper can not only overcome the local optimization to obtain the global optimal solution but also has faster convergence speed and higher prediction accuracy. This prediction model has obvious advantages compared with the other three improved prediction models in terms of fitting, accuracy, and generalization ability, which can provide a reliable theoretical basis for the prediction of gas emission in coal mining face under complex factors and propose a new idea for the application of artificial intelligence in the construction of intelligent mines. At the same time, the prediction model can also be applied to other fields.
Nonlinear-response neurons using a quantum-dot network for neuromorphic computing

This paper describes a neuromorphic-computing architecture using a quantum dot network. In the experiment, we confirmed that the quantum dot network operates as an artificial neuron with a nonlinear response to light intensity.
FedAggs: Optimizing Aggregate Queries Evaluation in Federated RDF Systems

With the increasing scale of RDF datasets and the security requirements of data management, federated RDF systems are becoming a research hotspot. However, the existing federated RDF systems only support basic queries in SPARQL 1.0, and cannot be compatible with complex queries in SPARQL 1.1 well, such as aggregate queries. In this demo, we develop a demonstration system, named FedAggs, which can support efficient aggregate queries in federated RDF system. It implements five common aggregate operators in federated RDF system: MAX, MIN, AVG, SUM and COUNT. FedAggs improves the efficiency of aggregate query processing by adopting a query decomposition optimization method, employing a cost-based optimal query plan generation algorithm and an incremental execution strategy according to the characteristics of five aggregate operators. Experimental results show that FedAggs has good query performance.
Constructing the three-qudit unextendible product bases with strong nonlocality

Unextendible product bases (UPBs) are interesting members of a family of orthogonal product bases. Here, we investigate the construction of 3-qudit UPBs with strong nonlocality. First, a UPB set in C 3 ⊗ C 3 ⊗ C 3 of size 19 is presented based on the shift UPBs. By mapping the system to a Rubik’s cube, we provide a general method of constructing UPBs in C d ⊗ C d ⊗ C d of size ( d – 1 ) 3 + 2 d + 5, whose corresponding Rubik’s cube is composed of four parts. Second, for the more general case where the dimensions of parties are different, we extend the classical tile structure to the 3-qudit system and propose the tri-tile structure. By means of this structure, a C 4 ⊗ C 4 ⊗ C 5 system of size 38 is obtained based on a C 3 ⊗ C 3 ⊗ C 4 system of size 19. Then, we generalize this approach to the C d 1 ⊗ C d 2 ⊗ C d 3 system which also consists of four parts. Our research provides a positive answer to the open question raised in by Halder et al. [ Phys. Rev. Lett . 122 040403 (2019)], indicating that there do exist UPBs that can exhibit strong quantum nonlocality without entanglement.
Online education as a temptation: unobvious challenges

The article is devoted to the phenomenal and procedural aspects of online practices implementation in modern education systems on the example of Ukraine. Using a number of sociological studies of educational practices in the Kharkiv region and Ukraine, the authors rethink effects and results of the intensification of educational online practices. Online is interpreted as a temptation that generates both large groups of supporters and many opponents. The consequences of online practices are represented in the form of non-obvious challenges, which are often interpreted in sociological and administrative discourse as advantages and positive effects, however, as the researchers demonstrate, they carry implied dangers. Specifically, the authors explore a number of temptations: less effort, less engagement, mobilization of participants in the educational process, commodification of education, symbolic struggle over the quality of education, unification of education, reforms in education, privatization and liberalization, the formation of an "attention economy". In this light, the authors isolate a number of dimensions and aspects of online education as a temptation, using the ideas of J. Habermas, N. Luhmann, Z. Bauman, S. Zizek, J. Ritzer, T. Eagleton and others. Separately, the article presents and discusses the conclusions and prospects for further research on this issue.
A Study on Pork Price Prediction Using LSTM

In this study, we propose LSTM, a technique for predicting pork prices to stabilize the supply and demand of pork. The LSTM model is trained using the attributes of day and month as additional inputs to historical time series data. The performance of the proposed LSTM model was compared to that of ARIMA, SARIMA, autoARIMA, and standard LSTM in terms of three performance metrics: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). As a result of the experiment, the error rate of the proposed LSTM model was significantly lower than that of other existing methods. In future research, it will be necessary to redefine the analysis of causal relationships such as livestock substitutes and supplements, and to conduct practical studies such as linking OpenAPI with big data of public institutions to improve the accuracy of price prediction.
A Novel Simon Light Weight Block Cipher Implementation in FPGA

For resource constrained devices, lightweight block cipher algorithm is highly indispensible. For low resource devices, use of light weight block ciphers ensures cost-efficiency. The most important problem in VLSI design implementation is balancing the parameter constraints such as power consumption, device utilization and latency. Light weight cryptography employs a cryptographic algorithm which indeed helps to reduce the resource constraints. The primary objective of the paper is to design an efficient Simon light weight block cipher implemented in FPGA. The Simon algorithm employing the structure for implementing XOR operations consumes more power, and it has critical path delay. But in our proposed system, clock gating and power gating methodology is utilized and it helps to reduce the delay and power consumption respectively. The design is implemented in Virtex-5 and Artix-7 FPGA devices and its performance is analyzed. From the obtained results, it is evident that the proposed methodology is efficient in terms of adaptive security level and also it is able to encrypt longer messages depending upon the sizes of encrypt and decrypt keys and blocks. Based on the obtained results, it is clearly viewed that the execution time and power consumption is highly reduced.
Augmentation of Local, Global Feature Analysis for online Character Recognition System for Telugu Language using Feed Forward Neural Networks (FFNN)

In this paper, we propose ANN based online handwritten character recognition for Telugu Language. In literature review, it is observed that Size of the database and preprocessing approaches plays prominent role in the recognition performance. Preprocessing techniques like normalization, interpolation, Uniformization, Smoothing, Slant Correction and resampling techniques are performed for better recognition performance. Local features like(x,y)co-ordinates, (x,y) (2 x,2 y) and the global features like tan( ) are considered as features for ANN modeling and Classification of 52 Telugu vowels and consonants. Recognition performance is evaluated by augmentation the local, global features and and tan ( ) Features. Theperformance is evaluated in terms of precision, recall and F-measure. Significant Improvement is reported by augmentation andby adopting preprocessing techniques. The database used for the study is HP-online Telugu database.
Hierarchically Reorganized Multi-Layer Fuzzy Neural Networks Architecture Driven With the Aid of Node Selection Strategies and Structural Network Optimization

In this study, a design methodology based on fuzzy sets inference and polynomial neural network(PNN) for hierarchically reorganized self-organizing network architecture is introduced to cope with over-fitting as well as multi-collinearity problems which generally appear in a conventional fuzzy neural network. The design method of the proposed self-organizing network structure provides an efficient solution to construct the hierarchically reorganized multi-layer fuzzy neural networks (HRmFNN) architecture through a synergy of multi-techniques such as L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization, probability theory, and multi-optimization. The overall network structure is realized with the aid of parallel network structure with newly added inputs as well as effective neuron selection method through the exponential-based roulette selection technique for each layer in HRmFNN, and the least square error estimation (LSE)-based learning method with L2-norm regularization is used for constructing the stabilized network architecture, and their ensuring design methodologies result in alleviating the overfitting phenomenon and also enhancing the generalization ability. For the performance enhancement of HRmFNN directly affected by some parameters such as the number of input variables, collocation of the specific subset of input variables, the number of membership functions per each variable, and the order of polynomial in the consequent parts of the fuzzy rules, multi-particle swarm optimization (MPSO) is exploited for the effectively structural as well as parametric optimization of the proposed network. That is, the multi-optimization helps achieve a compromise between the better generation performance and the alleviated over-fitting leading to the stabilization of the proposed multi-layered self-organizing network structure with the aid of synergistic multi-techniques such as a) L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization-based LSE learning, b) probability theory for effective neuron selection, and c) novel parallel network structure including newly added inputs and neuron selection method. The performance of the proposed network structure is quantified by comprehensive experiments and comparative analysis. It is also demonstrated through the application to cement compressive strength.
Creation of Effective Cooperation between Russia Youth and Foreign Higher Institutions in Research and Development Sphere

Currently, one of the most important factors of functioning and development of world society at the present stage is youth, which makes up about a fifth of the population of Russia and 18 % of the world's population.Youth plays an important role as a factor of social change, renewal, initiative, energy in the implementation of reforms and changes.Self-realization and self-organization of youth is possible on the basis of youth enterprises that contribute to the formation of their mentality, increase the level of their culture, the implementation of conceived projects and ideas, as well as aspirations.One of the priority directions of the development of universities is international activity, which must be an obligatory part of the education of specialists in various professional fields, who can know their way around foreign and domestic technologies freely and are competitive on the labour market.Russian universities are cooperating with international universities increasingly, what was caused by transitioning to the Bologna education process, Russia's entry into the WTO, the expansion of international cooperation.The article considers the directions of development of research cooperation of Russian and foreign youth enterprises; presents the best practices of cross-border interaction of youth enterprises; reviews the ways of development of research cooperation of Russian and foreign youth enterprises; analyses the system of criteria and algorithm of effective cooperation in the development of Russian and foreign youth research enterprises; suggests methodological recommendations for the competitive selection of Russian satellite universities as basis for research with the participation of youth.
Image Enhancement Techniques in Digital Image Processing

The procedure of image enhancement was to adjusting two or more pictures of the same scene. This procedure worried with assigning one picture as the reference also applying geometric change to alternate pictures so they adjust to suggestion. Ordinarily, the pictures are caught under variable conditions that can change camera setting. Misalignment can likewise be the consequence of lens also sensor or differentiate between catch gadgets. A geometric change maps areas in one picture to late areas in another. The progression of conclusive the ideal geometric change parameters key to the picture enhancement process. Picture enhancement was intermittently appropriate as a preparatory stride as a part of other picture hassling applications. For instance, you can utilize picture enhancement to adjust satellite pictures or to grouping of restorative pictures caught with diverse symptomatic modalities like MRI also SPECT. Picture enrollment permits you to examination of diverse picture estimations
String Matching Algorithm Based Filter for Preventing SQL Injection and XSS Attacks

Injection attacks are most often experienced computer security breaches. Among them, SQL injections with Boolean type of queries and cross-site scripts are mostly done. Probabilistic models fail to adapt to most of the new kind of attacks, due to the changing nature of these attacks. This paper proposes a novel technique for filtering SQL Boolean queries, and these queries are capable of bypassing the existing models but were trapped in this new model. The model uses Rabin-Karp algorithm which is based on the concept of string matching. An SQL query passed as user input is evaluated by the proposed query filter, which is designed to separate malicious queries from the normal queries and flag them as malicious. The proposed model of filtering was experimented on a JavaScript-based web application, designed to test the model with a number of Boolean queries. The results were promising with a maximum accuracy of 96%. Moreover, the model has proved to be effective against the Boolean SQL queries which shows that the SQL injection attacks could be prevented using this model.
A Comparison of Backtracking Algorithm in Time-Shared and Space-Shared VM Allocation Approaches Using CloudSim

Cloud is ubiquitous, and its computing is inevitable. In this post COVID-19 scenario cloud market has been increased multifold. During the pandemic and after the pandemic internet is the only medium of communication and governance throughout the world. This has attracted non-technical and technical users of internet as cloud users and cloud researchers. As a part of cloud computing Virtual Machine (VM) placed inside Physical Machine (PM) has taken a new dimension and migrations of VM from one PM to another are need of the hour. Numerous VM migration algorithms were already in the cloud market, and many of the new approaches are poured into the cloud. Our proposed backtracking approach is a proven algorithm for popular problems like N-Queen, Knapsack, etc. We have implemented the backtracking algorithm in time-shared and space-shared approaches in CloudSim, a cloud simulator in Java-eclipse platform. The experimental results show our backtracking in space-shared approach suits as a better candidate for VM migration algorithm.
The Stepchild of Informatics Education: File Management

colleges do not consider students' computational thinking skills, then why they test them at all, since illiteracy does not seem an obstacle to being accepted in tertiary education, if AP students are the best, what is the level below them like? Solution? Weak attempts "…school's AP program forwarded an email from the College Board to students on Sunday including tidbits of advice to prevent submission errors." "College Board's website instructs students with iPhones to change their camera settings so that photos save as JPEGs rather than HEICs." The College Board tried to help these students, but it was still not enough: "I thank the College Board for setting up a re-submission channel for students taking APs this week," Sisman said in a statement to The Verge. "I urge them to be fair to students who've already taken the APs and can't submit them. Many students will be in the military or working at summer jobs by June 1st and won't be able to take the makeup test." . "Now we might...
Evaluation of Performance Metrics of Thyroid Segmentation by Deep Learning Technique

Thyroid cancer is one of the commonly seen endocrine system cancer. Thyroid nodules appear as solid or fluid-filled masses on the thyroid. In many cases the thyroid nodules do not show any symptoms and due to this it leads to the critical situation up to death.All nodules are not cancerous and so it is very important to discriminate benign from malignant nodules. For diagnosing thyroid nodule the preferred imaging modality isUltrasound imaging.Due to inhomogeneous structure segmenting thyroid gland is a great challenge.Most of the researchers have implemented semi-automatic and automatic techniques to segment the nodules. In this paper we suggest a model to segment the region of interest by modifyingthe basic U-Netmodel. The performance metrics such as true positive, accuracy, F1-measure and dice coefficient is calculated and compared with basic model.
Question Answering Module Leveraging Heterogeneous Datasets

Question Answering has been a well-researched NLP area over recent years. It has become necessary for users to be able to query through the variety of information available - be it structured or unstructured. In this paper, we propose a Question Answering module which a) can consume a variety of data formats - a heterogeneous data pipeline, which ingests data from product manuals, technical data forums, internal discussion forums, groups, etc. b) addresses practical challenges faced in real-life situations by pointing to the exact segment of the manual or chat threads which can solve a user query c) provides segments of texts when deemed relevant, based on user query and business context. Our solution provides a comprehensive and detailed pipeline that is composed of elaborate data ingestion, data parsing, indexing, and querying modules. Our solution is capable of handling a plethora of data sources such as text, images, tables, community forums, and flow charts. Our studies performed on a variety of business-specific datasets represent the necessity of custom pipelines like the proposed one to solve several real-world document question-answering.
Graph Convolutional Networks for Cross-Modal Information Retrieval

In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.
Deep learning for subtyping the Alzheimer’s disease spectrum

In a recent article from Cell Reports Medicine, Kwak et al. generate novel insights about subtyping cognitively impaired individuals based on structural imaging. Quantifying heterogeneity in Alzheimer's disease via subtyping could help us harness new disease-modifying therapies and improve patient care by providing a more targeted approach.
Leveraging Collaborative Mobile Learning Instructional Pedagogy in The Era of COVID-19

Mobile collaborative learning is considered the next step of on-line collaborative learning by incorporating mobility as a key and breakthrough requirement. Indeed, the current wide spread of mobile devices and wireless technologies brings an enormous potential to e-learning, in terms of ubiquity, pervasiveness, personalization, flexibility, and so on. For this reason, Mobile Computer-Supported Collaborative Learning has recently grown from a minor research field to significant research projects covering a fairly variety of formal and specially informal learning settings, from schools and universities to workplaces, museums, cities and rural areas. Much of this research has shown how mobile technology can offer new opportunities for groups of learners to collaborate inside and beyond the traditional instructor-oriented educational paradigm. However, mobile technologies, when specifically applied to collaborative learning activities, are still in its infancy and many challenges arise...